Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.

Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and efficient in an era of new financial and regulatory imperatives, but previous surveillance approaches have used a simplified version of the definition. We applied a complete definition, including subjective elements identified through natural language processing of clinical notes. Through examination of documentation practices, we defined a set of rules that identified positively and negatively asserted symptoms of CAUTI. Our algorithm was developed on a training set of 1421 catheterizedpatients and prospectively validated on 1567 catheterizedpatients. Compared to gold standard chart review, our tool had a sensitivity of 97.1%, specificity of 94.5% PPV of 66.7% and NPV of 99.6% for identifying CAUTI. We discuss sources of error and suggestions for more computable future definitions.

A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center.

In an environment where there is increased demand for hospital beds, it is important that inpatient flow from admission to treatment to discharge is optimized. Among the many drivers that impact efficient patient throughput is an effective and timely discharge process. Early morning discharge helps align inpatient capacity with clinical demand, thereby avoiding gridlock that adversely affects scheduled surgical procedures, diagnostic procedures, and therapies. At our large, academic medical center, we hypothesized that an interdisciplinary approach to scheduled discharge order entry would increase the percentage of discharges occurring before 11:00 AM and improve overall discharge time. The pilot study involved moving rate-limiting steps to earlier in the discharge process, specifically medication reconciliation to the night before discharge and "discharge to home" order entry before 9:00 AM the morning of discharge. The baseline rate of discharges before 11:00 AM was 8% and significantly increased to 11% after the intervention (P = .02). Moreover, in the subset of patients (21%) for whom early medication reconciliation and discharge to home order entry were both executed, the percentage of patient discharges occurring before 11:00 AM increased to 29.7%, with an associated average discharge time of more than 3 hours earlier. No patient harm events were associated with this pilot project. There was no significant change in length of stay, and 30-day readmission rate improved significantly from 13.8% to 10.3% (P = .002). Our study demonstrates that a multidisciplinary approach using prescribed order entry and medication reconciliation is a low cost, safe, and effective way to increase early morning discharges and improve patient flow for large hospitals with high volumes of scheduled patient admissions.