To assess the impact of a 24-hour autocancellation of uncollected Clostridioides difficile samples in reducing reported healthcare-associated infections (HAIs).

Quality-improvement, before-and-after implementation study.

The study was conducted in 17 hospitals in Pennsylvania.

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.

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.

The 24-hour autocancellation of uncollected C. difficile orders reduced testing but did not result in reported HAI reduction.

To evaluate the impact of a diagnostic stewardship intervention on Clostridioides difficile healthcare-associated infections (HAI).

Quality improvement study.

Two urban acute care hospitals.

All inpatient stool testing for C. difficile required review and approval prior to specimen processing in the laboratory. An infection preventionist reviewed all orders daily through chart review and conversations with nursing; orders meeting clinical criteria for testing were approved, orders not meeting clinical criteria were discussed with the ordering provider. The proportion of completed tests meeting clinical criteria for testing and the primary outcome of C. difficile HAI were compared before and after the intervention.

The frequency of completed C. difficile orders not meeting criteria was lower [146 (7.5%) of 1,958] in the intervention period (January 10, 2022–October 14, 2022) than in the sampled 3-month preintervention period [26 (21.0%) of 124; P < .001]. C. difficile HAI rates were 8.80 per 10,000 patient days prior to the intervention (March 1, 2021–January 9, 2022) and 7.69 per 10,000 patient days during the intervention period (incidence rate ratio, 0.87; 95% confidence interval, 0.73–1.05; P = .13).

A stringent order-approval process reduced clinically nonindicated testing for C. difficile but did not significantly decrease HAIs.

To evaluate infectious pathogen transmission data visualizations in outbreak publications.

Scoping review.

Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7–1,963) and lasted a median of 214 days (range, 12–5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.