Predicting Call Abandonment in a Health Care Call Center Using Nonpersonal Operational Data: Machine Learning Study
Hilmi Al-Shakhshir, Sara Kier, Dan Hoke, Stephanie Hoiriis, Mary-Ellen Vicinus, Ravi Thadhani, Anant Madabhushi, Andrew Coristine, Sedat Ozcanan, Nevo Itzhak · 2026
Read Predicting Call Abandonment in a Health Care Call Center Using Nonpersonal Operational Data: Machine Learning Study by Hilmi Al-Shakhshir, Sara Kier, Dan Hoke, Stephanie Hoiriis, Mary-Ellen Vicinus, Ravi Thadhani, Anant Madabhushi, Andrew Coristine, Sedat Ozcanan, Nevo Itzhak (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13575546.txt.
Description
Hilmi Al-Shakhshir and co-authors asked in 2026 whether callers hanging up on a health system can be predicted without any personal data. Privacy rules bar the usual caller details, so the team used only routine operations numbers: wait times, staffing load, handling time, and time of day. They drew on over a million call records across four work phases.
Learning models trained on those spare signals still foresaw abandonment well enough to act on. Managers could add staff or reroute queues before patience runs out. The study shows access problems can be tackled inside strict privacy limits.
Catalog record
| CREATOR | Hilmi Al-Shakhshir, Sara Kier, Dan Hoke, Stephanie Hoiriis, Mary-Ellen Vicinus, Ravi Thadhani, Anant Madabhushi, Andrew Coristine, Sedat Ozcanan, Nevo Itzhak |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |