Indication alerts to improve problem list documentation

Author:

Grauer Anne1,Kneifati-Hayek Jerard1,Reuland Brian1,Applebaum Jo R2ORCID,Adelman Jason S12,Green Robert A12,Lisak-Phillips Jeanette1,Liebovitz David3,Byrd Thomas F3,Kansal Preeti3,Wilkes Cheryl3,Falck Suzanne4,Larson Connie5,Shilka John5,VanDril Elizabeth5,Schiff Gordon D6,Galanter William L457,Lambert Bruce L8

Affiliation:

1. Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA

2. Department of Quality and Patient Safety, New York-Presbyterian Hospital, New York City, New York, USA

3. Department of Medicine, Northwestern University, Chicago, Illinois, USA

4. Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA

5. Department of Pharmacy Practice, University of Illinois at Chicago, Chicago, Illinois, USA

6. Brigham and Women’s Hospital Center for Patient Safety Research, Harvard Medical School Center for Primary Care, Boston, Massachusetts, USA

7. Department of Pharmacy Systems, Outcomes and Policy, University of Illinois at Chicago, Chicago, Illinois, USA

8. Center for Communication and Health, Department of Communication Studies, Northwestern University, Chicago, Illinois, USA

Abstract

Abstract Background Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. Methods We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. Results Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. Conclusions Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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