Population Health and Care Management


Health Catalyst Editors

The Right Way to Build Predictive Models for the Most Vulnerable Patient Populations

Predictive artificial intelligence (AI) models can help health systems manage population health initiatives by identifying the organization’s most vulnerable patient populations. With these patients identified, organizations can perform outreach and interventions to maximize the quality of patient care and further enhance the AI model’s effectiveness.
The most successful models leverage a mix of technology, data, and human intervention. However, assembling the appropriate resources can be challenging. Barriers include multiple technology solutions that don’t share information, hundreds of possible, often disparate, data points, and the need to appropriately allocate resources and plan the correct interventions. When it comes to predictive AI for population health, simple models may harness the most predictive power, which allows for more informed risk stratification and identifies opportunities for patient engagement.

Daniel Orenstein, JD
Stephen Grossbart, PhD

2021 Healthcare Trends: What Leaders Need to Know from COVID-19 to New Administration Policies

While much of the healthcare industry was eager to put 2020 behind it, the new year brings its own challenges, concerns, and promises. Trends in the three main categories of new Biden administration policy, care delivery, and healthcare technology will shape 2021, with key issues including the long-term effects of COVID-19, future emergency preparedness, and the outlook for the Affordable Care Act (ACA). Healthcare leaders can prepare for this pivotal year by understanding critical areas to watch within these categories and how events, activities, and political appointments will affect the healthcare ecosystem.

Amy Flaster, MD, MBA

Effective Patient Stratification: Four Solutions to Common Hurdles

Accurate patient stratification, the first step of any effective population health strategy, identifies patients who will benefit most from a population health intervention. Successful patient stratification is critical when laying the foundation for any population health initiative, yet many health systems struggle with this step.
Care teams can apply four solutions to overcome common patient stratification hurdles, target the most impactable patients, and carry out population health initiatives:

Consider both the physical and the mental.
Prove and measure return on investment.
Complete data sets.
Transparent, customizable technology.

Tarah Neujahr Bryan

Introducing the Care Management Suite: A Data-Driven, Transparent Solution

COVID-19 has highlighted the healthcare imperative of effective care management—in particular, the ability for health systems to rapidly adapt their care management approach based on ever-changing healthcare terrain. Typical care management programs lack transparency, comprehensive data, and flexibility. This makes it difficult for care teams to easily change their care management programs based on patient population needs and opportunities. To meet these challenges, the Health Catalyst Care Management Suite leverages a transparent, data-driven strategy with expertise to help health systems maximize care management ROI.

Health Catalyst Editors

Population Health Management: A Path to Value

As value-based care (VBC) definitions and goals continue to shift, organizations struggle to create a roadmap for population health management (PHM) and to track associated costs and revenue. However, health systems can move forward with PHM amid the uncertainty by following the best practices of a path to value:

Begin with Medicare Advantage—a good growth opportunity with low barriers to entry.
Focus on ambulatory, not acute, care as it delivers more value.
Leverage registries based on utilization to identify the most impactable 3 to 10 percent of utilizers.
Simplify the physician burden by focusing on reasonable measures.

Health Catalyst Editors

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.

KimSu Marder

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.

Heather Schoonover

Many Health Systems Are Failing the LGBTQ+ Community—Two Ways to Improve

LGBTQ+ community members face unique challenges when accessing healthcare. Lack of knowledge among providers about the LGBTQ+ community leads to stigma, discrimination, and stereotypes that result in higher risk for cancers and substance abuse and higher rates of smoking. Poor health outcomes occur for multiple reasons—clinicians don’t know the best way to collect accurate health information and LGBTQ+ members don’t feel safe sharing personal health information.
The best way for health systems to improve healthcare delivery for the LGBTQ+ community is to rework the way they collect sexual orientation/gender identity data and educate clinicians about the health disparities LGBTQ+ members face.

Amy Flaster, MD, MBA
Eric Just

Succeeding in Population Health Management: Why the Right Tools Matter

The U.S. healthcare market projects that by 2022 90 million Americans will be in an ACO. The upward trend in population health management (PHM) makes the move towards risk-based contracts increasingly urgent for health systems. The industry has been largely unprepared for the shift, as it hasn’t established a clear definition of population health or solid guidelines on transitioning from volume to value. Organizations can, however, prepare for the demands of PHM by adopting a solution that manages comprehensive population health data, provides advanced analytics from new and complex challenges, and connects them with the deep expertise to thrive in a value-based landscape.

Health Catalyst Editors

Introducing the Health Catalyst Population Health Foundations Solution: A Data- and Analytics-first Approach to PHM

Introducing the Health Catalyst Population Health Foundations solution, which draws on integrated claims and clinical data, and provides essential, extensible tools and machine-learning capabilities for optimizing results in value-based risk arrangements. Accompanying solution services ensure that the strategic value of data is maximized to improve performance in risk contracts—and provide side-by-side subject matter expert partnership for establishing short- and long-term goals for population health management.

Health Catalyst Editors

Introducing Population Builder™: Stratification Module

The Health Catalyst Population Builder: Stratification Module allows healthcare organizations to identify the right patient populations in order to deliver the right care at the right time. The solution provides a seamless process for stratifying populations from multiple sources (EMR, claims, and clinical), using pre-defined, easily customized populations as building blocks. With a comprehensive view of the patients they manage, organizations can map populations along their continuum of care and confidently transition appropriate populations to population health interventions.

Jeff Selander

The Future of Employer Health Insurance

Employers are always looking for ways to reduce one of their biggest expenditures–the cost of providing health insurance to employees. Many employers have explored solutions such as adding wellness plans, reducing usage, and providing different provider access mechanisms, all with modest success.
Stemming the rising costs of health insurance requires management to understand and improve healthcare outcomes for their employee and dependent populations. Changing the future of employer health insurance will require a multi-faceted approach:

Driving additional value by reducing utilization of healthcare services within these employer populations.
Utilizing a wider lens through which to view performance of various providers, then making decisions based on those who are consistently providing low cost, high quality care.
Employer will need to combine their data with other companies across a geographic region to get a better picture of the provider landscape than has ever been possible before.

Adam Bell
Carol Owen
Dan Soule
Eric Crawford

Pairing HIE Data with an Analytics Platform: Four Key Improvement Categories

Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information.
By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas:

Machine learning
Professional services
Data governance

Health Catalyst Editors

Social Determinants of Health: Tools to Leverage Today’s Data Imperative

Social determinants of health (SDOH) data captures impacts on patient health beyond the healthcare delivery system. Traditional health data (e.g., from healthcare encounters) only tells a portion of the patient and population health story. To understand the full spectrum of health impacts (e.g., from environment to relationship and employment status), organizations need data from their patient’s daily lives. The urgency for SDOH data is particularly strong today, as value-based payment increasingly presses health systems to raise quality and lower cost. Without fuller insight into patient health (what happens beyond healthcare encounters) organizations can’t align with community services to help patients meet needs of daily living—prerequisites for maintaining good health.
Standardizing SDOH data into healthcare workflows, however, requires an informed strategy. Health systems will benefit by following a standardization protocol that includes relevant and comprehensive domains, engages patients, enables broader understanding of patient health, integrates with organizational EHRs, and is easy for clinicians to follow.

John Haughom, MD

Why Patient-Reported Outcomes Are the Future of Healthcare—and the Key to Ruth’s Independence

Patient-reported outcomes (PROs), defined as “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else,” are the future of healthcare.
In addition to helping people like 80-year-old-Ruth continue to live interpedently, PROs—interchangeable with the term patient-generated health data (PGHD)—have several benefits:

Effectively supplement existing clinical data, filling in gaps in information and providing a more comprehensive picture of ongoing patient health.
Provide important information about how patients are doing between medical visits.
Gather information on an ongoing basis—rather than just one point in time—and provide information relevant to preventive and chronic care management.

The new technologies that enable PROs and PGHD (e.g., sensors that detect whether Ruth takes food out of her refrigerator on a regular basis), generate important data outside of patients’ traditional care environments, sharing it with care teams to expand the depth, breadth, and continuity of information available to improve healthcare and outcomes.

Jeff Selander

Employer Health Plans: Keys to Lowering Cost, Boosting Benefits

Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality.
An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits:

Manage easily fixed cost issues.
Use healthcare cost savings to fund expanded benefits.

KimSu Marder

Identifying Vulnerable Patients and Why They Matter

The vulnerable individuals in a health system’s patient population are at risk of becoming some of the organization’s most complex and costly members. Because vulnerability can be determined by long-term health status and social determinants of health (versus acute episodes), managing risk for these patients relies on a whole-person approach to care. Fee-for-service reimbursement hasn’t incentivized this comprehensive approach to care, but, under value-based payment models, health systems are increasingly rewarded for care that keeps patients well.
The first challenge in addressing the needs of vulnerable patients is identifying those patients. Analytics-driven technologies can help health systems understand who is vulnerable in their populations and take actions to control risk for these patients.

Luke Skelley
Matt Denison
Rob McCrory

Six Challenges to Becoming a Data-Driven Payer Organization

As healthcare transitions from fee-for-service to value-based payment, payer organizations are increasingly looking to population health management strategies to help them lower costs. To manage individuals within their populations, payers must become data driven and establish the technical infrastructure to support expanding access to and reliance on data from across the continuum of care.
To fully leverage the breadth and depth of data that an effective health management strategy requires, payers must address six key challenges of becoming data driven:

Data availability.
Data access.
Data aggregation.
Data analysis.
Data adoption.
Data application.

Dennis Schmuland, MD, FAAFP
Sree Chaguturu, MD

Episode Analytics Now Mission Critical as Outcomes Meet Incomes: Partners HealthCare Paves Volume-To-Value Path With Late-Binding Data Warehouse

In this reprint from Microsoft, Dennis Schmuland, MD, FAAFP (Chief Health Strategy Officer, Microsoft US Health & Life Sciences), sits down with Sree Chaguturu, MD (Vice President and Chief Population Health Officer, Partners HealthCare) to learn how Partners HealthCare has prepared for the tipping point of value-based care.

KimSu Marder

Care Management Analytics: Six Ways Data Drives Program Success

To succeed in improving outcomes and lowering costs, care management leaders must begin by selecting the patients most likely to benefit from their programs. To identify the right high-risk and rising-risk patients, care managers need data from across the continuum of care and tools to help them access that knowledge when they need it.
Analytics-driven technology helps care managers identify patients for their programs and manage their care to improve outcomes and lower costs in six key ways:

Identifies rising-risk patients.
Uses a specific social determinant assessment to capture factors beyond claims data.
Integrates EMR data to achieve quality measures.
Identifies patients for palliative or hospice care.
Identifies patients with chronic conditions.
Increases patient engagement.

Holly Rimmasch

Four Population Health Management Strategies that Help Organizations Improve Outcomes

Population health management (PHM) strategies help organizations achieve sustainable outcomes improvement by guiding transformation across the continuum of care, versus focusing improvement resources on limited populations and acute care. Because population health comprises the complete picture of individual and population health (health behaviors, clinical care social and economic factors, and the physical environment), health systems can use PHM strategies to ensure that improvement initiatives comprehensively impact healthcare delivery.
Organizations can leverage four PHM strategies to achieve sustainable improvement:

Data transformation
Analytic transformation
Payment transformation
Care transformation

Kathleen Clary, BSN, MSN, DNP

Measuring the Value of Care Management: Five Tools to Show Impact

To earn legitimacy and resources within a healthcare organization, care management programs need objective, data-driven ways to demonstrate their success. The value of care management isn’t always obvious; while these programs may, in fact, be responsible for improvements in critical metrics, such as reducing readmissions, C-suite leaders need visibility into care management’s impact and processes to understand precisely how they’re improving care and lowering costs at their organizations.
Five analytics-driven technologies give healthcare leaders a comprehensive understanding of care management performance:

The Patient Stratification Application
The Patient Intake Tool
The Care Coordination Application
The Care Companion Application
The Care Team Insights Tool

Maureen Bisognano

Five Data-driven Patient Empowerment Strategies

Data plays a big role toward empowering patients to become more involved in their care. With data, digital tools, and education, patient empowerment can act like a blockbuster drug to produce exceptional outcomes.
Data empowers patients five ways:

Promotes patient engagement.
Produces patient-centered outcomes.
Helps patients practice self-care.
Improves communication with clinicians.
Leads to faster healing and independence.

Clinicians using creative, innovative care strategies, and patients with access to the right tools and technology, can produce remarkable results in terms of cost, health outcomes, and experience.

KimSu Marder

10 Motivational Interviewing Strategies for Deeper Patient Engagement in Care Management

Care management programs are most successful when patients are deeply engaged in their own care. Using the motivational interviewing technique, care managers work with patients to identify personal care goals and motivators to follow the care management program.
Ten strategies guide the motivational interviewing process, each focusing on patient-centered insights (e.g., pros and cons to following care management and barriers to adherence). With mobile technology to support these interactions, motivational interviewing can become a seamless, and vital, part of the care management workflow.

Kathleen Clary, BSN, MSN, DNP

Custom Care Management Algorithms that Actually Reveal Risk

Care management is a tool for population health that focuses scarce healthcare resources on the sickest patients. Care management leaders need to know who those sickest patients are (or may be). The static risk models typically used for stratifying patients into risk categories only paint a partial picture of health and are ineffective for modern care management programs. Custom algorithms are now capable of predicting risk based on multiple risk models and multiple data sources. They help care management teams confidently stratify patient populations to paint a complete picture of care needs and efficiently deliver care to those who need it most.
This article explains how custom algorithms work on static risk models to normalize risk scores and improve patient stratification, care management, and, ultimately, population health management.