Indication alerts to improve problem list documentation

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.

Development and validation of a self-updating gout register from electronic health records data

“…Indication alert prompting the documentation of gout in the problem list, triggered by the prescription of a ULT, could help solve this shortcoming. 38 …”

“…Indication alert prompting the documentation of gout in the problem list, triggered by the prescription of a ULT, could help solve this shortcoming. 38 The query in documents was complicated by the fact that gout, a prevalent word in French, is used commonly by patients and healthcare professionals. We used a combination of regular expression and natural language processing to exclude situations where the word gout was used in a negative context (eg, psychiatry) or referring to drugs (eg, drops of vitamins) and analyses (eg, drops of blood).…”

Enhancing gout management by creating a register using automated queries in electronic health records

“…In our study, most charts did not provide other diagnosis explaining the need for a urate lowering therapy. Indication alert prompting the documentation of gout in the problem list, triggered by the prescription of an urate lowering therapy, could help solve this shortcoming (31).…”