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Using Improvement Science in Healthcare to Create True Change

With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success. The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:

  1. Analyze the opportunity for improvement and define the problem.
  2. Scope the opportunity and set SMART goals.
  3. Explore root causes and set SMART process aims.
  4. Design interventions and plan initial implementation.
  5. Implement interventions and measure results.
  6. Monitor, adjust, and continually learn.
  7. Diffuse and sustain.
  8. Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.

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How Data Can Reduce Length of Stay and Keep the Revenue Stream Flowing

Many organizations face high costs and diminishing returns due to unnecessarily high length of stay (LOS) and readmission rates. Elevated LOS and readmission rates can indicate low quality care and also result in costly financial penalties. Therefore, addressing LOS and readmission rates can eliminate avoidable financial consequences, while keeping patients out of the hospital and less likely to develop hospital-acquired infections. Health systems can leverage analytic insight to reduce unnecessary patient LOS and readmission rates, resulting in lower costs for health systems and better health for patients, by applying three data-driven strategies:

  1. Implement process changes.
  2. Remove discharge barriers.
  3. Improve care transitions.

Three Data-Informed Ways to Drive Optimal Pediatric Care

Pediatric care has unique challenges, such as communicating with young patients through a parent or guardian and assessing pain levels with children. To overcome these challenges, organizations can rely on operational data to target pediatric improvement areas that lead to lower costs and higher profit margins. Leveraging operational data—instead of focusing solely on pediatric outcomes data—can reveal opportunities for health systems to improve pediatric patient access and, in turn, increase revenue. Organizations can deliver higher quality pediatric care while increasing profits by implementing three data-informed strategies:

  1. Maximize space utilization.
  2. Improve patient scheduling.
  3. Implement virtual care.

How Addressing Mental Health Can Improve Chronic Disease Outcomes and Cut Costs

Treating mental health is often a low priority for health systems because of its high costs and low reimbursement rate. But health systems should not underestimate the impact mental health has on one of their costliest areas—treating chronic diseases. As research links higher costs to patients with chronic disease and a mental health disorder compared to patients without a mental health disorder, organizations should consider mental health treatment a key part of chronic disease management. By following four steps, providers and care teams can address patients’ mental health, thereby improving chronic disease outcomes and lowering costs:

  1. Identify the patient population.
  2. Identify the financial impact.
  3. Develop a plan with experts.
  4. Measure the impact and show ROI.

Improving Sepsis Care: Three Paths to Better Outcomes

Sepsis affects at least 1.7 million U.S. adults per year, making it a pivotal improvement opportunity for healthcare organizations. The condition, however, has proven problematic for health systems. Common challenges including differentiating between sepsis and a patient’s acute illness and data access. In response, organizations must have comprehensive, timely data and advanced analytics capabilities to understand sepsis within their populations and monitor care programs. These tools can help organizations identify sepsis, intervene early, save lives, and sustain improvements over time.

2021 Asia-Pacific Healthcare Trends: Growing Digitization, Universal Health Coverage, and More

Along with the rest of the globe, 2021 healthcare trends across Asia-Pacific (APAC) countries will center on COVID-19 recovery and resuming the healthcare improvement journey. In the APAC region, however, a mix of developed and developing countries poses unique challenges, as healthcare access and basic infrastructure vary widely between urban and rural populations and economic levels. To shepherd healthcare out of the pandemic and enhance delivery overall in 2021, APAC nations will focus on increasing investment in digital health (including virtual care, machine learning, and EMR adoption), achieving universal health coverage, shifting more towards value, and improving payer-provider relationships.

Healthcare Process Improvement: Six Strategies for Organizationwide Transformation

Healthcare processes drive activities and outcomes across the health system, from emergency department admissions and procedures to billing and discharge. Furthermore, in the COVID-19 era’s uncertainty, process quality is an increasingly important driver in care delivery and organizational success. Given this broad scope of impact, process improvement is intrinsically linked to better outcomes and lower costs. Six strategies for healthcare process improvement illustrate the roles of strategy, skillsets, culture, and advanced analytics in healthcare’s continuing mission of transformation.

Six Ways Health Systems Use Analytics to Improve Patient Safety

With preventable patient harm associated with over 400,000 deaths in the U.S. annually, improving safety is a top priority for healthcare organizations. To reduce risks for hospitalized patients, health systems are using patient safety analytics and trigger-based surveillance tools to better understand and recognize the types of harm occurring at their facilities and intervene as early as possible. Six examples of analytics-driven patient safety success cover improvement in the following areas:

  1. Wrong-patient order errors.
  2. Blood management.
  3. Clostridioides difficile (C. diff).
  4. Opioid dependence.
  5. Event reporting.
  6. Sepsis.

A More Accurate Sepsis Identification Method: Leveraging Physiological Data

The traditional sepsis identification method—based on a combination of physician notes, coding, and billing—is often varied and too subjective, leading to inaccurate data. Because margins are tight and health systems can’t afford to waste any resources, clinical teams need to start with the most effective sepsis identification method. Using physiological data, such as vital signs, to identify sepsis is proving to be highly effective. With the physiological data approach, providers rely on the body’s response—rather than being steered by biases, anecdotal information, or reimbursement rates—to more accurately identify patients with sepsis. With a more effective approach to sepsis identification, providers can implement interventions sooner, leading to better outcomes.

The Top Five Insights into Healthcare Operational Outcomes Improvement

Effective, sustainable healthcare transformation rests in the organizational operations that power care delivery. Operations include the administrative, financial, legal, and clinical activities that keep health systems running and caring for patients. With operations so critical to care delivery, forward-thinking organizations continuously strive to improve their operational outcomes. Health systems can follow thought leadership that addresses common industry challenges—including waste reduction, obstacles in process change, limited hospital capacity, and complex project management—to inform their operational improvement strategies. Five top insights address the following aspects of healthcare operational outcomes improvement:

  1. Quality improvement as a foundational business strategy.
  2. Using improvement science for true change.
  3. Increasing hospital capacity without construction.
  4. Leveraging project management techniques.
  5. Features of highly effective improvement projects.

Using Improvement Science in Healthcare to Create True Change

With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success. The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:

  1. Analyze the opportunity for improvement and define the problem.
  2. Scope the opportunity and set SMART goals.
  3. Explore root causes and set SMART process aims.
  4. Design interventions and plan initial implementation.
  5. Implement interventions and measure results.
  6. Monitor, adjust, and continually learn.
  7. Diffuse and sustain.
  8. Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.

How to Design an Effective Clinical Measurement System (And Avoid Common Pitfalls)

As healthcare organizations strive to provide better care for patients, they must have an effective clinical measurement system to monitor their progress. First, there are only two potential aims when designing a clinical measurement system: measurement for selection or measurement for improvement. Understanding the difference between these two aims, as well as the connection between clinical measurement and improvement, is crucial to designing an effective system. This article walks through the distinct difference between these two aims as well as how to avoid the common pitfalls that come with clinical measurement. It also discusses how to identify and track the right data elements using a seven-step process.

Three Must-Haves for a Successful Healthcare Data Strategy

Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy. To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:

  1. Best practices to identify target behaviors and practices.
  2. Analytics to accelerate improvement and identify gaps between best practices and analytic results.
  3. Adoption processes to outline the path to transformation.

Improving Strategic Engagement for Healthcare CIOs with Five Key Questions

A healthcare CIO’s role can demand such an intense focus on technology that IT leaders may struggle to find natural opportunities to engage with their C-suite peers in non-technical conversations. To bridge the gap, healthcare CIOs can answer five fundamental questions to better align their programs with organizational strategic goals and guide IT services to their full potential:

  1. Whom do we serve?
  2. What services do we provide?
  3. How do we know we are doing a great job?
  4. How do we provide the services?
  5. How do we organize?

Survey Points to Major Burnout Concerns Among Clinicians

According to a November 2019 survey, 62 percent of clinicians and other healthcare professionals view burnout as a major problem industrywide. When asked for the best way to address clinician burnout problems, the most popular solution was less-complex workflows, which is the aim of emerging point-of-care analytics solutions. Responses to additional questions reveal more about clinician burnout experience and views on the technology designed to help:

  1. At your organization, how big of a problem is clinician burnout?
  2. What is the best way to solve clinician burnout problems?
  3. What are the biggest barriers to adopting closed-loop, point-of-care analytics capabilities at your organization
  4. What are the biggest problems arising from a lack of adopting closed-loop, point-of-care analytics capabilities?

Healthcare Quality Improvement: A Foundational Business Strategy

Waste is a $3 trillion problem in the U.S. Fortunately, quality improvement theory (per W. Edwards Deming) intrinsically links high-quality care with financial performance and waste reduction. According to Deming, better outcomes eliminate waste, thereby reducing costs. To improve quality and process and ultimately financial performance, an industry must first determine where it falls short of its theoretic potential. Healthcare fails in five critical areas:

  1. Massive variation in clinical practices.
  2. High rates of inappropriate care.
  3. Unacceptable rates of preventable care-associated patient injury and death.
  4. A striking inability to “do what we know works.”
  5. Huge amounts of waste.

Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data

Patient comments such as “I feel dizzy” or “my stomach hurts” can tell clinicians a lot about an individual’s health, as can additional background, including zip code, employment status, access to transportation, and more. This critical information, however, is captured as free text, or unstructured data, making it impossible for traditional analytics to leverage. Machine learning tools (e.g., NLP and text mining) help health systems better understand the patient and their circumstances by unlocking valuable insights residing unstructured data:

  1. NLP analyzes large amounts of natural language data for human users.
  2. Text mining derives value through the analysis of mass amounts of text (e.g., word frequency, length of words, etc.).