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Adoption Patterns of Clinical IT in Acute Care Hospitals: Potential Policy Levers Katya Fonkych Academy Health Annual Research Meeting.

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Presentation on theme: "Adoption Patterns of Clinical IT in Acute Care Hospitals: Potential Policy Levers Katya Fonkych Academy Health Annual Research Meeting."— Presentation transcript:

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

2 Measuring HIT Adoption

3 Focus of the Study is on Key Clinical HIT Systems in Hospitals E lectronic M edical R ecords (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 C omputerized P hysician O rder E ntry (CPOE) – Provides decision support at the time of ordering – Decreases medication errors P icture A rchiving and C ommunication S ystem (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 nations 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) 5*Cutting EdgeHave EMR with CPOE and PACS11% 4*AdvancedHave CPOE or PACS, and very likely to have EMR 23% 3MiddleMay have EMR, but unlikely38% 2SlowNo CPOE, no PACS, no EMR, less than 10 clinical HIT applications 19% 1LaggardsLess than 5 clinical HIT applications, only basic ones 10% * 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 hospitals 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 of competition/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 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 For Profits Non Profits

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 % Patients that go to hospitals with EMR system % in each category: CANational Medicare40% Medicaid30%44% Non-MCD-MCR48% - Commercial49% N/A - Indigent & self-pay8% N/A 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 hospitals 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 on quality/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

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