Network Effects and Diffusion of Health Information Technology
- Presenter:
Chair: Stephen T. Parente; Discussant: TBA Wed June 7, 2006 8:00-9:30 Room 313
Health information technology (HIT) is widely-regarded as a key strategic resource to increase efficiency and improve quality of care. Despite growing interest in HIT by managers and policymakers, few studies have examined the determinants of HIT adoption. In particular, little is known about the role of network effects in the diffusion of HIT across hospitals and affiliated organizations. This study seeks to address this gap in the literature by examining the effect of system membership on the adoption of HIT by hospitals, sub-acute, and ambulatory facilities. The primary data source is the 2004 HIMSS Analytics (HA) database, which contains detailed information on the adoption of HIT by 1,453 integrated health delivery systems. The sample includes 3,989 hospitals, 3,007 sub-acute, and 18,008 ambulatory care facilities. For hospitals and sub-acute facilities, the HIT variables are classified into 4 categories: financials and business office; medical records and administrative; management and human resources; and clinical and ancillary departments. Variables are also created for specific HIT, particularly advanced clinical applications such as computerized patient records and computerized physician order entry. The HA database is linked to two supplemental data sets: 1) 2002 AHA Annual Survey of Hospitals, which provides information on hospital (staffed beds, ownership type, teaching status, etc.) and system characteristics (centralization, physician arrangement, insurance product, etc.), and 2) 2004 Area Resource File, which provides information on area characteristics (rural, per capita income, etc.). I estimate multivariate regression models to test the effect of system characteristics on HIT adoption, controlling for facility and area characteristics. The first model uses logistic regression to estimate the factors correlated with the likelihood of adoption of specific HIT applications. The second model uses negative binomial regression to estimate the factors associated with the level of HIT adoption, as proxied by the count of applications adopted within each HIT category. Finally, I test the robustness of the results to the potential endogeneity of system membership using appropriate econometric methods. Preliminary results suggest that network effects are a strong determinant of HIT adoption. In particular, facilities in systems that are more centralized and offering an insurance product are much more likely to adopt advanced clinical IT as well as have a higher level of HIT adoption. The consolidation and integration of hospitals, physicians, and affiliated organizations into integrated health delivery systems has important implications for the adoption and diffusion of HIT. System membership may confer “network effects” because HIT adoption confers benefits to the adopter as well as other affiliated facilities. The study finds evidence that system governance and integration play a key role in HIT adoption. This implies that HIT adoption decisions are made at the system-level and that organizational structure plays a key role in the diffusion of HIT across facilities within a system.
