Healthcare worker feedback on duodenoscope reprocessing workflow and ergonomics.

BACKGROUND: The objectives of this survey study were to assess duodenoscope precleaning and manual cleaning times, identify human factors issues in duodenoscope reprocessing workflow or ergonomics, and ascertain any best practices in duodenoscope reprocessing. METHODS: Researchers developed the confidential, qualitative, online Duodenoscope Reprocessing Workflow and Ergonomic Design Human Factors Survey with an intended audience of healthcare workers (HCWs) who routinely perform duodenoscope precleaning or manual cleaning. The unrestricted survey link was distributed to target HCW email addresses in December 2020; the survey closed in January 2021. RESULTS: Three hundred and forty-one individuals completed the survey. Most respondents complete duodenoscope precleaning in 10 minutes or less and manual cleaning in 16-to-30 minutes. Most respondents' facilities use fixed distal endcap duodenoscopes. Most respondents experience pressure to work faster when cleaning duodenoscopes and reported that cleaning duodenoscopes caused fatigue or discomfort in at least one body part. Mentoring HCWs and retaining experienced staff were 2 primary duodenoscope reprocessing best practices identified by respondents. DISCUSSION AND CONCLUSIONS: To enhance duodenoscope cleaning, facilities should provide ample reprocessing work spaces with incorporated height-adjustable work surfaces, train HCWs on validated duodenoscope reprocessing instructions, provide step-by-step instructions for HCWs when duodenoscope cleaning is performed, mentor reprocessing HCWs, and retain experienced staff.

Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

INTRODUCTION: The objective of this study was to develop a semiautomated approach to screening cases that describe hazards associated with the electronic health record (EHR) from a mandatory, population-based patient safety reporting system. METHODS: Potentially relevant cases were identified through a query of the Pennsylvania Patient Safety Reporting System. A random sample of cases were manually screened for relevance and divided into training, testing, and validation data sets to develop a machine learning model. This model was used to automate screening of remaining potentially relevant cases. RESULTS: Of the 4 algorithms tested, a naive Bayes kernel performed best, with an area under the receiver operating characteristic curve of 0.927 ± 0.023, accuracy of 0.855 ± 0.033, and F score of 0.877 ± 0.027. DISCUSSION: The machine learning model and text mining approach described here are useful tools for identifying and analyzing adverse event and near-miss reports. Although reporting systems are beginning to incorporate structured fields on health information technology and the EHR, these methods can identify related events that reporters classify in other ways. These methods can facilitate analysis of legacy safety reports by retrieving health information technology-related and EHR-related events from databases without fields and controlled values focused on this subject and distinguishing them from reports in which the EHR is mentioned only in passing. CONCLUSIONS: Machine learning and text mining are useful additions to the patient safety toolkit and can be used to semiautomate screening and analysis of unstructured text in safety reports from frontline staff.

Ventilator-Related Adverse Events: A Taxonomy and Findings From 3 Incident Reporting Systems.

BACKGROUND: In 2009, researchers from Johns Hopkins University's Armstrong Institute for Patient Safety and Quality; public agencies, including the FDA; and private partners, including the Emergency Care Research Institute and the University HealthSystem Consortium (UHC) Safety Intelligence Patient Safety Organization, sought to form a public-private partnership for the promotion of patient safety (P5S) to advance patient safety through voluntary partnerships. The study objective was to test the concept of the P5S to advance our understanding of safety issues related to ventilator events, to develop a common classification system for categorizing adverse events related to mechanical ventilators, and to perform a comparison of adverse events across different adverse event reporting systems. METHODS: We performed a cross-sectional analysis of ventilator-related adverse events reported in 2012 from the following incident reporting systems: the Pennsylvania Patient Safety Authority's Patient Safety Reporting System, UHC's Safety Intelligence Patient Safety Organization database, and the FDA's Manufacturer and User Facility Device Experience database. Once each organization had its dataset of ventilator-related adverse events, reviewers read the narrative descriptions of each event and classified it according to the developed common taxonomy. RESULTS: A Pennsylvania Patient Safety Authority, FDA, and UHC search provided 252, 274, and 700 relevant reports, respectively. The 3 event types most commonly reported to the UHC and the Pennsylvania Patient Safety Authority's Patient Safety Reporting System databases were airway/breathing circuit issue, human factor issues, and ventilator malfunction events. The top 3 event types reported to the FDA were ventilator malfunction, power source issue, and alarm failure. CONCLUSIONS: Overall, we found that (1) through the development of a common taxonomy, adverse events from 3 reporting systems can be evaluated, (2) the types of events reported in each database were related to the purpose of the database and the source of the reports, resulting in significant differences in reported event categories across the 3 systems, and (3) a public-private collaboration for investigating ventilator-related adverse events under the P5S model is feasible.

Work-arounds slow electronic health record use.

The Pennsylvania Patient Safety Reporting System is a confidential, statewide Internet reporting system to which all Pennsylvania hospitals, outpatient-surgery facilities, and birthing centers, as well as some abortion facilities, must file information on medical errors.Safety Monitor is a column from Pennsylvania's Patient Safety Authority, the authority that informs nurses on issues that can affect patient safety and presents strategies they can easily integrate into practice. For more information on the authority, visit www.patientsafetyauthority.org. For the original article discussed in this column or for other articles on patient safety, click on "Patient Safety Advisories" and then "Advisory Library" in the left-hand navigation menu.

iPads, droids, and bugs: Infection prevention for mobile handheld devices at the point of care.

Health care providers are increasingly using wireless media tablets, such as the Apple iPad, especially in the hospital setting. In the absence of specific tablet disinfection guidelines the authors applied what is known about the contamination of other nonmedical mobile communication devices to create a "common sense" bundle to guide wireless media tablet infection prevention practices.