Abstract
Background: For almost a decade, public and private organizations have pressured hospitals to improve their patient safety records. Since 2008, the Centers for Medicare & Medicaid Services has no longer been reimbursing hospitals for secondary diagnoses not reported during the point of admission. This ruling has motivated some hospitals to engage in safety-oriented programs to decrease adverse events.
Purpose: This study examined which hospitals may engage in patient safety solutions and whether they create these patient safety solutions within their structures or use suppliers in the market.
Methodology: We used a theoretical model that incorporates the key constructs of resource dependence theory and transaction cost economics theory to predict a hospital's reaction to Centers for Medicare & Medicaid Services "never event" regulations. We present propositions that speculate on how forces conceptualized from the resource dependence theory may affect adoption of patient safety innovations and, when they do, whether the adopting hospitals will do so internally or externally according to the transaction cost economics theory.
Findings: On the basis of forces identified by the resource dependence theory, we predict that larger, teaching, safety net, horizontally integrated, highly interdependent, and public hospitals in concentrated, high public payer presence, competitive, and resource-rich environments will be more likely to engage in patient safety innovations. Following the logic of the transaction cost economics theory, we predict that of the hospitals that react positively to the never event regulation, most will internalize their innovations in patient safety solutions rather than approach the market, a choice that helps hospitals economize on transaction costs.
Practice Implications: This study helps hospital managers in their strategic thinking and planning in relation to current and future regulations related to patient safety. For researchers and policy analysts, our propositions provide the basis for empirical testing.