Automating the Identification of Safety Events Involving Machine Learning-Enabled Medical Devices.

With growing use of machine learning (ML)-enabled medical devices by clinicians and consumers safety events involving these systems are emerging. Current analysis of safety events heavily relies on retrospective review by experts, which is time consuming and cost ineffective. This study develops automated text classifiers and evaluates their potential to identify rare ML safety events from the US FDA's MAUDE.

Using Clinical Simulation to Evaluate AI-Enabled Decision Support.

Clinical simulation is a useful method for evaluating AI-enabled clinical decision support (CDS). Simulation studies permit patient- and risk-free evaluation and far greater experimental control than is possible with clinical studies. The effect of CDS assisted and unassisted patient scenarios on meaningful downstream decisions and actions within the information value chain can be evaluated as outcome measures.

How Well Do AI-Enabled Decision Support Systems Perform in Clinical Settings?

Real-world performance of machine learning (ML) models is crucial for safely and effectively embedding them into clinical decision support (CDS) systems. We examined evidence about the performance of contemporary ML-based CDS in clinical settings. A systematic search of four bibliographic databases identified 32 studies over a 5-year period.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings.

Aims And Objectives: To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings.

Method: PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy.

Artificial intelligence in medicine: has the time come to hang up the stethoscope?

The question of whether the time has come to hang up the stethoscope is bound up in the promises of artificial intelligence (AI), promises that have so far proven difficult to deliver, perhaps because of the mismatch between the technical capability of AI and its use in real-world clinical settings. This perspective argues that it is time to move away from discussing the generalised promise of disembodied AI and focus on specifics. We need to focus on how the computational method underlying AI, i.

Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.

Objective: This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.

Materials And Methods: We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings.

More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA.

Objective: To examine the real-world safety problems involving machine learning (ML)-enabled medical devices.

Materials And Methods: We analyzed 266 safety events involving approved ML medical devices reported to the US FDA's MAUDE program between 2015 and October 2021. Events were reviewed against an existing framework for safety problems with Health IT to identify whether a reported problem was due to the ML device (device problem) or its use, and key contributors to the problem.

Emergency department and urgent care clinician perspectives on digital access to past medical histories.

Objective: To explore emergency department (ED) and urgent care (UC) clinicians' perceptions of digital access to patients' past medical history (PMH).

Methods: An 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.

How Well Do Computerised Decision Support Systems Cover Nursing Standards of Practice? A Literature Review.

The use of computerized decision support systems (DSS) in nursing practice is increasing. However, research about who uses DSS, where are they implemented, and how they are linked with standards of nursing is limited. This paper presents evidence on users and settings of DSS implementation, along with specific nursing standards of practice that are facilitated by such DSS.

Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes.

Objective: The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes.

Materials And Methods: We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects.

How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices.

Objective: To examine how and to what extent medical devices using machine learning (ML) support clinician decision making.

Methods: We 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.

Emergency care access to primary care records: an observational study.

Objective: To measure lookup rates of externally held primary care records accessed in emergency care and identify patient characteristics, conditions and potential consequences associated with access.

Measures: Rates of primary care record access and re-presentation to the emergency department (ED) within 30 days and hospital admission.

Design: A 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.

Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment.

Background: 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.

Reduced Verification of Medication Alerts Increases Prescribing Errors.

Objective: Clinicians using clinical decision support (CDS) to prescribe medications have an obligation to ensure that prescriptions are safe. One option is to verify the safety of prescriptions if there is uncertainty, for example, by using drug references. Supervisory control experiments in aviation and process control have associated errors, with reduced verification arising from overreliance on decision support.

The Effect of Cognitive Load and Task Complexity on Automation Bias in Electronic Prescribing.

Objective: Determine the relationship between cognitive load (CL) and automation bias (AB).

Background: Clinical decision support (CDS) for electronic prescribing can improve safety but introduces the risk of AB, where reliance on CDS replaces vigilance in information seeking and processing. We hypothesized high CL generated by high task complexity would increase AB errors.

Automation bias in electronic prescribing.

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.

Automation bias and verification complexity: a systematic review.

Introduction: While potentially reducing decision errors, decision support systems can introduce new types of errors. Automation bias (AB) happens when users become overreliant on decision support, which reduces vigilance in information seeking and processing. Most research originates from the human factors literature, where the prevailing view is that AB occurs only in multitasking environments.