Effects of perceived cost, service quality, and customer satisfaction on health insurance service continuance

This paper aims to contribute to the universal discourse on financial services continuance behavior by examining the impact of service cost on customers\u27 service-quality perception and service continuance intention. It presents the results of an empirical study that has explored the impacts of service cost, service quality, and customer satisfaction on health insurance customers\u27 behavioral intention toward continuing or discontinuing with their service providers. Very few studies had examined the impact of service cost on service-quality perception. Our study attempts to fill that gap. A sample of 820 customers was surveyed, and 624 usable responses were analyzed with ANOVA, standard multiple regression, and logistic regression. Our findings indicate that, although highly satisfied health insurance customers will most likely retain their current service providers, customer dissatisfaction does not necessarily lead to discontinuance. Our results also provide some operational implications for health insurance managers, with strategies for reducing attrition and improving customer retention

FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

Abstract Background An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters. Methods We conducted a retrospective cohort study using the readily available clinical and administrative data from the electronic health records of a tertiary hospital in Singapore. The primary outcome was chosen as three or more inpatient readmissions within 12 months of index discharge. We used univariable and multivariable logistic regression models to build a frequent hospital admission risk score (FAM-FACE-SG) by incorporating demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute illness burden and markers of chronic illness burden. We further validated the risk score on a separate dataset and compared its performance with the LACE index using the receiver operating characteristic analysis. Results Our study included 25,244 patients, with 70% randomly selected patients for risk score derivation and the remaining 30% for validation. Overall, 4,322 patients (17.1%) met the outcome. The final FAM-FACE-SG score consisted of nine components: Furosemide (Intravenous 40 mg and above during index admission); Admissions in past one year; Medifund (Required financial assistance); Frequent emergency department (ED) use (≥3 ED visits in 6 month before index admission); Anti-depressants in past one year; Charlson comorbidity index; End Stage Renal Failure on Dialysis; Subsidized ward stay; and Geriatric patient or not. In the experiments, the FAM-FACE-SG score had good discriminative ability with an area under the curve (AUC) of 0.839 (95% confidence interval [CI]: 0.825–0.853) for risk prediction of frequent hospital admission. In comparison, the LACE index only achieved an AUC of 0.761 (0.745–0.777). Conclusions The FAM-FACE-SG score shows strong potential for implementation to provide near real-time prediction of frequent admissions. It may serve as the first step to identify high risk patients to receive resource intensive interventions