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Samsung Electronics: Quality Improvement Case Study

Samsung Electronics, a global leader in consumer electronics, has long been recognized for its commitment to innovation and quality. However, like any industry giant, the company has faced its share of quality improvement challenges.

In this case study, we will explore how Samsung Electronics identified opportunities for quality enhancement, implemented robust quality control measures, and leveraged technology to achieve excellence in their products.

By delving into the strategies and outcomes of their quality improvement initiatives, we can gain valuable insights into the complex world of quality management in the electronics industry and the lessons that can be applied to various business contexts.

Samsung Electronics: A Legacy of Innovation

Samsung Electronics has forged a legacy of innovation through its commitment to continuous technological advancement and groundbreaking product development. The company's journey towards innovation began with its inception, and it has consistently pushed the boundaries of what is possible in the tech industry.

Samsung's relentless pursuit of technological advancement is evident in its diverse range of products, from semiconductors and smartphones to home appliances and beyond. The company has consistently invested in research and development, driving progress in areas such as AI, 5G technology, and IoT. Samsung's ability to anticipate and meet the evolving needs of consumers has solidified its position as a global leader in innovation.

Furthermore, Samsung's legacy of innovation extends beyond product development. The company has actively contributed to shaping industry standards and technological ecosystems, driving progress and fostering collaboration within the tech community. By consistently delivering groundbreaking solutions, Samsung has cemented its reputation as a trailblazer in the global tech landscape.

This commitment to innovation not only sets Samsung apart but also paves the way for future advancements in technology.

Identifying Quality Improvement Opportunities

To identify quality improvement opportunities, Samsung Electronics must first analyze defects in production to pinpoint areas for enhancement.

Additionally, a comprehensive analysis of process efficiency is crucial to identify bottlenecks and inefficiencies.

Furthermore, a detailed examination of customer complaints can reveal valuable insights into areas that require improvement.

Defects in Production

Identifying and addressing defects in the production process is crucial for Samsung Electronics to continually improve the quality of its products. Effective defects management and production optimization are essential for ensuring customer satisfaction and maintaining a competitive edge in the market. The table below outlines key areas for identifying defects and optimizing production processes:

Process Efficiency Analysis

In the context of ensuring high-quality production, an essential aspect involves conducting a comprehensive analysis of process efficiency to identify opportunities for quality improvement. This entails a detailed examination of the manufacturing processes to streamline operations and enhance overall product quality. The analysis focuses on identifying areas for improvement and implementing changes to optimize efficiency and minimize waste.

Key elements of the process efficiency analysis include:

• Value Stream Mapping: Visualizing the production process to identify areas of improvement.
• Root Cause Analysis: Investigating the underlying reasons for inefficiencies or defects.
• Performance Metrics Tracking: Monitoring key performance indicators to assess process effectiveness.
• Standard Operating Procedures Review: Evaluating and updating procedures to ensure efficiency and quality.
• Continuous Improvement Initiatives: Implementing ongoing efforts to enhance processes and drive quality improvements.

This analytical approach enables Samsung Electronics to continuously enhance its operational efficiency and product quality.

Customer Complaints Analysis

Regularly monitoring and analyzing customer complaints is a crucial step in identifying opportunities for quality improvement at Samsung Electronics. By effectively addressing customer dissatisfaction, Samsung can enhance customer satisfaction and loyalty. The table below illustrates an example of a customer complaints analysis, outlining the types of complaints and their root causes.

Analyzing customer complaints allows Samsung to pinpoint areas for improvement, such as streamlining manufacturing processes, optimizing service operations, and enhancing product development. This approach is essential for maintaining high-quality standards and continuously improving customer satisfaction. Identifying and addressing the root causes of complaints can lead to sustained quality enhancement and increased customer loyalty.

Implementing Robust Quality Control Measures

As Samsung Electronics aims to fortify its quality control measures, the focus will be on implementing process improvement strategies to enhance product quality and reliability.

This will involve the meticulous integration of robust quality control processes throughout the production cycle, ensuring that performance metrics are continuously tracked and analyzed to identify areas for improvement.

Ultimately, the goal is to establish a comprehensive framework that safeguards product quality and customer satisfaction.

Process Improvement Strategies

Implementing robust quality control measures is a critical component of Samsung Electronics' process improvement strategies, ensuring the consistent delivery of high-quality products to customers.

To achieve this, the company employs the following strategies:

• Continuous Improvement : Samsung Electronics focuses on constantly refining its processes to enhance product quality and customer satisfaction.
• Quality Assurance : The company utilizes stringent quality assurance protocols to detect and rectify any deviations from established quality standards.
• Data-Driven Approaches : Samsung Electronics leverages data analytics to identify trends and potential areas for improvement within its manufacturing processes.
• Employee Involvement : The company encourages active participation from employees at all levels to contribute ideas for process enhancement and quality control.
• Supplier Collaboration : Samsung Electronics collaborates closely with its suppliers to ensure the quality of incoming components, thereby maintaining high standards throughout the production process.

Quality Control Implementation

Employing a comprehensive framework of stringent quality control measures, Samsung Electronics ensures the consistent delivery of high-quality products to its discerning customer base.

The company has implemented a multifaceted approach to quality control, encompassing thorough testing at every stage of production, stringent adherence to industry standards, and a robust feedback mechanism from customers and market data.

Samsung Electronics emphasizes continuous improvement in quality control through the use of advanced technologies, such as automated inspection systems and machine learning algorithms to detect and address potential defects proactively.

Furthermore, the company has established a culture of quality consciousness among its employees, ensuring that every individual is committed to upholding the highest standards.

Performance Metrics Tracking

A meticulous tracking of performance metrics is essential for ensuring the efficacy and success of robust quality control measures within Samsung Electronics. To achieve this, the company utilizes a comprehensive approach to performance metrics tracking, encompassing various key aspects:

• Quality Control: Samsung Electronics focuses on tracking quality control metrics such as defect rates, customer complaints, and product returns to identify areas for improvement.
• Productivity Tracking: The company also diligently tracks productivity metrics, including production yield, cycle times, and equipment downtime, to optimize operational efficiency.
• Data Analysis: Utilizing advanced data analysis tools, Samsung Electronics examines performance metrics to identify trends, root causes of issues, and opportunities for enhancing product quality and productivity.
• Continuous Improvement: The company emphasizes the continuous monitoring and tracking of performance metrics to drive ongoing quality improvements and operational enhancements.
• Benchmarking: Samsung Electronics employs benchmarking techniques to compare performance metrics against industry standards and best practices, facilitating a proactive approach to quality control and productivity tracking.

Overcoming Quality Improvement Challenges

Samsung Electronics faced significant hurdles in overcoming quality improvement challenges, requiring a comprehensive and strategic approach to drive meaningful change.

One of the primary challenges was integrating quality control measures across diverse product lines and manufacturing processes. Samsung addressed this by implementing a unified quality management system that standardized quality control protocols, enabling the company to consistently monitor and improve product quality.

Additionally, risk management posed a significant challenge due to the global scale of Samsung's operations. To mitigate this, Samsung developed a robust risk assessment framework that identified potential quality risks at various stages of production and supply chain, allowing for proactive intervention to prevent quality issues.

Another obstacle was fostering a quality-centric culture across all organizational levels. Samsung tackled this by instituting extensive training programs and incentivizing employees to prioritize quality. Furthermore, the company established clear quality improvement goals and regularly communicated progress to drive accountability and motivation.

Leveraging Technology for Quality Enhancement

Leveraging advanced technological solutions has become imperative for achieving significant enhancements in product quality across diverse industry sectors. For Samsung Electronics, technology integration and quality assurance techniques have played a pivotal role in driving continuous quality improvement.

The following approaches highlight how leveraging technology has led to quality enhancement at Samsung Electronics:

• Implementation of advanced data analytics tools for real-time quality monitoring
• Integration of Internet of Things (IoT) for predictive maintenance and quality control
• Utilization of artificial intelligence (AI) for automated quality inspection and defect detection
• Adoption of virtual reality (VR) for immersive quality testing and training simulations
• Deployment of blockchain technology for transparent supply chain management and quality tracking

Monitoring and Evaluating Quality Performance

The implementation of advanced data analytics tools, integration of Internet of Things (IoT), and utilization of artificial intelligence (AI) have significantly influenced the monitoring and evaluating of quality performance at Samsung Electronics. Quality measurement at Samsung Electronics involves the continuous collection and analysis of data from various sources, such as production processes, customer feedback, and supply chain operations. This data is then used to assess the performance of products and processes in real-time, enabling prompt corrective actions when deviations from quality standards are identified.

Continuous improvement is a key focus in Samsung Electronics' quality monitoring and evaluation processes. Through the use of advanced analytics, the company is able to identify trends, patterns, and potential areas for improvement. Furthermore, AI algorithms are employed to predict and prevent quality issues before they occur, contributing to proactive quality management.

The integration of IoT allows for real-time monitoring of equipment and processes, providing valuable insights into the production environment. This comprehensive approach to quality performance monitoring and evaluation enables Samsung Electronics to consistently enhance the quality of its products and processes, thus maintaining its position as a leader in the electronics industry.

Achieving Excellence: Quality Improvement Outcomes

Consistently delivering high-quality products and processes is the ultimate goal of Samsung Electronics' commitment to continuous improvement and proactive quality management. The company has implemented various quality improvement strategies and continuous improvement initiatives to achieve excellence in its outcomes.

Some of the key outcomes of these efforts include:

• Enhanced Product Quality : Through rigorous quality control measures and continuous monitoring, Samsung Electronics has significantly improved the overall quality of its products, leading to higher customer satisfaction and loyalty.
• Streamlined Processes : The implementation of lean manufacturing principles and process optimization techniques has resulted in streamlined operations, reduced waste, and improved efficiency across the organization.
• Improved Supplier Relationships : Samsung Electronics has focused on building strong partnerships with its suppliers, fostering collaboration, and ensuring that high-quality standards are maintained throughout the supply chain.
• Enhanced Employee Engagement : By promoting a culture of continuous improvement and providing employees with the necessary training and resources, Samsung Electronics has seen increased employee engagement and contribution to quality enhancement efforts.
• Market Leadership : As a result of its relentless pursuit of quality excellence, Samsung Electronics has solidified its position as a market leader, setting industry benchmarks for quality standards and innovation.

Through robust quality control measures, Samsung Electronics has been able to achieve excellence in quality improvement.

By leveraging technology and continuously monitoring and evaluating quality performance, the company has established a legacy of innovation in the industry.

Like a master craftsman refining a diamond, Samsung has honed its quality improvement processes to achieve brilliance in its products, setting a standard for others to follow.

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Quality Improvement Processes: The Basics and Beyond

By Kate Eby | February 15, 2019 (updated June 28, 2023)

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Who doesn’t want to provide their clients and customers with the best products and services? All businesses want to improve the quality of their offerings, but not everyone has the same idea of what constitutes the best or the highest quality. And people differ on how to achieve such aims. Especially in fields like healthcare and education, where evaluations often rely on results rather than tallies, a formal quality improvement (QI) process can be essential.

In this article, we will explore quality improvement processes in fields such as healthcare and education, uncover the must-haves in a good QI plan, and study the methods and tools to pursue a strong strategy. You’ll also find links to templates and best practices from a QI expert.

What Is Quality Improvement?

Quality improvement is a structured approach to evaluating the performance of systems and processes, then determining needed improvements in both functional and operational areas. Successful efforts rely on the routine collection and analysis of data. A quality improvement plan describes an ongoing, or continuous, process through which an organization’s stakeholders can monitor and evaluate initiatives and results.

Based on the thinking of such experts as W. Edward Demings, QI principles were developed in manufacturing in the 1940s. In the last two decades, QI processes have also become popular in healthcare and education.

Although organizations take many approaches, QI at its foundation concerns process management. If organizations operate according to many processes, by reviewing and improving one process at a time and leveraging the Pareto principle, they can more easily and gradually improve their entire system.

Quality improvement processes share these characteristics:

Quality improvement is data driven and regards the quantitative approach as the only reliable means to influence the qualitative elements. This principle is expressed in the following saying of quality improvement guru W. Edwards Deming: “The right data in the right format in the right hands at the right time.”

QI focuses on processes, not people. In other words, the individual is never at fault.

QI involves people as part of the improvement solution and looks for what is attributed to Deming as “the smart cogs,” the employees who are directly involved in and best understand the processes in an organization.

What Is the Main Purpose of Quality Improvement?

Quality improvement aims to create efficiencies and address the needs of customers. In healthcare, the main purpose of quality improvement is to improve outcomes. In healthcare settings, quality improvement may be associated with continuous quality improvement, the method used to identify problems and implement, monitor, and provide corrective action.

The Benefits of a Quality Improvement Process

A quality improvement process can offer organizations the following benefits:

Solutions that focus on failures in processes, not flaws in people

A reliance on objective, data-driven solutions, rather than subjective opinions, to identify inefficiencies, preventable errors, and inadequate processes

Improvements that provide better customer service, increased efficiency, greater safety, and higher revenues

A localized focus on testing small, incremental improvements that is less risky than a focus on making changes at one time

Data collection to monitor improvement efforts, which can provide the basis for reimbursement and certification programs, particularly in healthcare organizations

Primary Issues in Quality Improvement

Quality improvement plans are frequently measured in terms of results, employee and stakeholder satisfaction, ease of change, and cost. Quality improvement plans must also help companies understand how to meet the needs of diverse stakeholders (employees, customers, regulators, and others), find a method for prioritizing the improvement requirements of these stakeholders, comprehend the threshold of variation that will permit required change, and know how employees can succeed in a program if leadership support is inadequate.

Why Don’t People Believe in Quality Improvement Processes?

Who could fault an effort to make work more efficient or effective or to deliver higher-quality output to internal and external customers? No one, you would assume — yet employees often shudder at the mention of quality improvement efforts. Their suspicions have assorted origins:

Organizations don’t back change efforts with human resources and other shows of support, then express surprised disappointment when nothing improves.

Employees have previous experiences with efforts that produced no improvements.

Employees feel that “they’re coming after you.” They picture a punitive focus on the individual, rather than an effort to fix the process.

People who value action find data collection and analysis tiresome.

Carl Natenstedt is CEO of  Z5 Inventory , a hospital inventory management and reallocation platform. He says, “What most stands in the way of improvement is communication — more specifically, the lack thereof. It's almost inconceivable for an idea to be passed from the top level, the CFOs or regional directors, down to the people who will actually use the new solution day to day. Often, that's nurses who literally might not know their own email addresses.

Natenstedt notes, “The CFO, when they’re sending out a memo, assumes everybody is reading it. But if your team communicates exclusively by corkboard, how're you supposed to know what decisions have been made and how they affect you?”

Difficulties in Pursuing a Quality Improvement Process Plan

Here are some of the common difficulties in following through with a QI plan:

Expectations are not clear.

Leadership is not adequately engaged, making bottom-up initiatives difficult.

There is insufficient time and resources to properly implement the initiative.

In healthcare settings, some physicians don’t implement new systems until they have confidence in new processes.

There is an inadequate emphasis on the importance and use of new measures.

There is a poor level of collaboration between teams.

People underestimate the time required to implement a program.

The extent to which a result depends on a change can’t be measured because the extent of the original problem has not been measured.

Specific improvement cycles can’t be evaluated.

Costs can’t be evaluated.

Changes result from the Hawthorne effect rather than the QI program. In this interpretation of the Hawthorne effect, stakeholder behavior changes because their activities and results are monitored.

A small sample size makes generalizations impossible.

Solving some problems creates additional problems.

Targets are overly ambitious and therefore difficult to achieve.

There are too many diverse stakeholder conditions.

How to Succeed with a Quality Improvement Process Plan

According to Natenstedt, every successful QI plan needs a champion: “The most important factor contributing to successful implementation is highly committed senior leadership. For any quality improvement process, you need that leader who wants to make it happen. Success comes because someone at the top is pushing for it.”

In addition, Natenstedt says that QI projects flourish when stakeholders are invested in the outcome. “The projects that tend to go the best also tend to be the ones that tie back to the main mission of the organization,” he explains. “If you're trying to get traffic to flow better in the parking garage, nobody's committed. But if you're reducing infections, every employee gets involved, because people care about the quality of what they're providing.

“That’s when leadership has the crucial job of getting everybody on board. They accomplish this goal by explaining exactly why this particular solution is important and showing precisely who reaps the benefits,” Natenstedt continues.

Leadership for QI initiatives may be separate from the organizational structure and should best suit your particular system. In any case, leadership provides the needed resources, as well as the direction and support for core values and priorities. Because leadership is essential, it’s crucial to report any successes and obstacles back to them.

In addition to leadership, Natenstedt says teams need time, space, and opportunities to talk. “The primary team and other teams who will have great insights need collaborative, open, free thinking time. You have to get in a room, spend some time together, and not be afraid, no matter what you have to say or who you're saying it to — a no-stupid-ideas environment,” he adds.

Natenstedt emphasizes that different perspectives are essential: “Make sure the team is not dominated by one type of person or employee. Get a diverse range of voices — even clients' opinions. That fosters creative exchange.”

Other characteristics that contribute to a successful QI initiative include the following:

Offer consistent and continuous commitment.

Secure funds and other resources to support the plan.

Create a vision for quality by using shared goals within and across teams. Engage all stakeholders to help define priorities for safety or cost savings. Take a multidisciplinary approach that includes peers from all teams, as well as frontline workers who implement and champion changes.

Develop and agree on a plan for how to implement improvement activities, who will lead them, and how they will start.

Build a quality improvement team. Work with that team to prioritize and implement improvements.

Spread the word about initiatives and successes. Use one-on-one opportunities, newsletters, and other channels to discuss wins. Openly discuss successes and failures.

Publicize the successes. Stories of success build motivation. Encourage people to talk regularly about quality and contribute suggestions.

Educate stakeholders to prepare for initiatives by scheduling ongoing training and weekly meetings, especially with teams that have no previous experience with QI programs. Provide staff with the training and tools they need to measure and improve efforts. Seek outside support if necessary.

Educate stakeholders on the subject area of the initiative.

Collect the right data — however difficult — and use it well.

Measure progress regularly.

Use current resources as much as possible.

Identify incentives that help members of an organization appreciate and cultivate change. Incentives may be financial or nonfinancial. Think about reducing errors, improving communication, and diffusing tension.

Find influencers (i.e., people who others in the organization respect), and leverage that influence to spread ideas about change. Most people do not conduct research; they simply listen to the opinions of others.

Establish realistic goals. Don’t aim for 100 percent success.

Find an approach to metrics and documentation that suits your organization. “Within any project, you need a meaningful set of KPI (key performance indicators) that you can measure before and after,” says Natenstedt. “The old adage of ‘what gets measured gets improved’ is 100 percent true. And people will respond to the measurement simply because a measurement is being taken.”

Create a robust IT implementation to record data, changes, and plans, as well as to leverage electronic health records (EHRs) and public databases where appropriate.

Involve customers through surveys, exit interviews, and suggestion boxes. This information generates valuable ideas based on clients’ direct experiences with your services. In return, find user-friendly ways to help customers understand data.

Common Outcomes of Successful Quality Improvement Process Projects

Many organizations have found the following successes with QI:

Standardization eliminates the need for individual decision making.

By limiting options and changes, information technology (IT) forces functions that reduce errors. For example, IT eliminates redundant checks and barcodes by using computer-aided calculations.

When a culture encourages teams to report errors and near misses, they generate data that creates a foundation for understanding root causes.

A Case Study in Quality Improvement Process Implementation

As an example of what can go right and wrong in a QI plan, Carl Natenstedt tells the story of his company’s plan to remove and reallocate old product to save a hospital tens of thousands of dollars. “But those benefits weren't communicated to the clinicians who actually used the product every day,” he explains.

“When the time came for our team to come into the hospital and remove medical supplies, we were met with resistance. Nurses and doctors were worried about running out of what they needed, which is totally understandable. No one had presented them with the numbers or communicated with them. No one had said, ‘You actually don't need these eight extra boxes of sutures. We know because we've analyzed your usage history. You could reduce your patients' cost of care by X amount,’” he notes.

Resistance continued until senior leadership explained the benefits. “As soon as you quantify just how much you're helping your community, people are interested. They're excited,” Natenstedt says.

What Is the First Step in the Quality Improvement Process?

No matter which model you choose or what you call it, planning has to be the first step. You need to decide what problems you want to solve, how you will solve them, and how you’ll know when they are solved.

What Is a Quality Improvement Plan?

A quality improvement plan is the written, long-term commitment to a specific change and may even chart strategic improvement for an organization. A QI plan defines what your organization wants to improve, how it will make improvements, how it will test for success, and what are the anticipated outcomes and evidence of success. In essence, the plan becomes the monitoring and evaluation tool. Additionally, a QI plan provides the roadmap and outlines deliverables for grants, funding, or certification applications.

A plan differs from a QI project or QI program , both of which are considered subcategories of a plan. Projects grow out of the target areas you identify in the plan or those noted by stakeholders. With regular monitoring of changes, you can spotlight further targets for improvement.

Ensure that your quality improvement plans include the following elements:

Clearly defined leadership and accountability, as well as dedicated resources

Specified data and measurable results that suit your goals

Evidence-based benchmarks. In an evidence-based practice, teams determine the clinical or operational approaches that most often produce good outcomes, then create procedures to consistently implement those approaches. In benchmarking, employees learn about processes and results at comparable organizations. Then they consider how to implement similar processes in their own organization.

A mechanism for ensuring that you feed the data you collect back into the process. By doing so, you guarantee that you accomplish your goals. It also helps ensure that these goals are concurrent with improved outcome.

More than one QI method or tool

A built-in structure to keep the plan dynamic and ongoing

Attentive staff members who listen to the needs of all stakeholders

In healthcare, steps and measures that align with other quality assurance and quality improvement programs, such as those sponsored by Medicaid and HRSA

What Steps Are in the Quality Improvement Model?

Regardless of the framework you choose, the following six steps generally describe all quality improvement approaches:

Create a mission statement and vision statement to provide the organization with strategic direction.

Create improvement goals and objectives that provide quantitative indicators of progress, which can help to show areas of quality needs.

Analyze the background and context of the issues. Research and develop possible strategies to resolve the issue.

Choose specific interventions to implement.

Focus first on change in small and local structures. Test implementations to refine ideas and make change palatable to the individuals involved. You can scale successful plans to the larger organization.

Define a performance measurement method for your improvement project, and use existing data or collect data that you will use to monitor your successes.

Plan data collection and analysis.

Data is imperative to a QI project. Measure input, outcomes, and processes. Data management includes collecting, tracking, analyzing, interpreting, and acting on data.

Leadership and stakeholders usually review goals on an annual basis, but you should collect data more frequently.

No collection frequency works for all organizations, but you should specify the rate in your plan.

Charge one person or department with the responsibility of managing data. That way, if the designated staff member changes positions, it’s easy to locate and shift ownership.

Analyze and interpret data to identify opportunities for improvement. Analysis determines if data is appropriate, and interpretation identifies patterns.

Establish an improvement team.

Prepare the written QI plan.

Based on the plan, make changes to improve care, and continually measure whether those changes produce the improvements in service delivery that you wish to achieve.

Communicate successes to keep quality on the agenda.

Quality Improvement Tools and Frameworks

Quality improvement methods provide frameworks for pursuing change. Quality improvement tools provide strategies and documentation to gather and analyze data, as well as communicate results and conclusions.

The History of Quality Improvement

Most modern quality improvement approaches trace their history to modern efficiency experts, such as Walter Shewhart, who perfected statistical process control modelling. Other foundational methodologies include the Toyota Production System , which evolved into lean management. W. Edwards Deming heavily influenced both the former and the latter.

In healthcare, beginning in the 1960s, the Donabedian model became globally influential. Developed by Avedis Donabedian at the University of Michigan, the approach examines structure, process, and outcomes to inquire into the quality of care. Subsequently, healthcare organizations began to turn to frameworks used in other fields. Here are the most prominent of those additional frameworks:

Total Quality Improvement (TQM) : The TQM model is an approach involving organizational management, teamwork, defined processes, systems thinking, and change to create an environment for improvement. TQM espouses the view that the entire organization must be committed to quality and improvement to achieve the best results.

Continuous Quality Improvement (CQI) : The CQI process is designed to introduce frequent, small reviews and changes, and it operates on the principle that opportunities for improvement exist throughout the practice or institution. Popular in healthcare, CQI is used to develop clinical practices and has recently found traction in higher education. CQI incorporates QI methods such as PDSA (plan-do-study-act), lean, Six Sigma, and Baldrige. In healthcare in particular, CQI adopts/operates the Institute for Healthcare Improvement Model for Improvement.

Clinical Process Improvement (CPI) : CPI is a clinician-driven approach to addressing the many challenges and complexities that exist for modern healthcare providers. In CPI, teams follow the four QI steps: find a goal; gather data; assess data; and implement changes.

Commonly Recognized Quality Improvement Methods

Organizations choose methods based on their specific improvement goals. Each method offers varying advantages, depending on the company’s particular scenarios and environments:

Rapid-Cycle Quality Improvement : This method allows for quick integration of changes over short cycles.

ISO 9000 : The ISO 9001 standard of the ISO 9000 series is a framework that embraces continual improvement and certifies that an organization has an industry-recognized plan for pursuing quality.

Six Sigma : Six Sigma is a data-driven framework to eliminate waste. Using the DMAIC (define, measure, analyze, improve, and control) model, Six Sigma teams define a project or problem, review or measure historical experiences, analyze results, and decide on solutions that reduce variability in outcome. Teams then implement solutions and control or regularly monitor statistical output to ensure consistency. Six Sigma is closely related to PDSA, as it is based on Shewhart's PDCA (plan-do-check-act).

Malcolm Baldrige National Quality Award : This is an improvement criteria named for quality guru Malcolm Baldrige, a contemporary of Deming.

Toyota Production System or Lean Production : This approach emphasizes the elimination of waste or non-value-added processes. In healthcare, it’s used for process improvement in labs and pharmacies.

Plan-Do-Study-Act (PDSA) : This process improvement framework is fundamental to continual improvement and frequently provides steps for quality improvement in healthcare. PDSA, also known as the trial-and-learning cycle, promotes small changes and rapid adaptations and improvements. As such, it is suited to organizations that contain many units and processes that interact and yet often function independently. Within such companies, small, incremental adjustments can eventually have significant impact on the entire system.

5S or Everything in Its Place : This set of principles aims to make the workplace safe and efficient. 5S stands for the following Japanese terms and their English translations: seiton , set in order; seiri , sort; seiso , shine; seiketsu , standardize; and shitsuke , sustain. For further information on this topic, please see “ Everything You Need to Know About Lean Six Sigma .”

Human Factors (HFE) : HFE studies human capabilities and limitations and how they apply to the design of products, tools, and processes. HFE has a strong track record of success in improving manufacturing processes and is now proving helpful in clinical applications to bolster quality, reliability, and safety.

Zero Defects : This industrial management strategy centers on reducing and eliminating defects through a continuous focus on punctual and accurate performance. In the United States, this strategy was highly popular in the 1960s and early 1970s.

Quality Improvement Tools

The following tools work in conjunction with the quality improvement methodologies mentioned above:

Root Cause Analysis (RCA) : This is a way of looking at unexpected events and outcomes to determine the underlying causes and to recommend changes that are likely to fix the resultant problems and avoid similar problems in the future.

RCA Tools : RCA tools include the five whys, appreciation or situational analysis (“so what?”), and drilldowns. These tools reveal finer details of a larger picture, such as state-by-state data emerging from national data and fishbone diagram subprocesses emerging from general processes. In healthcare, RCA is applied after sentinel events , which are unanticipated events that result in death or serious physical or psychological injury to one or more patients in a clinical or healthcare setting. By definition, these events are not caused by a pre-existing illness.

Failure Modes and Effects Analysis (FMEA) : This is a systematic approach to identifying what could go wrong. Before an event, you apply FMEA to consider all adverse outcomes and mitigate these possibilities. Ideally, this mitigation process would take place both during the design phase and later, during implementation. The FMEA process addresses these questions:

Failure Modes : What could go wrong?

Failure Causes : Why could this happen?

Failure Effects : What would be the consequences of this failure?

Health Failure Modes and Effects Analysis (HFMEA): The U.S. Veterans Administration National Center for Patient Safety created the HFMEA for the purpose of risk assessment. Application of the tool includes identifying failure modes, then applying a hazard matrix score.

Statistical Process Control for Quality Improvement

Statistical process control (SPC), which measures and controls quality, started in manufacturing, but can apply in a range of other fields. SPC relies on the continuous collection of product and process measurements, as well as the subsequent subjection of said data to statistical analysis. In manufacturing, you collect data from machines in the production line. You can even compare data of different sizes and characteristics. For example, in Statistics and Data Analysis in Geology , John C. Davis writes, “In order to compare resultants from samples of different sizes, they must be converted into a standardized form. This is done simply by dividing the coordinates of the resultant by the number of observations, n …”

PDSA for Quality Improvement

The PDSA cycle of plan-do-study-act offers a common framework for improvement in healthcare, education, industry, and other areas. PDSA may take several cycles to test and perfect, but the cycles of implementation also disseminate ideas. The framework scales from small to large organizations.

At its core, PDSA maps to these active, iterative learning steps:

What do you want to do?

How will you know that a change has created an improvement?

What changes will create improvements?

Develop the plan.

Implement the changes.

Verify the results.

Make additional improvements.

The PDSA cycle can have three implementation expressions. The healthcare industry uses rapid-cycle problem solving, usability testing, and practice-policy communication loops; educational organizations use practice-policy communication loops. The three implementation expressions operate as follows:

Rapid-Cycle Problem Solving : This encourages teams to plan changes and make updates within three months, rather than within eight to 12 months. This approach is suited to electronic health record implementations and where fast improvements can make a big impact on stakeholders.

Usability Testing : With origins in software development, usability testing offers iterative testing with various small groups. The ideal series of tests is four to five, with different groups of four to five people. Each test reveals unique roadblocks and errors, usually starting with superficial problems and culminating in more fundamental issues. As you implement improvements, you may introduce new problems that require fixes.

Practice-Policy Communication Cycles : The practice-policy communication cycle model was articulated to explain how complex products and processes, such as legacy software systems, and organizations could discover where and how to make improvements. In this bottom-up approach, grassroots ( or practice ) levels maintain regular communication with management and top-tier ( or policy ) levels about requirements and changes. This ensures that the relevant party records improvements in the form of policy.

PDSA steps capture the following activities:

• Plan : The planning phase involves establishing goals, choosing areas of focus, identifying improvement strategies, and preparing the QI plan.

What does your organization or team want to accomplish?

Where do you need to improve service or care?

What about a workday or customer experience is most frustrating to employees and customers or clients?

Where can you be more efficient?

• Create an Initiative Team : Include representatives from a group or team affected by change, but consider including both functional and operational representatives. Include special team members as necessary. Some sources suggest that a team with a maximum of 10 members is the most effective. Establish an implementation and review the timeline for the initiative.

Determine Solutions : Solutions must fit the problem, align with the culture of the organization and the clients it serves, or provide technological upgrades or advancements. Here are some options for finding possible solutions:

Visit other facilities. Seeing what others do can help the team overcome internal resistance to change.

Glean information from conferences, subject-area literature, and academic literature.

Find benchmark practices for your speciality.

Talk to your clients. For example, in healthcare, survey your patients and their families.

Prepare the Plan : Add strategies and sub-strategies. Include answers to these questions:

What are the goals?

What are the objectives?

What areas do you want to improve?

What actions can accomplish these goals?

Which stakeholders will be affected and how?

Who can lead or champion the initiative?

What resources do you need: budgetary, human, or material?

What are the potential barriers? How can you overcome them?

How will you measure improvements and successes? Describe the benchmarks that will monitor progress in achieving objectives and goals. In clinical and other healthcare organizations, find metrics to determine whether team members adhere to new or revised practices. Do this to understand how practices influence patient care and to ascertain whether care is improving and to what extent.

What is the timeline?

How will you share your action plan?

Do : Implement the plan in short cycles and localized areas, executing small adjustments and evaluating changes on qualitative and quantitative bases. At this stage, you also collect data. Your plan should already indicate measurements that will provide the most impact and that will not pose a burden to staff and stakeholders. Ensure that you explain to the entire team why you are collecting data. Frame data collection as the attempt to learn what works. Teams will be more enthusiastic about making changes and collecting data when they are focused on the positive, rather than on finding problems and mistakes. Also, keep in mind that while data can highlight change over time, data and charts in and of themselves do not necessarily point to best practices.

Data is often best gathered in documents such as check sheets, flowcharts, swimlane maps, or run charts, which can also help with displaying and sharing data:

• Check Sheet : Also called a defect concentration diagram , a check sheet is adaptable to different situations. It is a structured form for collecting quantitative and qualitative data. Check sheets are among the seven basic quality tools.
• Swimlane Maps : Also known as deployment maps or cross-functional charts , swimlane maps describe processes according to functions, which are displayed in the lanes .
• Flowcharts : Flowcharts graphically describe processes.

Study : Use collected data to determine what works and what doesn’t work. Use run charts, control charts, and Pareto charts to visualize results. Share the results, especially the successes, to create enthusiasm through word of mouth. Don’t be troubled by what appear to be failures. Because localized changes are not applied to the entire organization, you can easily make and roll out incremental modifications (when perfected) to the rest of the organization.

Act : When the plan succeeds, extend the steps to the larger organization. Adapt processes as necessary. Monitor results by month or by quarter. Identify and manage any barriers to adoption, such as:

• Fear of change, fear of failure, or fear of loss of control
• Grief over the loss of familiar practices or people
• Lack of basic management expertise
• Inadequate staffing levels
• Inadequate information technology systems
• Lack of training in functional and operational jobs
• Outdated or unreasonable policies

What Is a Quality Improvement Process in Healthcare?

With its life-and-death focus, healthcare is a prime field for quality improvement initiatives. You can use QI processes for enterprises, clinics, labs, and individual practices. In healthcare, goals and objectives may be functional or operational, and they may include process measures and outcome measures. For example, you may improve your front-desk admissions process or your wound-care process. In healthcare, we measure improvements in terms of desired outcomes .

As this professional journal article states, “ Evidence for variable performance of colonoscopy indicates that patient outcomes could be improved by a constructive process of continuous quality improvement .” In this specific context of improving the performance of colonoscopies, this journal notes further that professionals can implement quality improvement by “educating endoscopists in optimal colonoscopic techniques, procedure documentation, interpretation of pathological findings, and scheduling of appropriate follow-up examinations.” Pathologists can improve their work through the “appropriate reporting of pathological findings. Continuous quality improvement is an integral part of a colonoscopy program.”

STEEP: A Quality Improvement Process in Healthcare

STEEP is a quality improvement tool unique to healthcare. Developed in 1999 by the Institute of Medicine (IOM), now the National Academy of Medicine, it is similar to FMEA and describes six goals for optimal patient care and safety:

Safety : Avoid injury to patients from the care that is intended to help them.

Timeliness : Reduce waits and harmful delays.

Effectiveness : Provide services, based on scientific knowledge, to all who could benefit — that is, avoid overuse. Refrain from providing services to those not likely to benefit — that is, avoid underuse.

Efficiency : Avoid waste.

Equitability : Provide care that does not vary in quality because of personal characteristics, such as gender, ethnicity, geographical location, and socioeconomic status.

Patient Centeredness : Provide care that is respectful of and responsive to individual patient preferences, needs, and values.

In addition to general challenges inherent in pursuing quality improvement, healthcare presents particular obstacles. For example, healthcare organizations face a likelihood of adverse events recurring, and they must anticipate overcoming resistance to change among key parties such as physicians, as much as 16 percent of whom may be unwilling to revise processes.

Nevertheless, QI cycles and data capture support applications for financial programs. In addition, certifications provide measures to contribute to public reporting schemes and offer data to support value-based payment models.

What Is a Quality Improvement Process in Nursing?

In nursing, the quality improvement process purports that the floor nurse is best situated to monitor the status of processes and make improvements. QI efforts from nurses can include safety issues (such as preventing patient falls), clinical issues (such as wound care and surgical procedures), and self-care for maintaining practitioner safety, health, and mental well-being.

What Is a Quality Improvement Process in Education?

Continuous quality improvement is the framework for consistent improvement in education, both in higher education and in K-12 in public education. Although common in manufacturing and healthcare, quality improvement methods for education are now beginning to blossom. Methodologies in education include Six Sigma, PDSA, PDCA (plan-do-check-act), and in a few cases, lean.

What Is a Quality Improvement Process in Information Systems?

Because they support data collection and analysis, information systems are key to the quality improvement processes of many types of organizations, especially healthcare. IT in healthcare leverages electronic health records (EHR) and health information exchanges (HIE), in addition to in-house data sources. Information systems can assist with such quality enhancements as generating patient reminders for screenings and preventive health checkups, as well as providing access to laboratory, radiology, hospital, and specialist reports and records.

What Is a Quality Improvement Process in Software?

Software quality management (SQM) is a management process that aims to develop and manage the quality of software to best ensure the product meets the standards expected by the customer. At the same time, it also meets regulatory and developer requirements.

What Is a Quality Improvement Process in Supply?

In terms of supply, quality improvement focuses on mutual objectives across the supply chain, rather than on competition between suppliers. QI in supply commonly adheres to Baldrige National Quality Award 2002 criteria, which emphasizes the needs of the end-customer, not just those of the next customer in chain. Some examples include the idea, expounded upon by Evan L. Porteus in “ Optimal Lot Sizing, Process Quality Improvement, and Setup Cost Reduction ,” that smaller lots provide less opportunity for defects and errors.

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Product Quality Improvement Based on Process Capability Analysis

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• Cite this conference paper

• Na Zhao 20 ,
• Yumin He 21 ,
• Mingxin Zhang 20 ,
• Gaosheng Cui 20 &
• Fuman Pan 20  

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 630))

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• IFIP International Conference on Advances in Production Management Systems

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Product quality is important to companies. Many factors affect the quality of products. Statistical process control (SPC) applies statistical methods to process control and can be utilized to improve the quality of products. This paper proposes an approach for product quality improvement based on problem analysis and statistical process control application. A framework is presented with the steps from problem identifying to problem solving to improve product quality. These steps include problem identification, problem analysis, SPC method determination, production analysis, cause analysis, and problem solving. A case study is made to a real manufacturing company. The proposed approach is applied to the company. The case company identified its production problem and made the actions on the production process improvement with the good result of product quality improvement. This research can provide a reference for manufacturing companies to apply SPC methods and statistical tools to production process control for product quality improvement.

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Park, M., Kim, J., Jeong, M., Hamouda, A., Al-Khalifa, K., Elsayed, E.: Economic cost models of integrated APC controlled SPC charts. Int. J. Prod. Res. 50 , 3936–3955 (2012)

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Minitab Homepage. http://www.minitab.com/zh-cn . Accessed Sep 2020

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Acknowledgment

The authors would like to thank the work of other people in the quality improvement project team. The authors would like to thank the session chair, Professor Natalia Bakhtadze and the referees for the valuable comments.

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Shanxi Aerospace Tsinghua Equipment Co., Ltd, Changzhi, 046000, Shanxi, People’s Republic of China

Na Zhao, Mingxin Zhang, Gaosheng Cui & Fuman Pan

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Correspondence to Yumin He .

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Zhao, N., He, Y., Zhang, M., Cui, G., Pan, F. (2021). Product Quality Improvement Based on Process Capability Analysis. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_63

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Quality improvement into practice

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• Peer review
• Adam Backhouse , quality improvement programme lead 1 ,
• Fatai Ogunlayi , public health specialty registrar 2
• 1 North London Partners in Health and Care, Islington CCG, London N1 1TH, UK
• 2 Institute of Applied Health Research, Public Health, University of Birmingham, B15 2TT, UK
• Correspondence to: A Backhouse adam.backhouse{at}nhs.net

What you need to know

Thinking of quality improvement (QI) as a principle-based approach to change provides greater clarity about ( a ) the contribution QI offers to staff and patients, ( b ) how to differentiate it from other approaches, ( c ) the benefits of using QI together with other change approaches

QI is not a silver bullet for all changes required in healthcare: it has great potential to be used together with other change approaches, either concurrently (using audit to inform iterative tests of change) or consecutively (using QI to adapt published research to local context)

As QI becomes established, opportunities for these collaborations will grow, to the benefit of patients.

The benefits to front line clinicians of participating in quality improvement (QI) activity are promoted in many health systems. QI can represent a valuable opportunity for individuals to be involved in leading and delivering change, from improving individual patient care to transforming services across complex health and care systems. 1

However, it is not clear that this promotion of QI has created greater understanding of QI or widespread adoption. QI largely remains an activity undertaken by experts and early adopters, often in isolation from their peers. 2 There is a danger of a widening gap between this group and the majority of healthcare professionals.

This article will make it easier for those new to QI to understand what it is, where it fits with other approaches to improving care (such as audit or research), when best to use a QI approach, making it easier to understand the relevance and usefulness of QI in delivering better outcomes for patients.

How this article was made

AB and FO are both specialist quality improvement practitioners and have developed their expertise working in QI roles for a variety of UK healthcare organisations. The analysis presented here arose from AB and FO’s observations of the challenges faced when introducing QI, with healthcare providers often unable to distinguish between QI and other change approaches, making it difficult to understand what QI can do for them.

How is quality improvement defined?

There are many definitions of QI ( box 1 ). The BMJ ’s Quality Improvement series uses the Academy of Medical Royal Colleges definition. 6 Rather than viewing QI as a single method or set of tools, it can be more helpful to think of QI as based on a set of principles common to many of these definitions: a systematic continuous approach that aims to solve problems in healthcare, improve service provision, and ultimately provide better outcomes for patients.

Definitions of quality improvement

Improvement in patient outcomes, system performance, and professional development that results from a combined, multidisciplinary approach in how change is delivered. 3

The delivery of healthcare with improved outcomes and lower cost through continuous redesigning of work processes and systems. 4

Using a systematic change method and strategies to improve patient experience and outcome. 5

To make a difference to patients by improving safety, effectiveness, and experience of care by using understanding of our complex healthcare environment, applying a systematic approach, and designing, testing, and implementing changes using real time measurement for improvement. 6

In this article we discuss QI as an approach to improving healthcare that follows the principles outlined in box 2 ; this may be a useful reference to consider how particular methods or tools could be used as part of a QI approach.

Principles of QI

Primary intent— To bring about measurable improvement to a specific aspect of healthcare delivery, often with evidence or theory of what might work but requiring local iterative testing to find the best solution. 7

Employing an iterative process of testing change ideas— Adopting a theory of change which emphasises a continuous process of planning and testing changes, studying and learning from comparing the results to a predicted outcome, and adapting hypotheses in response to results of previous tests. 8 9

Consistent use of an agreed methodology— Many different QI methodologies are available; commonly cited methodologies include the Model for Improvement, Lean, Six Sigma, and Experience-based Co-design. 4 Systematic review shows that the choice of tools or methodologies has little impact on the success of QI provided that the chosen methodology is followed consistently. 10 Though there is no formal agreement on what constitutes a QI tool, it would include activities such as process mapping that can be used within a range of QI methodological approaches. NHS Scotland’s Quality Improvement Hub has a glossary of commonly used tools in QI. 11

Empowerment of front line staff and service users— QI work should engage staff and patients by providing them with the opportunity and skills to contribute to improvement work. Recognition of this need often manifests in drives from senior leadership or management to build QI capability in healthcare organisations, but it also requires that frontline staff and service users feel able to make use of these skills and take ownership of improvement work. 12

Using data to drive improvement— To drive decision making by measuring the impact of tests of change over time and understanding variation in processes and outcomes. Measurement for improvement typically prioritises this narrative approach over concerns around exactness and completeness of data. 13 14

Scale-up and spread, with adaptation to context— As interventions tested using a QI approach are scaled up and the degree of belief in their efficacy increases, it is desirable that they spread outward and be adopted by others. Key to successful diffusion of improvement is the adaption of interventions to new environments, patient and staff groups, available resources, and even personal preferences of healthcare providers in surrounding areas, again using an iterative testing approach. 15 16

What other approaches to improving healthcare are there?

Taking considered action to change healthcare for the better is not new, but QI as a distinct approach to improving healthcare is a relatively recent development. There are many well established approaches to evaluating and making changes to healthcare services in use, and QI will only be adopted more widely if it offers a new perspective or an advantage over other approaches in certain situations.

A non-systematic literature scan identified the following other approaches for making change in healthcare: research, clinical audit, service evaluation, and clinical transformation. We also identified innovation as an important catalyst for change, but we did not consider it an approach to evaluating and changing healthcare services so much as a catch-all term for describing the development and introduction of new ideas into the system. A summary of the different approaches and their definition is shown in box 3 . Many have elements in common with QI, but there are important difference in both intent and application. To be useful to clinicians and managers, QI must find a role within healthcare that complements research, audit, service evaluation, and clinical transformation while retaining the core principles that differentiate it from these approaches.

Alternatives to QI

Research— The attempt to derive generalisable new knowledge by addressing clearly defined questions with systematic and rigorous methods. 17

Clinical audit— A way to find out if healthcare is being provided in line with standards and to let care providers and patients know where their service is doing well, and where there could be improvements. 18

Service evaluation— A process of investigating the effectiveness or efficiency of a service with the purpose of generating information for local decision making about the service. 19

Clinical transformation— An umbrella term for more radical approaches to change; a deliberate, planned process to make dramatic and irreversible changes to how care is delivered. 20

Innovation— To develop and deliver new or improved health policies, systems, products and technologies, and services and delivery methods that improve people’s health. Health innovation responds to unmet needs by employing new ways of thinking and working. 21

Why do we need to make this distinction for QI to succeed?

Improvement in healthcare is 20% technical and 80% human. 22 Essential to that 80% is clear communication, clarity of approach, and a common language. Without this shared understanding of QI as a distinct approach to change, QI work risks straying from the core principles outlined above, making it less likely to succeed. If practitioners cannot communicate clearly with their colleagues about the key principles and differences of a QI approach, there will be mismatched expectations about what QI is and how it is used, lowering the chance that QI work will be effective in improving outcomes for patients. 23

There is also a risk that the language of QI is adopted to describe change efforts regardless of their fidelity to a QI approach, either due to a lack of understanding of QI or a lack of intention to carry it out consistently. 9 Poor fidelity to the core principles of QI reduces its effectiveness and makes its desired outcome less likely, leading to wasted effort by participants and decreasing its credibility. 2 8 24 This in turn further widens the gap between advocates of QI and those inclined to scepticism, and may lead to missed opportunities to use QI more widely, consequently leading to variation in the quality of patient care.

Without articulating the differences between QI and other approaches, there is a risk of not being able to identify where a QI approach can best add value. Conversely, we might be tempted to see QI as a “silver bullet” for every healthcare challenge when a different approach may be more effective. In reality it is not clear that QI will be fit for purpose in tackling all of the wicked problems of healthcare delivery and we must be able to identify the right tool for the job in each situation. 25 Finally, while different approaches will be better suited to different types of challenge, not having a clear understanding of how approaches differ and complement each other may mean missed opportunities for multi-pronged approaches to improving care.

What is the relationship between QI and other approaches such as audit?

Academic journals, healthcare providers, and “arms-length bodies” have made various attempts to distinguish between the different approaches to improving healthcare. 19 26 27 28 However, most comparisons do not include QI or compare QI to only one or two of the other approaches. 7 29 30 31 To make it easier for people to use QI approaches effectively and appropriately, we summarise the similarities, differences, and crossover between QI and other approaches to tackling healthcare challenges ( fig 1 ).

How quality improvement interacts with other approaches to improving healthcare

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QI and research

Research aims to generate new generalisable knowledge, while QI typically involves a combination of generating new knowledge or implementing existing knowledge within a specific setting. 32 Unlike research, including pragmatic research designed to test effectiveness of interventions in real life, QI does not aim to provide generalisable knowledge. In common with QI, research requires a consistent methodology. This method is typically used, however, to prove or disprove a fixed hypothesis rather than the adaptive hypotheses developed through the iterative testing of ideas typical of QI. Both research and QI are interested in the environment where work is conducted, though with different intentions: research aims to eliminate or at least reduce the impact of many variables to create generalisable knowledge, whereas QI seeks to understand what works best in a given context. The rigour of data collection and analysis required for research is much higher; in QI a criterion of “good enough” is often applied.

Relationship with QI

Though the goal of clinical research is to develop new knowledge that will lead to changes in practice, much has been written on the lag time between publication of research evidence and system-wide adoption, leading to delays in patients benefitting from new treatments or interventions. 33 QI offers a way to iteratively test the conditions required to adapt published research findings to the local context of individual healthcare providers, generating new knowledge in the process. Areas with little existing knowledge requiring further research may be identified during improvement activities, which in turn can form research questions for further study. QI and research also intersect in the field of improvement science, the academic study of QI methods which seeks to ensure QI is carried out as effectively as possible. 34

Scenario: QI for translational research

Newly published research shows that a particular physiotherapy intervention is more clinically effective when delivered in short, twice-daily bursts rather than longer, less frequent sessions. A team of hospital physiotherapists wish to implement the change but are unclear how they will manage the shift in workload and how they should introduce this potentially disruptive change to staff and to patients.

Before continuing reading think about your own practice— How would you approach this situation, and how would you use the QI principles described in this article?

Adopting a QI approach, the team realise that, although the change they want to make is already determined, the way in which it is introduced and adapted to their wards is for them to decide. They take time to explain the benefits of the change to colleagues and their current patients, and ask patients how they would best like to receive their extra physiotherapy sessions.

The change is planned and tested for two weeks with one physiotherapist working with a small number of patients. Data are collected each day, including reasons why sessions were missed or refused. The team review the data each day and make iterative changes to the physiotherapist’s schedule, and to the times of day the sessions are offered to patients. Once an improvement is seen, this new way of working is scaled up to all of the patients on the ward.

The findings of the work are fed into a service evaluation of physiotherapy provision across the hospital, which uses the findings of the QI work to make recommendations about how physiotherapy provision should be structured in the future. People feel more positive about the change because they know colleagues who have already made it work in practice.

QI and clinical audit

Clinical audit is closely related to QI: it is often used with the intention of iteratively improving the standard of healthcare, albeit in relation to a pre-determined standard of best practice. 35 When used iteratively, interspersed with improvement action, the clinical audit cycle adheres to many of the principles of QI. However, in practice clinical audit is often used by healthcare organisations as an assurance function, making it less likely to be carried out with a focus on empowering staff and service users to make changes to practice. 36 Furthermore, academic reviews of audit programmes have shown audit to be an ineffective approach to improving quality due to a focus on data collection and analysis without a well developed approach to the action section of the audit cycle. 37 Clinical audits, such as the National Clinical Audit Programme in the UK (NCAPOP), often focus on the management of specific clinical conditions. QI can focus on any part of service delivery and can take a more cross-cutting view which may identify issues and solutions that benefit multiple patient groups and pathways. 30

Audit is often the first step in a QI process and is used to identify improvement opportunities, particularly where compliance with known standards for high quality patient care needs to be improved. Audit can be used to establish a baseline and to analyse the impact of tests of change against the baseline. Also, once an improvement project is under way, audit may form part of rapid cycle evaluation, during the iterative testing phase, to understand the impact of the idea being tested. Regular clinical audit may be a useful assurance tool to help track whether improvements have been sustained over time.

Scenario: Audit and QI

A foundation year 2 (FY2) doctor is asked to complete an audit of a pre-surgical pathway by looking retrospectively through patient documentation. She concludes that adherence to best practice is mixed and recommends: “Remind the team of the importance of being thorough in this respect and re-audit in 6 months.” The results are presented at an audit meeting, but a re-audit a year later by a new FY2 doctor shows similar results.

Before continuing reading think about your own practice— How would you approach this situation, and how would you use the QI principles described in this paper?

Contrast the above with a team-led, rapid cycle audit in which everyone contributes to collecting and reviewing data from the previous week, discussed at a regular team meeting. Though surgical patients are often transient, their experience of care and ideas for improvement are captured during discharge conversations. The team identify and test several iterative changes to care processes. They document and test these changes between audits, leading to sustainable change. Some of the surgeons involved work across multiple hospitals, and spread some of the improvements, with the audit tool, as they go.

QI and service evaluation

In practice, service evaluation is not subject to the same rigorous definition or governance as research or clinical audit, meaning that there are inconsistencies in the methodology for carrying it out. While the primary intent for QI is to make change that will drive improvement, the primary intent for evaluation is to assess the performance of current patient care. 38 Service evaluation may be carried out proactively to assess a service against its stated aims or to review the quality of patient care, or may be commissioned in response to serious patient harm or red flags about service performance. The purpose of service evaluation is to help local decision makers determine whether a service is fit for purpose and, if necessary, identify areas for improvement.

Service evaluation may be used to initiate QI activity by identifying opportunities for change that would benefit from a QI approach. It may also evaluate the impact of changes made using QI, either during the work or after completion to assess sustainability of improvements made. Though likely planned as separate activities, service evaluation and QI may overlap and inform each other as they both develop. Service evaluation may also make a judgment about a service’s readiness for change and identify any barriers to, or prerequisites for, carrying out QI.

QI and clinical transformation

Clinical transformation involves radical, dramatic, and irreversible change—the sort of change that cannot be achieved through continuous improvement alone. As with service evaluation, there is no consensus on what clinical transformation entails, and it may be best thought of as an umbrella term for the large scale reform or redesign of clinical services and the non-clinical services that support them. 20 39 While it is possible to carry out transformation activity that uses elements of QI approach, such as effective engagement of the staff and patients involved, QI which rests on iterative test of change cannot have a transformational approach—that is, one-off, irreversible change.

There is opportunity to use QI to identify and test ideas before full scale clinical transformation is implemented. This has the benefit of engaging staff and patients in the clinical transformation process and increasing the degree of belief that clinical transformation will be effective or beneficial. Transformation activity, once completed, could be followed up with QI activity to drive continuous improvement of the new process or allow adaption of new ways of working. As interventions made using QI are scaled up and spread, the line between QI and transformation may seem to blur. The shift from QI to transformation occurs when the intention of the work shifts away from continuous testing and adaptation into the wholesale implementation of an agreed solution.

Scenario: QI and clinical transformation

An NHS trust’s human resources (HR) team is struggling to manage its junior doctor placements, rotas, and on-call duties, which is causing tension and has led to concern about medical cover and patient safety out of hours. A neighbouring trust has launched a smartphone app that supports clinicians and HR colleagues to manage these processes with the great success.

This problem feels ripe for a transformation approach—to launch the app across the trust, confident that it will solve the trust’s problems.

Before continuing reading think about your own organisation— What do you think will happen, and how would you use the QI principles described in this article for this situation?

Outcome without QI

Unfortunately, the HR team haven’t taken the time to understand the underlying problems with their current system, which revolve around poor communication and clarity from the HR team, based on not knowing who to contact and being unable to answer questions. HR assume that because the app has been a success elsewhere, it will work here as well.

People get excited about the new app and the benefits it will bring, but no consideration is given to the processes and relationships that need to be in place to make it work. The app is launched with a high profile campaign and adoption is high, but the same issues continue. The HR team are confused as to why things didn’t work.

Outcome with QI

Although the app has worked elsewhere, rolling it out without adapting it to local context is a risk – one which application of QI principles can mitigate.

HR pilot the app in a volunteer specialty after spending time speaking to clinicians to better understand their needs. They carry out several tests of change, ironing out issues with the process as they go, using issues logged and clinician feedback as a source of data. When they are confident the app works for them, they expand out to a directorate, a division, and finally the transformational step of an organisation-wide rollout can be taken.

Education into practice

Next time when faced with what looks like a quality improvement (QI) opportunity, consider asking:

How do you know that QI is the best approach to this situation? What else might be appropriate?

Have you considered how to ensure you implement QI according to the principles described above?

Is there opportunity to use other approaches in tandem with QI for a more effective result?

How patients were involved in the creation of this article

This article was conceived and developed in response to conversations with clinicians and patients working together on co-produced quality improvement and research projects in a large UK hospital. The first iteration of the article was reviewed by an expert patient, and, in response to their feedback, we have sought to make clearer the link between understanding the issues raised and better patient care.

Contributors: This work was initially conceived by AB. AB and FO were responsible for the research and drafting of the article. AB is the guarantor of the article.

Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: This article is part of a series commissioned by The BMJ based on ideas generated by a joint editorial group with members from the Health Foundation and The BMJ , including a patient/carer. The BMJ retained full editorial control over external peer review, editing, and publication. Open access fees and The BMJ ’s quality improvement editor post are funded by the Health Foundation.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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15 Product Management Case Studies [Detailed Analysis][2024]

In today’s fast-paced and highly competitive business environment, effective product management has never been more crucial. It is a strategic catalyst that drives innovation and shapes how companies respond to evolving market demands and consumer preferences. This article delves into product management by examining 15 diverse global case studies, each showcasing the profound impact and key learnings derived from some of the world’s most influential companies. From Apple’s groundbreaking entry into the smartphone market to Spotify’s transformation of music consumption, and Toyota’s efficiency-driven Lean Production Model, these case studies offer a panoramic view of how strategic product management can lead to revolutionary changes in various industries. The article aims to provide valuable insights into the challenges faced, solutions implemented, and the overarching effects of these strategies, revealing how companies like Airbnb, Tesla, Zoom, Slack, Samsung, Netflix, and Patagonia have not only achieved market success but also set new benchmarks and trends in their respective domains. Through this exploration, we aim to equip current and aspiring product managers and business leaders with practical knowledge and inspiration to navigate the complex landscape of product management, driving innovation and success in their ventures.

Related: How to Build a Career in Product Management?

1. Apple Inc. – Reinventing the Smartphone

Task/Conflict:

Apple’s entry into the already crowded mobile phone market was a bold move, particularly with the objective of introducing a product that wasn’t just another addition but a complete redefinition of what a mobile phone could be. The challenge was to innovate in a way that would not only capture the market’s attention but also set a new standard for user interaction, functionality, and design in the smartphone industry.

The solution lay in the development of the iPhone, a device that combined a phone, an iPod, and an internet communicator. This integration, coupled with a pioneering touchscreen interface and a focus on user experience, positioned the iPhone not just as a product but as an ecosystem. Apple’s emphasis on design, functionality, and user interface created a product that stood out from its competitors.

Overall Impact:

• Revolutionized the smartphone industry.
• Set new standards for technology and user experience.

Key Learnings:

• Innovation can disrupt established markets.
• User-centric design is crucial in technology products.

2. Spotify – Transforming Music Consumption

In an era dominated by music piracy and declining physical album sales, Spotify faced the daunting task of reshaping how people accessed and paid for music. The challenge was not only technological but also cultural, requiring a shift in consumer habits and a rethinking of the existing music industry’s business model.

Spotify’s approach was to introduce a user-friendly music streaming service, offering a vast library of tracks with both a free, ad-supported model and a premium subscription option. This strategy addressed the issues of accessibility and affordability while respecting the rights of artists and producers, thus presenting an attractive alternative to illegal downloads.

• Influenced the revenue model of the entire music industry.
• Became a leader in music streaming.
• Innovative business models can redefine industries.
• Addressing consumer pain points is key to success.

3. Toyota – The Lean Production Model

Toyota was confronted with the challenge of enhancing efficiency and reducing waste in their production processes. The automotive industry, characterized by intense competition and high operational costs, demanded a strategy that not only improved production efficiency but also maintained high quality.

Toyota implemented the Lean Production Model, a revolutionary approach focusing on ‘Kaizen’ or continuous improvement. This methodology involved streamlining the manufacturing process, reducing waste, and empowering workers to contribute to ongoing improvements. The Lean Model emphasized efficiency, flexibility, and a relentless pursuit of quality in production.

• Enhanced operational efficiency and profitability.
• Established as a benchmark for manufacturing excellence.
• Efficiency and quality are pillars of manufacturing success.
• Continuous improvement drives operational excellence.

Related: Reasons to Study Product Management

4. Airbnb – Revolutionizing Hospitality

Airbnb aimed to carve out a new niche in the hospitality industry, which was traditionally dominated by hotels. The challenge was multifaceted, involving regulatory hurdles, building trust among users, and creating a reliable and scalable platform that connected homeowners with travelers seeking unique lodging experiences.

The solution was the creation of a user-friendly online platform that enabled homeowners to list their properties for short-term rental. This platform not only provided an alternative to traditional hotels but also fostered a sense of community and unique travel experiences. Airbnb focused on building a robust review system and transparent policies to overcome trust and safety concerns.

• Disrupted the traditional hotel industry.
• Became a leading figure in the sharing economy.
• Innovative platforms can create new market segments.
• Trust and transparency are crucial in community-driven businesses.

5. Tesla – Electrifying the Auto Industry

Tesla embarked on the ambitious goal of popularizing electric vehicles (EVs) as a sustainable and viable alternative to gasoline-powered cars. This task involved overcoming preconceptions about the performance, range, and practicality of EVs, as well as establishing the necessary infrastructure for their adoption.

Tesla’s approach was to develop high-performance, luxury electric vehicles that combined environmental friendliness with cutting-edge technology and stylish design. This strategy helped to change the perception of EVs from being seen as inferior alternatives to gasoline cars to desirable, high-tech vehicles. Tesla also invested in building a network of charging stations, further facilitating the practicality of EV ownership.

• Led the transition towards electric vehicle adoption.
• Influenced the auto industry’s direction towards sustainability.
• Sustainable technology can be aligned with luxury and performance.
• Changing consumer perceptions is key to introducing new technology.

6. Zoom – Simplifying Remote Communication

In a market crowded with various communication tools, Zoom faced the challenge of differentiating itself and proving its value. The goal was to provide a solution that was not only reliable and easy to use but also superior in terms of video and audio quality compared to existing offerings.

Zoom focused on creating a user-friendly platform that offered high-definition video and clear audio, even in low-bandwidth situations. This commitment to quality and reliability, combined with features like screen sharing, virtual backgrounds, and easy integration with other tools, made Zoom a preferred choice for businesses and individuals alike, especially during the COVID-19 pandemic.

• Became a staple tool for remote communication.
• Highlighted during the global shift to remote work due to the pandemic.
• Reliability and user experience are critical in technology solutions.
• Agility in adapting to market changes is vital.

Related: History & Origin of Product Management

7. Slack – Redefining Workplace Collaboration

Slack was developed with the vision of transforming the cluttered and inefficient landscape of workplace communication, dominated by email. The challenge was to create a platform that not only streamlined communication but also integrated various work tools to enhance productivity and collaboration.

The solution was an intuitive, chat-based platform that allowed for real-time messaging, file sharing, and integration with a wide range of work tools and applications. Slack’s focus on reducing the reliance on emails and consolidating communication into a single, searchable platform revolutionized team collaboration and internal communication in businesses.

• Changed the dynamics of team communication and collaboration.
• Became a central tool in many organizations for internal communication.
• Streamlining common practices can create significant market opportunities.
• Integration and user-friendliness are key in collaborative tools.

8. Samsung – Innovation in Electronics

Samsung’s challenge was to establish itself as a leader in the highly competitive and rapidly evolving consumer electronics market. This required keeping up with technological advancements and differentiating its products in terms of quality, innovation, and user experience.

Samsung’s strategy involved substantial investment in research and development, focusing on bringing innovative and high-quality products to the market. Their innovation commitment spanned various product categories, including smartphones, televisions, and home appliances. This focus on quality and technological advancement helped Samsung achieve a leading position in the global electronics market.

• Achieved a leading position in the consumer electronics market.
• Known for innovation and quality in product offerings.
• Innovation is crucial in technology sectors.
• Quality and continuous improvement attract consumer loyalty.

9. Netflix – Pioneering Streaming Services

Netflix’s journey began with the goal of transforming the traditional movie rental business. The challenge was to transition from a DVD rental service to an online streaming platform, requiring a technological shift and a change in consumer viewing habits and content distribution models.

The solution was a gradual but determined shift to an online streaming model, offering customers an extensive and ever-growing library of movies and TV shows. Netflix’s investment in original content and exclusive deals with production studios further enhanced their appeal. This strategic pivot catered to the growing demand for on-demand entertainment, free from physical media and broadcast schedules constraints.

• Redefined media consumption habits.
• Led the rise of online streaming services.
• Adaptability to technology and market trends is critical.
• Investing in original content can differentiate streaming services.

Related: Top Product Management Tools

10. Patagonia – Ethical Product Management

In a clothing industry often criticized for environmental and ethical issues, Patagonia aimed to differentiate itself by committing to sustainability and ethical practices. The challenge was not only to maintain profitability but also to influence consumer behavior and industry standards towards more responsible practices.

Patagonia’s approach included using sustainable materials, ensuring transparency in their supply chain, and advocating for environmental causes. Their commitment extended to initiatives like repairing products to extend their lifespan and encouraging responsible consumption. This strategy appealed to environmentally conscious consumers and set a new standard for corporate responsibility in the clothing industry.

• Became a model for sustainability in the clothing industry.
• Influenced both consumer and industry practices towards eco-friendliness.
• Sustainability can be a unique selling proposition.
• Ethical practices enhance brand loyalty and reputation.

11. Microsoft – Shifting to Cloud Computing

Microsoft faced significant challenges in adapting to the rapidly evolving technology landscape. The traditional software model of boxed products had grown increasingly obsolete due to a surge in cloud computing. Emerging competitors like Amazon Web Services and Google’s cloud platform gained momentum, providing flexible, scalable solutions that shifted the market’s preference away from on-premise software to on-demand, subscription-based models. Microsoft needed to transform its business approach and product portfolio to align with these market trends

Under CEO Satya Nadella’s leadership, Microsoft shifted focus to cloud computing, developing Azure as an end-to-end platform providing comprehensive infrastructure and software services. The company also transitioned its flagship Office suite to a cloud-based subscription model with Office 365. They emphasized flexibility, scalability, and security while ensuring seamless integration with existing Microsoft products. Investments in data centers globally and new pricing models enabled Microsoft to compete directly with other leading cloud providers.

• Transformed Microsoft into a leader in cloud computing.
• Significantly increased recurring revenue through subscription-based services.
• Implementation of emerging technologies is vital for staying ahead of market trends.
• Subscription models can create predictable and sustainable revenue streams.

12. Lego – Rebuilding a Toy Empire

Lego was at a crossroads in the early 2000s. The company had overextended its product lines, ventured into unrelated business areas, and faced fierce competition from digital entertainment sources like video games. The result was a decline in sales and profitability, jeopardizing the company’s future and threatening the iconic brand with irrelevance.

To rebuild its brand, Lego implemented a back-to-basics approach, refocusing on its core product, the Lego brick. It also streamlined its product lines and improved internal operations. Partnering with entertainment franchises such as Star Wars and Harry Potter, they launched themed Lego sets that resonated with younger generations. Lego expanded its reach into digital media with video games and movies like The Lego Movie, engaging customers through multiple channels and breathing new life into the brand.

• Restored profitability and renewed consumer interest in Lego products.
• Expanded their presence into digital media and entertainment.
• Diversification and partnerships can revitalize traditional products.
• Engaging customers across multiple channels strengthens brand loyalty.

Related: Inspirational Product Management Quotes

13. Dropbox – User-Friendly Cloud Storage

Dropbox faced the challenge of competing with tech giants including Google and Microsoft in the nascent cloud storage market. While these companies offered vast storage solutions integrated with their productivity suites, Dropbox needed to carve out a niche by appealing to users with an easy-to-use, reliable platform. They aimed to provide seamless file synchronization, security, and accessibility across devices.

Dropbox placed simplicity at the forefront, developing a cross-platform application that allowed users to sync files effortlessly across multiple devices. The system’s seamless synchronization and ease of use differentiated it from other cloud storage providers. They employed a freemium model that offered free storage with the option to upgrade for more capacity and features, attracting millions of users globally and enabling them to monetize their growing user base.

• Became a trusted name in cloud storage, with millions of users worldwide.
• Pioneered the freemium model, offering free and paid plans.
• User experience is a differentiator in competitive tech markets.
• Freemium models can attract users and convert them to paid subscriptions.

14. Nike – Personalizing Athletic Wear

Nike, already a leader in sports apparel, faced stiff competition from rivals like Adidas and Under Armour. The company needed a unique strategy to differentiate its products and capture the loyalty of a diverse, increasingly demanding customer base. Customers wanted personalized experiences, and Nike aimed to address this by providing a solution that matched their specific preferences in athletic wear.

Nike launched the NikeID program, which allowed customers to personalize their athletic gear online, choosing colors, patterns, and custom text. This innovation expanded the company’s appeal to athletes and fashion-conscious consumers alike, helping them express their individuality while boosting engagement. By streamlining the customization process and leveraging digital technology, NikeID created an experience that could be replicated globally, resulting in increased brand loyalty and revenues.

• Elevated customer engagement through personalized experiences.
• Expanded customization to a broad range of products, increasing brand loyalty.
• Personalization can differentiate brands in competitive markets.
• Engaging customers in the design process enhances brand value.

15. Procter & Gamble – Open Innovation with Connect + Develop

Procter & Gamble (P&G), known for a vast portfolio of consumer goods, recognized that the traditional R&D process was becoming slower and costlier, hampering the company’s ability to innovate. With the proliferation of specialized knowledge worldwide, P&G realized that internal expertise alone wouldn’t suffice fulfill the increasing demand for new products across its various brands. They needed to find a way to tap into external innovation to stay ahead of the competition.

P&G launched the Connect + Develop platform, an open innovation initiative that invited inventors, academics, and other companies to submit ideas and collaborate on new products. This platform enabled P&G to access global expertise and accelerate the product development process by integrating external solutions with their own internal capabilities. The platform generated new partnerships that broadened P&G’s R&D reach and enhanced the product pipelines for various brands, significantly improving efficiency and innovation.

• Increased innovation by sourcing solutions from a global network.
• Enhanced product pipelines across multiple categories.
• Open innovation can tap into global expertise for improved R&D.
• Collaborating beyond company boundaries accelerates product development.

Related: Product Management Failure Examples

Closing Thoughts

In conclusion, these case studies exemplify the transformative power of effective product management. They highlight the importance of understanding market needs, embracing innovation, focusing on user experience, and the value of ethical practices. Aspiring business leaders can draw valuable lessons from these examples to navigate challenges and drive success in their endeavors.

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The ACS Quality Framework is broken down into 38 components across 8 criteria. It’s designed to help ensure your quality improvement (QI) projects are both comprehensive and effective.

The framework is universal across all quality programs, and the criteria are organized around the 3 phrases of QI projects:

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Unlock the power of shared knowledge and best practices with our extensive Case Study Repository . The Case Study Repository showcases successful quality improvement initiatives across various programs, implemented by participants of the ACS Quality Programs. Learn from the experiences of others, draw inspiration from their achievements, and apply proven strategies to drive positive change within your own organization.

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50 Product Management Case Studies

We often wonder what kind of process other product teams have created, planned, and most importantly, how they have implemented it. That is why we at Producter have compiled 50 different case studies for you.

2 years ago   •   4 min read

We often wonder what kind of process other product teams have created, planned, and most importantly, how they have implemented it.

That is why we at Producter have compiled 50 different case studies for you.

Brought to you by Roadmape

1- Rules of Flow for Product Management: an AirBnB Case Study

“Engagement” is a term that is so overused in product management that it has almost lost its meaning. So often I’ve heard from teams, “We’ll measure the success of this test with engagement,” which could mean anything from feature click-through to bounce to we-aren’t-really-sure-this-will-drive-conversion-so-we’re-hedging-our-bet. Underneath, the reason this term has been co-opted and jargonized is that genuine, productive engagement can be ramped toward long-term customer loyalty. And loyalty pays off: a loyalty increase of 7% can boost lifetime profits per customer by as much as 85%, and a loyalty increase of 3% can correlate to a 10% cost reduction ( Brand Keys ).

2- The Psychology of Clubhouse’s User Retention (...and churn)

3- Netflix Q1 ’21 Subscriber Growth Miss: Can We Avoid Another One?

As a data analyst supporting a mobile subscription business , Netflix’s Q1 ’21 subscriber growth miss is a classic example of when I would get called for recommendations to prevent a miss in the future. I thought this would make an interesting case study to discuss my approach to finding insights to drive subscriber growth. Sadly I’m not a Netflix employee and will be limited to publicly available data but the wealth of information on the Internet about Netflix is sufficient to generate insights for this case study.

4- Amazon Go Green

As part of the Design Challenge from productdesign.tips, our team came together to find ways for Amazon to encourage more sustainability on their e-commerce platform. As with any unsolicited design project, the challenge comes with a lack of access to application analytics and technical feasibilities. Nonetheless, the question remains: How might we design checkout screens for an e-commerce app to help people recycle the goods they buy?

5- Quora Case Study – The Wonderful World of Quora

Quora has become a substantive resource for millions of entrepreneurs and one of the best sources for Business to Business market. Majorly used by writers, scholars, bloggers, investors, consultants, students this Q/A site has much to offer in terms of knowledge sharing, connection building and information gathering.

6- Building a product without any full-time product managers

Jambb is an emerging social platform where creators grow their communities by recognizing and rewarding fans for their support. Currently, creators monetize fan engagement through advertisements, merchandise, and subscriptions, to name a few. However, this only represents 1% of fans, leaving the other 99% (who contribute in non-monetary ways) without the same content, access, and recognition that they deserve.

8- What if you can create Listening Sessions on Spotify

Summary: The project was done as a part of a user experience design challenge given to me by a company. I was given the brief by them to work on a feature of Spotify and I spent around 25–30 hours on the challenge in which I went through the entire process, from the research to testing.

9- Redesigned Apple Maps and replicated an Apple product launch for it

Quick-fire question; what is the single most important and widely used feature in a phone — asides from texting and instant messaging friends, coworkers and family? Maybe you guessed right, perhaps this feature is so integrated into your life that you didn’t even think about it — either way, it is your phone’s GPS. It is reasonable to say that GPS technology has changed society’s lives in ways we never could’ve imagined. Gone are the days of using physically printed maps and almanacks, when we now have smartphones with navigation apps. Since the launch of the iPhone and the App Store, consumers have been able to use different apps for their personal navigation needs. Everyone has a preference, and apps have come out to try and address every need.

10- Intuitive design and product-led growth

In 2018, Miro was hardly a blip on the radar in the Design world. Fast forward two years, and suddenly Miro is solidly the number one tool for brainstorming and ideation.

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What is customer segmentation, learnings about product development strategy in 2022, keep reading, boost product management with slack: a comprehensive guide to producter's slack integration, mastering the art of product management: 10 essential strategies for success, what is user research.

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• Published: 05 June 2024

A quality improvement study on how a simulation model can help decision making on organization of ICU wards

• Danielle Sent   ORCID: orcid.org/0000-0002-4703-5345 1 , 2 ,
• Delanie M. van der Meulen   ORCID: orcid.org/0000-0002-1678-2057 1 ,
• Andres Alban   ORCID: orcid.org/0000-0002-9552-4039 3 ,
• Stephen E. Chick   ORCID: orcid.org/0000-0002-8026-1571 4 ,
• Ilse J.A. Wissink   ORCID: orcid.org/0000-0002-5551-9868 5 ,
• Alexander P.J. Vlaar   ORCID: orcid.org/0000-0002-3453-7186 5 &
• Dave A. Dongelmans   ORCID: orcid.org/0000-0001-8477-6671 5  

BMC Health Services Research volume  24 , Article number:  708 ( 2024 ) Cite this article

53 Accesses

Metrics details

Intensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs.

A discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients.

The simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources.

Conclusions

The choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management.

Peer Review reports

Introduction

During the 1900s it became clear that clustering of the most critically ill patients was beneficial for their clinical outcomes [ 1 ]. As a result of these findings, Intensive Care Units (ICUs) were developed. At present the question has arisen to what degree clustering of ICU patients by their condition is beneficial for patients and society. In other words, whether a specialized ‘dedicated ICU’ is in favor of a ‘non-dedicated ICU’ with a mixed patient population [ 2 , 3 ]. According to literature, both designs have pros and cons, while strong evidence in favor of one of the two designs is lacking [ 4 , 5 , 6 ].

As a result of an ageing population, a continuous increase of healthcare expenditures and shortages in the available medical workforce, a supply-demand mismatch in ICU capacity has arisen in recent years [ 7 , 8 ]. The capacity strain resulting from this mismatch inevitably leads to increased rescheduling of elective ICU admissions, and to rejection of unplanned patients in need of critical care. To serve as many patients as possible and make efficient use of scarce labor and capital resources, unnecessary capacity loss resulting from unoccupied available beds should be minimized. This raises the question on the ideal ICU design. In this study the non-dedicated ICU refers to general pooling of resources in an ICU with a mixed patient population, and a dedicated ICU refers to an ICU design with specific wards for patients with similar conditions in which dedicated resources are available that are not shared with other wards. In the context of this paper ‘efficient’ is used for a design with as less rejections, rescheduling, and empty beds as possible. In a situation where resources are scarce it is important to be able to see the effects of choices that should be made. By providing a model we give insights into the effects on performance of various designs of ICUs. Based on the principals of pooling, a higher quantity of patients has access to ICU care in a non-dedicated design [ 9 ]. Besides, if certain specialisms regularly deal with long-stay patients, this quickly leads to stagnation of admissions and an increase in the number of cancellations in a dedicated design. However, all this only holds when lengths of ICU stays and other process metrics are otherwise the same for both designs.

According to some studies a dedicated-ICU is associated with lower mortality rates, shorter length of stay, shorter duration of intubation and less blood-stream-infections [ 2 , 10 , 11 , 12 , 13 ], whilst other studies were not able to confirm these findings [ 14 , 15 , 16 ]. One optimistic paper regarding dedicated ICU was from Mirski et al. which showed a dedicated ICU design to be related to a 25–45% reduced length of stay, resulting in reduced healthcare expenditures [ 13 ]. We still find the idea that a dedicated ICU could result in decreased length of ICU stay interesting and want to explore whether these effects could outweigh the efficiency of a non-dedicated ICU. Song et al. as well as Li et al. [ 17 , 18 ] suggest that behaviours might change in a dedicated ICU, for example in reducing rework or overhead associated with multi-tasking, or with greater sense of ownership, resulting in somewhat shorter length of stay and without loss of quality of care. There is a general trend towards decreased length-of-stay in hospitals has been observed in the past decades [ 19 ], and studies show the importance of admissions and discharge policies on ICU performance [ 18 ], where admission policy may include features of the likelihood a bed might be immediately available or alternatively a wait or a rerouting of patients might be required, or potentially a need to reschedule elective surgeries. Together, these and other studies suggest that technology and organization changes have roles to play in improving length of stay and quality of care. Taken together, it remains unclear whether a dedicated ICU results in improved outcomes for individual patients. If length of ICU stay and other process metrics do not outperform the efficiency of a non-dedicated design, a dedicated ICU is expected to lead to increased capacity strain which in itself is associated with deterioration in healthcare delivery, suboptimal patient outcomes and decreased job satisfaction among medical staff [ 7 ].

In this paper we aim to describe and use a simulation model to help ICU departments to visualize trade-offs between a dedicated and a non-dedicated ICU design. Department-specific characteristics can be imported in such a model to study performance of both designs in terms of bed occupancy, rejection, and rescheduling rates. Depending on local preferences, a model can be optimized for one of the performance measures, at the expense of other measures. In conjunction with other operation characteristics such as staffing and quality management, a simulation model can be useful to gain insights into the effects of decisions that should be made. Besides, models such as these can be utilized to reorganize the ICU department and manage expectations to staff and policy makers in case of abrupt changes in circumstances [ 20 ]. We did not also include a wait room, as did [ 21 ], because our application was motivated by COVID-19 response, where scenarios of capacity expansion, allocation of that capacity, and the potential need to reroute urgent patients to other facilities, space permitting, was more relevant, as well as the potential role of rescheduling elective procedures in support of urgent critical care needs. That said, the model’s scenario selection allows for an assessment for potential total bed capacity planning, as well as estimates of performance metrics for tradeoffs between allocating that capacity for specialisms versus having a non-dedicated design.

This paper followed the SQUIRE 2.0 guideline for reporting on quality improvement studies [ 22 ]. The ICU from which admission data are used in this paper, is a large academic hospital in the Netherlands housing a multidisciplinary non-dedicated ICU with a bed capacity of 28 beds divided over four units. INSEAD and Amsterdam UMC cooperated to create a base simulation model which enables to model the performance of a dedicated or non-dedicated design in hypothetical scenarios. It visualizes performance of both designs in terms of rescheduling, rejection, and bed occupancy rates.

Patient inflow data of the local non-dedicated ICU were used, and patients were labeled either as being planned or unplanned. Planned patients were defined as patients that arrived at the ICU via other medical wards in which they had a planned admission. Unplanned patients came straight from the emergency room or from other wards in emergency situations. Arrival, length of ICU stay, rejection, and rescheduling data were also obtained from the local ICU over the years 2015 ( N  = 1881) and 2016 ( N  = 2170). Out of those, 1,779 and 2,043 patients were admitted, and 102 and 127 patients were rejected, respectively. The overall average arriving patients per day was therefore 5.55. In total, 66% of all arrived patients (2,668 patients) were unplanned, and out of them 9% (229 patients) were rejected. Among all patients, 82% (3,117 patients) were admitted on weekdays and 18% (705 patients) were admitted on weekends. Only unplanned patients could be rejected, planned patients were rescheduled if necessary, and in our model the bed partition was, at the expense of rescheduling rates, optimized for rejection rates. Furthermore, all ICU patients were assigned to a medical specialism based on their diagnose on admission (CAPU = cardiopulmonary surgery, CARD = cardiology, INT = internal medicine, CHIR = surgery, NEC = neurosurgery or NEU = neurology or other). The simulation model was coded in R, and an online version is available at https://daniellesent.shinyapps.io/ICU-model/ . Parameters of the model were calibrated to hospital scheduling and LOS data for both planned and unplanned patients for each specialism, and bed capacity characteristics of the hospital. Since no association between arriving patients is expected, the arrival of the unplanned patients is assumed to be a Poisson process. The Poisson assumption was tested using the number of unplanned arrivals per day with a chi-squared test and find p -values of 0.42 and 0.39, respectively, not rejecting our assumption. The arrival process was estimated independently for weekday and weekend arrivals. The arrival of planned patients is follows a categorical distribution, where the probabilities are given by the fraction of days in the two-year period that a given number of patients arrived. The distribution family of the LOS was chosen using the tool Stat::FitTM. The simulation ran for a period of 3,770 days (approximately ten and a half years), where the first 120 days were burned-in to warm up the queue. The remaining ten years were use for evaluation purposes. The model was verified by checking several special cases of inputs to theoretical values provided by queueing theory. More in depth specifications of the model and it evaluation can be found in the article of Alban et al. [ 23 ].

For this study, we extended the base model to be able to run four clinically relevant scenarios that help decision-making on ICU design under varying hypothetical circumstances. First the base case model will be shown with a maximum capacity of 28 beds, the total number of beds that were available at the ICU of the hospital the data was obtained from. The base case model compares a dedicated with a non-dedicated ICU design. In this scenario the dedicated ICU is divided into four specialized units, based on the current infrastructure of the building. This structure uses a partition of six beds for unit CAPU, eleven beds for a combination unit of CARD/INT/Other, five beds for unit CHIR and six beds for a combination unit of NEC and NEU. This partition was found to be optimal for 28 beds in terms of all three performance measures. Yet, in practice bed capacity fluctuates due to specific circumstances such as workforce constraints, a pandemic or holidays either increasing or decreasing the number of beds available for patients. Therefore, a second model shows the performance for a dedicated and a non-dedicated ICU under varying bed capacities, to complement other studies of ICU dedicated versus flexible capacity [ 17 ], which may be influenced as well by patient mix [ 21 ]. A third scenario visualizes the situation in which a dedicated ICU design decreases length of ICU stay (LOS) in advance of a non-dedicated ICU, as was shown by Mirski et al. [ 13 ]. Finally, a scenario with an increased inflow of unplanned patients is simulated, motivated by the COVID pandemic in which the inflow increased dramatically. To do so, a new simulation population was created by decreasing the original inter-arrival rates. Taken together, the graphs may show the numbers of beds required in dedicated or non-dedicated settings in order to achieve a given threshold for metrics of interest, thereby presenting a guage with which to assess potential behavioural effects that might be active [ 18 ]. Ethical approval was not required, only anonymized data containing date and time of admission and (if applicable) departure and specialism were used for the model. No further patient data was used.

The simulation of the first scenario is shown in Fig.  1 and presents the base-case model with 28 beds for a non-dedicated and a dedicated ICU design in terms of a (occupancy), b (rejection) and c (rescheduling) rates.

Each measure is reported for the overall performance and for performance of the four different clinical diagnose groups (CAPU, CHIR, INT/CAR, NEU/NEC). As the general ICU is not divided into units, the values correspond to the performance specific to the cluster of patients assigned to the respective dedicated unit. The overall rejection rate for the non-dedicated ICU is 7.7% versus 18.1% for the dedicated ICU. The overall rescheduling rates are 13.0% versus 100.5% (meaning that some patients had to be rescheduled more than once). The overall occupancy rate lies lower in the dedicated ICU (75.1% versus 67.4% for non-dedicated and dedicated respectively). Note that the non-dedicated ICU has lower rejection and rescheduling rates despite the higher occupancy rate. The rescheduling simulation model shows a high standard error, which originates from the fact that the number of rescheduled patients per day is mostly very low, and binary (yes rescheduled or not rescheduled).

Comparison of the non-dedicated and dedicated ICU in terms of occupancy ( a ), rejection ( b ) and rescheduling ( c ) rates in base-case situation

Figure  2 shows the performance of a dedicated (a) and a non-dedicated (b) ICU with varying bed capacity, ranging from 14 to 50 beds. While the non-dedicated ICU starts out with a higher occupancy rate than the dedicated ICU (86.9% vs. 76.8%), both end up with a similar occupancy rate at the capacity of 50 beds (44.9% vs. 44.6%). Between the minimum and maximum bed capacity that we tested, the occupancy rate in the non-dedicated ICU is constantly higher than in the dedicated ICU. Note that for the dedicated ICU it is observed that at a capacity of 34, 37, 42 and 46 beds, the rescheduling rate drops. At these steps, the capacity for the specialism with most planned patients was increased with one bed, resulting in dropping of the rescheduling rate. For the non-dedicated ICU, the rejection rate and rescheduling rate both come close to their minimum around a bed capacity of 30 beds, while for the dedicated ICU this is seen at a bed capacity of 45 beds. The rescheduling rate is steadily higher than the rejection rate. The comparison of Fig.  2 a and b also allows for comparison of the minimum number of beds required to reach a rejection rate below 5%, which is a target in ICU organization management in the Netherlands [ 24 ]. In the non-dedicated ICU setting, 30 beds are required to meet this target, while in the dedicated ICU 38 beds would be needed. With a bed capacity of 30 beds, the non-dedicated ICU would have an average occupancy of 71.7%, while a bed capacity of 38 beds in the dedicated ICU leads to an average occupancy of 56.3%. The average rescheduling rates for this number of beds lie at 7.4% - and 39.4%, for the non-dedicated and dedicated ICU respectively.

Performance of a dedicated ( a ) and a non-dedicated ( b ) ICU with a varying bed capacity

Figure  3 shows a simulation of the dedicated ICU setting, where an average length of ICU stay (LOS) reduction is assumed. It demonstrates that in the dedicated ICU, bed occupancy rates decrease steadily as the average LOS becomes shorter. In the base-case situation, with no LOS reduction, the overall occupancy rate was 67.4% in the dedicated ICU, while at a hypothetical 30% LOS reduction it lies at 53.3%. With a hypothetical 30% reduction in LOS, rejection rates decrease to 7.5% and rescheduling rates decrease to 28.6%. As described in the first scenario, in the non-dedicated ICU setting the average rejection, rescheduling and occupancy rates were 7.7%, 13% and 75.1%, respectively.

Hypothetical lengths of stay reduction in a dedicated ICU design

The last scenario is shown in Fig.  4 for a non-dedicated design (a) and a dedicated design (b). In this scenario the simulation model ran for various increases (10%, 20% and 30%) of unplanned patients of each specialism. The 0% increase bar shows the results for the base-case patient inflow. In the non-dedicated ICU design the occupancy rates are 75.1%, 78.8% and 83.4% for a patient inflow of 0%, and an increase of unplanned patients of 10%, 20% and 30% respectively. In the dedicated ICU the occupancy remains lower with rates of 67.4%, 70.2%, 74.7% and 77.5% respectively. In the non-dedicated ICU setting the rejection rates (7.7%, 9.7%, 16.3% and 22.8%) and rescheduling rates (13%, 17.2%, 33.1% and 51.5%) increase gradually as the inflow of unplanned patients increases. In a dedicated ICU design the rejection rates are higher for the same increase in inflow, 18.1%, 21.3%, 27.9% and 34.0%, respectively. The rescheduling rates for 0, 10, 20 and 30% increase in unplanned patient inflow are 100.5%, 106.3%, 129.5% and 151.2% respectively, meaning that some patients will be rescheduled more than once.

Increase in unplanned patient inflow in a non-dedicated ( a ) and a dedicated ( b ) ICU design

This study aimed to provide insights in the capacity performance of a dedicated ICU in comparison with a non-dedicated ICU by using a discrete event simulation model. Previous literature shows no consensus on a preferred design [ 2 , 10 , 12 , 13 , 14 , 15 , 16 ]. However, it is known that organising a non-dedicated ICU has its challenges because effective management is amongst other factors depending on the establishment of continuous professional development, ensuring that all personnel is equipped to respond to the diverse needs characteristic of non-dedicated ICUs we note that our simulations do not answer the question of whether a non-dedicated ICU should or should not be preferred over a dedicated ICU. We quantify the performance, in terms of the number of rejected and rescheduled patients and the occupancy rate of the ICU’s. Our simulation model was developed using admission data of a large university hospital ICU in the Netherlands.

The first scenario showed that the non-dedicated ICU design dominates the dedicated ICU design in terms of occupancy, rejection, and rescheduling rates in the base-case scenario. The second scenario showed that the dedicated ICU needed eight more beds to accomplish a desired rejection rate below 5%, when compared to the non-dedicated ICU design. Yet, the model showed that scaling up with eight beds to a total of 38 beds in a dedicated design resulted in a bed occupancy rate of 56.3%, which can be seen as inefficient use of expensive and scarce ICU resources. The non-dedicated ICU should scale up with at least two more beds to be able to have a rejection rate below 5%, as is described in the national guidelines [ 24 ]. Figure  2 b shows that the rescheduling rates for the dedicated ICU remain high, up to a high number of available beds. This is likely because in our analysis the bed partition was optimized for rejection rates, clearly at the expense of rescheduling rates. The third scenario explored the theory that a dedicated ICU leads to a shorter average LOS due to the specialization of healthcare delivery as was shown by Mirski et al. in 2001 [ 13 ]. The simulation model showed that if a dedicated design would lead to a 30% reduction in average LOS, the non-dedicated ICU would still outperform the dedicated ICU in terms of lower rescheduling rates and higher occupancy rates of available beds. The rejection rates for the dedicated (7.5%) and non-dedicated (7.7%) ICU would be almost equal. One might wonder whether a significant decrease in LOS is still realistic today. The fourth scenario studied unexpected increases in unplanned patient inflow. The non-dedicated ICU adapts easier to the situation in terms of higher bed occupancy rates. In the case of a 30% increase in unplanned patients, 22.5% of the beds remains empty in a dedicated design, while this is 16.6% for the non-dedicated ICU. Thus, more beds remain empty while at the same time rejection and rescheduling rates increase in the dedicated ICU.

The simulation models made clear that from a resource efficient perspective a non-dedicated ICU outperforms a dedicated ICU. Yet, some studies found improved individual patient outcomes in a dedicated ICU design [ 2 , 10 , 11 , 12 , 13 ], whilst others did not [ 14 , 15 , 16 ]. We quantified some surprising ‘jump’ effects for the specialisms that some other papers have not seen – e.g. when there is a long-hauler. Song et al. [ 17 ] as well as Li et al. [ 18 ] suggest that behaviours might change in the dedicated – a greater sense of ownership of patients, and in our case greater sense of specialization for a given specialism. This would suggest that using the same service rates for both the simulation of the non-dedicated ICU as well as the dedicated ICU, might not be realistic, since dedicated ICU’s might have slightly better length of stay for instance. In this light, it should be noted that it remains unclear whether these potential positive effects in the dedicated ICU are associated with the level of specialization or are a result of structural lower bed occupancy rates in this setting. Contrary to low bed occupancy, high occupancy rates could result in higher rejection and rescheduling rates, which are associated with higher mortality rates and inferior patient prognosis [ 7 , 25 , 26 , 27 , 28 , 29 ]. Hence, the previously found positive effects of a dedicated design in some studies could also have been effects of structural lower bed occupancy rates in this setting, instead of being an effect of a higher level of specialization. Furthermore, specialization and impact on outcome may also follow a U-shape curve in which higher level of specialization may also result in not being able to diagnose and treat complications of another domain. Taken together with other studies that did not find evidence for improved outcomes for individual patients in a dedicated ICU design, the potential gains of a dedicated design remain uncertain, while capacity benefits of a non-dedicated design are evident [ 14 , 15 , 16 ].

Strengths of this study are the development and application of a simulation model that quantifies trade-offs that are important in capacity management in ICUs. ICU’s can use local admission data to personalize the model. Limitations of this study are that while designing our simulation model, we were not able to include daily practice issues such as the limitation of the number of available beds due to sickness or holiday leave of nursing staff, changes in inflow of patients throughout the year, etcetera. The simulation furthermore quantifies three capacity trade-off measures, but does not include assessment of quality, survival, service times, costs and other measures that are also involved in the multi-criteria decision of ICU design. Further, the simulation model was only used in one single-center case, and we did not explore other ICU designs or more flexible models. Other industries showed for example that if products are interchangeable in a multi-factory supply network (long-chain-model), in which each factory has a backup (e.g. factory one has two as backup, factory two has three as backup, etc.) then the performance improves [ 30 ]. However, for ICU care delivery, it is unknown what the quality and safety effects are if for example a dedicated surgery patient, is admitted to a dedicated cardiology ward.

Future research could expand the simulation model and add for example a dedicated ‘isolation’ unit to support ICU design decision making during a pandemic such as COVID-19 or outbreak of more regular infectious diseases that require patients to be treated isolated from other patients.

The model we present shows how a simulation can be utilized to make trade-offs between clinical goals in terms of rejection and rescheduling rates and efficiency in terms of occupancy rates. It helps to find a bed occupancy rate at which the rejection and rescheduling rates are acceptable for a specific ICU in different scenarios. The insights gained from the model can support decision making on the local ICU design. The model showed that a non-dedicated design outperforms a dedicated ICU in terms of higher efficiency. These metrics, as a function of capacity and design, are useful inputs to complement local data on quality and local specialization skills to support a local ICU design decision.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Online access to the R code of the event simulation model is available on reasonable request.

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Acknowledgements

The authors would like to thank Nathalie Nikodym, MSc and Alice Lvova, MSc for their work on earlier parts of the (original) model.

Alban and Chick acknowledge the support of the European Union through the MSCA-ESA-ITN project (676129).

Chick acknowledges research funding as the Novartis Chair of Healthcare Management at INSEAD.

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Danielle Sent & Delanie M. van der Meulen

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Management Department, Frankfurt School of Finance & Management, Frankfurt am Main, Germany

Andres Alban

Technology and Operations Management, INSEAD, Fontainebleau, France

Stephen E. Chick

Department of Intensive Care Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands

Ilse J.A. Wissink, Alexander P.J. Vlaar & Dave A. Dongelmans

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Substantial contributions to the conception and design of the work: Danielle Sent, Andres Alban, Stephen E. Chick, Dave A. Dongelmans. Substantial contributions to the acquisition, analysis, or interpretation of data for the work: Danielle Sent, Delanie M. van der Meulen, Ilse J.A. Wissink, Alexander P.J. Vlaar, Dave A. Dongelmans. Wrote the first draft of the manuscript: Delanie M. van der Meulen, Danielle Sent. Revised the manuscript: Andres Alban, Stephen E. Chick, Ilse J.A. Wissink, Dave A. Dongelmans. Final approval of the version to be submitted for publication and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: all authors.

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Sent, D., van der Meulen, D.M., Alban, A. et al. A quality improvement study on how a simulation model can help decision making on organization of ICU wards. BMC Health Serv Res 24 , 708 (2024). https://doi.org/10.1186/s12913-024-11161-2

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Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

• Lu, Qi-Peng
• Mao, Xiao-Dong
• Wan, Hua-Jun
• Wang, Ci-Yong

To analyse and continually improve existing issues in the quality improvement process of medical linear accelerators (LINACs) and enhance the quality control management of LINACs. Data were collected from eight LINACs (sourced from three manufacturers) at Zhejiang Cancer Hospital using Excel diaries between January 2019 and December 2020. The data description and analysis were performed using the analytic hierarchy process, SPSSAU and Excel software, and mean-time-to-repair (MTTR)/mean-time-between-failure (MTBF) metrics. Continuous quality improvement was executed using the quality control circle (QCC) quality management method. After quality improvement, the risk frequency of 'LINAC down' events decreased by 43.63% and downtime was reduced by 40.45%. The weight of downtime risk improved by 73.69%. The MTTR recovery value increased by 31.90%, and MTBF reliability increased by 2.97 h. The simulation results demonstrated that the proposed quality improvement measures could effectively decrease the frequency and duration of downtimes, consequently extending the normal operational time of LINACs. Transitioning from instant repair to preventative maintenance can enhance the operational efficiency of equipment and yield economic benefits for hospitals. The QCC method and the event risk evaluation model are effective in reducing the downtime of LINACs and improving their quality control management.

• Medical linear accelerators;
• Quality control circle;
• Risk weight;
• Mean time to repair;
• Mean time between failure;
• Medical equipment management

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Quality improvement and healthcare: The Mayo Clinic quality Academy experience

Nneka i. comfere.

b Department of Dermatology, Mayo Clinic, Rochester, MN, United States

John C. Matulis, III

c Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, United States

John C. O'Horo

a Division of Infectious Diseases, Joint Appointment Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States

Associated Data

What is Quality Improvement (QI)? Paul Batalden and Frank Davidoff, in 2008, described QI as “the combined and unceasing efforts of everyone—healthcare professionals, patients and their families, researchers, payers, planners and educators—to make the changes that will lead to better patient outcomes (health), better system performance (care) and better professional development” [1] . The concept of QI intimately links front-line staff with a fundamental responsibility to improve the systems they work in. In essence, Quality Improvement asserts that everyone has two jobs: first, to do the job they were trained to do, and second to improve the system in which they do that job [2] .

While this definition is recent, the application of formal QI methods to healthcare has been evolving over the past century [3] , [4] , [5] . Important historical eras, individuals and events which have influenced the evolution of Quality Improvement in healthcare is shown in Table 1 . QI methodologies, originally used in industry, include frameworks such as the Plan-Do-Study-Act (PDSA), the Malcolm Baldridge model, Lean and Six-Sigma. All of these QI frameworks require adhering to an iterative, methodical process where the underlying system is systematically examined at the project outset. A number of different tools may be employed to understand variation in underlying performance. Typically, a detailed exploration of the underlying system, and thus an understanding of the drivers of variation in performance need to be made clear before solutions are generated. Interventions to address those performance gaps, which must not have been pre-determined, may evolve during the project and are tailored to the specific context of the clinical setting.

Important milestones in the evolution of QI in healthcare.

Dates	Event Description	Importance
1911	Ernest Codman opens his “end results” hospital in Boston	Errors were reported and shared, with the intent of improving performance
1920s–1940s	Walter Shewhart and W. Edwards Deming pioneer Quality Management within manufacturing	Applied foundational concepts of studying a system, understanding variation and understanding human behavior to improve performance
1951	Joint Commission established	Formal regulatory oversight of hospitals
1966	Avedis Donabedian publishes “Evaluating the Quality of Medical Care”	Established framework for evaluating Quality in healthcare including the concept of Structure-Process and Outcome Measures
1980	Toyota develops its Lean production system; Motorola develops Six-Sigma tools	Principles from these approaches to improvement are eventually widely adopted in healthcare
1986	The National Demonstration Project on Quality Improvement in Health Care (NDP) is launched	First modern, large-scale effort bringing together thought leaders and innovators in Healthcare Improvement
1991	Institute for Healthcare Improvement is established	IHI has been the leading organization promoting Quality Improvement in Health Care over the past 25 years
1994	Lucian Leape publishes “Error in Medicine”	Describes the prevalence and underlying drivers of medical error
1998	“To err is human” report published by the Institute of Medicine	Increased public attention brought to Quality and Safety within healthcare
2006	“Keystone study” published by Pronovost et al.	Establishes the importance of the standard use of checklists to improve patient safety; subsequently leads to recognition of the role of context in application of Quality Improvement interventions
2015	MACRA (Medicare access and CHIP reauthorization act) passed	Numerous incentives created to link payment for healthcare services to the Quality of care provided

Infrastructures which formally support the adoption and application of these formal QI frameworks are now present in many large healthcare organizations within the United States [6] . While these methodologies have been widely adopted in American healthcare, they have shown variable success in improving system performance and patient outcomes in healthcare [7] , [8] , [9] . There are many common challenges to full, consistent implementation of sound QI methods across any healthcare organization: competing strategic priorities, inadequate leadership support, limited QI education, limited physician engagement, inappropriate focus on interventions, and lack of recognition for QI are among the challenges that organizations face [6] , [10] , [11] . Working to overcome these and other barriers in an expanding healthcare work-force is a challenge that healthcare organizations will continue to face in the years ahead.

Here, we describe the Mayo Clinic Quality Academy’s (MCQA) blended approach to application of QI methods across a diverse and complex healthcare organization.

1. The blended approach: an introductory toolkit for quality improvement

There are a variety of different tools used for quality improvement in healthcare. At Mayo Clinic, we view the “plan-do-study-act” (PDSA) cycle as the fundamental tool used in our quality improvement framework. Using this framework, a small test of change can identify a potential solution to a problem, and using appropriate techniques, scaled and improved to a larger level. In this “blended framework,” we view the tools of six sigma and lean as complementary ways of going from a PDSA cycle to a larger comprehensive quality project. We fit the larger project into the Define-Measure-Analyze-Improve-Control (DMAIC) framework from Six Sigma, though the phases of DMAIC this can be applied to lean and other projects (see Fig. 1. ).

The Blended Approach To Quality Improvement.

1.1. “Just do it”

The most basic kind of quality improvement effort “just do it.” This is a simple, low risk local project where a problem can be identified and fixed by a small group of people who completely control that process. These are things like fixing a broken piece of equipment or eliminating a clearly unnecessary step in an isolated workflow. Empowering workers to feel that they can contribute to the quality of their work is critical to creating a “culture of quality,” and recognizing that not every improvement has to be a project helps ensure that quality improves continuously. In a “just do it” situation, however, there is a risk of not understanding the upstream and downstream effects that a change may have, so adequate understanding of the systems processes and global workflows remains important when deciding if an opportunity for improvement is really of the “just do it” variety. The most effective kind of “just do it” interventions may be seen as part of a larger project, where some low hanging fruit are identified as targets for short-cycle interventions and implemented before the larger project is completed. This is similar to a “Kaizen blitz” approach.

The PDSA is a step up from the “just do it” mentality. In these projects, a structured planning approach is used to appropriately plan an intervention, test on a small level, and iteratively improve and enlarge the project. A tool or process could be piloted by an individual, studied, improved, adopted by a work unit, improved again, and rolled out to a larger department. PDSA cycles allow for more opportunities for studying some of the downstream impacts a change may have, and optimizing a process. More importantly, PDSA brings in change management principles, allowing for studying how to adapt and implement successful models of change from one area or pilot into other similar areas, studying the culture and workflow as it spreads throughout.

Lean is an approach to reduction of waste. The goal of Lean is to improve value as perceived by the customer (in healthcare, typically the patient) by recognizing those activities that do not add value and reducing or eliminating them. The most common example in healthcare is waiting and delays. Although there are certain wait time that are unavoidable (e.g. analytic time for laboratory tests), the amount of time spent in a exam room, hospital room or waiting room without any kind of provider is a known dissatisfier to patients and contributes little to their overall care. Lean projects identify which sort of wasteful activities like that can be reduced through serial process improvements, such as improving the flow of persons or materials throughout an emergency department to reduce the total “down time” a patient may have during an intense evaluation.

The aggressive elimination of waste in Lean involves attempting to understand what is key to a system and identifying where value is not being added. This may include steps where a process is redone or over-worked, or where a inadequate product or resource is delivered to a required step. Tools such as value stream mapping are used to understand where value is added, and where bottlenecks exist that can be eliminated to allow for efficient and timely delivery of a quality product.

5S is probably the best known example of a “lean” tool, though lean can be adopted without 5S (and to a certain extent, 5S can be implemented without a broader Lean approach). In 5S, a workplace is organized in such a way as to reduce inefficiency and waste. In healthcare, examples typically include stockrooms, laboratories and even procedure carts. The first “S” is “sorting,” going through the area and removing that which is truly not necessary. Items which are not needed make it harder to find those that are in an efficient way, and by removing them, work can be more effective. The second “S” is “set in order,” where items are rearranged to fit the workflows; commonly required tools are co-located to make them easier to find and quicker to gather together. The third S is “Shine,” where a workplace is clean and organized enough to make it obvious when something is out of place. This leads to the fourth “S,” “standardize,” where procedures are put in place to help ensure that the organizational schema is easy to follow. Visual guides may be implemented to make it clear what belongs where, checklists to ensure restocking follows the same protocol, and training materials to make sure all those who work in this space know how to optimally apply it. The final S “Sustain,” looks to move the first 4S’s into the organizational culture, encouraging workers to continuously find areas for improvement, and reassess if an area slips into disorder to restart the process.

Although typically thought of as a way of organizing physical space, 5S principles have been successfully applied to time in schedule-based projects, and to virtual space with one of the quality academy’s most successful offerings “take control of your e-mail,” a 5S approach to optimizing in box management. Thus, the principles can be applied to a broad array of situations where there is a gap in efficiency.

1.4. Six sigma

Six Sigma is traditionally associated with quality improvement, and has its roots in manufacturing. Unlike Lean, where the emphasis is on waste reduction and improving efficiency, Six Sigma targets defects and finds ways to reduce defects. Indeed, it gets its name from the target – that a defect should only occur at the sixth standard deviation (sigma), or 3.4 defects per million opportunities. This is equivalent to a 99.99966% rate of quality performance. Put in healthcare terms, this can seem daunting, but if we are to accept only a target of 99.9% quality, every day in the USA, this would work out to 11 babies being given to the wrong patient [12] . In 2008, transfusion fatalities due to mismatch operated at a 19.4 DMPO/6.1 sigma level [13] . Although the six sigma level may be more conceptual or aspirational for many areas in healthcare, in many more, it can and has been achieved.

Six sigma works via a five step process. In the first step, a problem is “defined.” Ensuring an adequate definition of the gap between the current state and an achievable future state informs all future steps in a six sigma project. Without a clear vision or criteria for success, a six sigma project cannot succeed. The define stage is summed up in the (perhaps apocryphal) quote from Yogi Berra “If you don’t know where you’re going, you’ll end up someplace else.” Tools often used in the “define” stage include stakeholder assessment, project charters, critical to quality trees, and aim statements.

The second step, “Measure,” emphasizes the statistical and scientific elements of Six Sigma. A thorough baseline evaluation of the present state to assess current functionality in a way that’s reliable and reproducible so that performance can be re-measured and re-analyzed as needed. The measure phase may include mapping a process, histograms, observations and check sheets.

In the third phase, “Analyze,” data from the measurement is reviewed to determine what driving the gap in quality identified in “define.” This emphasizes some statistical tools like control charts, correlation analysis and Pareto diagrams, but can also include more qualitative analytic tools like root cause analysis and cause and effect diagrams. From the analyze phase, drivers of the quality gap are identified and targeted for improvement.

The fourth phase, “Improve,” is where the improvements are actually developed and trialed. There are some standard improvements for specific project (e.g. some of the Lean toolkit if waste is identified as a driver, future state mapping and brainstorming to effectively crowdsource solutions), but often times, this ends up being a phase of iterative improvement, harkening back to the PDSA cycle. With appropriate measurement tools and analysis, however, these PDSA cycles can be more targeted for more complicated problems than a simple “just do it” problem could hope to achieve.

Once a project is successful, it moves into the “control” phase, where the team seeks to ensure gains aren’t lost by transitioning ownership to a group that will continue to function once the quality project team is disbanded, and appropriate guard rails are in place to prevent backsliding. This often includes such features as a measurement which can continue to be followed and an action plan if defects or adverse events exceed a certain threshold. Control phase methodology is often a stumbling point for otherwise successful projects, as sustaining the gains as processes and tools are transitioned into other operational owners is inherently difficult. A combination of tools to ensure ongoing compliance (e.g. checklists) and measures to follow.

All of these tools exist on a continuum, and tools from Six Sigma may be applied to smaller projects and vice versa. Also, although presented linearly, there is often a need to loop back to a prior step and cycle through again when problems are better understood. A culture that accepts and encourages continuous improvement is more important to effective QI work than any stream of steps or individual process.

2. Tuberculosis and quality improvement

Tuberculosis care is an area ripe for quality improvement initiatives. In any situation where patients, practitioners, guidelines and healthcare agencies attempt to simultaneously engage for a time-sensitive project, the complexity will inherently lend itself to inefficiency and errors. Individuals make such a system work through meticulous attention detail and follow up, but this comes at the expense of the valuable provider’s time and energy.

Many of the problems surrounding tuberculosis care are typical Lean problems. The processes followed by clinics and public health to enroll patients in tuberculosis treatment are paperwork-heavy, and can be a cause of extensive over- and re-processing, common targets for Lean initiatives. Another area which would be well suited to lean is mis-triage, where a patient may be referred to a tuberculosis clinic for active TB when they actually have latent TB, or vice versa. In either situation, matching the required resources to the task cannot be done efficiently a priori , and methodically studying the drivers to this kind of waste would be a perfect application of lean.

TB care also presents typical six sigma problems. In the context of patient care, loss to follow up, missed appointments and preventable drug toxicities are all “defects” in the Six Sigma sense of the word, and each are the end result of a complex series of processes and opportunities that can be defined, measured, analyzed, improved and ultimately controlled.

These are some typical examples of quality improvement work applied to this setting, but ultimately, effective QI in TB care will have to be driven by knowledgeable TB providers. In Japanese manufacturing, the term “get to Gemba” is used to describe the need for management or quality improvement personnel to walk the production line and understand the processes by which their products are made before they work on changing them. TB needs to have leaders educated in quality tools.

3. The Mayo Clinic Quality Academy

The Mayo Clinic Quality Academy (MCQA) was established in 2006 as a ‘grass-roots’ effort to support ongoing quality improvement activities in the institution. This effort was led by health systems engineering staff and a handful of Mayo physician and administrative leaders who had pursued external training in quality improvement (QI). MCQA’s functions fulfill the Education mission of our institution’s three shield mission (Education, Practice and Research), and reports to the Executive Dean of the Mayo Clinic College of Medicine and Sciences. MCQA’s primary mission is to develop core knowledge and capabilities in quality improvement (QI) and to build capacity within the Mayo Clinic staff to apply QI methods to address identified gaps in quality. In addition to providing support to individuals and teams engaged in QI, MCQA also leads institutionally directed strategic large-scale collaboratives to address Enterprise-wide quality deficiencies.

MCQA is staffed by 50 QA faculty, 4 quality improvement advisors with expertise in QI and 8 operational staff (operations manager and administrative assistants). Our faculty design curriculum, teach courses, coach and mentor teams engaged in QI. MCQA staff attend to the administrative needs of our various subgroups including an Education committee, Communications group, Mayo Clinic Quality Fellows Program, Curriculum Oversight Subcommittee and an Office of QI Scholarship. The MCQA staff are also responsible of coordinating and planning all QI related activities across all campuses including annual quality conferences, Lean collaborative team presentations and Quality grand rounds events among many other events to recognize and share QI efforts broadly.

The MCQA offers a broad range of services including a comprehensive curriculum composed of face-face and online courses, collaboratives, longitudinal certification programs, team coaching and mentoring, QI project review and faculty development offerings. This curriculum is reviewed periodically, based upon learner feedback, current knowledge about successes and failures of the applications of existing QI methods and practice needs as determined by the Practice Quality Subcommittee. Quality improvement training at all levels is designed to support two key components of Mayo Clinic’s strategic plan, 1) to deliver the highest-value, and most trusted care for our patients and 2) to achieve operational excellence by improving and maintaining efficiency, productivity, and quality (outcomes, safety, patient experience) in order to provide high quality, affordable outcomes at a low cost. Primary services offered by the MCQA include, the development and delivery of a broad-based QI curriculum, coaching and mentoring QI teams, consultation with work unit leaders on QI training needs, curation of training resources, tracking and monitoring QI training of staff.

MCQA faculty comprise physicians, advanced practice providers, nurses, allied health staff and administrative staff in areas that are involved in direct and indirect patient care. Faculty holds primary assignments in other areas of the institution, and have QI expertise. They have successfully trained and coached individuals and teams in quality improvement, resulting in measurable improvements in inter-professional staff engagement and empowerment, scholarship and improved clinical and financial outcomes. Outcome metrics include numbers of staff (trainees, physicians, allied health staff) who have taken courses through MCQA or who have achieved certification at the Bronze, Silver and Gold levels, periodic surveys of MCQF participants, and team-reported time, cost and FTE savings associated with interventions.

MCQA courses are drawn from existing frameworks in Six Sigma, Lean, PDSA and problem solving. A collaborative model of work unit based, inter-professional training is a successful model for teaching quality improvement to teams at Mayo Clinic. When an educational intervention is desired to aid work unit based quality improvement activities, QA provides consultative services and work unit based didactic teaching and coaching resources to these teams. Our collaboratives aim to engage health care teams that work together in practice and strive to address deficiencies in the quality or safety of health care. In these collaboratives, didactic coursework on specific QI tools and methods is coupled with their application in a quality improvement project using the DMAIC framework to address identified gaps in quality.

Several strategies are employed to engage and empower staff in quality improvement training. The Mayo Clinic Quality Fellows (MCQF) certification program is one of such strategies. This is a longitudinal certification program that certifies staff at escalating levels of QI competency (Bronze, Silver, and Gold) from novice through expert levels. MCQF certification is eligible to all staff at Mayo Clinic. At the Bronze level, staff learn the importance of quality improvement in their daily work, understand their role in recognizing and addressing gaps in quality and how these may impact patient care, recognize key components of quality including safety, outcomes and patient experience. The Silver level is focused on the application of QI tools and methods to eliminate quality deficiencies in practice. At the Gold level, participants demonstrate competencies in facilitating, coaching and leading project teams through process improvement, incorporating change management principles in the course of the project and leading the diffusion and dissemination of improvements throughout the practice.

MCQA arose organically in our institution in response to a growing recognition amongst staff of the value of quality improvement in advancing our daily work and ultimately ensuring delivery of high quality and safe patient care. The evolution of MCQA has resulted in continual refinement of our curricular content, delivery methods, mentorship and coaching capabilities and our recognition and reward activities. These experiences have yielded many lessons that would be of benefit to others seeking to institute similar programs or strategies. Key attributes of a successful educational QI program include:

• • Early senior leadership support to champion the quality movement and ensure adequate allocation of resources
• • Visible reward of staff and recognition of their QI accomplishments
• • Provision of opportunities for both Individual and team-based QI training
• • Celebration and promotion of a culture of quality improvement
• • Monitor and respond to results, both quantitative and qualitative

MCQA leadership continually works with institutional leaders and the Practice Quality Subcommittee to align curriculum development efforts with current quality initiatives and objectives. Working through MCQA faculty who have education and quality improvement expertise, faculty design and teach courses to equip Mayo Clinic staff with the necessary knowledge and skills to meet the needs of the both the patient and the practice.

4. Conclusion

Quality improvement is more traditionally associated with manufacturing and business due to its historical roots. Nonetheless, these same tools can and have been used to deliver better care at lower cost at the bedside. Educating clinicians in effective quality improvement techniques is critical to the future of healthcare. Our blended approach of quality improvement methodologies coupled with health care subject matter expertise has made the Mayo Clinic Quality Academy successful in this charge.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jctube.2020.100170 .

Appendix A. Supplementary data

The following are the Supplementary data to this article:

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