CATCH-IT: The Relationship between Electronic Health Record Use and Quality of Care over Time

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    CATCH-IT: The Relationship between Electronic Health Record Use and Quality of Care over Time - Presentation Transcript

    1. The Relationship between Electronic Health Record Use and Quality of Care over Time, Zhou et al. Daniel Beemsigne HAD5726 October 5, 2009
    2. Why do a CATCH-IT report?
      • Multidisciplinary field
      • No standard way of reporting findings
      • Create a standard
      • Promote research and foster debate
    3. What is an EHR?
      • An electronic health record (EHR) (also electronic patient record or computerised patient record ) is an evolving concept defined as a longitudinal collection of electronic health information about individual patients or populations. It is a record in digital format that is capable of being shared within across different health care settings, by being embedded in network-connected enterprise-wide information system.
      • (Wikipedia, 2009)
    4. Background
      • Increase quality of care by:
        • providing timely access to patients’ health information;
        • tracking patients over time to ensure that they receive guideline-recommended care;
        • and offering decision-support mechanisms to reduce medical errors.
      • Randomized controlled trials demonstrate clearly that quality improvement occurs when specific decision support is in place
      • Benefits of EHR adoption and use may be time-dependent
    5. Research Question
      • How does the quality of care delivered in ambulatory care practices vary according to duration of EHR adoption and usage?
    6. Methods
      • Study design involved two data sources:
        • (1) a statewide survey of physicians’ adoption and use of EHR and;
        • (2) statewide (Massachusetts) data on physicians’ quality of care as indicated by their performance on widely used quality measures.
      • These two sets of data were integrated and linked for each included physician.
    7. Methods
      • Stratified random sample
        • i.e., Hospital based, large and rural practices in MA, by specialty, etc.
      • Sample size calculated to provide 80% power to detect differences of 10% or greater
      • Eight page questionnaire
      • Surveys mailed with a $20 honorarium to a doctor in each practice
      • Sample weights were used to adjust for overall representation
      • Simon SR, Kaushal R, Cleary PD, et al. Correlates of electronic health record adoption in office practices: A statewide survey. J Am Med Inform Assoc 2007 Jan–Febr;14(1):110 –7.
    8. Methods
      • Analysis
        • Examined the cross-sectional relationship between having an EHR and concurrent indicators of quality of care
        • Carried out a longitudinal analysis to study the trend of EHR adoption and usage as well as to examine the association between the duration of EHR usage and quality of care
        • Compared the HEDIS® quality measures of respondents to the length of time using an EHR
    9. Blog: Finally, a positive comment!
      • “ Wouldn't it be cool if they compared quality of care versus use of a specific feature in EHR?"
    10. Methods
    11. Methods
    12. Results: Recall the Research Question
      • How does the quality of care delivered in ambulatory care practices vary according to duration of EHR adoption and usage?
    13. Results
      • EHR adoption – increase over time
      • Availability and Use of EHR core functions
      • Difference b/w EHR users and non-users?
        • No statistical difference
      • Having an EHR was not related to physicians’ reported financial incentives for patient satisfaction or clinical quality.
      • Page 462
      How does the quality of care delivered in ambulatory care practices vary according to duration of EHR adoption and usage?
    14. Limitations
      • HEDIS data:
        • Exclusion criteria made respondents younger, more recently graduated, smaller practices, more patient visits, female, non-hospital based
      • Survey did not record the length of time using specific EHR features
        • Prevented analysis of the associations between the duration of using a specific EHR feature and quality of care.
      • The sample sizes for long-time users were relatively small, only 9 MD’s – hard to detect ∆’s
    15. Blog: What is adoption?
      • EHR availability vs. adoption vs. use
      • Feature availability vs. use
    16. Assumptions
      • EHR adoption and availability and use of EHR core functions by year were estimated based on these assumptions:
        • If a physician indicated in the 2005 survey that a feature was available in his or her EHR system, we assumed that the feature had been available since the time when the practice first began using an EHR.
        • The same assumption was also applied to the extent of usage of the EHR feature.
    17. Implications
      • Only two references cited for relationship of EHR use and quality of care (growing body of literature)
      • We found no evidence that quality of care improved with increasing duration of EHR usage
      • Paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow transformation
    18. Implications
      • Study showed that the usage of decision support among EHR users was low: only 23.5% in 2005, compared to its availability, which was 65.0% among EHR adopters
      • The 2005 EHR adoption rate will need to triple in less than a decade (for MA), which represents a challenge for health care policy
      • Stronger incentives or more extensive programs to support physician office transformation may be needed
    19. Validity
      • Unmeasured confounding factors may have obscured true associations
        • Models included “all” available covariates
      • HEDIS rates used
        • Derived from claims data
      • Self-reported
        • Similar results to nationwide survey
        • Follow-up surveys indicated usage rate stability and increased usage of features
      • External: State of MA, may not be generalizable to the USA
    20. Conclusions
      • No association between duration of using an EHR and quality of ambulatory care
      • In general, EHR use was not associated with improved quality of care
      • The old standby: more research needed
    21. Discussion
    22. Reductionism or study complex systems?
      • Often complex socio-technical systems with multiple components are evaluated: difficult to determine relative contribution / importance of their different components (communication –community – content)
      • Do we need to evaluate “comprehensive systems” (with p2p as one of many components), or should we break them down to different components and evaluate these separately?
      • Are the components “additive” in their effect, or is the sum bigger than its parts?
    23. Which is the chicken? Egg?
      • In the UK, with the combination of near-universal EHR implementation, decision support, and substantial pay-for-performance, very high levels of quality performance have been achieved,
    24.  
    25. HEDIS Questions answered
      • Was there any relationship with HEDIS scores and physician payment with the insurers?
        • Open question
      • Where HEDIS scores determined by all of the physicians' patients?
        • HEDIS scores are determined with claims data
      • How do HEDIS scores determine quality?
        • Do they determine quality?
    26. HEDIS vs. other databases
      • Would any of the following have been a better choice? If so, why? If not, why not?
    27. Massachusetts Health Care Quality and Cost Council
    28. Agency for Healthcare Research and Quality
    29. Ontario Health Quality Council
    30. CIHI
    31. HEDIS vs. other databases
      • Would any of the following have been a better choice? If so, why? If not, why not?
    32. Discussion
      • The authors conducted a longitudinal analysis for EHR adoption duration (years) vs. HEDIS measures, but wouldn't it be worthwhile to perform a stratified analysis which separated physicians based on their actual usage of EHR or EHR features (most, some, none) vs. HEDIS scores?
    33. Adjustment for patient characteristics
      • Would this be considered a valid risk adjustment measure?
      • How would the investigator collect data on patient characteristics?
      • What are the legal implications of such data collection?
    34. Standards? In eHealth? HA!
      • Develop an EHR usage measure that would perform a standardized method for reporting on usage for EHRs.
    35. Alternative Measures?
      • Discuss in class some measures that may have been more appropriate assessing the impact of EHR use on quality of patient care
    36. Thanks
      • Questions/comments/concerns
    37. Posted on Slide Share
      • www.slideshare.net

    + dbeemsignedbeemsigne, 1 month ago

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