International journal of medical informatics
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The COVID-19 pandemic and its socio-economic impacts have disrupted our health systems and society. We sought to examine informatics and digital health strategies that supported the primary care response to COVID-19 in Australia. Specifically, the review aims to answer: how Australian primary health care responded and adapted to COVID-19, the facilitators and inhibitors of the Primary care informatics and digital health enabled COVID-19 response and virtual models of care observed in Australia. ⋯ COVID-19 has transformed Australian primary care with the rapid adaptation of digital technologies to complement "in-person" primary care with telehealth and virtual models of care. The pandemic has also highlighted several literacy, maturity/readiness, and micro, meso and macro-organisational challenges with adopting and adapting telehealth to support integrated person-centred health care. There is a need for more research into how telehealth and virtual models of care can improve the access, integration, safety, and quality of virtual primary care.
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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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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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Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease. ⋯ Tongue features can improve the prediction accuracy of the diabetes risk prediction model formed by classical machine learning model significantly. In addition to the excellent performance, Stacking model and ResNet50 model which were recommended had non-invasive operation and were easy to use. Stacking model and ResNet50 model had high precision, low false positive rate and low misdiagnosis rate on detecting hyperglycemia. While on detecting blood glucose value in critical state, Stacking model and ResNet50 model had a high sensitivity, a low false negative rate and a low missed diagnosis rate. The study had proved that the differential changes of tongue features reflected the abnormal glucose metabolism, thus the diabetes risk prediction model formed by a combined TCM tongue diagnosis and machine learning technique was feasible.
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Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels. ⋯ Four machine learning models had good discriminative ability of triage. XGBoost demonstrated a slight advantage over other models. These models could be used for differential triage of low-risk patients and high-risk patients as a strategy to improve efficiency and allocation of finite resources.