• Date and time: Thursday 3 April 2025, 2pm to 3pm
  • Location: Via Zoom only (not recorded) - please join the mailing list to receive the Zoom link
  • Audience: Open to staff, students (postgraduate researchers only)
  • Admission: Free admission, booking not required

Event details

Background: Antibiotic resistance is an ongoing public health crisis fuelled by overuse and misuse of antibiotics.  Various financial incentive programmes to reduce the rate of inappropriate antibiotic prescriptions have been implemented and studied empirically. However, there have not been analytical studies to evaluate payment model contract design features and the potential for payment models to impact diagnosis decision making. We examine the impact of action-based incentive payments on reducing inappropriate antibiotic prescriptions in primary care, where 30%–50% of antibiotic prescriptions are inappropriate. 

Methods: We develop a stylized physician compensation model to study the interaction between a payer and a provider. The payer offers a payment contract, with a bonus tied to the prescription, to maximize social welfare, considering total costs of providing care and social harm from antibiotic resistance. Given the contract offered and their own opportunity cost associated with factors such as fear of misdiagnosis and time spent explaining to patients why antibiotics are not indicated, the provider chooses whether or not to prescribe antibiotics to patients for whom antibiotics are not clinically indicated. We consider four cases: when diagnostic accuracy relies on symptom presentation versus additional diagnostic testing and when the opportunity cost of not prescribing antibiotics is public versus private information of the provider. 

Results and Discussion: When there is no information asymmetry, an action-based incentive payment can coordinate care and achieve the first-best policy, decreasing the rate of inappropriate prescribing, even when incentive payments can affect the diagnosis behavior. However, when the diagnosis relies on additional objective testing, the first-best policy results in fewer inappropriate antibiotic prescriptions when the test has high specificity. Therefore, when an accurate technical diagnostic is available, a simple to implement action-based incentive payment can be effective in reducing inappropriate antibiotic prescribing. In the realistic setting where the provider’s opportunity cost is private information, financial incentive imposes a moral hazard on the provider’s prescription decision and an action-based incentive payment cannot eliminate inappropriate antibiotic prescribing. In these settings, the introduction of point of care diagnostics to aid in objective diagnostic criteria will reduce the unintended consequences of the contract.

Case study:  Finally, we apply our framework to patients presenting to primary care providers with a sore throat where 85%–90% of infections are viral. We apply our model to demonstrate that a bonus payment may influence physician diagnostic behavior at the expense of health outcomes in patients with bacterial infections when diagnostic accuracy relies on symptom presentation alone.  We demonstrate the benefit of a bonus to reduce antibiotic prescribing behavior conditional on the use of a point-of-care rapid antigen test and we calculate the optimal bonus payment for not prescribing antibiotics to patients diagnosed with viral infections causing sore throat.

Work with Salar Ghamat, Mojtaba Araghi, and Michael Silverman

If you are not a member of University of York staff and are interested in attending a seminar, please contact adrian.villasenor-lopez@york.ac.uk or dacheng.huo@york.ac.uk so that we can ensure we have sufficient space. Please also use these contacts if you wish to be added to the mailing list.

Professor Lauren Cipriano, Ivey Business School and Western University

Bio:  Lauren Cipriano is a Professor at the Ivey Business School and in the Department of Medicine and Department of Epidemiology & Biostatistics at Western University where she holds the Canada Research Chair in Healthcare Analytics, Management, and Policy. She earned her PhD in Management Science and Engineering and MS in Statistics at Stanford University. Lauren’s research interests focus on the application of statistics, decision analysis, operations research, and systems analysis to health policy problems. Previously, Lauren worked at the Institute for Technology Assessment at Massachusetts General Hospital. Lauren was the 2018 winner of the Dr. Maurice McGregor Award for Health Technology Assessment and Lauren is the Deputy Editor of Medical Decision Making and MDM Policy & Practice. 

Contact

For more information on these seminars, contact Adrián Villaseñor-Lopez or Dacheng Huo: