Use of electronic health record data to evaluate overuse of cervical cancer screening
- PMID: 22268215
- PMCID: PMC3392856
- DOI: 10.1136/amiajnl-2011-000536
Use of electronic health record data to evaluate overuse of cervical cancer screening
Abstract
Background: National organizations historically focused on increasing use of effective services are now attempting to identify and discourage use of low-value services. Electronic health records (EHRs) could be used to measure use of low-value services, but few studies have examined this. The aim of the study was to: (1) determine if EHR data can be used to identify women eligible for an extended Pap testing interval; (2) determine the proportion of these women who received a Pap test sooner than recommended; and (3) assess the consequences of these low-value Pap tests.
Methods: Electronic query of EHR data identified women aged 30-65 years old who were at low-risk of cervical cancer and therefore eligible for an extended Pap testing interval of 3 years (as per professional society guidelines). Manual chart review assessed query accuracy. The use of low-value Pap tests (ie, those performed sooner than recommended) was measured, and adverse consequences of low-value Pap tests (ie, colposcopies performed as a result of low-value Pap tests) were identified.
Results: Manual chart review confirmed query accuracy. Two-thirds (1120/1705) of low-risk women received a Pap test sooner than recommended, and 21 colposcopies were performed as a result of this low-value Pap testing.
Conclusion: Secondary analysis of EHR data can accurately measure the use of low-value services such as Pap testing performed sooner than recommended in women at low risk of cervical cancer. Similar application of our methodology could facilitate efforts to simultaneously improve quality and decrease costs, maximizing value in the US healthcare system.
Conflict of interest statement
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References
-
- Kaiser Family Foundation Percent Annual Increase in National Health Expenditures (NHE) per Capita vs. Increase in Consumer Price Index (CPI), 1980-2009. 2011. http://facts.kff.org/chart.aspx?ch=212 (accessed 20 Jun 2011).
-
- American College of Physicians ACP's High-Value, Cost-Conscious Care Initiative. 2011. http://www.acponline.org/advocacy/events/state_of_healthcare/cost_initia... (accessed 10 Jun 2011).
-
- National Quality Forum Overuse. 2011. http://www.qualityforum.org/Topics/Overuse.aspx (accessed 20 Jun 2011).
-
- American Medical Association PCPI and PCPI Approved Quality Measures. 2011. http://www.ama-assn.org/apps/listserv/x-check/qmeasure.cgi?submit=PCPI (accessed 24 Jun 2011).
-
- Chung J, Kaleba E, Wozniak G. Empirical Applications for Assessing the Efficiency and Value of Healthcare Using the Physician Consortium for Performance Improvement Physician Performance Measures. American Medical Association, 2008. http://www.ama-assn.org/ama1/pub/upload/mm/370/empirical_applications.pdf (accessed 24 Jun 2011).
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