Health Information Technology and Patient Outcomes AHRQ Sponsored Evidence and Next Steps

1. Health Information Technology and Patient Outcomes AHRQ Sponsored Evidence and Next Steps Stephen T. Parente, Ph.D., University of Minnesota Jeffrey McCullough, Ph.D., University of Minnesota Jean Abraham, Ph.D., University of Minnesota Martin S. Gaynor, Ph.D., Carnegie Mellon University September 28, 2010

2. Presentation Overview • Policy & industry context for research • Data & empirical methods • Results & interpretation • Policy Prescription for more HIT and better (or at least more) clinical effectiveness data

3. What do We Know about National IT Impact Measured by Data in the US • Actually, very little. • Studies generally extrapolate from case examples in a set of national sites. • Very little mainstream health insurer success stories.

4. The Issue With Regional Insurer EMR Cases Applied to the Nation… • Hi, I’m a PPO design and I have 85%+ market. I also rule the FEHBP market and TRICARE. • Hi, I’m a HMO design and I have -15% market. Oh, and I’m the model for EMR success stories in Colorado, West Coast, North Central PA and Massachusetts. I’m so ACO ready!!!

5. What if No One Wants a Trojan Rabbit? • Sir Bedivere the Wise: “Now once we have gotten all the physicians to buy a Stimulus Bill-financed Dell computers from Wal Mart for an EMR install, we can distribute the software to them to place more data entry onto their existing workflow and then pay them less when we use the system to tell them they are under-performing in their new ACO/medical home.” Bonus Film Points: Also from 1975: Chouinard A. Shall I not ask for whom the [electronic] medical record is kept? CMAJ 1975. Start of SNOMED

6. Conceptual Model • The conceptual model for our proposed analysis is an economic model of technical production. • We assume hospitals produce a number of different outputs: quantity (Q) and quality (Z) subject to the following technical production relation, as • L: Labor • K: Capital • IT: IT Systems • patient attributes affecting efficiency (e.g., severity) • hospital specific factors • 1st derivatives with respect to outputs are positive • 1st derivatives with respect to inputs are negative • 2nd derivatives with respect to inputs are positive

7. Data for Empirical Investigation • We measure HIT value by combining hospital- and patient-level data during 1997-2007. • Sources: • Medicare inpatient admissions during our study period – the 100% MedPar inpatient Medicare claims data file. These data provide patient-specific outcomes and severity adjustment measures. • The Healthcare Information and Management Systems Society (HIMSS) Analytics Database provides detailed hospital IT adoption data for a variety of applications including: • electronic medical records (EMR), • nurse charts, and • picture archiving communications systems (PACS). • HIMSS Analytics comprises a near census of acute care, urban, nonfederal US hospitals. • American Hospital Association’s (AHA’s) annual survey which describes hospital characteristics.

8. Econometric Approach - 1 • We regress patient-level PSIs on a set of HIT variables, patient-level controls, and hospital fixed effects. • Each of the PSIs is a binary variable equal to 1 if an adverse event occurred and zero otherwise. • Control variables include patient age, gender (female=1, else=0), race (non-white=1, else 0), risk score, and year of admission. • HIT variables are a set of three binary indicators for the presence of EMR, nurse charting, and PACS. • HIT variables were lagged by one year to reflect anecdotal evidence and expert interviews indicating that HIT value is realized one or more years subsequent to adoption.

9. Econometric Approach - 2 • HIT value may change with time through unobserved learning and innovation. • We include a set of nine HIT-by-year interaction terms allowing HIT to have a different affect in each year. • interactions of the binary HIT application variables with binary indicators for the years 2000, 2001, and 2002 respectively. • Finally, we control for unobserved hospital attributes by including hospital-specific fixed effects. • Creates over 2,700 binary variables, one for each hospital in the study. These fixed effects control for hospital attributes that are stable across time such as bed size and patient case load described • This design controls for unobserved time-invariant quality differences. Effectively, this specification controls for some types of selection in the HIT adoption process.

10. Descriptive Statistics

11. Health IT Adoption

13. Change in Patient Safety by Technology

14. IT Total and Year-Specfic Effects

15. Main Findings • EMR investments improve patient safety by reducing infections due to medical care • Others (PACS & Nurse Charting) Health IT Systems are not as effective as EMR • EMR’s affect on patient safety grows with time • We find limited evidence wide-spread HIT value

16. Contribution & Policy Implications • Demonstrates the use of large scale claims data analysis to study health IT impact. More could be done: • More recent years • Other populations besides Medicare • Evidence suggest savings will not be quite as big as projected. • For new initiatives that are part of health reform, it will be critical for them to show their value using nationally generalizable data, since it is available for analysis and the fiscal stakes have not been higher.

17. Going Forward • Use National Data to track how IT investments are influencing measure outcomes with claims data linked to available clinical data TODAY (e.g., lab results and Imaging URLs) for appropriate CPT codes. • If Phase IV Post-launch drugs and devices can use claims data for monitoring effectiveness of treatments and avoidance of adverse events – why not the federal government with Medicare Parts A, B and D data. • Zhan & Miller (2003) set a great precedent for AHRQ to publish the code to measure medical errors. There needs to be far more efforts in this direction to gauge national impact of health IT.

18. Today’s World What $30 billion better build Congress <90% Income Federal Government Main Street Medical Technology Big Business Insurers/Banks Physicians 99% Income 91-99% Income Courts Hospitals

19. If Government Really Wants HIT Acceleration - Consider: • What: Federal/State health benefits require providers to pay using a national health card technology platform. • Government Why: Want clinical data attached to claim for de-identified comparative effectiveness data pipeline. Bonus – technology platform to mitigate prevent fraud as ‘pay for’. • Provider Why: I’ll get paid in 4 days and under for 90% claims • The Big How: • Augment Medicare Administrative Contracts (MACs) for 2011-12 to include card use and require linked clinical data for approximately 100 HCPCS/CPT codes as pilot – more later. • Augment TRICARE contracts to do the same. • States put contracts out for competitive bid following TRICARE model. • FEHBP buts this required specification as well.

20. Next Steps to Dead End:Integrated Health Care Demonstration Project R-18 • Proposed a Trial Integrated Health Card (combine clinical data with claims transmission for new locations: • University of Minnesota employees and dependents (>35,000 lives) • Minnesota Care (Minnesota’s variant of state Medicaid program) • Additional support • Metavante – Issues card technology / payment hub (without clinical link) for >30K lives including all of Minnesota’s public programs and other state programs – would provide all demonstration technology and consulting support gratis. • Comments from Study Panel Reviewers (paraphrased): • 1) Claims data is inferior for measuring outcomes and should be not be encouraged as a platform because it is not consistent with an ACO. • 2) The researchers are well regarded but the technology (e.g., Medavante - >$10 billion firm) does not exist. • 3) Great idea – go for it – Best ebayer ever A+++++++ • My question – Other than building a company and evaluating it (my current hobby), could this ever be expedited for demonstration funds given the stakes at end.