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ObjectiveTo examine how and to what extent medical devices using machine learning (ML) support clinician decision making.MethodsWe searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed.ResultsOf 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision.ConclusionLeveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians’ interactions with them.

Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored. We conducted a randomized controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention. In a 3-way, within-subject randomized design, doctors completed 9 clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (eg, random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and antihypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured. In total, 102 doctors from all 7 geographical regions of Indonesia participated. Most (n=85, 83%) participants were 26-35 years of age, and 57 (56%) were male, with a median of 6 (IQR 4.75) years of clinical experience. AI-based CDS improved risk assessment by 27% (χ22 (n=102)=48.875, P<.001) when compared to unassisted risk assessment, equating to 1 additional correct risk classification for every 3.7 patients where doctors used AI (number needed to treat=3.7, 95% CI 2.9-5.2). The prescription of statins also improved by 29% (χ22 (n=102)=36.608, P<.001). In pairwise comparisons, doctors who were assisted by the AI-based CDS correctly assessed significantly more cases (z=-5.602, n=102, adjusted P<.001) and prescribed the appropriate statin more often (z=-4.936, adjusted P<.001, medium effect size r=0.35) when compared with the control. AI-assisted cases required less time (estimated marginal means 63.6 s vs 72.8 s, F2, 772.8=5.710, P=.003). However, improvements in the prescription of aspirin and antihypertensives did not reach statistical significance (P=.08 and P=.30, respectively). No improvement was observed in referral decisions. Participants generally viewed AI-based CDS positively, with 81 (79%) agreeing or strongly agreeing that they would follow its recommendations and 82 (82%) indicating they would use it if given access. They believed CDS could enhance the efficiency of risk assessment, particularly in high-volume primary care settings, while noting the need to verify AI recommendations against clinical guidelines for each patient. Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of health care resources and doctors' time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.

ObjectiveTo explore emergency department (ED) and urgent care (UC) clinicians’ perceptions of digital access to patients’ past medical history (PMH).MethodsAn online survey compared anticipated and actual value of access to digital PMH. UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) was used to assess technology acceptance. Quantitative data were analysed using Mann-Whitney U tests and qualitative data were analysed using a general inductive approach.Results33 responses were received. 94% (16/17) of respondents with PMH access said they valued their PMH system and all respondents with no digital PMH access (100%; 16/16) said they believed access would be valuable. Both groups indicated a high level of technology acceptance across all UTAUT2 dimensions. Free-text responses suggested improvements such as increasing the number of patient records available, standardisation of information presentation, increased system reliability, expanded access to information and validation by authoritative/trusted sources.DiscussionNon-PMH respondents’ expectations were closely matched with the benefits obtained by PMH respondents. High levels of technology acceptance indicated a strong willingness to adopt. Clinicians appeared clear about the improvements they would like for PMH content and access. Policy implications include the need to focus on higher levels of patient participation, and increasing the breadth and depth of information and processes to ensure patient record curation and stewardship.ConclusionThere appears to be strong clinician support for digital access to PMH in ED and UC; however, current systems appear to have many shortcomings.

Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored. The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall. Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured. Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=-4.08, P<.001) and simple tasks (Z=-2.24, P=.03). Complex tasks took significantly longer to complete (Z=-2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=-3.30, P=.001). However, there was no effect on errors. Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.

BackgroundArtificial intelligence (AI) tools could assist emergency doctors interpreting chest X-rays to inform urgent care. However, the impact of AI assistance on clinical decision-making, a precursor to enhanced care and patient outcomes, remains understudied. This study evaluates the effect of AI assistance on clinical decisions of emergency doctors interpreting chest X-rays.MethodJunior and senior residents, emergency registrars and consultants working in Australian emergency departments were eligible. Doctors completed 18 clinical vignettes involving chest X-ray interpretation, representative of typical patient presentations. Vignettes were randomly selected from a bank of 49 based on the emergency medicine curriculum and contained a chest X-ray, presenting complaint, relevant symptoms and observations. Of the 18 vignettes, each doctor was randomly assigned to have half assisted by a commercial AI tool capable of detecting 124 different chest X-ray findings. Four vignettes contained X-rays known to produce incorrect AI findings. Primary outcomes were correct diagnosis and patient management. X-ray interpretation time, confidence of diagnosis, perceptions about the AI tool and the differential impact of AI assistance by seniority were also examined.Results200 doctors participated. AI assistance increased correct diagnosis by 5.9% (95% CI 2.7 to 9.2%) compared with unassisted vignettes, with the largest increase among senior residents (11.8%; 95% CI 5.2% to 18.3%). Patient management increased by 3.2% (95% CI 0.1% to 6.4%). Confidence in diagnosis increased by 5% (95% CI 3.4% to 6.6%; p<0.001) and interpretation time increased by 4.9 s (p=0.08). Incorrect AI findings decreased correct diagnosis by 1% for false-positive (p=0.9) and 9% for false-negative findings (p=0.1). Participants found the AI tool helpful for interpreting chest X-rays, highlighting missed findings, but were neutral on its accuracy.ConclusionImprovements in diagnosis and patient management without meaningful increases in interpretation time suggest AI assistance could benefit clinical decisions involving chest X-ray interpretation. Further studies are required to ascertain if such improvements translate to improved patient care.

Background Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. Methods One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. Results Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% ( p  < .0001, n  = 120), 46.6% ( p  < .0001, n  = 70), and 39.2% ( p  < .0001, n  = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% ( p  < .0001, n  = 120), 24.5% ( p  < .009, n  = 82), and 26.7% ( p  < .0001, n  = 120). Participants made commission errors, 65.8% ( p  < .0001, n  = 120), 53.5% ( p  < .0001, n  = 82), and 51.7% ( p  < .0001, n  = 120). Task complexity and interruptions had no impact on AB. Conclusions This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

ObjectiveTo measure lookup rates of externally held primary care records accessed in emergency care and identify patient characteristics, conditions and potential consequences associated with access.MeasuresRates of primary care record access and re-presentation to the emergency department (ED) within 30 days and hospital admission.DesignA retrospective observational study of 77 181 ED presentations over 4 years and 9 months, analysing 8184 index presentations in which patients’ primary care records were accessed from the ED. Data were compared with 17 449 randomly selected index control presentations. Analysis included propensity score matching for age and triage categories.Results6.3% of overall ED presentations triggered a lookup (rising to 8.3% in year 5); 83.1% of patients were only looked up once and 16.9% of patients looked up on multiple occasions. Lookup patients were on average 25 years older (z=−9.180, p<0.001, r=0.43). Patients with more urgent triage classifications had their records accessed more frequently (z=−36.47, p<0.001, r=0.23). Record access was associated with a significant but negligible increase in hospital admission (χ2 (1, n=13 120)=98.385, p<0.001, phi=0.087) and readmission within 30 days (χ2 (1, n=13 120)=86.288, p<0.001, phi=0.081).DiscussionEmergency care clinicians access primary care records more frequently for older patients or those in higher triage categories. Increased levels of inpatient admission and re-presentation within 30 days are likely linked to age and triage categories.ConclusionFurther studies should focus on the impact of record access on clinical and process outcomes and which record elements have the most utility to shape clinical decisions.

Crowe spent more than 12 years working in job shops, climbing his way up the ladder from machine operator to manager before taking on a job as a CNC machining instructor at a technical college near St. Louis. Visitors to IMTS can hear that message for themselves when Crowe takes the stage at the Job Shops Workshop - Day 1, September 12, in the first session on workforce retention in development. Jones says that outreach to students as young as elementary school age is vitally important, as research has shown that children form opinions about manufacturing and start plotting their career direction as young as fourth and fifth grades.

To get there, Russell focused on four key growth opportunities: 1.Tracking Leads to Mutual Understanding During the early days of the ownership transition, the need for an ERP solution was not obvious to all. Overall, new software has been critical to managing the company's growing workload, which consists of an average of 300 open jobs at any given time, Russell says. 2.Company Acquisitions With software and production processes under control, Russell set out to expand capabilities and capacity through acquisitions. [...]an appreciation of how multitasking machines can accelerate output may have been the biggest catalyst for Rolar's growth, Russell says.

Up-Front Automation Kippen got his start in the machining industry while living in the San Francisco Bay area where he started working at a job shop as a machine operator in 2009. [...]Kippen credits the software for saving approximately 75 hours per month in administrative work - savings that are further magnified by additional revenue from producing additional parts on the shop floor. 2. Setting Up Shop for CAM The Vermont facility's office area still reflects its heritage as a tractor repair shop, with various hooks and markings along the walls where engine belts were kept in stock and stout workbenches that were made for repair work.