EHRs, e-reminders don't impact racial disparities in cancer screening

Despite concerns to the contrary, electronic health records and e-reminders may not necessarily increase treatment disparities between white and non-white patients, at least when it comes to cancer screening, according to a recently published study in the Journal of the American Medical Informatics Association.

The researchers, from Brigham and Women's Hospital and Harvard Medical School, conducted a visit-based study of rates of breast, cervical and colon cancer screening orders between white and non-white patients at locations that did and did not use the technology to see whether EHRs and e-reminders changed the differences in screening rates. 

Of the 2.4 billion U.S. adult primary care visits reviewed, the screening order rates didn't differ between white and non-white patients for breast or cervical cancer. For colon cancer, non-white patients were more likely to receive a screening order than white patients, overall.

"Despite hopes and fears about [health IT], EHRs and e-reminders did not ameliorate or exacerbate racial differences in cancer screening order rates," the researchers concluded.

The report does not address whether the Meaningful Use program, as opposed to EHR use, may unintentionally create or increase health disparities. Evidence indicates that the more successful Meaningful Use attesters are those providers who have more resources to earn the incentive money to begin with. That, in turn, leaves smaller, rural hospitals and community health centers that treat a disproportionate number of Medicaid, uninsured and low income patients, lagging even further behind and more likely to incur financial penalties, which could adversely affect patient care.

Twenty-four members of the House of Representatives have asked the Office of the National Coordinator for Health IT and the Centers for Medicare & Medicaid Services to leverage the development of Stage 3 of the Meaningful Use program to reduce and potentially eliminate health disparities, claiming that the program as currently designed does not go far enough.

To learn more:
- read the abstract

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