Evolution of Personalized Care

Personalized care has long been associated with improved clinical outcomes and patient satisfaction. However, a question remains: Was care truly personalized in the past, or is precision medicine just the beginning of a truly tailored approach to Healthcare?

The concept of personalized care has seen quite a transformative journey over the years with the growing adoption of modern technologies and a deeper, ever-maturing knowledge of human biology and disease mechanisms. We are witnessing a paradigm shift from the traditional "one-size-fits-all" approach to more tailored and individualized treatments that are rooted in an understanding that each individual is unique, and so is their disease process. 

Cohort-Based segmentation: Initial years of personalized care

An aging population, a rising prevalence of chronic conditions, and a promising value-based care ecosystem have pushed Healthcare organizations to shift from population-based care to personalized care strategies. This led to the adoption of cohort-based segmentation, the process of grouping patients based on shared characteristics, such as medical history, demographic factors, or predicted Healthcare needs. Cohort-based segmentation combined with a Healthcare Provider’s clinical expertise, guarantees the delivery of scientifically substantiated treatments and medicines to unique individual scenarios. It is widely adopted to deliver focused Healthcare interventions with efficient resource allocations without losing the focus on personalization.

Traditionally, cohorts have been defined using rule-based or standard algorithm-based methods, such as Hierarchical Condition Categories (HCCs) or Adjusted Clinical Groups (ACGs). A rule-based system might group patients by age and diagnosed chronic conditions, while standard algorithms could use statistical methods to find patterns in patient data, identifying those at risk of hospital readmission. 

While these approaches provide a valuable starting point, they often fail to capture the complex interplay of factors such as medication adherence, lifestyle behaviors, and social determinants of health that can significantly impact disease progression and treatment response.

Resources For Living program used Social Isolation index to identify high risk MA members for social isolation and clinical risk. Permitted interventions to address loneliness and social isolation.

The Role of clinical guidelines for cohort-based member segmentation

Clinical guidelines are evidence-based recommendations that provide a framework to make informed decisions about appropriate treatments, diagnostics, and care pathways for specific clinical circumstances. In the context of personalized care, clinical guidelines can enable precise member segmentation. By mapping patient data against established clinical guidelines, Healthcare organizations can identify cohorts or subgroups of patients who share specific clinical characteristics, risk factors, or treatment requirements. 

With the CMS Interoperability & Prior Authorization Final Rule (CMS-0057-F), there is a lot of traction in the industry to convert clinical guidelines into Clinical Quality Language (CQL) Rules for Documentation Template Requirement (DTR) workflow. Leveraging this conversion, the adoption of CQL-based clinical guidelines can be used to enhance member segmentation efforts. CQL-based clinical guidelines can be used to automate the process of identifying patients who meet specific criteria or require specific interventions based on their individual characteristics and clinical data. 

For example, a CQL-based clinical guideline for diabetes management could be used to automatically identify patients with uncontrolled blood glucose levels, comorbidities such as hypertension or obesity, or those who are overdue for recommended screenings or follow-up visits. These patients could then be segmented into appropriate cohorts for targeted interventions, personalized care plans, or enrollment in disease management programs.

AI/ML-powered cohort optimization: Better but not the best

We understand that the success of personalization depends on the ability to accurately identify and optimize patient cohorts. This was a challenge that troubled the industry for a long time. With the rise of big data and machine learning capabilities, Healthcare organizations began employing AI/ML models to optimize cohort segmentation. Cohort optimization, segmenting populations into further granular, clinically relevant subgroups, offers a transformative solution to the healthcare industry. By integrating diverse data streams, from electronic health records (EHRs) and claims data to socioeconomic and genomic insights, a 360-degree view of each patient can be constructed revealing hidden patterns and nuances that traditional segmentation models overlook.