An electronic documentation system improves the quality of admission notes: a randomized trial

OBJECTIVE

There are concerns that structured electronic documentation systems can limit expressivity and encourage long and unreadable notes. We assessed the impact of an electronic clinical documentation system on the quality of admission notes for patients admitted to a general medical unit.

METHODS

This was a prospective randomized crossover study comparing handwritten paper notes to electronic notes on different patients by the same author, generated using a semistructured electronic admission documentation system over a 2-month period in 2014. The setting was a 4-team, 80-bed general internal medicine clinical teaching unit at a large urban academic hospital. The quality of clinical documentation was assessed using the QNOTE instrument (best possible score = 100), and word counts were assessed for free-text sections of notes.

RESULTS

Twenty-one electronic-paper note pairs (42 notes) written by 21 authors were randomly drawn from a pool of 303 eligible notes. Overall note quality was significantly higher in electronic vs paper notes (mean 90 vs 69, P < .0001). The quality of free-text subsections (History of Present Illness and Impression and Plan) was significantly higher in the electronic vs paper notes (mean 93 vs 78, P < .0001; and 89 vs 77, P = .001, respectively). The History of Present Illness subsection was significantly longer in electronic vs paper notes (mean 172.4 vs 92.4 words, P = .0001).

CONCLUSIONS

An electronic admission documentation system improved both the quality of free-text content and the overall quality of admission notes. Authors wrote more in the free-text sections of electronic documents as compared to paper versions.

What happens to patients who leave hospital against medical advice?

BACKGROUND

Patients who leave hospital against medical advice (AMA) may be at risk of adverse health outcomes and readmission. In this study we examined rates of readmission and predictors of readmission among patients leaving hospital AMA.

METHODS

We prospectively studied 97 consecutive patients who left the general medicine service of an urban teaching hospital AMA. Each patient was matched according to age, sex and primary diagnosis with a control patient who was discharged routinely. Readmission rates were examined using Kaplan-Meier analysis. Regression models were used to test the hypothesis that readmissions among patients discharged AMA followed a biphasic curve.

RESULTS

Patients who left AMA were much more likely than the control patients to be readmitted within 15 days (21% v. 3%, p < 0.001). Readmissions occurred at an accelerated pace during the first 15 days, followed by a 75-day period during which readmissions occurred at a rate comparable to that among the control patients. Among the patients who left AMA, being male and having a history of alcohol abuse were significant predictors of readmission within 15 days; however, these characteristics were common among the patients who left AMA. In the Cox proportional hazard models, leaving AMA was the only significant predictor of readmission (adjusted hazard ratio 2.5, 95% confidence interval 1.4-4.4).

INTERPRETATION

The significantly increased risk of readmission among general medicine patients who leave hospital AMA is concentrated in the first 2 weeks after discharge. However, it is difficult to identify which patients will likely be readmitted.