Health Informatics via Machine Learning for the Clinical Management of Patients

Clifton, David A.; Niehaus, Kate; Charlton, Peter; Colopy, Glen Wright

doi:10.15265/iy-2015-014

Cited by 21 publications

(7 citation statements)

References 59 publications

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“…Furthermore, the ability to make a prediction does not necessarily equate to effective decision-making by a policymaker, especially where the underlying assumptions of ML, required to make said prediction, are not fully understood (Athey, 2017). A review analysed literature on the application of ML methods with data collected over time, mainly during routine clinical care, and its impact on the clinical management of patients (Clifton et al, 2015). Generally, no effects on health outcomes were found.…”

Section: Using His and Big Data To Learn Understand And Inform Decision-makingmentioning

confidence: 99%

Developments in healthcare information systems in Ireland and internationally

Walsh¹,

Domhnaill²,

Mohan³

2021

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The mission of the Economic and Social Research Institute is to advance evidencebased policymaking that supports economic sustainability and social progress in Ireland. ESRI researchers apply the highest standards of academic excellence to challenges facing policymakers, focusing on 12 areas of critical importance to 21st Century Ireland.The Institute was founded in 1960 by a group of senior civil servants led by Dr T.K. Whitaker, who identified the need for independent and in-depth research analysis to provide a robust evidence base for policymaking in Ireland.Since then, the Institute has remained committed to independent research and its work is free of any expressed ideology or political position. The Institute publishes all research reaching the appropriate academic standard, irrespective of its findings or who funds the research.The quality of its research output is guaranteed by a rigorous peer review process. ESRI researchers are experts in their fields and are committed to producing work that meets the highest academic standards and practices.The work of the Institute is disseminated widely in books, journal articles and reports. ESRI publications are available to download, free of charge, from its website. Additionally, ESRI staff communicate research findings at regular conferences and seminars.The ESRI is a company limited by guarantee, answerable to its members and governed by a Council, comprising 14 members who represent a cross-section of ESRI members from academia, civil services, state agencies, businesses and civil society. The Institute receives an annual grant-in-aid from the Department of Public Expenditure and Reform to support the scientific and public interest elements of the Institute's activities; the grant accounted for an average of 30 per cent of the Institute's income over the lifetime of the last Research Strategy. The remaining funding comes from research programmes supported by government departments and agencies, public bodies and competitive research programmes.

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Section: Using His and Big Data To Learn Understand And Inform Decision-makingmentioning

confidence: 99%

Developments in healthcare information systems in Ireland and internationally

Walsh¹,

Domhnaill²,

Mohan³

2021

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“…This is due to hospitalised patients generally being admitted to wards that are capable of handling patients in their condition. It is also due to the fact that using machine learning for the monitoring of inpatients for degradation has a very rich literature and would require a review of it is own (Clifton et al 2015 ). As a result, we only focus on works that explicitly predict admissions or transferrals of patients.…”

Section: Intra-hospital Resource Managementmentioning

confidence: 99%

Machine learning in patient flow: a review

et al. 2021

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This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.

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“…It can be highly useful for a future application of pharmacogenetic testing in daily practice. [29][30][31][32] Although pharmacogenetic testing is a promising und evolving tool in precision medicine, pre-emptive testing, except for mandatory diagnostics for certain prescriptions, is not covered by insurance companies and not adequately used as standard of care in Germany in most cases. 33 Several studies indicate that pharmacogenetics can promote the reduction of healthcare costs by preventing ADRs and can increase patient's safety in therapy with drugs.…”

Section: Introductionmentioning

confidence: 99%

Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR

et al. 2020

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IntroductionPre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective.Methods and analysisThe EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment.Ethics and disseminationThe protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences.Trial registration numberGerman Clinical Trials Register, DRKS00013909.

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Health Informatics via Machine Learning for the Clinical Management of Patients

Abstract: Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Cited by 21 publications

References 59 publications

Developments in healthcare information systems in Ireland and internationally

Developments in healthcare information systems in Ireland and internationally

Machine learning in patient flow: a review

Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR

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