Does a mobile clinical decision-support system (CDSS) help improve the quality of primary health care?

2021 ◽  
Author(s):  
Agustín Ciapponi
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Primary Health

Advanced Issues of Health Informatics and Clinical Decision Support System in Global Health Care

2018 ◽  
pp. 153-170 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Global Health ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Health Informatics ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Global Health Care

This chapter indicates the advanced issues of health informatics; the advanced issues of Clinical Decision Support System (CDSS); CDSS and Computerized Physician Order Entry (CPOE); the false positive alerts in CDSS; and CDSS and biomedical engineering. Health informatics and CDSS are the advanced health care technologies with the support of many technological fields. Health informatics and CDSS apply various computerized devices to provide enhanced health-related outcomes in terms of problem solving, analytical thinking, and decision making. Health informatics and CDSS help clinicians and health care providers to make complex information useful in supporting clinical decisions, thus delivering the best standard of care for each patient. The chapter argues that utilizing health informatics and CDSS has the potential to increase health outcomes and reach strategic goals in global health care.

VP29 Designing A Mobile Clinical Decision Support System For Dementia

2019 ◽  
Vol 35 (S1) ◽  
pp. 83-83
Author(s):  
Noemí Robles ◽  
Carme Carrion i Ribas ◽  
Marta Aymerich
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Behavioral Interventions ◽  
Clinical Decision Support System ◽  
Scientific Evidence ◽  
Clinical Decision ◽  
Cognitive Therapies

IntroductionE-health offers the opportunity of supporting the management of several diseases, but most of these tools are far from being based on scientific evidence and demonstrating their effectiveness and efficacy. The PSICODEM Project aims to develop a mobile personalized clinical decision support system (CDSS) based on evidence for contributing to e-health interventions addressed to the management of dementia that require not only a pharmacological approach but also psychosocial interventions for improving patients’ quality of life and reducing emotional, cognitive and behavioral symptoms. The present communication focuses on the identification of the evidence on which the CDSS algorithm will be developed.MethodsThree systematic reviews were carried out in order to identify the existing scientific evidence published in relation to the effectiveness of behavioral, emotional and cognitive therapies addressing dementia (January 2009 to December 2017). The main databases were consulted (PubMed, Cochrane Library, PsychoInfo) and only randomized control trials (RCT) were considered. Articles were reviewed by two independent reviewers. The quality of the selected publications was assessed according to the SIGN criteria.ResultsForty-seven RCTs were selected for cognitive therapies, thirty-two for emotional ones and fifteen for behavioral interventions. Those therapies with more support of evidence were skills training for cognitive therapies and reminiscence interventions for emotional interventions; however, in behavioral interventions a variety of therapeutically approaches were found. Wide differences were found between studies in terms of types and levels of dementia, forms of intervention (number, length and frequency of sessions) and outcome measures.ConclusionsIn-depth analysis of evidence will allow the identification of those interventions more appropriate for each patient according to their symptoms and level of dementia. According to this evidence, the mobile CDSS algorithm will be developed. Additionally, these findings point out the gaps in psychosocial intervention research.

scholarly journals Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
David L. Chin ◽  
Michelle H. Wilson ◽  
Ashley S. Trask ◽  
Victoria T. Johnson ◽  
Brittanie I. Neaves ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
System Data

A Clinical Decision Support System

2016 ◽  
pp. 270-294 ◽  
Author(s):  
Jalel Akaichi ◽  
Linda Mhadhbi
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Medical Resources ◽  
Health Care Institutions ◽  
Patient State

Inadequate response and bad decisions taken by mobile physicians may lead to bad consequences threatening rescued people lives. Moreover, there are growing information that overload physicians when facing urgent cases. In order to facilitate the on road decision making for the mobile physicians, we propose a clinical decision support system based on an ontology driven approach for effective emergency management that allows finding out as quickly as possible the needed medical resources and reserves the most suitable health care institutions according to the patient state. Specifically, this work permits to localize rapidly the closets health care institution to the emergency scene, to find out the needed medical resources to deal with the patient first diagnosis, to match the localized health care institutions that contain the necessary medical resources to fulfil the patient determined needs, and to rank medical institutions, according to urgent case requirements, in order to allow the mobile physician to perform the adequate choice of one of them.

A Clinical Decision Support System

2016 ◽  
pp. 1589-1613
Author(s):  
Jalel Akaichi ◽  
Linda Mhadhbi
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Medical Resources ◽  
Health Care Institutions ◽  
Patient State

Inadequate response and bad decisions taken by mobile physicians may lead to bad consequences threatening rescued people lives. Moreover, there are growing information that overload physicians when facing urgent cases. In order to facilitate the on road decision making for the mobile physicians, we propose a clinical decision support system based on an ontology driven approach for effective emergency management that allows finding out as quickly as possible the needed medical resources and reserves the most suitable health care institutions according to the patient state. Specifically, this work permits to localize rapidly the closets health care institution to the emergency scene, to find out the needed medical resources to deal with the patient first diagnosis, to match the localized health care institutions that contain the necessary medical resources to fulfil the patient determined needs, and to rank medical institutions, according to urgent case requirements, in order to allow the mobile physician to perform the adequate choice of one of them.

Clinical Decision Support System for the Activity of Evidence based Computation

2020 ◽  
pp. 50-57
Author(s):  
Ali Кhusein ◽  
Urquhart A
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Future Research ◽  
Evidence Based

The application of the Clinical Decision Support Systems (CDSS) in the process of facilitating the activity of the evidence-centred treatment project effect enhances the quality of the healthcare services. The main purpose of this article is to define and illustrate the basis of the processes of the evidencecentred decision support tracking at the two thousand AMIA symposium spring. The analysis has been done on the basis of protocol issues when capturing the evidence-centred practices in machine interpretation and repositories for supporting and developing the CDSS for evidence-centred treatment. As a result, the research recommendations are based on five areas: capturing literature-centered and practice-centred evidence in the interpretation of machine knowledge and bases; creating maintainable methodological and technical elements for computer-centred decision support CDSS; assessing the medical costs and effects for clinical decision support system and the manner in which the systems affect the organizational best practices; disseminating and identifying the works based on work-flow sensitivity approach for the system and creating the public policy which will effectively provide the incentives meant to implement CDSS to enhance the quality of healthcare services. The paper is concluded with an assumption of evidence-based medicine aspect being strong. However, future research is still recommended in CDSS to potentially realize more defined benefits of the systems.

Web-Based Clinical Decision Support System for Triage of Vestibular Patients

2003 ◽  
Vol 128 (1) ◽  
pp. 48-53 ◽  
Author(s):  
William C. Kinney
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support System ◽  
Health Care System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Web Based ◽  
Care System

OBJECTIVES: We sought to use a clinical decision support system (CDSS) to save costs and to improve scheduling of vestibular patients in an otolaryngology clinic. STUDY DESIGN AND SETTING: We conducted a concurrent review of 50 vestibular patients scheduled in the University of Missouri otolaryngology clinic with or without testing based on the outcome of a CDSS. The CDSS was implemented using Web-based technology. Charges incurred by the health care system through tests determined by the CDSS were compared with those incurred using the standard procedure of ordering hearing tests and electronystagmography for all patients. RESULTS: Thirty-nine tests were prescheduled using the CDSS. Twenty-five additional tests were ordered after the visit. The CDSS resulted in savings of $37,904.00 in charges to the health care system. The CDSS showed high specificity and variable sensitivity. CONCLUSION: A Web-based CDSS can be used to better manage and coordinate patient encounters. SIGNIFICANCE: One important reason to use a CDSS in health care management is to lower costs.

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