Annual AJMC® Health IT Issue Covers Machine Learning, Telehealth, Portable Licensure

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There are opportunities for savings and better care delivery if the health system takes advantage of machine learning, telehealth and portable licensure, according to research and commentary appearing in the annual health information technology (IT) issue of The American Journal of Managed Care.

Now in its ninth year, the annual health IT issue features an essay from special guest editor Ilana Graetz, PhD, who is an associate professor in the Department Policy & Management at the Rollins School of Public Health at Emory University. Graetz discusses how a decade after passage of the American Recovery and Reinvestment Act, electronic health records (EHRs) and predictive tools have arrived that can help target interventions to those patients most likely to drive up healthcare costs. Unfortunately, she writes, technical, legal and institutional hurdles prevent them from being used to their full potential. Also, she writes, it is clear that EHR systems are not silver bullets that will automatically result in better coordination of care and quality.

Now that the nations healthcare providers have achieved widespread adoption of EHRs, the next phase of research, highlighted in this years annual health IT issue, explores innovative methods for using data to improve population health, the role of trust and provider relationships, and enduring barriers to interoperability, says Graetz.

Highlights of this years issue include:

  • A commentary by Pooja Chandrashekar, AB, and Sachin Jain, MD, MBA, on the need to move away from state-based medical licensure, which would allow physicians to reach patients in underserved areas through telemedicine; this would enable better distribution of specialists and mental health professionals.
  • A study by researchers at Penn State and the RAND Corp. found that while adoption of a certified EHR is rising, only 38 percent of clinics reported having all 16 health IT functionalities in 2016, with patient-facing features among those less likely to be in place.
  • Two different studies on machine learning had different results: A study of the use of machine learning in predicting reliance on primary care in the Veterans Administration Medicare system found that it brought only modest improvements. However, researchers from Jvion found their algorithm could predict which patients were likely to visit the emergency room or be readmitted to the hospital within 90 days, based on capturing simple data points: age, gender, race and a persons address.
  • Finally, this months issue features an interview with cardiologist and digital medicine researcher Eric Topol, MD; this is the first of a series that will appear in the journal to mark its 25th anniversary. Topol discusses the cultural barriers to using more technology and eliminating the number of people involved in healthcare.

About¯The American Journal of Managed Care

The American Journal of Managed Care¯(AJMC) is a multimedia peer-reviewed, MEDLINE-indexed journal that keeps industry leaders on the forefront of health policy by sharing digital research relevant to industry decision-makers. Other brands in the¯AJMC¯family include¯The American Journal of Accountable Care,¯Evidence-Based Oncology„¢ and¯Evidence-Based Diabetes Management„¢. These comprehensive multimedia brands bring together stakeholder views from payers, providers, policymakers and other industry leaders in managed care. AJMC is a brand of MJH Life Sciences„¢, the largest privately held, independent, full-service medical media company in North America dedicated to delivering trusted health care news across multiple channels.

AJMC® Media

Alexandra Ventura

609-716-7777, ext. 121

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