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592. Measurement Noise and Judgment Bias in Hypertension Management

Invited abstract in session MC-15: Healthcare Analytics, stream OR in Health Services (ORAHS).

Monday, 12:30-14:00
Room: 18 (building: 116)

Authors (first author is the speaker)

1. Vedat Verter
Smith School of Business, Queen's University
2. Manaf Zargoush
McMaster University
3. Mehmet Gumus
Desautels Faculty of Management, McGill University

Abstract

Hypertension is a major public health issue and the most important modifiable risk factor for cardiovascular disease. Effective hypertension management is hindered by (i) the noise in blood pressure (BP) measurements (technology factor) and (ii) the physicians’ judgment bias (human factor). The latter refers to the physician’s error in inferring the patient’s true BP from the measurements. This study investigates the role of these two factors in hypertension management from the perspective of the clinical value of information.

We present an analytical framework with two main modules: a learning module that models how clinical judgments about the patient’s underlying BP are made (with and without bias) and an optimization module that models how optimal treatment decisions are made under various learning strategies.

Our results suggest that the value of information concerning the patients’ underlying BP depends on (i) the physician's judgment bias and decision flexibility, cardiovascular risk as well as the short-term and long-term BP variability of the patient, and the measurement noise of the device. In addition, among the two types of judgment bias (i.e., under-estimation and over-estimation), the value of information is much higher under the under-estimation bias, where the clinician undervalues the significance of the observed BP in inferring the true underlying BP.

Keywords

Status: accepted

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