A Blessing or a Curse? Teletriage Service in Healthcare
Topic: |
A Blessing or a Curse? Teletriage Service in Healthcare |
Time&Date: |
9:00 am - 10:30 am, March 8, 2024 (Friday) |
Venue |
Zoom |
Zoom Link: |
https://cuhk-edu-cn.zoom.us/j/8913862860?pwd=WDdDbFBRbW9hSTVyTTRTancvbmI0dz09 |
Speaker: |
Prof. Krista Li (Indiana University) |
Abstract: |
Teletriage is a telehealth service that uses telecommunication technologies to screen patients remotely to determine patients’ condition and need for medical treatment. This paper investigates how teletriage service affects patients’ decisions to seek medical treatment and when healthcare providers (e.g., medical businesses, hospitals, or clinics) should launch teletriage. We show that teletriage can draw more mild patients to seek treatment while deterring more severe patients. Consequently, even if teletriage is costless, healthcare providers might abandon this service, despite the fact that teletriage always improves patient surplus. Patients can be better off seeking medical treatment from a provider who charges a higher treatment price. Adoption of teletriage leads some providers to increase their price of medical treatment but other providers to reduce it. When providers can charge a fee for patients to use teletriage, they always launch teletriage and may choose to offer teletriage for free. Patients can be better off if providers charge patients a non-zero fee to access teletriage service. These results caution healthcare policymakers that teletriage can exacerbate healthcare inefficiency instead of alleviating it. Nevertheless, to improve patient surplus, the government should sometimes subsidize providers to offer teletriage or allow providers to charge a teletriage fee. |
Biography: |
Krista Li is Blanche “Peg” Philpott Professor and Associate Professor of Marketing at the Kelley School of Business at Indiana University. Her research contributes to theory and practice on how firms can improve marketing strategies by leveraging information (e.g., consumer data, private data) and consumers’ behavioral biases (e.g., time-inconsistent preferences, loss aversion, fairness, and status preferences). |