International journal of medical informatics
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The rapid global spread of the SARS-CoV-2 virus has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources and design targeted policies for vulnerable subgroups have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. ⋯ Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 72 %, 79 %, 89 %, and 90 % for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization:age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression; (2) for mortality: age, immunosuppression, chronic renal insufficiency, obesity and diabetes; (3) for ICU need: development of pneumonia (if available), age, obesity, diabetes and hypertension; and (4) for ventilator need: ICU and pneumonia (if available), age, obesity, and hypertension.
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Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties, such as poor structure and heterogenicity of the raw EMR data, are encountered when introducing AI with ICU data. Labor-intensive work, including manual data entry, personal medical records sorting, and laboratory results interpretation may hinder the progress of AI. In this article, we introduce the developing of an AI algorithm designed for sepsis diagnosis using pre-selected features; and compare the performance of the AI algorithm with SOFA score based diagnostic method. ⋯ Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well.
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Observational Study
Physician use of speech recognition versus typing in clinical documentation: A controlled observational study.
Speech recognition (SR) is increasingly used directly by clinicians for electronic health record (EHR) documentation. Its usability and effect on quality and efficiency versus other documentation methods remain unclear. ⋯ Participants felt that SR saves them time, increases their efficiency and allows them to quickly document more relevant details. Quality analysis supports the perception that SR allows for more detailed notes, but whether dictation is objectively faster than typing remains unclear, and participants described some scenarios where typing is still preferred. Dictation can be effective for creating comprehensive documentation, especially when physicians like and feel comfortable using SR. Research is needed to further improve integration of SR with EHR systems and assess its impact on clinical practice, workflows, provider and patient experience, and costs.
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Neuropathic pain (NP) remains a major debilitating condition affecting more than 26% of breast cancer survivors worldwide. NP is diagnosed using a validated 10-items Douleur Neuropathique - 4 screening questionnaire which is administered 3 months after surgery and requires patient-doctor interaction. To develop an effective prognosis model admissible soon after surgery, without the need for patient-doctor interaction, we sought to [1] identify specific pain characteristics that can help determine which patients may be susceptible to NP after BC surgery, and 2) assess the utility of machine learning models developed in objective [1] as a knowledge discovery tool for downstream analysis. ⋯ Modern machine learning models show improvements over traditional least square regression in predicting of DN4-interview score. Penalized regression methods and the Gradient boosting model out-perform other models. As a predictor discovery tool, machine learning algorithms identify relevant predictors for DN4-interview score that remain statistically significant indicators of neuropathic pain in the classification model. Anxiety, type of surgery and acute pain on movement remain the most useful predictors for neuropathic pain.
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Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and complex medical histories, which can make disposition planning more challenging. Machine learning (ML) approaches have been previously used to inform decision-making surrounding ED disposition in the general population. However, little is known about the performance and utility of ML methods in predicting hospital admission among older ED patients. We applied a series of ML algorithms to predict ED admission in older adults and discuss their clinical and policy implications. ⋯ To the best of our knowledge, this is the first study to predict hospital admission in older ED patients using a series of geriatric syndromes and functional assessments. We were able to predict hospital admission in older ED patients with good accuracy using the items available in the interRAI ED Contact Assessment. This information can be used to inform decision-making about ED disposition and may expedite admission processes and proactive discharge planning.