View source for Improving completeness of electronic problem lists through clinical decision support

Problems are medical conditions documented in the patient's electronic health record [[EMR#Problem list|Problem List]] using predefined codes (such as [[SNOMED|SNOMED]] codes). This article describes improvements in documented problems when clinical decision support alerts providers to options based on other documentation in the EMR. <ref name="Wright2012">Wright A, Pang J, Feblowitz JC, et al. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. Journal of the American Medical Informatics Association : JAMIA. 2012;19(4):555-561. doi:10.1136/amiajnl-2011-000521. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/22215056.</ref>

==Abstract<ref name="Wright2012">Wright A, Pang J, Feblowitz JC, et al. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. Journal of the American Medical Informatics Association : JAMIA. 2012;19(4):555-561. doi:10.1136/amiajnl-2011-000521. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/22215056.</ref>==
===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 However, problem lists are often incomplete or out of date.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.

===Trial Registration===
ClinicalTrials.gov: NCT01105923.

==Comments==
Problem list reconciliation is a hot topic in healthcare, especially as connectivity between physicians office and hospital EHRs attempting to meet Meaningful Use requirements leads to a dissonance between the long-term problem management perspective of the primary care physicians versus the short-term acute condition resolution perspective of the hospitalist and consults. Because acute care providers are less likely to think in terms of long-term problem management and yet need to take those problems into consideration both in plan of care during the hospital stay as well as upon discharge, clinical decision support as described in this study is extremely promising.

==References==
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