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:
1. Identifies rising-risk patients.
2. Uses a specific social determinant assessment to capture factors beyond claims data.
3. Integrates EMR data to achieve quality measures.
4. Identifies patients for palliative or hospice care.
5. Identifies patients with chronic conditions.
6. Increases patient engagement.
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The foundation of successful care management is identifying the right high-risk and rising-risk patients to participate in a care management program. To reliably select the patients most likely to benefit from care management, health systems need data from across the continuum of care, as claims data (the most common criteria for patient identification) alone doesn’t give a full picture of a patient’s health. Care managers need other critical data, such as clinical data, pharmaceutical data, and social determinants of health data (e.g., homelessness, abuse, lack of insurance, and
lack of access to routine care), to truly understand a patient’s
risk and plan effective intervention.
Health systems can’t make informed decisions about care management without near real-time access to all patient data and care management analytics-driven decision support tools. This article discusses how care managers today use analytics-driven technologies to effectively identify patients for their programs and manage their care to improve outcomes and lower costs.
Before the transition to value-based care reimbursement models, care management departments primarily managed patients as primary care providers (PCPs) referred them. As health systems take on more financial risk, however, care management programs need to be more selective by focusing on the patients most likely to benefit, and they need to understand how to best allocate their resources (e.g., number of available care managers and patient capacity).
The right patients for care management are not necessarily the highest-cost patients. If cost were the only consideration, identifying patients based on claims or utilization data alone would be easy. However, patient identification is rarely so simple; here are two examples:
Analytics help care managers identify impactable patients for care management by better understanding patients’ current treatment and challenges, care management goals based on their situations, care setting changes, medications, treatments, and more.
Analytics-driven care management tools help care managers identify the right patients for their programs and deliver optimal care in key ways:
Patient stratification technology, such as the Health Catalyst Patient Stratification tool, leverages data (including PCP and ED visits) to find patients who are not identified as high risk, but who could become high-cost, high-priority patients. With EMR data, it’s fairly easy to identify patients who have chronic conditions by how frequently they visit their PCP or the ED, or those who have an acute condition based on hospitalization records. However, what about patients whose circumstances are less obvious? For example, how do care managers determine whether care management is appropriate for the patient who is borderline hypertensive, but not yet on medication, or the patient who has gained significant weight, but has not yet developed weight-related conditions (e.g., diabetes, cardiac issues, or respiratory issues)?
Patient stratification also uses risk models to help care managers determine which patients are at risk of becoming ill in the future. With this understanding of rising risk, the organization’s leadership and care management leadership can determine where to strategically focus care management efforts for the most impact. The organization can also customize algorithms to target specific populations as their care management and population health strategy develops.
Social determinants significantly affect the overall health of the patient and the likelihood of good health outcomes. The care manager can conduct a social determinant assessment, and, based on those findings, address barriers to good care (e.g., homelessness, cultural and language barriers, financial stressors, etc.).
For example, Health Catalyst uses an INSIGHT (Independent Neighborhood Socioeconomic Indicators for Geo-based Health Trends) score to calculate a patient’s social determinants. INSIGHT bases scoring on the most recent U.S. mortality rates and publicly available socioeconomic data from the U.S. Census; data represents a county and/or zip code-based view of the overall socioeconomic status of a given area’s population.
Care managers can use data on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measures, vaccinations, and preventive diagnostic tests, etc., (both age specific and condition specific) to meet performance measures while improving patient outcomes.
Care managers may use patient stratification to develop an algorithm that can identify multiple admissions, or certain diagnoses data, including terminal diagnosis or multiple admissions for a serious condition, to determine when patients need palliative or hospice care. Care managers must work collaboratively with palliative and hospice agencies to explain the level of care needed, especially for patients with severe symptoms and terminal diagnoses who require more intensive palliative management. After identifying patient-appropriate goals for palliative care or hospice, the care manager, along with the PCP, starts a goals-of-care conversation to guide the patient and family in determining the best care going forward. A care planning tool (e.g., the Health Catalyst Care Coordination application) offers customizable hospice assessments and can also include palliative assessments (again, customizable by the client).
By identifying patients with chronic conditions, such as COPD, CHF, cardiac issues, and diabetes, care managers can intervene and ultimately reduce medical costs while improving care for this population. More than two-thirds of all health care costs are for treating chronic diseases. The National Council on Aging estimates that 95 percent of health care costs for older Americans can be attributed to chronic diseases.
Mobile patient engagement tools (e.g., the Health Catalyst Care Companion application), enhance care management’s impact by giving patients immediate access to their care managers. A timely response from a care manager may, for example, help a patient decide between going to the ED and managing the situation without emergency care. Patients can also report their own outcomes assessments, giving their care teams an even fuller picture of their health status.
Cost data alone doesn’t give organizations a robust understanding of care management needs within their populations. To provide the right care for the right patient at the right time, care managers must have analytics-driven technology to understand which patients will benefit most from care management and to plan the best program for each patient.
Health systems that use analytics tools to drive their care management programs, from patient identification to care coordination, will improve outcomes and cost savings. The combination of the right data with an experienced care manager can decrease cost, improve outcomes, and, ultimately, provide quality care for the patients who need the most support.
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