Adoption Patterns of Clinical IT in Acute Care Hospitals: Potential Policy Levers

1. University of Southern California Center for Healthcare Financing, Policy and Management Adoption Patterns of Clinical IT in Acute Care Hospitals:Potential Policy Levers Katya Fonkych fonkych@usc.edu Academy Health Annual Research Meeting June, 2007

2. Measuring HIT Adoption

3. Focus of the Study is on Key Clinical HIT Systems in Hospitals • Electronic Medical Records (EMR) • Backbone of the entire clinical HIT –> interactions with other software • Speeds up care processes, reduces duplications, improves coordination • Helps to produce clinical documentation, billing and quality data • Computerized Physician Order Entry (CPOE) • Provides decision support at the time of ordering • Decreases medication errors • Picture Archiving and Communication System (PACS) • Digital images in radiology and cardiology departments • Eliminates film-related costs, reduces duplication of tests

4. Data Sources for HIT Measurement • HIMSS (2004) - survey of software applications in: • 80% of nation’s acute community hospitals (all but small independent) • 20% of ambulatory care physicians (all part of hospital systems) • Sum of 2 measures of adoption: installed and signed a contract: • EMR in ambulatory clinics: 13% including contracted • Inpatient CPOE: 22% including contracted • Inpatient PACS: 42% including contracted • Not measured in the dataset: • Inpatient EMR system (instead there are some components) • Integration of HIT infrastructure and its utilization • Derived an overall measure of clinical HIT sophistication – HIT scale from 1 to 5 • Using data on adoption of over 50 different clinical IT applications

5. Hospitals Classified by Clinical HIT Adoption Level (HIT scale) * Top 2 categories approximate adoption of a basic EMR system

6. Hypotheses

7. Reimbursement Policies Could Be Responsible for HIT Adoption Pattern • Per diem and FFS reimbursements do not provide much incentive to reduce LOS by improving efficiency • Capitation increases hospital’s financial benefit from HIT • E.g. closed systems like Kaiser & VA • Medicare DRG payments provide financial incentive to reduce LOS • But lower reimbursement rate may reduce the capital available for investment • PACS’ benefits are mostly in efficiency improvements per procedure that accrue to a provider regardless of reimbursement policy • Most CPOE benefits are in safety/quality of care, and in reduced utilization that may not benefit provider under some reimbursement policies

8. Non-Reimbursement Factors May Influence HIT Adoption • Economies of scale mean higher ROI for larger hospitals, unless very complex • Rich hospitals are more able to adopt • Higher reimbursement level is a function of favorable patient mix • Market power • Donations • Network externalities: • HIT can improve care-coordination only if majority of providers in the community adopt • Conflicting views on the effect ofcompetition/market power: • Observed lower prices now are more important than unobserved quality in future: only market power allows for such “luxury” as investing in quality • Expected improved efficiency in the future induces hospitals to adopt HIT to beat their competitors • Locally concentrated multi-provider systems can reap the benefits of coordinated HIT investment => network externalities are internalized

9. Hypothesized Relationships • Disadvantaged hospitals may need help with HIT: • Small hospitals, • Rural hospitals • Safety net (underpaid) hospitals (e.g. with high Medicaid patient mix) • Nonprofits may be quicker to adopt because they can afford to trade profit for quality • Capitation provides largest incentive to improve efficiency with HIT • HIT adoption is clustered within local areas and hospital systems due to network externalities • Address the debate on whether competition or market power is helpful for HIT adoption

10. Methods of Empirical Analysis • Descriptive analysis to find disadvantaged hospitals and patients: • Correlations and tabs • Cross-sectional analysis of adoption prevalence: • OLS and Ordered Logit => HIT scale (1 to 5) • Logit => CPOE, PACS & Top 2 categories on HIT scale (~ EMR) • PACS • Inter-temporal incidence analysis for a limited sample and variable mix • 6 alternative national models • Different mix of variables • Different sample sizes • Look for robust results • Californian sample is small but has complete set of variables

11. Independent Variables: • Hospital type, size and location: Non-profit, rural, large and small urban, log bedsize, academic, pediatric, contract-managed • System variables: small geographically concentrated system, small dispersed or large hospital system (versus single hospital) • % revenues and patients from: Managed care (HMO and PPO for sub-sample), Medicare, Medicaid • Mix: Outpatient/inpatient, long-term care share, DRG case-mix index • Financial status: Profit margin & unrestricted contributions (only for California sample) • Competition:HHI (good measure for California only) • Capitation: % revenues (good measure for California only) Sources: HIMSS, AHA Hospital survey, Medicare Impact files, OSHPD for California) – 2004 and 2003

12. Empirical Findings

13. Non Profits For Profits Mission versus Profit • Regression results: • for-profits do not differ significantly on average level of HIT sophistication • but have significantly lower adoption of CPOE, top two HIT categories (EMR proxy) &even PACS (despite ROI?) • Academic and pediatric hospitals have very high HIT adoption, especially CPOE

14. Size Matters - Especially for PACS Small hospitals constitute almost ½ of the facilities, but only ¼ of patients Regression Results: • log size is significant and positive across all models for HIT scale & PACS • But insignificant for CPOE

15. Lower Adoption in Hospitals with High Share of Medicare & Medicaid Patient-level: Differences (above median): Hospital-level: Differences (above median): • High Medicare share hospitals have 1.5 to 2 times lower HIT adoption • High Medicaid share hospitals are no different • Because many are academic Regression results: • Shares of Medicare & Medicaid patients (revenues) are negative & significant for HIT scale & CPOE • Less robust results for PACS

16. Managed Care & Capitation Are Associated with Higher Adoption Managed care: • % revenues from managed care matter only for CPOE (+) • % revenues from HMO (includes capitation) significant for HIT scale and CPOE, but not PACS • PPO & POS do not matter Capitation: • E.g. Kaiser is getting towards 100% adoption • Even when Kaiser is excluded: • % revenues from capitation have positive & significant effect on HIT scale • Increase from 0% to 50% would move hospital 2 positions up • from 2 (slow adopter) to 4 (advanced adopter) • Positive effect on CPOE, but no effect on PACS

17. Adoption Spreads within Hospital Systems & Market Power Matters • Adoption by other hospitals from the same system is the largest determinant of hospital’s HIT adoption: 75% correlation (HIT scale) • System-level adoption is higher than individual: % of systems that have at least one hospital with: • Top HIT (EMR) 65% • CPOE 41% • PACS 75% • Non-adopting systems are more effective as a policy target, than an individual non-adopting hospital Regression results: • HIT adoption in a hospital positively depends on local adoption rates • Small and geographically concentrated multi-hospital systems have higher adoption, than independent hospitals • Geographically dispersed and larger hospital systems have lower adoption • Market power is associated with high probability of adoption of EMR and PACS, but not necessarily CPOE

18. Conclusions:Addressing Market Failures • Over 30% adopted => time to focus on ROI • Relate HIT adoption polices to quality measurement • Pay-for-Performance programs to reward quality improvement • Payers to coordinate & finance HIT investment • Can demand data onquality/efficiency from hospitals in return • Broader use of mechanisms that internalize most benefits of improved efficiency: • Capitation • DRG-like payments • Kaiser exemplifies this

19. Strengthening CMS Involvement is Critical for Broader Adoption Reasons: • Medicare and Medicaid patients have less access to the benefits from clinical HIT (higher quality and safety) • Hospitals with high share of Medicare and Medicaid lack capital for HIT adoption • CMS eventually pays for everyone => • most interested in better health outcomes Policy Levers: • CMS can coordinate efforts to pay for HIT adoption/use • Capitation helps! • Current “Disproportionate share” payments for hospitals with high share of Medicare/Medicaid/indigent • make amount conditional on either HIT adoption/performance/publicizing data on quality • Or separate subsidy for high share hospitals

20. Government Subsidies for HIT? • Subsidy for Smaller Hospitals: • Due to insufficient economies of scale, it is a costly to subsidize • ½ of all hospitals, and quarter of all patients in <100 bed hospital • Promote vendor development of a simpler and less expensive EMR system, i.e. modules on-line + interaction • Rural hospitals are no less disadvantaged than hospitals from large urban areas after size is taken into account • Grants & incentives for geographic areas and hospital systems with low overall adoption – spillovers of information, experience & coordination of investments can help to spread the diffusion further