Practices that employ a “one size fits all’ model, where same level of care is provided to every patient, risk being clinically ineffective and unprofitable because of needless resource-intensive workflows. To maximize efficiency and profit margins, healthcare practices must analyze their patient pool and customize care management accordingly. Risk Stratification allows to correctly evaluate and assign a vulnerability status to patients by strategically segmenting them in groups of high, medium and low risk, for planning, development and implementation of a personalized care plan and to target resources efficiently at lower costs.

Risk Stratification Methods:

There are many methods used for risk stratification each able to help practices implement care coordination more efficiently. These include the Hierarchical Condition Categories HCCs, Adjusted Clinical Groups ACGs, Chronic Comorbidity Count CCC, Elder Risk Assessment ERA, etc. Condition count (number of conditions per patient) is a core metric in many of the aforementioned models to segment patients in risk categories.  Some of these models are quite complex and costly to integrate in individual practices. However, simpler ones are also effective, particularly for small scale practices. The ACGs developed by John Hopkins University, for example, stratifies risk with a critical review and testing process and is considered to be extremely efficient for growing practices.

Practices can also adopt and tailor these models according to organizational needs and patient influx.

Risk-Stratified Care Management; a Two Step Approach:

1. Consolidated Data, Automated Analytics and Post Analysis Workflows:

Access to actionable data and updated Electronic Health Records helps practices identify and single out a cohort of high-risk patients. A well-rounded examination is needed to design, manage and ultimately implement care models. Automated analytics and workflows that sort patients by the type and number of conditions, or comorbidities, can be generated for monitoring the progress of individual patients along common disease trajectories and plan their interventions accordingly.

2. Subjective Considerations

While objective data and empirical models are crucial to maintain an effective Risk-Stratified Care Management, subjective physician insights are equally important. Automated models alone cannot analyze and compare objective metrics against behavioral, demographic and clinical data as well as lifestyle and other social and environmental factors that can work to alleviate or potentially exacerbate individual patient risk status. For example, a patient diagnosed, some months back, with Type-2 diabetes could be categorized as high risk. However, he might have since begun exercising and intermittent fasting; and have lost 30 pounds as a result, while also taking his medication as prescribed. A subjective case specific analysis will lead the concerned physician to assign such patient a lower risk level. Therefore, combining objective and subjective input can remarkably improve Risk Stratification and in extension care plans and resource management.