Events Calendar

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San Jose Health IT Summit
2017-04-13 - 2017-04-14    
All Day
About Health IT Summits U.S. healthcare is at an inflection point right now, as policy mandates and internal healthcare system reform begin to take hold, [...]
Annual IHI Summit
2017-04-20 - 2017-04-22    
All Day
The Office Practice & Community Improvement Conference ​​​​​​The 18th Annual Summit on Improving Patient Care in the Office Practice and the Community taking place April 20–22, 2017, in Orlando, FL, brings together 1,000 health improvers from around the globe, in [...]
Stanford Medicine X | ED
2017-04-22 - 2017-04-23    
All Day
Stanford Medicine X | ED is a conference on the future of medical education at the intersections of people, technology and design. As an Everyone [...]
2017 Health Datapalooza
2017-04-27 - 2017-04-28    
All Day
Health Datapalooza brings together a diverse audience of over 1,600 people from the public and private sectors to learn how health and health care can [...]
The 14th Annual World Health Care Congress
2017-04-30 - 2017-05-03    
All Day
The 14th Annual World Health Care Congress April 30 - May 3, 2017 • Washington, DC • The Marriott Wardman Park Hotel Connecting and Preparing [...]
Events on 2017-04-13
San Jose Health IT Summit
13 Apr 17
San Jose
Events on 2017-04-20
Annual IHI Summit
20 Apr 17
Orlando
Events on 2017-04-22
Events on 2017-04-27
2017 Health Datapalooza
27 Apr 17
Washington, D.C
Events on 2017-04-30
Articles

Scientists Say EHRs Can Help Identify High-Risk Pregnancy Patients

The use of electronic health records could help identify high-risk pregnancy patients who require treatment to avoid medical complications, according to an article published in the Johns Hopkins Public Health magazine, FierceEMR reports.

Researchers — assisted by Johns Hopkins University’s Center for Population Health IT — are conducting a pilot program that uses predictive modeling and natural language processing to sort through the text in EHRs of pregnant Medicaid beneficiaries.

The researchers are looking for information such as whether beneficiaries smoke or live in abusive environments. Those beneficiaries typically do not receive regular or follow-up care, according to FierceEMR.

After the EHR data identify the high-risk beneficiaries, the researchers can contact them about receiving needed care.