Predicting Call Abandonment in a Health Care Call Center Using Nonpersonal Operational Data: Machine Learning Study.

Hilmi Al-Shakhshir¹, Sara Kier², Dan Hoke³, Stephanie Hoiriis³, Mary-Ellen Vicinus⁴, Ravi Thadhani³, Anant Madabhushi^1,5

¹Atlanta VA Medical Center, Decatur, GA, United States.

²Executive Consulting Group Management Consultants, Atlanta, GA, United States.

³Emory Healthcare, Atlanta, GA, United States.

⁴Disruptive Innovations, Atlanta, GA, United States.

⁵Department of Biomedical Engineering, Wallace H. Coulter, Emory University, Atlanta, GA, United States.

PubMed DOI

Abstract

BACKGROUND: Call abandonment is a critical barrier to patient access in health care call centers; yet, predictive modeling efforts are limited by strict privacy regulations that restrict the use of personal or behavioral data. Whether abandonment can be accurately predicted using only anonymized operational metrics remains unclear. OBJECTIVE: This study evaluated the feasibility, performance, and…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.