Michael Fairley is a Ph.D. student in the Department of Management Science & Engineering at Stanford University.
Research Area: Health Policy
Honors & Awards
Stanford Interdisciplinary Graduate Fellowship, Office of the Vice Provost for Graduate Education (VPGE)
- Optimizing interventions across the HIV care continuum: A case study using process improvement analysis OPERATIONS RESEARCH FOR HEALTH CARE 2020; 25
OPTIMIZING PORTFOLIOS OF RESEARCH STUDIES DESIGNED TO INFORM ECONOMIC EVALUATIONS
SAGE PUBLICATIONS INC. 2020: E253–E254
View details for Web of Science ID 000509275600219
Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
2020; 23 (6): 734–42
Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods' use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.
View details for DOI 10.1016/j.jval.2020.02.010
View details for PubMedID 32540231
OPTIMAL ALLOCATION OF CLINICAL TRIAL SAMPLE SIZE TO SUBPOPULATIONS WITH CORRELATED PARAMETERS
SAGE PUBLICATIONS INC. 2020: E260–E261
View details for Web of Science ID 000509275600223
PRACTICAL CONSIDERATIONS FOR THE EFFICIENT COMPUTATION OF THE EXPECTED VALUE OF SAMPLE INFORMATION TO PRIORITIZE RESEARCH IN HEALTH CARE
SAGE PUBLICATIONS INC. 2020: E63–E64
View details for Web of Science ID 000509275600063
- Improving the efficiency of the operating room environment with an optimization and machine learning model HEALTH CARE MANAGEMENT SCIENCE 2019; 22 (4): 756–67
Improving the efficiency of the operating room environment with an optimization and machine learning model.
Health care management science
The operating room is a major cost and revenue center for most hospitals. Thus, more effective operating room management and scheduling can provide significant benefits. In many hospitals, the post-anesthesia care unit (PACU), where patients recover after their surgical procedures, is a bottleneck. If the PACU reaches capacity, patients must wait in the operating room until the PACU has available space, leading to delays and possible cancellations for subsequent operating room procedures. We develop a generalizable optimization and machine learning approach to sequence operating room procedures to minimize delays caused by PACU unavailability. Specifically, we use machine learning to estimate the required PACU time for each type of surgical procedure, we develop and solve two integer programming models to schedule procedures in the operating rooms to minimize maximum PACU occupancy, and we use discrete event simulation to compare our optimized schedule to the existing schedule. Using data from Lucile Packard Children's Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital.
View details for PubMedID 30387040
Cost-effectiveness of Intensive Blood Pressure Management.
2016; 1 (8): 872-879
Among high-risk patients with hypertension, targeting a systolic blood pressure of 120 mm Hg reduces cardiovascular morbidity and mortality compared with a higher target. However, intensive blood pressure management incurs additional costs from treatment and from adverse events.To evaluate the incremental cost-effectiveness of intensive blood pressure management compared with standard management.This cost-effectiveness analysis conducted from September 2015 to August 2016 used a Markov cohort model to estimate cost-effectiveness of intensive blood pressure management among 68-year-old high-risk adults with hypertension but not diabetes. We used the Systolic Blood Pressure Intervention Trial (SPRINT) to estimate treatment effects and adverse event rates. We used Centers for Disease Control and Prevention Life Tables to project age- and cause-specific mortality, calibrated to rates reported in SPRINT. We also used population-based observational data to model development of heart failure, myocardial infarction, stroke, and subsequent mortality. Costs were based on published sources, Medicare data, and the National Inpatient Sample.Treatment of hypertension to a systolic blood pressure goal of 120 mm Hg (intensive management) or 140 mm Hg (standard management).Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually.Standard management yielded 9.6 QALYs and accrued $155 261 in lifetime costs, while intensive management yielded 10.5 QALYs and accrued $176 584 in costs. Intensive blood pressure management cost $23 777 per QALY gained. In a sensitivity analysis, serious adverse events would need to occur at 3 times the rate observed in SPRINT and be 3 times more common in the intensive management arm to prefer standard management.Intensive blood pressure management is cost-effective at typical thresholds for value in health care and remains so even with substantially higher adverse event rates.
View details for DOI 10.1001/jamacardio.2016.3517
View details for PubMedID 27627731