A clinical classification framework for identifying persons with high social and medical needs: The COMPLEXedex-social determinants of health (SDH)

Background First-generation algorithms resulted in high-cost features as a representation of need but unintentionally introduced systemic bias based on prior ability to access care. Improved precision health approaches are needed to reduce bias and improve health equity. Purpose To integrate nursing expertise into a clinical definition of high-need cases and develop a clinical classification algorithm for implementing nursing interventions. Methods Two-phase retrospective, descriptive cohort study using 2019 data to build the algorithm (n = 19,20,848) and 2021 data to test it in adults ≥18 years old (n = 15,99,176). Discussion The COMPLEXedex-SDH algorithm identified the following populations: cross-cohort needs (10.9%); high-need persons (cross-cohort needs and other social determinants) (17.7%); suboptimal health care utilization for persons with medical complexity (13.8%); high need persons with suboptimal health care utilization (6.2%). Conclusion The COMPLEXedex-SDH enables the identification of high-need cases and value-based utilization into actionable cohorts to prioritize outreach calls to improve health equity and outcomes

Evaluation of a practical expert defined approach to patient population segmentation: a case study in Singapore

Abstract Background Segmenting the population into groups that are relatively homogeneous in healthcare characteristics or needs is crucial to facilitate integrated care and resource planning. We aimed to evaluate the feasibility of segmenting the population into discrete, non-overlapping groups using a practical expert and literature driven approach. We hypothesized that this approach is feasible utilizing the electronic health record (EHR) in SingHealth. Methods In addition to well-defined segments of “Mostly healthy”, “Serious acute illness but curable” and “End of life” segments that are also present in the Ministry of Health Singapore framework, patients with chronic diseases were segmented into “Stable chronic disease”, “Complex chronic diseases without frequent hospital admissions”, and “Complex chronic diseases with frequent hospital admissions”. Using the electronic health record (EHR), we applied this framework to all adult patients who had a healthcare encounter in the Singapore Health Services Regional Health System in 2012. ICD-9, 10 and polyclinic codes were used to define chronic diseases with a comprehensive look-back period of 5 years. Outcomes (hospital admissions, emergency attendances, specialist outpatient clinic attendances and mortality) were analyzed for years 2012 to 2015. Results Eight hundred twenty five thousand eight hundred seventy four patients were included in this study with the majority being healthy without chronic diseases. The most common chronic disease was hypertension. Patients with “complex chronic disease” with frequent hospital admissions segment represented 0.6% of the eligible population, but accounted for the highest hospital admissions (4.33 ± 2.12 admissions; p < 0.001) and emergency attendances (ED) (3.21 ± 3.16 ED visits; p < 0.001) per patient, and a high mortality rate (16%). Patients with metastatic disease accounted for the highest specialist outpatient clinic attendances (27.48 ± 23.68 visits; p < 0.001) per patient despite their relatively shorter course of illness and high one-year mortality rate (33%). Conclusion This practical segmentation framework can potentially distinguish among groups of patients, and highlighted the high disease burden of patients with chronic diseases. Further research to validate this approach of population segmentation is needed