The Importance of Risk Stratification in Population Health Management
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The Importance of Risk Stratification in Population Health Management
Published on January 6, 2017
Abhinav Shashank
CEO at Innovaccer - Hiring passionate go to market folks!
As value-based care becomes the primary focus of healthcare organizations, providers now need to broach Population Health Management and to manage it with risk stratification with a delicate balance. It is now important for providers to focus on patients’ individual and distinct health signs and take decisions to take their journey forward to better and quality health.
Before providers deliver care, they should have a prior knowledge of who their patients are, identifying them and finding who needs the care most. In this era of value-based care, risk stratification becomes a necessity: to sort patients into high, moderate and low health risk tiers and delivering appropriate care to address their health needs.
The Need for Risk Stratification
Risk stratification, to put simply, is the process of dividing patients into “buckets” based on their vital health signs, their lifestyles, and medical history. Risk stratification is a framework applied for complete population health management, combining several individual risk scores, demographic and socioeconomic characteristics and medical records to create a comprehensive patient profile.
Risk stratification is the foundational step for targeting patients at various levels of risks, and further, scheduling follow-ups and keeping them aligned with their care plans. Here’s why risk stratification is important:
Predict risks: Risk stratification can help providers to proactively identify patients at risk of unplanned hospital admissions. Almost one-third of all the readmissions that take place in the United States are preventable.
Patient-specific care plans: Identifying patient-specific risk factors that may pose a threat in future can help physicians and health coaches develop care plans tailored to their needs. Understanding population health trends: With a continuous assessment of risk factors and the use of risk scores, providers can understand their patient population and find answers to critical questions. All in all, it’s the need of the hour to implement risk stratification in any successful population health management model to classify patients into high-risk, low-risk, and rising-risk groups and to achieve the Triple Aim: better health outcomes, quality care and lower costs of care.
Overview of Risk Stratification Methods
There are several models available to stratify a population by risk. Here are a few widely used and recognized ones. :
Hierarchical Condition Categories (HCCs): The HCC models were designed as part of the Medicare Advantage Program by CMS. It incorporates 70 conditions, all selected from ICD codes and also brings in expected health expenditures. Adjusted Clinical Groups (ACG): ACG was developed by John Hopkins University with the ability to stratify risk with a critical review and testing process. It uses both inpatient and outpatient diagnoses and predicts hospital utilizations. Elder Risk Assessment (ERA): ERA works for the population over 60 years of age and makes use of their demographic data to assign a risk score to each patient. Chronic Comorbidity Count (CCC): CCC is to total count of selected comorbid conditions spanning over six categories, using the public data from the Agency for Healthcare Research and Quality.
The Use of Data
Data analytics is the current buzzword in healthcare, and rightly so. Access to actionable data combined with right analysis helps not only in predicting outcomes, but also improves the ability of care teams to align available resources to what a patient needs. Although data is just a start and not an endpoint, it can be used in several ways to make the process of risk stratification less cumbersome.
Optimal use of data: Going beyond clinical and claims data to socioeconomic data and other relevant information that describe a patient and integrating multiple sources of information to let providers understand what works best for a patient. Performing analytics: Identification of underlying risk factors will alert providers well-in-time of any complication that might occur. Monitoring growth and outcomes: Data from previous successes and failures can help care teams redesign care plans and ensure complete patient-centric care. Incorporating data into risk scores: Creating risk scores as a blend of behavioral, demographic and clinical data will provide physicians with a holistic view of patients to improve outcomes across the care continuum. A value-focused organization used Medicaid data, along with real-time hospital admissions, discharge, and transfer data (ADT) from about 80% of hospitals across the state with the aim to provide more than 1800 Primary Care Practices with insights into patient risk status and population health trends. Within the last one year, their efforts have been rewarded with a 5 percent drop in total Medicaid costs along with a 26 percent reduction in inpatient admissions and half the number of preventable readmissions.
The Road Ahead
Risk stratification, in all true sense, is a catalyst in developing successful population health management plans. Only an effective implementation of risk stratification combined with communication and monitoring will guarantee appropriate patient-centric care. The availability of data is not an issue; even the access to data is a challenge that can be combated. The real challenge is transformation – it needs more than analytics and insights; it calls for actionable plans from providers, payers, and patients.
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