International journal of medical informatics
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Emergency departments in the United Kingdom (UK) experience significant difficulties in achieving the 95% NHS access standard due to unforeseen variations in patient flow. In order to maximize efficiency and minimize clinical risk, better forecasting of patient demand is necessary. The objective is therefore to create a tool that accurately predicts attendance at emergency departments to support optimal planning of human and physical resources. ⋯ This paper described a heuristic-based fuzzy logic model for predicting emergency department attendances which could help resource allocation and reduce pressure on busy hospitals. Valid and reproducible prediction tools could be generated from these hospital data. The methodology had an acceptable accuracy over a relatively short time period, and could be used to assist better bed management, staffing and elective surgery scheduling. When compared to other prediction models usually applied for emergency department attendances prediction, the proposed heuristic model had better accuracy.
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Nursing triage documentation is the first free-form text data created at the start of an emergency department (ED) visit. These 1-3 unstructured sentences reflect the clinical impression of an experienced nurse and are key in gauging a patient's illness. We aimed to predict final ED disposition using three commonly-employed natural language processing (NLP) techniques of nursing triage notes in isolation from other data. ⋯ Nursing triage notes can be used to predict final ED patient disposition, even when used separately from other clinical information. These findings have substantial implications for future studies, suggesting that free text from medical records may be considered as a critical predictor in research of patient outcomes.
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Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers. ⋯ For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.
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The management of hypertrophic cardiomyopathy (HCM) patients requires the knowledge of risk factors associated with sudden cardiac death (SCD). SCD risk factors such as syncope and family history of SCD (FH-SCD) as well as family history of HCM (FH-HCM) are documented in electronic health records (EHRs) as clinical narratives. Automated extraction of risk factors from clinical narratives by natural language processing (NLP) may expedite management workflow of HCM patients. The aim of this study was to develop and deploy NLP algorithms for automated extraction of syncope, FH-SCD, and FH-HCM from clinical narratives. ⋯ Automated extraction of syncope, FH-SCD and FH-HCM using NLP is feasible and has promise to increase efficiency of workflow for providers managing HCM patients.
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Medical Information Technology may be understood as an interdisciplinary study of the conception, design, development, adoption and use of Information Technology (IT) innovations for healthcare provision, management and planning. Concerning the use of IT in reproductive health, the aim of the diverse range of currently available applications (apps) is to assist in family planning, antenatal, intrapartum and postpartum care, along with neonatal and infant healthcare. End users are healthcare workers or women. Studies evaluating the effectiveness of these solutions have demonstrated promising results reflecting adherence to healthcare services and recommendations, information on management and risk identification in pregnancy, improvement in women's satisfaction with healthcare received, in addition to financial benefits for the healthcare system. ⋯ The systematic review demonstrated that it is an arduous task to search for mobile digital solutions that meet the guidelines for clinical use during antenatal care. Although the apps analyzed have great potential for use in different contexts, the bulk of these software systems are unavailable for "prompt delivery", since the test version cannot be downloaded or access is restricted.