The impact of autocancellation of uncollected Clostridioides difficile specimens after 24 hours on reported healthcare-associated infections: A quality improvement intervention.

OBJECTIVE: To assess the impact of a 24-hour autocancellation of uncollected Clostridioides difficile samples in reducing reported healthcare-associated infections (HAIs). DESIGN: Quality-improvement, before-and-after implementation study. SETTING: The study was conducted in 17 hospitals in Pennsylvania. INTERVENTIONS: Clostridioides difficile tests that are not collected within 24 hours are automatically canceled ("autocancel") through the electronic health record. The intervention took place at 2 facilities (intervention period November 2021-July 2022) and subsequently at 15 additional facilities (April 2022-July 2022). Quality measures included percentage of orders canceled, C. difficile HAI rate, percent positivity of completed tests, and potential adverse outcomes of canceled or delayed testing. RESULTS: Of 6,101 orders, 1,090 (17.9%) were automatically canceled after not being collected for 24 hours during the intervention periods. The reported C. difficile HAI rates per 10,000 patient days did not significantly change. These rates were 8.07 in the 6-month preintervention period and 8.77 in the intervention period for facilities A and B combined (incidence rate ratio [IRR], 1.09; 95% CI, 0.88-1.34; P = .43), and were 5.23 HAIs per 10,000 patient days in the 6-month preintervention period and 5.33 in the intervention period for facilities C-Q combined (IRR, 1.02; 95% CI, 0.79-1.32; P = .87). From the preintervention to the intervention periods, the percent positivity rates of completed C. difficile tests increased by 1.1% for facilities A and B and by 1.4% for facilities C-Q. No adverse outcomes were observed. CONCLUSIONS: The 24-hour autocancellation of uncollected C. difficile orders reduced testing but did not result in reported HAI reduction.

Clinical Decision Support for In-Hospital AKI.

AKI carries a significant mortality and morbidity risk. Use of a clinical decision support system (CDSS) might improve outcomes. We conducted a multicenter, sequential period analysis of 528,108 patients without ESRD before admission, from October of 2012 to September of 2015, to determine whether use of a CDSS reduces hospital length of stay and in-hospital mortality for patients with AKI. We compared patients treated 12 months before (181,696) and 24 months after (346,412) implementation of the CDSS. Coprimary outcomes were hospital mortality and length of stay adjusted by demographics and comorbidities. AKI was diagnosed in 64,512 patients (12.2%). Crude mortality rate fell from 10.2% before to 9.4% after CDSS implementation (odds ratio, 0.91; 95% confidence interval [95% CI], 0.86 to 0.96; P=0.001) for patients with AKI but did not change in patients without AKI (from 1.5% to 1.4%). Mean hospital duration decreased from 9.3 to 9.0 days (P<0.001) for patients with AKI, with no change for patients without AKI. In multivariate mixed-effects models, the adjusted odds ratio (95% CI) was 0.76 (0.70 to 0.83) for mortality and 0.66 (0.61 to 0.72) for dialysis (P<0.001). Change in adjusted hospital length of stay was also significant (incidence rate ratio, 0.91; 95% CI, 0.89 to 0.92), decreasing from 7.2 to 6.0 days for patients with AKI. Results were robust to sensitivity analyses and were sustained for the duration of follow-up. Hence, implementation of a CDSS for AKI resulted in a small but sustained decrease in hospital mortality, dialysis use, and length of stay.

Using Bioinformatics to Treat Hospitalized Smokers: Successes and Challenges of a Tobacco Treatment Service.

BACKGROUND: Hospitals face increasing regulations to provide and document inpatient tobacco treatment, yet few blueprint data exist to implement a tobacco treatment service (TTS). METHODS: A hospitalwide, opt-out TTS with three full-time certified counselors was developed in a large tertiary care hospital to proactively treat smokers according to Chronic Care Model principles and national treatment guidelines. A bioinformatics platform facilitated integration into the electronic health record to meet evolving Centers for Medicare & Medicaid Services meaningful use and Joint Commission standards. TTS counselors visited smokers at the bedside and offered counseling, recommended smoking cessation medication to be ordered by the primary clinical service, and arranged for postdischarge resources. RESULTS: During a 3.5-year span, 21,229 smokers (31,778 admissions) were identified; TTS specialists reached 37.4% (7,943), and 33.3% (5,888) of daily smokers received a smoking cessation medication order. Adjusted odds ratios (AORs) of receiving a chart order for smoking cessation medication during the hospital stay and at discharge were higher among patients the TTS counseled > 3 minutes and recommended medication: inpatient AOR = 7.15 (95% confidence interval [CI] = 6.59-7.75); discharge AOR = 5.3 (95% CI = 4.71-5.97). As implementation progressed, TTS counseling reach and medication orders increased. To assess smoking status ≤ 1 month postdischarge, three methods were piloted, all of which were limited by low follow-up rates (4.5%-28.6%). CONCLUSION: The TTS counseled approximately 3,000 patients annually, with increases over time for reach and implementation. Remaining challenges include the development of strategies to engage inpatient care teams to follow TTS recommendations, and patients postdischarge in order to optimize postdischarge smoking cessation.