Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS: Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS: A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION: Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04000087.

Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study.

BACKGROUND: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. OBJECTIVE: This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. METHODS: A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. RESULTS: Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool's promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. CONCLUSIONS: The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087.

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

Drive-through Satellite Testing: An Efficient Precautionary Method of Screening Patients for SARS-CoV-2 in a Rural Healthcare Setting.

The first documented case of COVID-19 in the United States occurred on January 30th, 2020. Soon after, a global pandemic was declared in March 2020 with each state issuing stay at home orders based on population, risk for community transmission and current number of positive cases. A priority for each region was to develop efficient systems for testing large patient volumes in a safe manner to reduce the risk of community transmission. A community based United States health care system in the upper mid-west implemented a drive through testing site in an attempt to divert suspected cases of COVID-19 away from larger patient areas while protecting staff and patients. This commentary outlines the planning, work flow and challenges of implementing this drive through testing site in a rural community setting.

Physical and Metabolic Changes after Ileal Pouch-Anal Anastomosis: A Case Study.

This case study examined changes in body composition, resting metabolic rate (RMR), aerobic capacity, and daily physical activity in a patient who had ulcerative colitis and underwent ileal pouch-anal anastomosis (IPAA) surgery. Body composition, RMR, and peak oxygen consumption (VO2peak) were assessed prior to surgery and four, eight, and 16 weeks after IPAA surgery. Daily physical activity data were extracted from a wrist-worn activity tracker preoperatively and 16 months postoperatively. At baseline, total body mass was 95.3 kg; body fat, 11.6%; lean body mass, 81.1 kg; RMR, 2416 kcal/d; and VO2peak, 42.7 mL/kg/min. All values decreased from baseline at four weeks postoperatively, body mass was 85.2 kg (-10.5%); body fat, 10.9% (-6.0%); lean body mass, 73.1 kg (-9.9%); RMR 2210 kcal/d (-8.5%) and VO2peak, 25.5 mL/kg/min (-40.3%). At 16 weeks postoperatively, most parameters were near their baseline levels (within 1-7%), exceptions were VO2peak, which was 20.4% below baseline, and RMR, which increased to nearly 20% above baseline. After the patient had IPAA surgery, his total and lean body masses, RMR, and aerobic capacity were markedly decreased. Daily physical activity decreased postoperatively and likely contributed to the decreased aerobic capacity, which may take longer to recover compared to body composition and RMR parameters.

Multi-ingredient pre-workout supplements, safety implications, and performance outcomes: a brief review.

In recent years, a new class of dietary supplements called multi-ingredient pre-workout supplements (MIPS) has increased in popularity. These supplements are intended to be taken prior to exercise and typically contain a blend of ingredients such as caffeine, creatine, beta-alanine, amino acids, and nitric oxide agents, the combination of which may elicit a synergistic effect on acute exercise performance and subsequent training adaptations compared to single ingredients alone. Therefore, the purpose of this article was to review the theoretical rationale and available scientific evidence assessing the potential ergogenic value of acute and chronic ingestion of MIPS, to address potential safety concerns surrounding MIPS supplementation, and to highlight potential areas for future research. Though direct comparisons between formulations of MIPS or between a MIPS and a single ingredient are challenging and often impossible due to the widespread use of "proprietary blends" that do not disclose specific amounts of ingredients in a given formulation, a substantial body of evidence suggests that the acute pre-exercise consumption of MIPS may positively influence muscular endurance and subjective mood, though mixed results have been reported regarding the acute effect of MIPS on force and power production. The chronic consumption of MIPS in conjunction with a periodized resistance training program appears to augment beneficial changes in body composition through increased lean mass accretion. However, the impact of long-term MIPS supplementation on force production, muscular endurance, aerobic performance, and subjective measures is less clear. MIPS ingestion appears to be relatively safe, though most studies that have assessed the safety of MIPS are relatively short (less than eight weeks) and thus more information is needed regarding the safety of long-term supplementation. As with any dietary supplement, the use of MIPS carries implications for the athlete, as many formulations may intentionally contain banned substances as ingredients or unintentionally as contaminants. We suggest that athletes thoroughly investigate the ingredients present in a given MIPS prior to consumption. In conclusion, it appears that multi-ingredient pre-workout supplements have promise as an ergogenic aid for active individuals, though further information is required regarding long-term efficacy and safety in a wider variety of populations.