The following article appeared recently.
Improved Patient Problem List Enhances Diagnoses
New clinical decision support tool gives doctors a better patient problem list, reminding doctors of those medical issues and leading to improved care, say researchers at Brigham & Women's Hospital in Boston.
By Ken Terry, InformationWeek
January 30, 2012
A new clinical decision support tool developed by researchers at Harvard-affiliated Brigham & Women's Hospital in Boston can increase the completeness of patient problem lists in electronic health records (EHRs). Having all of a patient's diagnoses on a single list helps physicians provide better care, because they're more likely to treat a condition such as diabetes or hypertension if they're reminded of that problem when a patient visits.
Unfortunately, as a recent paper in the Journal of the American Medical Informatics Association noted, medical problem lists are often incomplete. In a previous study of a primary care network affiliated with Brigham & Women's, the authors found that "completeness ... ranged from 4.7% for renal insufficiency or failure to 50.7% for hypertension, 61.9% for diabetes, to a maximum of 78.5% for breast cancer, and other institutions have found similar results." 
To increase the comprehensiveness of the diagnosis list, Adam Wright, MD, assistant professor of medicine at Brigham & Women's and Harvard Medical School, and his colleagues designed a "problem inference" tool that uses billing codes, lab results, medications, and other data to infer the missing diagnoses.
After validating the tool for 17 health conditions, the researchers set out to prove that it worked in the real world. They conducted a randomized trial involving 11 primary-care clinics in the Brigham & Women's network. The practices included 28 clinical areas, which were evenly divided between intervention and control sites.
The results of the six-month trial were promising. The physicians in the intervention sites accepted 41% of the 17,000 alerts about missing diagnoses that they received. They also added 70% of the problems in the alerts to their problem lists. Including new and old problems, the intervention sites added nearly three times as many diagnoses to the lists as the control sites did.
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The .pdf for the full paper is found here:
The abstract is found here:
J Am Med Inform Assoc doi:10.1136/amiajnl-2011-000521
- Research and applications
Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial
- Adam Wright1,2,3,
- Justine Pang1,2,
- Joshua C Feblowitz1,2,
- Francine L Maloney2,
- Allison R Wilcox2,
- Karen Sax McLoughlin1,
- Harley Ramelson1,2,3,
- Louise Schneider1,3,
- David W Bates1,2,3,4
Author Affiliations
1. 1Division of General Internal Medicine, Brigham & Women's Hospital, Boston, Massachusetts, USA
2. 2Partners HealthCare, Boston, Massachusetts, USA
3. 3Harvard Medical School, Boston, Massachusetts, USA
4. 4Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, USA
- Correspondence to Dr Adam Wright, Brigham and Women's Hospital, 1620 Tremont St, Boston, MA 02115, USA; awright5@partners.org
- Received 2 August 2011
- Accepted 5 December 2011
- Published Online First 3 January 2012
Abstract
Background Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.
Objective To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.
Study Design and Methods Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods.
Results 17 043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.
Conclusion Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.
----- End Abstract.
A very interesting study - especially revealing just how incomplete problem lists can be and how important they are in making sure all the relevant issues are addressed at a visit!
It also makes it clear that maintaining quality information is actually hard work!
David.
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