DATA-DRIVEN ASSESSMENT OF SHORT-TERM HEALTH RISK USING CLINICAL AND BEHAVIORAL INDICATORS IN TELEHEALTH MONITORING
DOI:
https://doi.org/10.65327/hsn.v12i1.2484Keywords:
Telehealth monitoring, Short-term health risk, Clinical indicators, Behavioral indicators, Logistic regressionAbstract
Telehealth monitoring is a chance to use routinely collected clinical and behavioral data to identify patients at higher risk for short-term health outcomes. This study assessed the association of demographic, physiological, behavioral, and healthcare-utilization indicators with 30-day high-risk status in a telehealth monitoring dataset. The secondary analytical observational design comprised 1,800 patient records of adult patients with complete data on age, sex, BMI, blood pressure, resting heart rate, daily steps, medication adherence, sleep duration, prior hospital admissions and risk status within 30 days. Descriptive statistics, Mann–Whitney U test, Fisher's exact test, Spearman correlation and binary logistic regression were used. There was significant class imbalance as only 14 patients were identified as high risk within 30 days. The high-risk individuals had an older age profile, higher diastolic blood pressure, higher resting heart rate, lower daily step count and poorer medication adherence. Logistic regression also found positive age, DBP, and RHR association with short-term risk, while daily steps and medication adherence had inverse associations with short-term risk. Correlations between most predictors were low, indicating that predictors were complementary aspects of the health status. These findings suggest that combining clinical and behavioral measures could enhance short term risk assessment for telehealth monitoring. However, the high-risk group is small, which limits the stability of estimates. Longitudinal data sets are needed to be larger, and external validation is required prior to clinical implementation.
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