Modeling Patients' Progression through Health-Related Social Needs.

Abstract

This study sought to characterize how a population experienced health-related social needs (HRSNs) over time.We employed hidden Markov modeling using data extracted from a natural language processing state machine from 2018 to 2020 to examine whether a patient experienced any food, legal, transportation, employment, financial, or housing needs. Characteristics of patients transitioning into low/high-risk states were compared. We also identified the frequency at which patients transitioned according to their risk state.Our results identified that five hidden states best represented how patients are experiencing HRSNs longitudinally. Of 48,055 patients, 80% were categorized in states 1 and 2, labeled as low risk. Nine percent, 8%, and 3% of the study population were labeled as medium, high, and very high risk, respectively. Results also showed that low and high-risk patients (states 1, 2, and 5) only transition states once every year and a half, while patients in medium and high-risk states transition approximately once per year.Low and very high-risk patients tend to remain in the same state over time, suggesting that low-risk patients may have the means to maintain a healthy state while very high-risk patients have a difficult time resolving multiple HRSNs. Early screening and immediate interventions may be beneficial in mitigating the persistent harm of unaddressed HRSNs.