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The Impact of Digital Technologies, Data Analytics and AI on Nursing Informatics:
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 4, 2022
Charlene H. Chu, Aaron Conway, Lindsay Jibb, Charlene E. Ronquillo
There are three broad categories in which data analysis of routinely collected healthcare data will prove particularly useful in the future: organizational performance, resource allocation and the integration of local data into clinical decision support systems (CDSS). At the organizational level, presentation of performance metrics in real time is a feature of analytics used in current nursing practice, for example, the creation of dashboards containing data visualizations to communicate real-time performance metrics. The analyses of routinely collected healthcare data like 30-day hospital readmissions and patient falls are common markers used to judge healthcare quality (Lambert et al., 2016). Predictive data analytics can also be applied for resource allocation, for example, predictive data analytics can provide insights into the optimal composition of nursing teams to improve the quality of care (Spetz, 2021). Lastly, Rajkomar and colleagues demonstrate how state-of-the-art, deep learning methods were able to integrate local data into the CDSS to predict mortality risk from raw EHR records. Such advancements can produce highly accurate insights and improve quality of care (Rajkomar et al., 2018).
Clinical Data Analytics
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
A clinical decision support system (CDSS) is an intelligent system that generates pertinent information based upon inputted patient data to suggest a course of actions. The action could be the diagnosis of an ailment, choice of a medication, a possible surgery, their combinations and the patient-care. CDSS is a special type of decision support system (DSS) that integrates knowledge management, intelligent inferencing, machine learning, data warehousing and electronic health records. The intelligent inferencing and machine learning techniques include cluster analysis, Bayesian decision network, uncertainty-based reasoning, data mining and content-based information retrieval from EHR. CDSS is needed to help the medical practitioners make a quick informed decision that involves handling larger amount of data and deduction using rule-based systems that can handle uncertainties and probability-based reasoning.
Swarm Intelligence and Evolutionary Algorithms for Heart Disease Diagnosis
Published in Sandeep Kumar, Anand Nayyar, Anand Paul, Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development, 2019
In this way, clinical decision support systems (CDSS) are well suitable and desired for the healthcare sector. Primarily, the medical sector involves voluminous amount of data and information referring to diversified medical scenarios and conditions. Usually, such huge amount of medical data is stored in dedicated storage bases. This is an added advantage to analyze the data within the dedicated medical domain. The healthcare process involves various phases namely screening of patients, diagnosis of diseases, treatment procedure for patients, and prognosis phases. Each of these phases generates huge data and has medical and health information buried within it [5,6].
Clinical Decision Support System (CDSS) in primary care: from pragmatic use to the best approach to assess their benefit/risk profile in clinical practice
Published in Current Medical Research and Opinion, 2022
Iacopo Cricelli, Ettore Marconi, Francesco Lapi
Clinical Decision Support Systems (CDSSs) are software-based tools intended to support physicians in clinical decision making. They are commonly administered using electronic healthcare records (EHR) interacting with other clinical workflows, which have been made easier by the growing adoption of EHR over the last decades1. In essence, several patient’s characteristics are matched to a computer-based clinical knowledge so notifying physicians on patient-specific actions to be taken2,3. There are several CDSS intended to primary care physicians, given that General Practitioners (GPs) have a multidimensional approach to their patients, who are usually over the sixties, suffer from concurrent chronic diseases and are exposed to polypharmacy. In this context, being compliant with evidence-based clinical practice guidelines (CPGs) may be heterogenous and unsatisfactory, so leading to suboptimal disease management. CDSSs operating on long-term follow-up are what mainly fits the GPs’ needs. However, the need of being compliant to Medical Device Regulation (MDR) raises several questions for public health authorities, clinicians, and researchers. Therefore, this manuscript aims to discuss an Italian experience regarding CDSSs and how to assess their evidence-based implementation according to the current MDR.
Analysis of medication patterns for pediatric asthma patients in emergency department: Does the sequence placement of glucocorticoids administration matter?
Published in Journal of Asthma, 2021
Hoon Jang, Mustafa Ozkaynak, Claudia R. Amura, Turgay Ayer, Marion R. Sills
Our approach and findings suggest that adding asthma medication sequence to clinical process improvement efforts may enhance ED care for asthma exacerbations. For example, an alert that reminds providers to order glucocorticoids after the second rescue medication is administered may improve the timeliness of glucocorticoid dosing within the asthma medication sequence, and thereby improve length of stay and admission rates (50). More generally, by considering first clinical interventions and real-time condition of the patient, clinical decision support systems (CDSS) can guide clinicians for the next steps. Temporal (e.g. sequential) perspectives provide critical insights for the development of CDSS by providing the needed information in a timely manner and at the right point in the clinical workflow (51,52).
SERIES: eHealth in primary care. Part 3: eHealth education in primary care
Published in European Journal of General Practice, 2020
Elisa J. F. Houwink, Marise J. Kasteleyn, Laurence Alpay, Christopher Pearce, Kerryn Butler-Henderson, Eline Meijer, Sanne van Kampen, Anke Versluis, Tobias N. Bonten, Jens H. van Dalfsen, Petra G. van Peet, Ybranda Koster, Beerend P. Hierck, Ilke Jeeninga, Sanne van Luenen, Rianne M. J. J. van der Kleij, Niels H. Chavannes, Anneke W. M. Kramer
Health care professionals often lack knowledge regarding existing eHealth applications [19–21]. This knowledge includes (1) what apps exist for what purpose, (2) whether they are safe, evidence-based and effective, and (3) how apps should be implemented in daily practice. Health care providers can benefit from skills to appraise those aspects. In the case of Mr Jones, several eHealth applications could support Dr Smith. For example, regarding the ‘inform’ domain, a website aimed at GPs covers genetics (www.huisartsengenetica.nl) and includes information on hereditary forms of high cholesterol and referral criteria to the department of Clinical Genetics [22,23]. Regarding ‘monitor and track,’ eHealth has proven effective in improving cholesterol levels [24]. An example of ‘interaction’ is Shared Decision Making between patient and health care provider. However, Groenhof et al., did not report a clear clinical benefit of clinical decision support systems (CDSS) in terms of cardiovascular risk factor levels and target attainment, and they concluded that some features of CDSS seem more promising than others.