International journal of medical informatics
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The timely identification of patients for hospitalization in emergency departments (EDs) can facilitate efficient use of hospital resources. Machine learning can help the early prediction of ED disposition; however, application of machine learning models requires both computer science skills and domain knowledge. This presents a barrier for those who want to apply machine learning to real-world settings. ⋯ In comparison with the conventional approaches, the use of autoML improved the predictive ability for the need for hospitalization. The findings can optimize ED management, hospital-level resource utilization and improve quality. Furthermore, this approach can support the design of a more effective patient ED flow for pediatric asthma care.
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Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. ⋯ There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.
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Observational Study
An observational study of postoperative handoff standardization failures.
Patient handoffs from an operating room (OR) to an intensive care unit (ICU) require precise coordination among surgical, anesthesia, and critical care teams. Although several standardized handoff strategies have been developed, their sustainability remains is poor. Little is known regarding factors that impede handoff standardization. ⋯ Compliance failures are prevalent in all handoff phases, leading to poor adherence with standardization. We propose theoretically grounded guidelines for designing "flexibly standardized" bundled handoff interventions for ensuring care continuity in OR to ICU transitions of care.
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Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data. ⋯ Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
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Information technologies have been vital during the COVID-19 pandemic. Telehealth and telemedicine services, especially, fulfilled their promise by allowing patients to receive advice and care at a distance, making it safer for all concerned. Over the preceding years, professional societies, governments, and scholars examined ethical, legal, and social issues (ELSI) related to telemedicine and telehealth. Primary concerns evident from reviewing this literature have been quality of care, access, consent, and privacy. ⋯ Clinicians and organizations need updated guidelines for ethical use of telemedicine and telehealth care, and decision- and policy-makers need evidence to inform decisions. The variety of newly implemented telemedicine services is an on-going natural experiment presenting an unparalleled opportunity to develop an evidence-based way forward. The paper recommends evaluation using an applied ethics, context-sensitive approach that explores interactions among multiple factors and considerations. It suggests evaluation questions to investigate ethical, social, and legal issues through multi-method, sociotechnical, interpretive and ethnographic, and interactionist evaluation approaches. Such evaluation can help telehealth, and other information technologies, be integrated into healthcare ethically and effectively.