Veterans Health Administration: EMR Foundation for Gains Data-Mining Benefits
For an industry driven by advanced knowledge and technological innovation, American health care is shockingly behind the curve on adoption of information technology. Only 1.5% of U.S. hospitals have adopted comprehensive electronic medical records systems (Jha et al., 2009). As of 2006, only 20% of U.S. hospitals had implemented electronic medical records (Arnst, 2006). The U.S. is lags behind several OECD countries in per capita spending on health IT (eHealth101, 2006) and is perhaps more than a decade behind international leaders in health IT (Anderson et al., 2006). Without serious investment in health IT, most American hospitals can’t take advantage of data mining.
An exception to this absence of data-mining capability is found in the Veterans Health Administration. The VA began developing the nation’s first functioning electronic medical record system in the late 1970s (Longman, 2009) and computerized medical records in all of its approximately 1300 facilities by 2000 (Arnst, 2006). VA hospitals using VistA—Veterans Health Information Systems and Technology Architecture—constitute nearly half of the hospitals in the U.S. that have implemented comprehensive electronic medical records (Jha et al., 2009). With VistA, the VA has become the “unlikely leader” in maintaining electronic records that can be mined for insights that produce significant improvements in care and cost efficiency.
The VA has used data mining to improve practices in a number of ways. VA researchers have mined VistA data to target rewards for surgical teams that beat quality and safety benchmarks (and to identify underperforming surgical teams) and to sift through 12,000 medical records to evaluate and improve treatments for diabetes (Longman, 2009). The VA’s Center for Imaging of Neurodegenerative Diseases has used Weka to apply Random Forest and Support Vector Machine algorithms to brain imaging studies (Young, 2009). VA data mining also helped discover the link between arthritis medication Vioxx and heart attacks (Longman, 2009).
One obstacle to optimal data mining in VistA is the diversity of local data dictionaries. Local users can customize data dictionaries to meet unique local needs. That flexibility is a significant part of the system’s success (Brown et al., 2003). However, those different data dictionaries complicate efforts to combine and analyze data across the nationwide system. The VA’s efforts to create national standard dictionaries to translate local dictionaries support not only better immediate transactions such as e-prescribing (Brown et al. 2003) but improved large-scale data mining. The VA’s system has been sufficiently successful that other government hospitals in the U.S. and abroad are adopting and adapting VistA for their facilities (Longman, 2009).
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Young, K. (2009). Diagnostic Data Mining for Multi-modal Brain Image Studies. Veterans Health Administration Center for Imaging of Neurodegenerative Diseases. Retrieved August 2, 2009, from http://www.cind.research.va.gov/research/multi_modal_brain.asp