Key Attributes of a Medical Learning Community Mentor at One Medical School.

PURPOSE: The purpose of this study was to discover the elements required for a successful learning community (LC) faculty member educator of medical students. METHOD: The authors in this qualitative study evaluated six 90-min focus groups of faculty members. The groups included 31 experienced and 19 inexperienced LC faculty members at the University of Texas Southwestern Medical School. After achieving excellent interrater reliability, transcriptions of the discussions were subjected to thematic analysis using ATLAS.ti software. RESULTS: Five major themes emerged: (1) LC faculty characteristics/competency, (2) suggested faculty development methods, (3) factors outside the LC environment influencing student relationships, (4) student attributes influencing teaching techniques, and (5) measuring and improving history and physical skills. Faculty characteristics/competency subthemes included role-modeling, mentoring, and teaching competence. Suggested faculty development methods subthemes included assessing and giving feedback to faculty, peer development, and learning from experts. Experienced LC faculty focused more attention on teaching competence and mentoring competence than inexperienced LC faculty. DISCUSSION: The themes with the most extensive discussion among the experienced LC faculty groups may represent qualities to be sought in future mentor recruitment and faculty development. Future studies could build on this study by similarly investigating student perceptions.

Viewing Prevention of Catheter-Associated Urinary Tract Infection as a System: Using Systems Engineering and Human Factors Engineering in a Quality Improvement Project in an Academic Medical Center.

BACKGROUND: Urinary tract infections (UTIs) are the most commonly reported health care-associated infection (HAI) in the United States. Among UTIs acquired in the hospital, approximately 75% are associated with urinary catheters, with an estimated 15%-25% of all hospitalized patients receiving urinary catheters during their hospitalization. Despite ambitious national goals to reduce these infections, catheter-associated urinary tract infection (CAUTI) has not decreased in the United States. METHODS: Systems engineering (SE) and human factors engi- neering (HFE) methods were used to reduce urinary catheter utilization and CAUTIs in a three-year (June 1, 2012-May 31, 2015) quality improvement project in a 610-bed academic medical center. These methods were used to define the factors leading to CAUTI and promote standardization of urinary catheter utilization, insertion, and maintenance. RESULTS: The total systemwide CAUTI count decreased from 135 cases at baseline to 74 cases at the end of the project's Year 1, to 59 cases at the end of Year 2, and 25 cases at the end of Year 3-alone, an 81.5% reduction from baseline. The control chart showed a steady decline in the CAUTI count within a few months after the project's start. By the end of Year 3, on the basis of an average attributable-per-patient cost of CAUTI ($1,007 per case), the estimated annual avoidable CAUTI costs decreased from approximately $135,945 to $25,175 per year. Urinary catheter utilization decreased by 27.3% during the same three-year period, and the systemwide CAUTI standardized infection ratio (SIR) decreased from 3.2 to 0.51 (84.1% from baseline). CONCLUSION: SE and HFE methods and principles can effectively decrease urinary catheter utilization and CAUTI incidence in an academic medical center hospital environment.

Public reporting of patient safety metrics: ready or not?

In its 1999 report, the Institute of Medicine estimated that medical error leads to between 44,000 and 98,000 deaths per year. Given that statistic, public reporting of quality and safety metrics is a welcome response that may serve to reduce the rate of adverse events and restore patients' trust in the health care system. To ensure that any public reporting system fulfills its potential, several questions must be addressed: Are we measuring the right metrics? Are the metrics accurate, valid, and is their public reporting effecting change? Based on a review of the literature, it is clear that current metrics suffer from low reliability, low validity, and possibly minimal relevance to the intended consumer. To improve data collection and analysis, both physicians and health care consumers need to be involved in the design and collection of metrics. Until we have a valid, reliable, and actionable data set at our fingertips, it would behoove patients, providers, and institutions to look at outcome and safety metrics with a skeptical and discerning eye.

An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data.

BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between January 2007 and August 2008. Data were extracted from an electronic medical record. The performance of the electronic model was compared with mortality and readmission models developed by the Center for Medicaid and Medicare Services (CMS models) and a HF mortality model derived from the Acute Decompensated Heart Failure Registry (ADHERE model). RESULTS: The 30-day mortality and readmission rates were 3.1% and 24.1% respectively. The electronic model demonstrated good discrimination for 30 day mortality (C statistic 0.86) and readmission (C statistic 0.72) and performed as well, or better than, the ADHERE model and CMS models for both outcomes (C statistic ranges: 0.72-0.73 and 0.56-0.66 for mortality and readmissions respectively; P < 0.05 in all comparisons). Markers of social instability and lower socioeconomic status improved readmission prediction in the electronic model (C statistic 0.72 vs. 0.61, P < 0.05). CONCLUSIONS: Clinical and social factors available within hours of hospital presentation and extractable from an EMR predicted mortality and readmission at 30 days. Incorporating complex social factors increased the model's accuracy, suggesting that such factors could enhance risk adjustment models designed to compare hospital readmission rates.