The Effect of IT Capital on Hospital Efficiency

1. The Effect of IT Capital on Hospital Efficiency Michael G. Housman,1 Lorin M. Hitt,1 Kinga Z. Elo,2 Nick Beard,2 1: The Wharton School, University of Pennsylvania 2: PricewaterhouseCoopers HIT Interest Group Meeting

2. Research Questions • Previous Research on Hospital IT: • Found a positive influence of IT and medical IT capital on hospital output (Menon, Lee, & Eldenburg, 2002) • Cost-reductions in hospitals adopting IT, 3-5 years after adoption (Borzekowski, 2002) • Evidence that aggressive implementers improved efficiency more than other hospitals (Atkinson & Cockerill, 2006) • Research Questions: • Does hospital IT investment improve operating performance and/or lower costs? • Do clinical versus administrative applications create different benefits? • How do these benefits change with different time horizons? Is there a lagged effect? HIT Interest Group Meeting

3. Data Sources • HIMSS Analytics Database (formerly Dorenfest 3000+) • Data on IT implementation (hardware/software) in U.S. hospitals • Sample size of 4,000+ hospitals from 1999 to 2005 • Data on 40 clinical and administrative hospital applications • AHA Annual Survey Database • Data on hospital resources (capital and labor) and outputs • Sample size of 6,000+ hospitals (all AHA members) • Solucient ProviderView (from Medicare Cost Reports) • Medicare data on hospital revenues, costs, and profits • Sample size of 6,000+ hospitals (all Medicare providers) HIT Interest Group Meeting

4. Hospital Sample vs. Population HIT Interest Group Meeting

5. Hospital Software Applications HIT Interest Group Meeting

6. 100 All applications 100 CPOE 80 80 PACS EMR 60 60 Cardiology Pharmacy 40 40 Lab Billing 20 Scheduling 20 Registration 0 No applications 0 Measuring IT Adoption • Calculated each hospital’s position on an IT index by weighting each application according to price • An expert provided cost estimates for each application type within a representative 600-bed hospital, based on real price proposals from several software companies that were adjusted by hospital size Simple Application Count Index All applications make an equal contribution to the overall “IT index.” Price-Weighted Application Index Applications make a contribution to the overall “IT index” according to their typical price. HIT Interest Group Meeting

7. Distribution of IT Capital Index Scores For-profit hospitals Not-for-profit hospitals HIT Interest Group Meeting

8. Specifications and Assumptions • Utilize a translog functional form • Flexibility, minimal set of assumptions, and cross-elasticity effects (Christensen, et al., 1973; Berndt, 1990) • Production function is assumed to be linearly homogeneous in labor and material costs • IT and capital are treated as quasi-fixed inputs and treated as fixed in the short run • Measure of capital (revenue-adjusted bed size) is less easily adjusted than traditional forms of capital (Brown & Christensen, 1981) • Estimated by seemingly unrelated regression (SUR) • Stochastic frontier analyses (SFA) were run from the data and the results were not qualitatively different than those presented HIT Interest Group Meeting

9. Economic Model • Results were obtained by solving for the following system of equations with IT and capital treated as ‘quasi fixed’ (treated as fixed in the short term): • VC = Total hospital costs – operating expenses • PW = Wages (adjusted by state-averages) • PM = Material costs (drugs, supplies charged) • Y = Outputs (discharges and outpatient visits) • IT = IT capital index score • K = Other quasi-fixed capital, measured by revenue-adjusted bed size • SL = Labor cost share in total operating cost • SM = Material cost share in total operating cost • SL+ SM = 1 • Z = Controls (patient case mix, scope of services, ownership status, state, urban/rural, disproportionate share, teaching hospital, medical school affiliation) (1) (2) HIT Interest Group Meeting

10. SUR Results HIT Interest Group Meeting

11. IT Index vs. Variable Costs per Bed HIT Interest Group Meeting

12. Validity of Assumptions • The required regularity conditions for a well-behaved cost function are satisfied in our analysis • The symmetry and homogeneity of degree one in factor prices restrictions that were imposed on our system are verified (Berndt, 1991) • Monotonicity satisfied because the estimated marginal cost of labor is positive • Non-negativity conditions are also satisfied since the predicted cost shares of labor are positive • Strict quasi-concavity of input prices is also satisfied because second-order derivatives of labor are all non-positive • Coefficient on wage is positive as expected, implying that production costs increase as the value of input increases • The parameter estimate of the output measures are also positive confirming that the cost function is well behaved and specified HIT Interest Group Meeting

13. Overall Results • Higher levels of in IT capital appear to be associated with reduced short-term operating costs • Effect appears only after a threshold level of investment (tipping point) has been reached • Initial increases in IT capital may entail significant ‘start-up’ expenses (networking infrastructure, recruitment of IT staff) which increase costs initially • Non-profit hospitals appear less efficient than for-profit hospitals • Reach the tipping point at higher levels of IT capital and efficiency gains are smaller HIT Interest Group Meeting

14. Clinical vs. Administrative Applications • Administrative applications show efficiency gains, even at low levels of IT capital investment • Higher marginal gain in for-profit hospitals • Stronger long-term marginal effects (2-year lags) • Clinical applications show no efficiency gains in non-profit hospitals but do appear to improve efficiency in for-profit hospitals • No difference in efficiency gains over longer time periods HIT Interest Group Meeting

15. Limitations • Treatment of inputs and outputs ignores some differences across hospitals • Do control for differences in patient case mix and service intensity • Do not adjust for differences in healthcare quality • 4-year panel controls for mortality rates with no substantive changes • Self-reported hospital data creates some consistency and comparability issues • Definition of software applications, accounting rules, etc. HIT Interest Group Meeting

16. Next Steps • Follow-up project examines the link between IT investment and hospital quality • MEDPAR data used to assess patient outcomes • AHRQ quality indicators used to represent clinical processes • Preliminary results strongly support the notion that IT adoption improves performance with both patient outcomes and clinical process indicators • Process scores much more strongly associated with clinical than administrative applications HIT Interest Group Meeting

17. Acknowledgements • This research is being conducted as a collaborative effort between The Wharton School and PricewaterhouseCoopers HIT Interest Group Meeting