Ordering Clostridioides difficile diagnostics without appropriate clinical indications can result in inappropriate antibiotic prescribing and misdiagnosis of hospital onset C. difficile infection. Manual processes such as provider review of order appropriateness may detract from other infection control or antibiotic stewardship activities.

We developed an evidence-based clinical algorithm that defined appropriateness criteria for testing for C. difficile infection. We then implemented an electronic medical record–based order-entry tool that utilized discrete branches within the clinical algorithm including history of prior C. difficile test results, laxative or stool-softener administration, and documentation of unformed bowel movements. Testing guidance was then dynamically displayed with supporting patient data. We compared the rate of completed C. difficile tests after implementation of this intervention at 5 hospitals to a historic baseline in which a best-practice advisory was used.

Using mixed-effects Poisson regression, we found that the intervention was associated with a reduction in the incidence rate of both C. difficile ordering (incidence rate ratio [IRR], 0.74; 95% confidence interval [CI], 0.63–0.88; P = .001) and C. difficile–positive tests (IRR, 0.83; 95% CI, 0.76–0.91; P < .001). On segmented regression analysis, we identified a sustained reduction in orders over time among academic hospitals and a new reduction in orders over time among community hospitals.

An evidence-based dynamic order panel, integrated within the electronic medical record, was associated with a reduction in both C. difficile ordering and positive tests in comparison to a best practice advisory, although the impact varied between academic and community facilities.

The spatial and temporal extent of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time.

We sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model.

The probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection: floors (odds ratio [OR], 67.8; 95% credible interval [CrI], 36.3–131) and high-touch elevated surfaces (OR, 7.39; 95% CrI, 4.31–13.1). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR, 1.6; 95% CrI, 1.03–2.53). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with the duration of the local pandemic wave.

Distance from a patient’s bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.

Multidrug-resistant organisms (MDROs) colonizing the healthcare environment have been shown to contribute to risk for healthcare-associated infections (HAIs), with adverse effects on patient morbidity and mortality. We sought to determine how bacterial contamination and persistent MDRO colonization of the healthcare environment are related to the position of patients and wastewater sites.

We performed a prospective cohort study, enrolling 51 hospital rooms at the time of admitting a patient with an eligible MDRO in the prior 30 days. We performed systematic sampling and MDRO culture of rooms, as well as 16S rRNA sequencing to define the environmental microbiome in a subset of samples.

The probability of detecting resistant gram-negative organisms, including Enterobacterales, Acinetobacter spp, and Pseudomonas spp, increased with distance from the patient. In contrast, Clostridioides difficile and methicillin-resistant Staphylococcus aureus were more likely to be detected close to the patient. Resistant Pseudomonas spp and S. aureus were enriched in these hot spots despite broad deposition of 16S rRNA gene sequences assigned to the same genera, suggesting modifiable factors that permit the persistence of these MDROs.

MDRO hot spots can be defined by distance from the patient and from wastewater reservoirs. Evaluating how MDROs are enriched relative to bacterial DNA deposition helps to identify healthcare micro-environments and suggests how targeted environmental cleaning or design approaches could prevent MDRO persistence and reduce infection risk.