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: Analyze the opportunity for improvement and define the problem. Scope the opportunity and set SMART goals. Explore root causes and set SMART process aims. Design interventions and plan initial implementation. Implement interventions and measure results. Monitor, adjust, and continually learn. Diffuse and sustain. Communicate Quantitative and Qualitative Results. With the right approach, an improvement team can measure the results and know if the changes they made will…

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Putting Patients Back at the Center of Healthcare: How CMS Measures Prioritize Patient-Centered Outcomes

Today’s healthcare encounters are too often marked by more clinician screen time than patient-clinician engagement. Increasing regulatory reporting burdens are diverting clinician attention from their true priority—the patient. To put patients back at the center of care, CMS introduced its Meaningful Measures framework in 2017. The initiative identifies the highest priorities for quality measurement and improvement, with the goal of aligning measures with CMS strategic goals, including the following: Empowering patients and clinicians to make decisions about their healthcare. Supporting innovative approaches to improve quality, safety, accessibility, and affordability.

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Healthcare Data Literacy: A Must-Have for Becoming a Data-Driven Organization

The journey for healthcare organizations to become data driven is complex but absolutely critical for success in today’s increasingly digitized environment. Data literacy is an essential capability because it empowers team members at every level of the organization—from individual learners to executives—to aggregate, analyze, and utilize data to drive decision making. To optimize data usage and reach high levels of data literacy, health systems can create a data literacy program based on four foundational elements: Infrastructure Access Support Privacy and Security

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AI in Healthcare: Finding the Right Answers Faster

Health systems rely on data to make informed decisions—but only if that data leads to the right conclusion. Health systems often use common analytic methods to draw the wrong conclusions that lead to wasted resources and worse outcomes for patients. It is crucial for data leaders to lay the right data foundation before applying AI, select the best data visualization tool, and prepare to overcome five common roadblocks with AI in healthcare: Predictive Analysis Before Diagnostic Analysis Leads to Correlation but Not Causation. Change Management Isn’t Considered Part of the Process. The Wrong Terms to Describe the Work. Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions. Lack of Agreement on Definitions Causes Confusion. As AI provides more…

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News Roundup: January 31, 2020

As AI and ML are rapidly making their way to the forefront of the healthcare world, some leaders feel that advanced analytics isn't gaining traction too quickly, while others are embracing its capabilities. In this week's new roundup: how AI can help leaders drive systemwide outcomes improvement; four ways AI/ML will transform healthcare in 2020, health systems utilizing ML; the power of data and AI to improve the payer-provider relationship; and the benefits of AI in medical imaging.

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AI-Assisted Decision Making: Healthcare’s Next Frontier

While many healthcare organizations have implemented Artificial Intelligence (AI) and Machine Learning (ML) tools at the point of care, few have successfully applied them to high-level decision making. A new frontier is expanding AI from artificial intelligence to augmented intelligence; traditional AI focuses on improving analytics efficiency while augmented intelligence is about improving the decision-making ability of healthcare leaders. This article addresses the capabilities health systems should embrace and provides two examples of how AI can assist with leaders with their most important decisions. Healthcare leaders’ biggest needs of from AI are the ability to separate signal from noise and make decisions that impact the future.

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Creating a Data-Driven Research Ecosystem with Patients at the Center

As patient data because one of the healthcare industry’s most valuable assets, organizations are establishing new practices around accessing and handling data. In question is the practice of de-identifying patient data for widespread cross-organizational data collaboration without compromising patient privacy. But because deeper and richer data drives better clinical understanding and, ultimately, better outcomes, does separating patients from their health data and how it’s used give researchers and developers the best insights? Or do data users risk losing critical connection with the patients and insights into therapies their lives, disease, treatments, and deaths that contribute to new therapeutic approaches? It’s time to consider a progressive approach to patient data that keeps the patients involved by informing them when and how…

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Weekly News Roundup: January 24, 2020

Understanding the current healthcare climate and upcoming trends in 2020 can help healthcare organizations stay abreast of important changes and be prepared for the future. In this week's news roundup: the top trends that will disrupt healthcare in 2020, seven trends impacting the design of healthcare environments, the main trends driving mergers and acquisitions, and more.

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The Top Three 2020 Healthcare Trends and How to Prepare

After a tumultuous 2019, healthcare organizations are pivoting to make sense of the latest changes and prepare to face the top 2020 healthcare trends: Consumerism—Can health systems respond to the consumer demands of better access and price transparency? Financial Performance—With mergers, acquisitions, and private sector companies entering the healthcare arena, how will traditional hospitals and clinics compete? Social Issues—How will health organizations respond to the opioid crisis and consider social determinants of health as part of the care process to provide comprehensive treatment? As health systems struggle to survive amidst constant change, they must look forward and proactively prepare for what’s to come in 2020.

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Achieving Stakeholder Engagement: A Population Health Management Imperative

To succeed in population health management (PHM), organizations must overcome barriers including information silos and limited resources. Due to the systemwide nature of these challenges, widespread stakeholder engagement is an imperative in population-based improvement. An effective PHM stakeholder engagement strategy incorporates the following: Includes as many stakeholders as possible at the beginning of the journey. Meets the unique analytics and reporting needs of the organization. Enables users to measure, and therefore manage, PHM outcomes. Provides the real-time analytics value-based care requires.

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The Medicare Shared Savings Program: Four Tools for Better Profit Margins and High-Quality Care

Medicare patients make up the majority of health systems’ revenue; yet, organizations earn only a one percent profit while caring for this population. Despite historically low profit margins, Medicare can be lucrative for health systems, and through the Medicare Shared Savings Program, healthcare organizations can increase revenue with four tools: The ability to aggregate and analyze data. The ability to align financial incentives between payers and providers. The ability to engage patients in behavior or lifestyle modifications. The ability to garner support from clinicians and encourage them to lead the shift to VBC. As the shift from fee-for-service to value-based care continues, health systems can leverage MSSP to deliver the highest level of care while also increasing profit margins.

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Weekly News Roundup: January 17, 2020

Artificial intelligence (AI) and machine learning will play an even bigger role in healthcare in 2020. In this week's news roundup: AI and machine learning trends; why AI in healthcare is primarily a change management problem; how Christiana Care is personalizing the "black box" of machine learning; and much more.

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Removing Barriers to Clinician Engagement: Partnerships in Improvement Work

With clinicians driving many of the decisions that affect health system quality and cost, they’re an essential part of successful improvement efforts. Clinicians are, however, notoriously overburdened in today’s healthcare setting, and getting their buy-in for additional projects is often a big challenge. To successfully partner with these professionals in improvement work, health systems must develop engagement strategies that prioritize clinician needs and concerns and leverage data that’s meaningful to clinicians. Improvement leaders can approach clinician engagement on three levels: Clinician-led local programs. Department- or division-level programs. Leadership-level growth and improvement programs.

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Weekly News Roundup: January 10, 2020

Healthcare is moving towards an era of personalized medicine in which providers customize treatments for the individual patient. In this week's news roundup: How data-driven precision medicine individualizes treatment; who will benefit from precision medicine; and the major barriers to implementing precision medicine programs.

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Artificial Intelligence in Healthcare: A Change Management Problem

The key to successfully leveraging artificial intelligence (AI) in healthcare rests not wholly in the technical aspects of predictive and prescriptive machines but also in change management within healthcare organizations. Better adoption and results with AI rely on a commitment to the challenge of change, the right tools, and a human-centered perspective. To succeed in change management and get optimal value from predictive and prescriptive models, clinical and operational leaders must use three perspectives: Functional: Does the model make sense? Contextual: Does the model fit into the workflow? Operational: What benefits and risks are traded?

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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: Whom do we serve? What services do we provide? How do we know we are doing a great job? How do we provide the services? How do we organize?

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Six Need-to-Know Guidelines for Successful Care Management

In a job that changes every minute, care managers don’t have much time to think as they tackle unpredictable situations. Care managers stay on track amid the distractions by following six key elements of successful care management: Act as an advocate for the patient. Practice cultural competence. Garner support from leaders. Develop effective communication skills. Prioritize patients based on up-to-date data. Don’t ever forget that the patient is a human being first. As care managers practice these six crucial components for successful care management, the patient’s health and well-being will always be the top priority for everyone involved, which translates to better outcomes and lower costs.

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Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Personalized Care

Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.

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Weekly News Roundup: December 20, 2019

With 2019 coming to a close, cutting costs will be top of mind in 2020 for most healthcare organizations. This week's news roundup focuses on financial alignment: three strategies for healthcare financial transformation; managing the most expensive patients; tactics to drive revenue cycle efficiency; and healthcare's pricing problems explained in four charts.

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ICD-10 PCS: Harnessing the Power of Procedure Codes

The transition to ICD-10 in 2015 saw the number of available procedure codes increase from roughly 3,000 to more than 70,000. This change gives clinicians the ability to code procedures to a much higher degree of specificity and provides health systems the ability to unlock powerful clinical insights into how inpatient procedural care is delivered. This article covers the benefits and drawback of ICD-10 PCS, as well as concrete ways health systems can use these procedure codes to provide new clinical insights. The article also walks through the anatomy of the seven-digit alphanumeric codes and provides specific clinical examples of how healthcare organizations can slice and dice this data.

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Three Key Strategies for Healthcare Financial Transformation

To succeed in today’s rapidly evolving business environment, healthcare organizations must have accurate financial data. Approximately 50 percent of CMS payments are now tied to a value component; hospital operating margins are at an all-time low; and consumer demands are rising with their costs. In order to meet these new challenges, health systems must shift their strategy or risk being left behind. This article details the operational, organizational, and financial strategies that drive financial transformation, as well as examples of how to obtain and utilize financial data, find waste reduction opportunities, and much more.

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

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

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Weekly News Roundup: November 22, 2019

As the need for data-driven improvement becomes more urgent, health systems are finding that their current approaches to data management and analytics are failing to keep up. Organizations must evolve towards a broad data management solution to satisfy healthcare's increasing demands for data storage and near-real time insights.

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

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