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.

Mothers and Babies Benefit From Cost-Saving Substance Use Disorder Program

Community Health Network (CHNw) observed higher than national rates of maternal substance use disorder, with a higher number of pregnant women having positive drug screens for opioids, cocaine, amphetamines, barbiturates, and benzodiazepines. It developed a care coordination and substance use program to help reduce the incidence of substance use disorders among pregnant women. Using its data platform and analytics applications, CHNw was able to evaluate the impact of various process measures on patient outcomes.

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:

1. Empowering patients and clinicians to make decisions about their healthcare.
2. Supporting innovative approaches to improve quality, safety, accessibility, and affordability.

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:

1. Infrastructure
2. Access
3. Support
4. Privacy and Security

Closed-Loop Analytics Supports Data-Driven Medical Management

Acuitas Health improved access to data for its partner clinicians by using its data platform and closed-loop analytics to integrate data from more than ten disparate systems. Clinicians receive patient-specific details before the patient visit, allowing them to identify opportunities for health maintenance, improve quality, support data-driven medical decision making, increase adoption of best practices, and improve hierarchical condition category (HCC) coding.

The Doctor’s Orders For Engaging Physicians to Drive Improvements

During this webinar, our presenters will:

• Identify the levels of physician leadership in your organization you can engage to drive improvement.
• Pinpoint the types of data and information of most interest to physician leaders.
• Propose several ways data to use data to engage physicians in leading improvement work.
• Help you develop at least one mechanism you can use to better engage physicians in improvement work at your organization.

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:

1. Predictive Analysis Before Diagnostic Analysis Leads to Correlation but Not Causation.
2. Change Management Isn’t Considered Part of the Process.
3. The Wrong Terms to Describe the Work.
4. Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions.
5. Lack of Agreement on Definitions Causes Confusion.

As AI provides more efficiency and power in healthcare, organizations still need a collaborative approach, deep understanding of data processes, and strong leadership to effect real change.

Data Advances Improvements in Stroke Care Delivery, Reducing Mortality

Thibodaux Regional Health System had implemented evidence-based stroke care interventions in its emergency department. However, the organization was not meeting its established goals for early identification and treatment. With strong leadership support and the help of analytics, the organization’s stroke care transformation team was able to identify opportunities for improvement, culminating in improved care delivery through facility-wide automated alerts and a reduction in the need to transfer patients to other facilities for treatment.

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.

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.

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 their data is used to earn trust and engagement, making patients partners in data-driven healthcare transformation.

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.

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.

Millions Saved: Complex Care Coordination Reduces Total Cost of Care

OneCare Vermont, an accountable care organization (ACO), is focused on reducing costs by reforming payment models. As the organization methodically and rapidly moves toward value-based payments, it is challenging current delivery methods and seeking to engage providers and patients in new care models. To be successful, OneCare needed to implement strategies to effectively drive change. With robust data analytics, it was able to prioritize opportunities for improvement and ultimately change the way care is coordinated and delivered throughout its network. Results include nearly $20M in positive, value-based financial results in just one year.

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:

1. Includes as many stakeholders as possible at the beginning of the journey.
2. Meets the unique analytics and reporting needs of the organization.
3. Enables users to measure, and therefore manage, PHM outcomes.
4. Provides the real-time analytics value-based care requires.

Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for Analytic Maturity

During this webinar, Dale will:

• Review the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI.
• Share his observations of maturity in the market.
• Provide an update on the current state of the HIMSS Adoption Model for Analytic Maturity.

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:

1. The ability to aggregate and analyze data.
2. The ability to align financial incentives between payers and providers.
3. The ability to engage patients in behavior or lifestyle modifications.
4. 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.

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:

1. The ability to aggregate and analyze data.
2. The ability to align financial incentives between payers and providers.
3. The ability to engage patients in behavior or lifestyle modifications.
4. 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.

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.

Achieving Self-Service Analytics with Rapid Response Analytics

Billings Clinic had its data located within multiple different source systems, which limited access to the data and decreased trust in the data. The available tools were difficult for non-analysts to use and understand, creating resistance to self-service analytics. To breakdown data silos, ensure a gold standard for metrics, and optimize its analytics use, Billings Clinic deployed a data platform and analytics application across its organization.