Improving Compliance With Institutional Performance on Train of Four Monitoring.

Background: We performed a multistep quality improvement project related to neuromuscular blockade and monitoring to evaluate the effectiveness of a comprehensive quality improvement program based upon the Multi-institutional Perioperative Outcomes Group (MPOG) Anesthesiology Performance Improvement and Reporting Exchange (ASPIRE) metrics targeted specifically at improving train of four (TOF) monitoring rates. Methods: We adapted the plan-do-study-act (PDSA) framework and implemented 2 PDSA cycles between January 2021 and December 2021. PDSA Cycle 1 (Phase I) and PDSA Cycle 2 (Phase II) included a multipart program consisting of (1) a departmental survey assessing attitudes toward intended results, outcomes, and barriers for TOF monitoring, (2) personalized MPOG ASPIRE quality performance reports displaying provider performance, (3) a dashboard access to help providers complete a case-by-case review, and (4) a web-based app spaced education module concerning TOF monitoring and residual neuromuscular blockade. Our primary outcome was to identify the facilitators and barriers to implementation of our intervention aimed at increasing TOF monitoring. Results: In Phase I, 25 anesthesia providers participated in the preintervention and postintervention needs assessment survey and received personalized quality metric reports. In Phase II, 222 providers participated in the preintervention needs assessment survey and 201 participated in the postintervention survey. Thematic analysis of Phase I survey data aimed at identifying the facilitators and barriers to implementation of a program aimed at increasing TOF monitoring revealed the following: intended results were centered on quality of patient care, barriers to implementation largely encompassed issues with technology/equipment and the increased burden placed on providers, and important outcomes were focused on patient outcomes and improving provider knowledge. Results of Phase II survey data was similar to that of Phase I. Notably in Phase II a few additional barriers to implementation were mentioned including a fear of loss of individualization due to standardization of patient care plan, differences between the attending overseeing the case and the in-room provider who is making decisions/completing documentation, and the frequency of intraoperative handovers. Compared to preintervention, postintervention compliance with TOF monitoring increased from 42% to 70% (28% absolute difference across N = 10 169 cases; P < .001). Conclusions: Implementation of a structured quality improvement program using a novel educational intervention showed improvements in process metrics regarding neuromuscular monitoring, while giving us a better understanding of how best to implement improvements in this metric at this magnitude.

An app a day: Results of pre- and post-surveys of knowledge, attitudes, and practices (KAP) regarding antimicrobial stewardship principles among nurses who utilized a novel learning platform.

Background: Nurses perform several functions that are integral for antimicrobial stewardship (AMS). However, nurses are underrepresented in research and underutilized in implementation of AMS interventions. The objective of this pilot study was to assess the effect of asynchronous microlearning on inpatient nursing staff knowledge, attitudes, and practices (KAP) regarding AMS principles. Methods: A team of pharmacists, physicians, and nurses developed 9 case-based, multiple-choice questions with accompanying educational explanations on associated AMS principles. One case was delivered to participants daily via an institutional web-based application (QuizTime). A KAP survey with 20 questions on a 5-point Likert scale was administered before and after the intervention. Survey results were compared using a Wilcoxon signed-rank test. Results: Participants' mean survey score after the intervention demonstrated statistically significant improvement for 18 (90%) of 20 items compared to before the intervention. Participants' confidence improved in key AMS activities: (1) differentiating between colonization and infection (mean difference, 0.63; P < .001), (2) identifying unnecessary urine cultures and inappropriate treatment of urinary tract infections (mean difference, 0.94; P < .001), (3) recognizing opportunities for intravenous to oral therapy conversion (mean difference, 1.07; P < .001), and (4) assessing for antibiotic-associated adverse effects (mean difference, 0.54; P < .001). Conclusions: Nursing education provided through an asynchronous, microlearning format via a mobile platform resulted in statistically significant improvement in most KAP topics. Nurses are integral members of a multidisciplinary AMS team, and novel education methods can help equip them with the necessary AMS tools. This pilot study forms the basis for expanded AMS educational efforts in all healthcare professionals.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals.

PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. RESULTS: Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. CONCLUSIONS: The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Considering Clinician Competencies for the Implementation of Artificial Intelligence-Based Tools in Health Care: Findings From a Scoping Review.

BACKGROUND: The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding. OBJECTIVE: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. METHODS: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. RESULTS: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. CONCLUSIONS: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.

Effect of Smartphone App-Based Education on Clinician Prescribing Habits in a Learning Health Care System: A Randomized Cluster Crossover Trial.

Importance: Effective methods for engaging clinicians in continuing education for learning-based practice improvement remain unknown. Objective: To determine whether a smartphone-based app using spaced education with retrieval practice is an effective method to increase evidence-based practice. Design, Setting, and Participants: A prospective, unblinded, single-center, crossover randomized clinical trial was conducted at a single academic medical center from January 6 to April 24, 2020. Vanderbilt University Medical Center clinicians prescribing intravenous fluids were invited to participate in this study. Interventions: All clinicians received two 4-week education modules: 1 on prescribing intravenous fluids and 1 on prescribing opioid and nonopioid medications (counterbalancing measure), over a 12-week period. The order of delivery was randomized 1:1 such that 1 group received the fluid management module first, followed by the pain management module after a 4-week break, and the other group received the pain management module first, followed by the fluid management module after a 4-week break. Main Outcomes and Measures: The primary outcome was evidence-based clinician prescribing behavior concerning intravenous fluids in the inpatient setting and pain medication prescribing on discharge from the hospital. Results: A total of 354 participants were enrolled and randomized, with 177 in group 1 (fluid then pain management education) and 177 in group 2 (pain management then fluid education). During the overall study period, 16 868 questions were sent to 349 learners, with 11 783 (70.0%) being opened: 10 885 (92.4%) of those opened were answered and 7175 (65.9%) of those answered were answered correctly. The differences between groups changed significantly over time, indicated by the significant interaction between educational intervention and time (P = .002). Briefly, at baseline evidence-concordant IV fluid ordered 7.2% less frequently in group 1 than group 2 (95% CI, -19.2% to 4.9%). This was reversed after training at 4% higher (95% CI, -8.2% to 16.0%) in group 1 than group 2, a more than doubling in the odds of evidence-concordant ordering (OR, 2.56, 95% CI, 0.80-8.21). Postintervention, all gains had been reversed with less frequent ordering in group 1 than group 2 (-9.5%, 95% CI, -21.6% to 2.7%). There was no measurable change in opioid prescribing behaviors at any time point. Conclusions and Relevance: In this randomized clinical trial, use of smartphone app learning modules resulted in statistically significant short-term improvement in some prescribing behaviors. However, this effect was not sustained over the long-term. Additional research is needed to understand how to sustain improvements in care delivery as a result of continuous professional development at the institutional level. Trial Registration: ClinicalTrials.gov Identifier: NCT03771482.