scholarly journals A Clinical Recommendation System to Maternity Care

2020 ◽  
pp. 1-20
Author(s):  
Eliana Pereira ◽  
Filipe Portela ◽  
António Abelha
Keyword(s):  
Health Professionals ◽  
Maternity Care ◽  
Recommendation System ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Triage System ◽  
Main Requirement ◽  
Simulation Techniques ◽  
Clinical Recommendation ◽  
Using Data

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, a pre-triage system was developed and implemented in Centro Hospitalar do Porto in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. At the end the main indicators achieved with this system are presented in the Business Intelligence Platform already deployed. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

scholarly journals A Clinical Recommendation System to Maternity Care

2016 ◽  
pp. 64-83 ◽  
Author(s):  
Eliana Pereira ◽  
Filipe Portela ◽  
António Abelha
Keyword(s):  
Health Professionals ◽  
Maternity Care ◽  
Recommendation System ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Triage System ◽  
Main Requirement ◽  
Simulation Techniques ◽  
Clinical Recommendation ◽  
Using Data

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, a pre-triage system was developed and implemented in Centro Hospitalar do Porto in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. At the end the main indicators achieved with this system are presented in the Business Intelligence Platform already deployed. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

scholarly journals Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: a systematic review of experimental and observational studies

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053886
Author(s):  
Teumzghi F Mebrahtu ◽  
Sarah Skyrme ◽  
Rebecca Randell ◽  
Anne-Maree Keenan ◽  
Karen Bloor ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Health Professionals ◽  
Comparative Research ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Link Type ◽  
Care Outcomes ◽  
Allied Health Professional

ObjectiveComputerised clinical decision support systems (CDSS) are an increasingly important part of nurse and allied health professional (AHP) roles in delivering healthcare. The impact of these technologies on these health professionals’ performance and patient outcomes has not been systematically reviewed. We aimed to conduct a systematic review to investigate this.Materials and methodsThe following bibliographic databases and grey literature sources were searched by an experienced Information Professional for published and unpublished research from inception to February 2021 without language restrictions: MEDLINE (Ovid), Embase Classic+Embase (Ovid), PsycINFO (Ovid), HMIC (Ovid), AMED (Allied and Complementary Medicine) (Ovid), CINAHL (EBSCO), Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), Social Sciences Citation Index Expanded (Clarivate), ProQuest Dissertations & Theses Abstracts & Index, ProQuest ASSIA (Applied Social Science Index and Abstract), Clinical Trials.gov, WHO International Clinical Trials Registry (ICTRP), Health Services Research Projects in Progress (HSRProj), OpenClinical(www.OpenClinical.org), OpenGrey (www.opengrey.eu), Health.IT.gov, Agency for Healthcare Research and Quality (www.ahrq.gov). Any comparative research studies comparing CDSS with usual care were eligible for inclusion.ResultsA total of 36 106 non-duplicate records were identified. Of 35 included studies: 28 were randomised trials, three controlled-before-and-after studies, three interrupted-time-series and one non-randomised trial. There were ~1318 health professionals and ~67 595 patient participants in the studies. Most studies focused on nurse decision-makers (71%) or paramedics (5.7%). CDSS as a standalone Personal Computer/LAPTOP-technology was a feature of 88.7% of the studies; only 8.6% of the studies involved ‘smart’ mobile/handheld-technology.DiscussionCDSS impacted 38% of the outcome measures used positively. Care processes were better in 47% of the measures adopted; examples included, nurses’ adherence to hand disinfection guidance, insulin dosing, on-time blood sampling and documenting care. Patient care outcomes in 40.7% of indicators were better; examples included, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity and triaging appropriateness.ConclusionCDSS may have a positive impact on selected aspects of nurses’ and AHPs’ performance and care outcomes. However, comparative research is generally low quality, with a wide range of heterogeneous outcomes. After more than 13 years of synthesised research into CDSS in healthcare professions other than medicine, the need for better quality evaluative research remains as pressing.

Adjustment of Medical Observations Influenced by Emotional State

2018 ◽  
Vol 9 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Firas Zekri ◽  
Afef Samet Ellouze ◽  
Rafik Bouaziz
Keyword(s):  
Decision Support ◽  
Fuzzy Systems ◽  
Emotional State ◽  
Strong Dependence ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Observation ◽  
Using Data

Research in neurophysiology and neuropsychology have established a strong dependence between emotion, subjectivity and decision-making. Otherwise, medical observations are used as one of the main inputs of clinical decision support systems (CDSS) which are designed to support patients with chronic progressive diseases. However, these observations are influenced when confronted with a critical emotional state and they are likely to be subjective. To generate efficient results, CDSS must bring these subjective observations closer to the reality by using data describing the observer's emotional state. To solve this issue, the authors of this article propose to identify the dependency relationship between observations and emotions. Then they provide a solution that moderates the patient and caregivers' observations within a medical decision support system, so that it can generate efficient results. Finally, they propose two fuzzy systems to adjust the influence of emotional state on medical observation. These two systems make the medical observation closer to the current condition of the patient.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

scholarly journals Clinician acceptance of complex clinical decision support systems for treatment allocation of patients with chronic low back pain

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stephanie Jansen-Kosterink ◽  
Lex van Velsen ◽  
Miriam Cabrita
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Low Back ◽  
Research Model ◽  
Online Questionnaire ◽  
Treatment Allocation

Abstract Background The uptake of complex clinical decision support systems (CDSS) in daily practice remains low, despite the proven potential to reduce medical errors and to improve the quality of care. To improve successful implementation of a complex CDSS this study aims to identify the factors that hinder, or alleviate the acceptance of, clinicians toward the use of a complex CDSS for treatment allocation of patients with chronic low back pain. Methods We tested a research model in which the intention to use a CDSS by clinicians is influenced by the perceived usefulness; this usefulness, in turn is influenced by the perceived service benefits and perceived service risks. An online survey was created to test our research model and the data was analysed using Partial Least Squares Structural Equation Modelling. The study population consisted of clinicians. The online questionnaire started with demographic questions and continued with a video animation of the complex CDSS followed by the set of measurement items. The online questionnaire ended with two open questions enquiring the reasons to use and not use, a complex CDSS. Results Ninety-eight participants (46% general practitioners, 25% primary care physical therapists, and 29% clinicians at a rehabilitation centre) fully completed the questionnaire. Fifty-two percent of the respondents were male. The average age was 48 years (SD ± 12.2). The causal model suggests that perceived usefulness is the main factor contributing to the intention to use a complex CDSS. Perceived service benefits and risks are both significant antecedents of perceived usefulness and perceived service risks are affected by the perceived threat to autonomy and trusting beliefs, particularly benevolence and competence. Conclusions To improve the acceptance of complex CDSSs it is important to address the risks, but the main focus during the implementation phase should be on the expected improvements in patient outcomes and the overall gain for clinicians. Our results will help the development of complex CDSSs that fit more into the daily clinical practice of clinicians.

Evaluation and cost estimation of laboratory tests overuse through a CCDSS in a university hospital

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Vallone ◽  
A Tamburrano ◽  
C Carrozza ◽  
A Urbani ◽  
A Cambieri ◽  
...  
Keyword(s):  
Cost Estimation ◽  
Laboratory Tests ◽  
Quality Care ◽  
Clinical Decision Support Systems ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
University Hospital ◽  
Specific Patient ◽  
Global Rate ◽  
Common Laboratory

Abstract Computerized Clinical Decision Support Systems (CCDSS) are information technology-based systems that use specific patient characteristics and combine them with rule-based algorithms. The aim of this study is to conduct a survey to measure and assess the over-utilization rates of laboratory requests and to estimate the monthly cost of inappropriate requests in inpatients of the “Fondazione Policlinico Universitario A. Gemelli IRCCS” Care Units. This observational study is based on the count of rules violations for 43 different types of laboratory tests requested by the Hospital physicians, for a total of 5,716,370 requests, over a continuous period of 20 months (from 1 July 2016 to 28 February 2018). Requests from all the hospital internal departments (except for Emergency, Intensive Care Units and Urgent requests) were monitored. The software intercepted and counted, in silent mode for the operator, all requests and violations for each laboratory test among those identified. During the observation period a mean of 285,819 requests per month were analyzed and 40,462 violations were counted. The global rate of overuse was 15.2% ± 3.0%. The overall difference among sub-groups was significant (p < 0.001). The most inappropriate exams were Alpha Fetoprotein (85.8% ± 30.5%), Chlamydia trachomatis PCR (48.7% ± 8.8%) and Alkaline Phosphatase (20.3% ± 6.5%). All the exams, globally considered, generated an estimated avoidable cost of 1,719,337€ (85,967€ per month) for the hospital. This study reports rates (15.2%) similar to other works. The real impact of inappropriateness is difficult to assess, but the generated costs for patients, hospitals and health systems are certainly high and not negligible. Key messages It would be desirable for international medical communities to produce a complete panel of prescriptive rules for all the most common laboratory exam. That is useful not only to reduce costs, but also to ensure standardization and high-quality care.

Sign in / Sign up

Export Citation Format

Share Document