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June 2, 2007HIT Interest Group Meeting1 The Effect of IT Capital on Hospital Efficiency Michael G. Housman, 1 Lorin M. Hitt, 1 Kinga Z. Elo, 2 Nick Beard,

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Presentation on theme: "June 2, 2007HIT Interest Group Meeting1 The Effect of IT Capital on Hospital Efficiency Michael G. Housman, 1 Lorin M. Hitt, 1 Kinga Z. Elo, 2 Nick Beard,"— Presentation transcript:

1 June 2, 2007HIT Interest Group Meeting1 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

2 June 2, 2007HIT Interest Group Meeting2 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?

3 June 2, 2007HIT Interest Group Meeting3 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)

4 June 2, 2007HIT Interest Group Meeting4 Hospital Sample vs. Population YearDatasetsObs. Merged Dataset Obs. Match Pct. 1999 AHA6116 AHA- HIMSS- Solucient 314351.4% HIMSS4132 Solucient5338 2000 AHA6044 AHA- HIMSS- Solucient 292048.3% HIMSS4045 Solucient4753 2001 AHA6003 AHA- HIMSS- Solucient 289748.3% HIMSS4029 Solucient4773 2002 AHA6013 AHA- HIMSS- Solucient 279846.5% HIMSS4023 Solucient4816 2003 AHA6008 AHA- HIMSS- Solucient 293448.8% HIMSS4005 Solucient5054 2004 AHA6072 AHA- HIMSS- Solucient 310851.2% HIMSS3989 Solucient5119 Hospital SampleHospital Pop. 1,978 hospitals6,072 hospitals Nbr.Pct.Nbr.Pct. Hospital Beds: < 50 beds1336.7%1,69027.8% 50 to 100 beds1899.6%1,34122.1% 100 to 200 beds58229.4%1,39022.9% 200 to 300 beds44422.5%72812.0% 300 to 400 beds27614.0%3966.5% 400 to 500 beds1497.5%2103.5% 500+ beds20510.4%3175.2% Geographic Region: East46223.4%95115.7% West37318.9%71311.7% South71636.2%263743.4% Midwest42721.6%177129.2% Ownership Status: For-Profit42121.3%1,20619.9% Non-Profit1,54278.0%3,18152.4% Local Govt.150.8%1,44323.8% Federal Govt.00.0%2424.0%

5 June 2, 2007HIT Interest Group Meeting5 Hospital Software Applications ADMINISTRATIVE APPLICATIONSCLINICAL APPLICATIONS Base Admin. and Financial:Financial Decision Support:Ancillary Department: Accounts PayableCase Mix AnalysisLaboratory Benefits AdministrationClinical Decision SupportPACS Credit/CollectionsCost AccountingRadiology Electronic ClaimsExecutive Info Sys EligibilityFlexible BudgetingClinical Department: General LedgerOutcomes and Quality Mgmt.Cardiology Managed Care Contract Mgmt.Emergency Department Materials ManagementMedical Records:Intensive Care (Critical Care) Nurse StaffingAbstractingPharmacy Patient BillingChart DeficiencySurgery Patient RegistrationChart Tracking/Locator PayrollEncoderEnterprise Clinical: Personnel AdministrationMaster Patient IndexClinical Data Repository Premium BillingMedical Record ImagingClinical Documentation Time & AttendanceTranscriptionOrder Communication/Results Point of Care (Med/Surg Bedside)

6 June 2, 2007HIT Interest Group Meeting6 Measuring IT Adoption Calculated each hospitals 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 0 100 No applications All applications 0 100 PACS CPOE EMR Cardiology Pharmacy Lab Billing Scheduling Registration 60 80 40 20 60 80 40 20 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.

7 June 2, 2007HIT Interest Group Meeting7 Distribution of IT Capital Index Scores For-profit hospitalsNot-for-profit hospitals

8 June 2, 2007HIT Interest Group Meeting8 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

9 June 2, 2007HIT Interest Group Meeting9 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 P W = Wages (adjusted by state-averages) P M = 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 S L = Labor cost share in total operating cost S M = Material cost share in total operating cost S L + S M = 1 Z = Controls (patient case mix, scope of services, ownership status, state, urban/rural, disproportionate share, teaching hospital, medical school affiliation) (1) (2)

10 June 2, 2007HIT Interest Group Meeting10 SUR Results lopexSLSL CoefficientStd. ErrorCoefficientStd. Error IT0.752(2.74)**-0.012(2.38)* IT 2 -0.313(2.79)** IT × FP-0.095(2.67)** IT × ladjpd-0.05-1.19 IT × lwage-0.027-1.6 IT × lcapital0.029-0.66 servicescope0.002(5.25)**0.002(13.89)** NFP0.248(10.10)**0.065(21.38)** case mix index0.202(12.77)**0.037(6.35)** Lurban-0.029-1.33-0.006(2.30)* dispr-0.009-1.33-0.006(2.30)* coth0.033(3.57)** med_school0.017(2.78)** year 2 0.008(12.89)**

11 June 2, 2007HIT Interest Group Meeting11 IT Index vs. Variable Costs per Bed

12 June 2, 2007HIT Interest Group Meeting12 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 EquationObsParmsRMSER-sqChi 2 P lopex4821730.1629170.95168,010,0000.000 SLSL 4821600.0618470.8543482,8790.000

13 June 2, 2007HIT Interest Group Meeting13 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

14 June 2, 2007HIT Interest Group Meeting14 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

15 June 2, 2007HIT Interest Group Meeting15 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.

16 June 2, 2007HIT Interest Group Meeting16 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

17 June 2, 2007HIT Interest Group Meeting17 Acknowledgements This research is being conducted as a collaborative effort between The Wharton School and PricewaterhouseCoopers

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