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Applying Decision Science to the Prioritization of Healthcare-Associated Infection Initiatives

Tsai, Terry H. MSPH, PhD*; Gerst, Michael D. MS, PhD; Engineer, Cyrus MHS, DrPH*‡; Lehmann, Harold P. MD, PhD*

doi: 10.1097/PTS.0000000000000416
Original Article: PDF Only

Objectives Improving patient quality remains a top priority from the perspectives of both patient outcomes and cost of care. The continuing threat to patient safety has resulted in an increasing number of options for patient safety initiatives, making choices more difficult because of competing priorities. This study provides a proof of concept for using low-cost decision science methods for prioritizing initiatives.

Methods Using multicriteria decision analysis, we developed a decision support model for aiding the prioritization of the four most common types of healthcare-associated infections: surgical site infections, central line–associated bloodstream infections, ventilator-associated events, and catheter-associated urinary tract infections. In semistructured interviews, we elicited structure and parameter values of a candidate model, which was then validated by six participants with different roles across three urban teaching and nonteaching hospitals in the Baltimore, Maryland area.

Results Participants articulated the following structural attributes of concern: patient harm, monetary costs, patient mortality, reputational effects, and patient satisfaction. A quantitative decision-making model with an associated uncertainty report for prioritizing initiatives related to the four most common types of healthcare-associated infections was then created.

Conclusions A decision support methodology such as our proof of concept could aid hospital executives in prioritizing the quality improvement initiatives within their hospital, with more complete data. Because hospitals continue to struggle in improving quality of care with tighter budgets, a formal decision support mechanism could be used to objectively prioritize patient safety and quality initiatives.

From the *Johns Hopkins University, Baltimore; †University of Maryland, College Park; and ‡Towson University, Towson, MD.

Correspondence: Terry H. Tsai, MSPH, PhD, Johns Hopkins University, School of Medicine (e-mail:

The authors disclose no conflict of interest.

The study was funded by Agency for Healthcare Research and Quality (R03 HS23298).

We used the TreeAge Pro 2014, R1.0. TreeAge Software (software available at to create the decision trees and sensitivity analyses described in this study.

Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (

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