25 Feb Non-Medical Workers and Mobile Technology Can Help Predict Hospital Readmissions
MedicalResearch.com Interview with:
Dr. Andrey Ostrovsky
Andrey Ostrovsky, MD
CEO | Co-Founder
Care at Hand
Medical Research: What is the background for this study?
Dr. Ostrovsky: Hospital readmissions are a large source of wasteful healthcare spending, and current care transition models are too expensive to be sustainable. One way to circumvent cost-prohibitive care transition programs is complement nurse-staffed care transition programs with those staffed by less expensive nonmedical workers. A major barrier to utilizing nonmedical workers is determining the appropriate time to escalate care to a clinician with a wider scope of practice. The objective of this study is to show how mobile technology can use the observations of nonmedical workers to stratify patients on the basis of their hospital readmission risk.
Medical Research: What are the main findings?
Dr. Ostrovsky: The risk score derived from the nonmedical workers’ observations had a significant association with 30-day readmission rate with an odds ratio (OR) of 1.12 (95 percent confidence interval [CI], 1.09–1.15) compared to an OR of 1.25 (95 percent CI, 1.19–1.32) for the risk score using nurse observations. Risk scores using nurse interpretation of nonmedical workers’ observations show that patients in the high-risk category had significantly higher readmission rates than patients in the baseline-risk and mild-risk categories at 30, 60, 90, and 120 days after discharge. Of the 1,064 elevated-risk alerts that were triaged, 1,049 (98.6 percent) involved the nurse care manager, 804 (75.6 percent) involved the patient, 768 (72.2 percent) involved the health coach, 461 (43.3 percent) involved skilled nursing, and 235 (22.1 percent) involved the outpatient physician in the coordination of care in response to the alert.
Medical Research: What should clinicians and patients take away from your report?
Dr. Ostrovsky: The predictive nature of the 30-day readmission risk scores is influenced by both nurse and nonmedical worker input, and both are required to adequately triage the needs of the patient.
Medical Research: What recommendations do you have for future research as a result of this study?
Dr. Ostrovsky: Although this preliminary study is limited by a modest effect size, it demonstrates one approach to using technology to contribute to delivery model innovation that could curb wasteful healthcare spending by tapping into an existing underutilized workforce.
Medical Research: Is there anything else you would like to add?
Response: Nurses are required to adequately address alerts. But nurses don’t have to be on the frontline. A more balanced approach to staffing care transitions would include non-medical staff as frontline workers supervised by nurses.
Citation:
Andrey Ostrovsky, MD; Lori O’Connor, RN; Olivia Marshall; Amanda Angelo; Kelsy Barrett; Emily Majeski; Maxwell Handrus, MS; Jeffrey Levy. “Predicting 30- to 120-Day Readmission Risk among Medicare Fee-for-Service Patients Using Nonmedical Workers and Mobile Technology.” Perspectives in Health Information Management (Winter 2016): 1-20.
Andrey Ostrovsky, MD (2016). Non-Medical Workers and Mobile Technology Can Help Predict Hospital Readmissions MedicalResearch.com
Last Updated on February 25, 2016 by Marie Benz MD FAAD