The advantages of reduced measurement burden, rich clinical context, and longitudinal data have made electronic data, in particular data from health IT systems, the target of a growing interest in supporting and measuring care coordination processes in new ways. In this report, we provide an assessment of the potential for such measurement, based on input from experts. Their insight suggests much reason for optimism about the possibility of measuring care coordination using electronic data sources, albeit tempered by the reality of many challenges that must be overcome to make such measurement feasible. A key observation from our discussions with these experts is the rapidity with which the health IT landscape is changing. That rapid change will almost assuredly help resolve many of challenges in the current health IT environment, but does introduce its own challenge in predicting what will and will not be possible in the future. Even recommendations pertaining to near-term measurement opportunities identified in this report may become outdated before they are fully implemented.
Many of the challenges with using health IT data for care coordination measurement identified by the panelists are indicative of a field still in the early stages of growth. While well-defined datasets and definitions of data elements are generally not available today, our discussions with panelists and review of background materials suggest that much improvement in this area is already underway. Thus, the opportunity for measure development must respond to and take advantage of this dynamic environment. Even beyond measure development, monitoring existing indicators in light of this environment will be essential as new data developments may pave the way for indicator improvements.
Our findings suggest a need for continued dialog with a wide range of experts at local, regional, and national levels. We hope this report offers a starting point for that discussion, and further identification of opportunities for focusing on operational mechanisms that are important to producing highly coordinated patient-centered care. Measurement offers one such operational mechanism, but finding exactly what can be measured well locally and then compared regionally or nationally to motivate improvements in performance is an ongoing process. The rich opportunities likely to be feasible with the growth of electronic data are both exciting and daunting. Directing attention to measuring coordination processes has the potential to bring together many health care stakeholders and ultimately deliver on these opportunities.
References
1. McDonald K, et al. Care Coordination. Vol. 7 of: Shojania K, McDonald K, Wachter R, Owens D, editors. Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies. Technical Review 9 (Prepared by the Stanford University-UCSF Evidence-based Practice Center under contract 290-02-0017). AHRQ Publication No. 04(07)-0051-7. Rockville, MD: Agency for Healthcare Research and Quality. June 2007. Available at: https://www.ahrq.gov/downloads/pub/evidence/pdf/caregap/caregap.pdf
2. National Quality Forum. National Priorities Partnership. Available at: http://www.qualityforum.org/Setting_Priorities/NPP/Input_into_the_National_Quality_Strategy.aspx . Accessed August 19, 2011.
3. McDonald K, et al. Care Coordination Atlas (Prepared by Stanford University under subcontract to Battelle on Contract No. 290-04-0020). AHRQ Publication No. 11-0023-EF. Rockville, MD: Agency for Healthcare Research and Quality, November 2010. Available at: http://www.ahrq.gov/qual/careatlas/
4. Schultz E, et al. Care Coordination Measures Project: Phase I Technical Report. Contract No. 290-04-0020. February 15, 2011.
5. National Quality Forum. Preferred Practices and Performance Measures for Measuring and Reporting Care Coordination: A Consensus Report. Washington, DC: National Quality Forum, 2010.
6. Henricks WH. "Meaningful use" of electronic health records and its relevance to laboratories and pathologists. J Pathol Inform 2:7.
7. Persell SD, et al. Assessing the validity of national quality measures for coronary artery disease using an electronic health record. Arch Intern Med Nov 13 2006;166(20):2272-2277.
8. Baker DW, et al. Automated review of electronic health records to assess quality of care for outpatients with heart failure. Ann Intern Med Feb 20 2007;146(4):270-277.
9. Tang PC, et al. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc Jan-Feb 2007;14(1):10-15.
10. Kmetik KS, et al. Exceptions to outpatient quality measures for coronary artery disease in electronic health records. Ann Intern Med Feb 15;154(4):227-234.
11. Persell SD, et al. Changes in performance after implementation of a multifaceted electronic-health-record-based quality improvement system. Med Care Feb;49(2):117-125.
12. Miller P, Peters A. All-Payer Claims Databases 2.0: The Next Evolution. Durham, N.H.: APCD Council and National Association of Health Data Organizations. 2011.