health information technology and patient outcomes ahrq sponsored evidence and next steps
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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 - PowerPoint PPT PresentationTRANSCRIPT
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
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
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.
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!!!
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
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
0),,;,,,,( ITKLZQF .
( 0, ZQ FF )
( 0,, ITKL FFF )
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.
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.
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.
Descriptive Statistics
Hospital descriptive statistics, 1999 values
Variables Sample Average Early adopters Late adopters Non-adoptersBedsize 187 233*** 197 179Visits 129,619 173,272** 136,549 122,467COTH Membership 8% 18%*** 8% 7%Multihosp. System 69% 67% 72% 68%Nonprofit 71% 87%*** 71% 68%**For-profit 15% 1%*** 15% 16%Government 15% 12%** 14%* 15%% Medicare 47% 46% 46% 47%% Medicaid 18% 17% 18% 18%
Number 2,846 247 522 2,077* denotes significance at p=0.10, ** at p=0.05, and *** at p=0.001
BY EMR adoption
Health IT Adoption
0%
10%
20%
30%
40%
50%
60%
70%
80%
1999 2000 2001 2002
Figure 1. HIT Application Diffusion
EMRPACSNurse Chart
Descriptive Stats of Medicare Hospital Admissions in 1999, by type of IT Invested
Electronic Imaging Nursing IT No EMR/PACSPatient Attributes Medical Records PACS Systems Nursing IT
Age 73.4 73.4 73.8 74.0Female Gender (%) 56.5% 57.2% 57.2% 57.8%Non-White (%) 15.6% 10.8% 15.2% 16.4%Risk Score 2.39 2.38 2.30 2.18
Patient Safety Indicator (PSI) admission rate per 1,000 patientsAggregate PSI 8.628 8.407 7.831 6.794Complications of Anesthesia 0.191 0.135 0.244 0.204Infection due to Medical Care 3.061 2.893 2.804 2.389Post-operative Hemorrage or Hematoma 2.665 3.006 2.498 2.415Postoperative Pulmonary Embolism or Deep Vein Thrombosis 13.547 12.768 12.812 12.708Post-operative Wound Dehiscence 4.201 3.499 4.011 3.876
Hospital Attributes of Patients AdmittedNumber of Hospital Beds 368.5 340.0 331.6 293.4Number of Nurses on Staff 575.3 515.0 469.2 405.4% Medicaid Admissions 13.3% 13.2% 13.9% 15.9%% Medicare Admissions 47.7% 47.8% 49.3% 48.9%
Admissions in 1999 879,723 167,115 5,118,437 2,375,527
Based on a all Medicare patients treated at 2,707 US hospitals
Change in Patient Safety by TechnologyChange in Patient Safety by Technology (per 1,000 admissions)
All YearsElectronic Medical Records Infection due to Medical Care -0.490 *
Post-operative Hemorrage or Hematoma -0.240 *Postoperative Pulmonary Embolism or DVT -1.000 *
PACS Infection due to Medical Care -0.610 *
Post-operative Hemorrage or Hematoma -0.072Postoperative Pulmonary Embolism or DVT -2.000 *
Clinical/Nusing IT Infection due to Medical Care -0.390 *
Post-operative Hemorrage or Hematoma -0.009Postoperative Pulmonary Embolism or DVT 0.300
Notes: * denotes a reduction/increase in clinical error with t-statistic significant at p<.001
IT Total and Year-Specfic EffectsYear-specific and system-specific effect of Health IT on patient safety indicator, adjusted by patient risk and hospital specific attributes (per 1,000 admission)
IT in allYears 2000 2001 2002
Infection due to Medical Care Electronic Medical Records -0.2912** -0.0134** -0.0391** -0.0685**
PACS -0.3056 -0.1010 0.0533 0.0512Clinical/Nusing IT -0.0053 -0.0331 -0.0632 0.1479**
Post-operative Hemorrage or Hematoma Electronic Medical Records 0.0503 0.0866 0.1916 -0.1513
PACS -2.2799 -0.0079 -0.3930 -0.0645Clinical/Nusing IT -0.0213 0.0717 -0.0245 -0.0113
Postoperative Pulmonary Embolism or DVT Electronic Medical Records 0.0870 0.3299 0.9670** 0.5225
PACS 2.3452 0.3784 0.9017 0.8966Clinical/Nusing IT 0.1933 -0.2254 -0.2466 0.0188
*denotes significance at p=0.10, ** at p=0.05, and *** at 0.001Note: Health IT effects are lagged one year
Year-specific IT Impact
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
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.
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.
Physicians
Congress Main Street Medical Technology
Courts
Federal Government
<90% Income
Insurers/Banks 99% Income 91-99% Income
Big Business
Hospitals
Today’s World What $30 billion better build
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.
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.