Evaluating the Impact of a Risk Assessment System With Tailored Interventions in Germany: Protocol for a Prospective Study With Matched Controls

Abstract

Background With a worldwide increase in the elderly population, and an associated increase in health care utilization and costs, preventing avoidable emergency department visits and hospitalizations is becoming a global priority. A personal emergency response system (PERS), consisting of an alarm button and a means to establish a live connection to a response center, can help the elderly live at home longer independently. Individual risk assessment through predictive modeling can help indicate what PERS subscribers are at elevated risk of hospital transport so that early intervention becomes possible. Objective The aim is to evaluate whether the combination of risk scores determined through predictive modeling and targeted interventions offered by a case manager can result in a reduction of hospital admissions and health care costs for a population of German PERS subscribers. The primary outcome of the study is the difference between the number of hospitalizations in the intervention and matched control groups. Methods As part of the Sicher Zuhause program, an intervention group of 500 PERS subscribers will be tracked for 8 months. During this period, risk scores will be determined daily by a predictive model of hospital transport, and at-risk participants may receive phone calls from a case manager who assesses the health status of the participant and recommends interventions. The health care utilization of the intervention group will be compared to a group of matched controls, retrospectively drawn from a population of PERS subscribers who receive no interventions. Results Differences in health care utilization and costs between the intervention group and the matched controls will be determined based on reimbursement records. In addition, qualitative data will be collected on the participants’ satisfaction with the Sicher Zuhause program and utilization of the interventions offered as part of the program. Conclusions The study evaluation will offer insight into whether a combination of predictive analytics and case manager-driven interventions can help in avoiding hospital admissions and health care costs for PERS subscribers in Germany living at home independently. In the future, this may lead to improved quality of life and reduced medical costs for the population of the study. Trial Registration Deutsches Register Klinischer Studien (DRKS), DRKS00017328; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017328 International Registered Report Identifier (IRRID) DERR1-10.2196/17584

Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States

“…In a recently completed randomized clinical trial, we developed workflows that integrate daily PERS-based risk of 30-day emergency hospital transport with care pathways [17], resulting in 49% fewer EMS encounters in the intervention group [18]. In a currently running prospective study in Germany [19], the predictive model described herein is used to predict subscribers risk for 30-day emergency transport, followed by a case manager assessment, and tailored interventions for high-risk subscribers. The number of patients who will ultimately benefit from a combination of prediction and intervention will depend on various factors including the population size and prevalence of emergency health care use, performance of the predictive model and risk threshold above which patients are considered to be high risk, and efficacy of the interventions provided to high-risk patients.…”

“…In our prospective study in Germany [19], the predicted risk scores drive proactive outreach-if the risk is above a certain threshold, the patient may be contacted by the case manager. Due to the low prevalence of hospital transport in the German PERS population, setting the value of the risk threshold is a trade-off between finding many true positive cases (ie, a high sensitivity) and reducing the number of false positives (ie, a high PPV), as shown in Table 2 and also reported by other predictive modeling studies of emergency health care use [12,28].…”

“…In this study, a study nurse contacted high-risk subscribers, conducted additional triaging and, if deemed necessary, provided them with interventions including educational support, nurse home visits, or primary care physician referral. Based on the positive findings in the United States, we are investigating if a PERS-based risk prediction system with tailored interventions could also be applied in the German health care setting [19]. Similar to the US study, the German study requires a predictive model of risk of hospital transport in PERS users.…”