“We fundamentally believe in the importance of care management to support the success of our population health initiatives. We must help our patients manage the interplay of their conditions and achieve the best outcomes.”
– Sreekanth Chaguturu, MD
Vice President for Population
Unprecedented changes in the healthcare payment system have resulted in health organizations across the country investing in the pursuit of the Institute for Healthcare Improvement’s (IHI’s) Triple Aim to improve population health, improve patient experience and outcomes, and reduce costs per capita.1 Health organizations must develop effective population health management strategies, and they need the right data and analytics to inform their initiatives.
Once armed with the information to make data-driven decisions, leading healthcare providers are implementing care management programs, which have proven to be helpful mechanisms for achieving the Triple Aim. Many healthcare organizations have identified specific patient cohorts to monitor the impact of care management interventions on individual and population health outcomes.
Data-driven care management programs that target high-risk and rising-risk patients can achieve impressive results, including:
The shift to value-based care and changes to healthcare payment models are prompting healthcare leaders to renew their focus on the Triple Aim. Population health, which is best defined as the health outcomes of a group of individuals, including the distribution of such outcomes within the group, is at the heart of these conversations because it impacts all three of those important dimensions.2 Organizations with effective population health initiatives rely on analytics to help their leaders make data-driven decisions—and those analytics are essential to every step, from identifying patient cohorts to measuring the effectiveness of initiatives.3
An organization’s success in managing population health is dependent upon the ability to make informed decisions about its entire strategy. The key strategic pieces include identifying populations or cohorts of interest, obtaining health outcomes data for the cohorts (such as mortality, disease burden and injury, and behavioral factors), examining experience of care, and determining per capita cost (total cost of care, and hospital and emergency department utilization rate and/or cost). An analysis of these combined data points provides organizations with much-needed insights to design and deliver the right set of services that improve care, improve population health, and reduce costs per capita. Organizations must measure and evaluate the effectiveness of their initiatives as related to all facets of the Triple Aim.
An increasingly common and critical component of effective population health strategies is care management. It is defined as “a set of activities designed to assist patients and their support systems in managing medical conditions and related psychosocial problems more effectively, with the aims of improving patients’ functional health status, enhancing the coordination of care, eliminating the duplication of services, and reducing the need for expensive medical services.” 4
Literature supports that care management is a helpful mechanism for improving the Triple Aim, but organizations have limited success in this realm if they don’t have access to the right data and analytics. Even the first step of identifying which patients would receive the greatest benefit from participating in a care management program, and stratifying their risk, is a challenge for many organizations. So, they often simplify their approach, just identifying the highest cost patients to be members of the cohort. However, the single data point of cost is not enough to inform the design and delivery of services. The best predictive models integrate data from multiple sources, enabling organizations to identify patients who are at risk but are not yet too sick to benefit from the program. These advanced models, which also look at medication information, diagnostic testing, and social determinants of health, are better at predicting both rising risk and future costs. These models are also better at risk stratification than older models that rely on historical claims data alone.
Having a sophisticated predictive model is vital, but it’s still not valuable unless someone takes appropriate action on the information. After identifying the high- and rising-risk patients, an expert needs to perform a comprehensive assessment of the patient’s health needs and available social support. That detailed assessment, to plan for services that will support patients in improving their health and reduce costs, requires much more information than what primary care physicians gather during ordinary visits. To determine needs and align services, organizations need information about functional status, which identifies how patients go about their daily living activities. This assessment may include questions about meal preparation, physical activity, transportation, financial resources, social participation, and social support. Organizations also need insights on patient and caregiver preferences, because a care plan that does not align with the patient’s preferences cannot be effective.
While, care management is imperative for healthcare organizations, it’s nearly impossible for primary care providers to perform this complex and comprehensive assessment as a part of their routine clinical work. That’s where care managers come in. After the completion of this thorough assessment, care managers must then develop a care plan that addresses the patient’s needs. The care plan should be tailored to the individual patient’s needs, and should be something in which the patient can successfully participate. The multi-faceted plan should address both immediate needs and longterm care goals, and it should clearly identify who is responsible for each service. When the care plan has been established, care managers can turn their attention to monitoring the patient’s health status and communicating with the patient. Although experienced care managers are best suited to perform this work, they are in short supply.
Addressing these challenges and getting started with a care management program can be difficult and overwhelming. Thankfully, success stories are available from peer organizations that have already tackled some of the challenges and achieved impressive results. Many healthcare organizations have leveraged information from their Health Catalyst Analytics Platform, including their Late-Binding™ Data Warehouse (EDW) and broad suite of analytics applications, to support the identification of specific patient cohorts to monitor the impact of care management interventions on individual and population health outcomes. The programs featured below focus on different patient populations, but they share major commonalities. The themes of aggregating data to identify and risk stratify potential patients, focusing on care coordination functions, developing processes to improve patient engagement, and importantly, measuring performance, are prevalent throughout the examples (see Figure 1).
Data-driven care management programs that target high-risk and rising-risk patients can improve the patient experience and outcomes, improve population health, and reduce costs per capita. The five health systems highlighted here have implemented innovative programs with exciting results, which have profound implications for providers across the country.
At Partners, patients enrolled in the iCMP are reaping the benefits, including improved patient satisfaction and:
Allina’s HF management program decreased readmission rates by three percentage points, and its CKRI program is improving outcomes and cost, including:
Care management will continue to be a critical component of population health strategies, and organizations will need to adopt data-driven strategies to be effective. In the future, healthcare leaders will refine and improve the accuracy of risk prediction models and will work to implement evidenced-based interventions that correlate directly with the patient’s risk level.