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.

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:

1. Clinician-led local programs.
2. Department- or division-level programs.
3. Leadership-level growth and improvement programs.

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.

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:

1. Functional: Does the model make sense?
2. Contextual: Does the model fit into the workflow?
3. Operational: What benefits and risks are traded?

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?

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.

Clinical Decision Support: Driving the Last Mile

Join Scott Weingarten, MD, MPH, and Dale Sanders to explore clinical decision support in a joint webinar. Dr. Weingarten is recognized throughout the U.S. and international healthcare space as a physician and for his contributions to decision support, including his role in founding Zynx and Stanson Health. Dale brings a technologist’s viewpoint to the conversation, informed by his background in computer-aided decision support in the healthcare, military, and national intelligence sectors.

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.

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.

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.

Data-Driven Care Transformation Continues to Reduce Deaths from Sepsis

Thibodaux Regional Health System had improved sepsis mortality rates, yet it recognized the challenges associated with sustaining and further improving care outcomes. Driven by a mission of patient-centered excellence that starts with the chief executive officer, the Board, and leadership, Thibodaux Regional’s sepsis care transformation team utilized its data platform and analytics applications to help facilitate data-driven, sustained improvements.

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?

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?

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.).

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.

Improving Percutaneous Coronary Intervention Reduces Adverse Events and Length of Stay

UnityPoint Health evaluated its percutaneous coronary intervention (PCI) performance and identified the opportunity to further improve. The health system decided to identify ways to improve its PCI outcomes. With its data operating system and a robust suite of analytics tools, UnityPoint Health took a data-driven approach to improving its PCI outcomes.

Powering Digital Transformation in Healthcare

The EMR was the start of the digital transformation in healthcare, but there is so much more to come. The digital wave has come to healthcare… Are you ready?

Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:

1. Acquire
2. Organize
3. Standardize
4. Analyze
5. Deliver
6. Orchestrate
7. Extend

These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.