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12:00 AM - TEDMED 2017
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TEDMED 2017
2017-11-01 - 2017-11-03    
All Day
A healthy society is everyone’s business. That’s why TEDMED speakers are thought leaders and accomplished individuals from every sector of society, both inside and outside [...]
AMIA 2017 Annual Symposium
2017-11-04 - 2017-11-08    
All Day
Call for Participation We invite you to contribute your best work for presentation at the AMIA Annual Symposium – the foremost symposium for the science [...]
Beverly Hills Health IT Summit
2017-11-09 - 2017-11-10    
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, [...]
Forbes Healthcare Summit
2017-11-29 - 2017-11-30    
All Day
ForbesLive leverages unique access to the world’s most influential leaders, policy-makers, entrepreneurs, and artists—uniting these global forces to harness their collective knowledge, address today’s critical [...]
Events on 2017-11-01
TEDMED 2017
1 Nov 17
La Quinta
Events on 2017-11-04
AMIA 2017 Annual Symposium
4 Nov 17
WASHINGTON
Events on 2017-11-09
Beverly Hills Health IT Summit
9 Nov 17
Los Angeles
Events on 2017-11-29
Forbes Healthcare Summit
29 Nov 17
New York
Articles

Algorithm Uses EHR Data To Identify Diabetes at Earliest Possible Date

diabetes

Researchers have developed an algorithm that can evaluate electronic health records to determine whether the patient has diabetes, FierceHealthIT reports.

Details of Algorithm

The algorithm, published in BioMedCentral, focuses on determining the earliest possible data of diagnosis in close to real time.

It evaluates information that is regularly documented and can be extracted from structured data fields, including:

  • Past medical history;
  • Problem list;
  • Medications; and
  • Laboratory results.

Each element is given a point value. After reaching a certain threshold, the algorithm identifies the presence of diabetes and calculates the earliest date that the disease could have been diagnosed.

Algorithm’s Accuracy

The researchers compared the algorithm’s findings with the opinion of a physician and found that the algorithm agreed on the date of diagnosis in 78.4% of cases.

It established a date of diagnosis that was within three months of the physician’s date in 94% of cases.

Experts say the algorithm could be effective in reaching patients who do not visit a physician regularly (Hall, FierceHealthIT, 8/2). Source