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.
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.
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.
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.
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:
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.
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.
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:
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.
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:
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.
Influential healthcare financial trends in 2017 emerged in three areas:
Transitions in payment.
Disruption from familiar players and newcomers.
Emerging data skillsets.
Uncertainty has been a common theme for 2017. Organizations continue waiting for clarity on the future of the Affordable Care Act (ACA), while working to implement value-based care. Changes from established healthcare organizations as well as the arrival of prominent newcomers (e.g., Amazon) add to the unsettled outlook, as do emerging data skillsets.
Amid the uncertainty, however, healthcare is clearly continuing on the path to patient-centered care. Organizations best positioned for 2018 will understand their performance in 2017’s top three healthcare financial trends as they evaluate their preparedness for the coming year.
8 in 10 Hospitals Stand Pat on Population Health Strategy, Despite Uncertainty Over the Affordable Care Act’s Future
A 2017 survey by Health Catalyst shows that despite uncertainty about the future of the Affordable Care Act, 80 percent of healthcare executives have not paused or otherwise changed their population health management strategy. 68 percent said that PHM is “very important” to their healthcare delivery strategy, while fewer than 3 percent said it was not important at all. The results show that executives view the move to value-based care as inevitable, and they view a PHM strategy as an integral part of their future efforts.
The documentary, “A Coalition of the Willing: Data-Driven Population Health and Complex Care Innovation in Low-Income Communities” shows how precision medicine and care management can be effective tools for successful population health. The film highlights three programs that use data to hotspot populations of high-risk, high-need patients, and then deploy unique, targeted care management inventions. The documentary, which initially aired during the 2017 Healthcare Analytics Summit, presents hopeful solutions, scalable across diverse patient populations, that are leading to exceptional results and the future of healthcare transformation.
Population health management (PHM) is in its early stages of maturity, suffering from inconsistent definitions and understanding, overhyped by vendors and ill-defined by the industry. Healthcare IT vendors are labeling themselves with this new and popular term, quite often simply re-branding their old-school, fee-for-service, and encounter-based analytic solutions. Even the analysts —KLAS, Chilmark, IDC, and others—are also having a difficult time classifying the market. In this paper, I identify and define 12 criteria that any health system will want to consider in evaluating population health management companies. The reality of the market is that there is no single vendor that can provide a complete PHM solution today. However there are a group of vendors that provide a subset of capabilities that are certainly useful for the next three years. In this paper, I discuss the criteria and try my best to share an unbiased evaluation of sample of the PHM companies in this space.
In today’s data-rich healthcare environment, patient registries put knowledge in front of the people who will use it to improve outcomes and population health. Non-IT professionals (e.g., clinicians and researchers) often don’t have direct, timely access to operational and clinical data. As a result, organizations miss out on important improvement opportunities and data-driven point-of-care decisions. Knowledge too often remains siloed in the enterprise data warehouse (EDW) or among specialized groups.
Patient registries remove these barriers. It allows clinicians and researchers to make informed choices and frees up data analysts to focus on their priority areas.
Unnecessary barriers to practice and licensing limitations have severe consequences for health systems’ population health initiatives, especially as the nationwide shortage of healthcare practitioners continues to grow:
Delayed access to clinicians.
Decreased access to care, particularly primary care and care in rural areas.
Limited labor supply.
Increased costs of services.
Loss of potential revenue for healthcare organizations.
Using clinical nurse specialists as an example—one of many critical roles in population health management—effectively demonstrates the importance of removing unnecessary barriers to practice, from reductions in unnecessary readmissions and reduced length of stay (LOS), to less frequent ED visits and higher patient satisfaction.
The bottom line, when it comes to barriers to practice, is that removing them (with solutions like uniform regulations) will do more than improve population health management—it will also reduce costs and improve patient outcomes.
A comprehensive care management program organizes many moving parts into an efficient workflow and brings order to the complex, often messy, world of healthcare. Care coordination harmonizes the workflow of clinicians, patients, family, social workers, and therapists, to name a few. It facilitates medication reconciliation, care compliance, appointment scheduling, and communication with patients, as well as engagement between patients and the care team. Care coordination concentrates on the highest-utilization, highest-cost patients to produce better clinical, operational, and financial outcomes, the bottom line goals for healthcare systems involved in population health and value-based care.
This article details the benefits of, and barriers to, care management and coordination, their role in population health, and the technology that’s helping to automate this area of healthcare.
An effective population health management program must include three systems: Healthcare Analytics, Best Practice, and Adoption. Organizations with only one or two of these systems often display symptoms of weak and ineffective capability for population health management. But when you have a analytics foundation based upon a data warehouse, combined with evidence-based practices contained in a best practice system, and the ability to deploy and implement systematic changes to healthcare processes, health systems are truly prepared to manage population of patients.
Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.
Patient engagement is critical as we move toward population health—as patients who engage in their own care by following medical recommendations and making healthy nutrition and lifestyle choices will have better outcomes and experiences.
There isn’t, however, a clear path to successful patient engagement. Fortunately, public health can lend several established principles that may help us better involve patients in their own care:
Using systematic, population-level solutions that require less individual effort.
Engaging patients on interpersonal and community levels as well as personal.
Identifying root-cause, assessing and capitalizing on strengths, and engaging stakeholders.
Using strategies from behavioral economics to help individuals make good choices.
Anticipating failure and learning from it.
This article examines how to define population health through a review of the top analytics research firms. It lands on a single theme, but in the process it uncovers six common categories of IT capabilities required to successfully manage population health:
These six strategic components define the population health ecosystem, and successful organizations must multitask across these domains, working with an enterprise data warehouse, if they hope to thrive in value-based healthcare and become true partners and assets in their respective communities.
Population Health can mean many different things depending on whom you ask or what you read. The one common element among all the definitions is the focus on outcomes. These outcomes can be related to quality (successfully treating the patient), experience (the patient’s satisfaction with the care that was provided), or cost outcomes (reducing errors and a decline in length of stay). In the end it means delivering the highest quality care for patients at the lowest possible cost over and over again. To ensure these outcomes are tangible, sustainable, and transferable a three-system approach is necessary: a best practices system (to determine what should be done), an analytic system (to tell the organization how it’s doing versus the goal), and an adoption system (to report the results to the organization).
Given the variety of payment contractors and models, ACOs have their hands full when it comes to assessing risk and managing population health. EMRs are one source of data for painting a partial picture of the population; claims and HIE data are limited, and the promise of FHIR is still a work in progress. To complete the picture, external data sources are necessary. Those are available in a variety of ways, including demographic analysis, and external EMRs from physician practices independent of the ACO. There are many challenges to stratifying risk, but there also many creative ways to pull in the data for accurately identifying the patient population, improving their health, and reducing costs.
Two variables are required in the value-based healthcare equation if it is to add up to a profitable contract. One variable, optimizing the care for the patient population, is commonly included and is a focus for most healthcare systems involved in managing population health. However, a second variable, getting the right dollars in order to care for that population, is often overlooked. And yet this variable is easier to attain. It’s a matter of appropriately assessing the risk of the population by addressing inaccurate diagnoses coding. Here, we offer four methods for solving this variable: identifying high-risk gaps over time, persistent diagnosis tracking, identifying code adequacy, and identifying likely diagnoses.