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1.
Sensors (Basel) ; 23(2)2023 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-36679786

RESUMO

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.


Assuntos
Algoritmos , Sistemas de Apoio a Decisões Clínicas , Lógica Fuzzy , Aprendizado de Máquina
2.
Nutrients ; 15(2)2023 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-36678303

RESUMO

Prenatal health is important for both mother and child. Additionally, the offspring's development is affected by the mother's diet. The aim of this study was to assess whether a Clinical Decision Support System (CDSS) can improve adherence to the Mediterranean diet in early pregnancy and whether this change is accompanied by changes in nutritional status and psychological parameters. We designed a three month randomised controlled clinical trial which was applied to 40 healthy pregnant women (20 in the CDSS and 20 in the control group). Medical history, biochemical, anthropometric measurements, dietary, and a psychological distress assessment were applied before and at the end of the intervention. Pregnant women in the CDSS group experienced a greater increase in adherence to the Mediterranean diet, as assessed via MedDietScore, in the first trimester of their pregnancy compared to women in the control group (p < 0.01). Furthermore, an improved nutritional status was observed in pregnant women who were supported by CDSS. Anxiety and depression levels showed a greater reduction in the CDSS group compared to the control group (p = 0.048). In conclusion, support by a CDSS during the first trimester of pregnancy may be beneficial for the nutritional status of the mother, as well as for her anxiety and depression status.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Dieta Mediterrânea , Feminino , Humanos , Gravidez , Mães , Estado Nutricional , Gestantes
3.
BMC Med Educ ; 23(1): 16, 2023 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-36627640

RESUMO

BACKGROUND: Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. METHODS: A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. RESULTS: The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). CONCLUSIONS: This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.


Assuntos
Inteligência Artificial , Músculo Esquelético , Médicos , Humanos , Computadores , Pessoal de Saúde , Radiografia , Sistemas de Apoio a Decisões Clínicas , Músculo Esquelético/diagnóstico por imagem , Músculo Esquelético/lesões
4.
Int J Med Sci ; 20(1): 79-86, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36619220

RESUMO

Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Humanos , Inteligência Artificial , Oncologia/métodos , Tomada de Decisão Clínica/métodos , Prognóstico
5.
Health Informatics J ; 29(1): 14604582231152792, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36645733

RESUMO

OBJECTIVES: Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called "priority queue" in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. MATERIAL AND METHODS: We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. RESULT: The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. CONCLUSION: By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Telemedicina , Humanos , Computação em Nuvem
6.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 54(1): 208-216, 2023 Jan.
Artigo em Chinês | MEDLINE | ID: mdl-36647669

RESUMO

A clinical decision support system (CDSS) integrated with electronic health records helps physicians at the grassroots make patient-appropriate and evidence-based treatment decisions and improves the efficiency of diagnosis and treatment. Furthermore, using ontologies to build up the medical knowledge base and patient data for CDSS enhances the automation and transparency of the reasoning process of CDSS and helps generate interpretable and accurate treatment recommendations. Herein, we reviewed the relevant ontologies in the field of diabetes treatment and the progress and challenges concerning ontology-based CDSSs. Firstly, we elaborated on the current status and challenges of diabetes treatment in China, highlighting the urgent need to improve the efficiency and quality of medical services. Then, we presented background information about ontologies and gave an overview of the framework, methodology, and features of using ontologies to construct CDSS. After that, we reviewed the ontologies and instances of ontology-based CDSS in the field of diabetes treatment in China and abroad and summarized their construction methods and features. Last but not the least, we discussed the future prospects of the field, suggesting that integrating evidence-based medicine with ontologies to build a reliable clinical recommendation system should be the current focus of CDSS development.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Diabetes Mellitus , Humanos , Diabetes Mellitus/terapia , China
7.
BMC Prim Care ; 24(1): 23, 2023 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-36670354

RESUMO

BACKGROUND: Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow. METHODS: A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed. RESULTS: The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue". CONCLUSIONS: The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Medicina Geral , Clínicos Gerais , Humanos , Medicina de Família e Comunidade , Encaminhamento e Consulta
8.
Anesth Analg ; 136(2): 327-337, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36638512

RESUMO

BACKGROUND: Volatile anesthetic consumption can be reduced by minimizing excessive fresh gas flows (FGFs). Currently, it is unknown whether decision support tools embedded within commercial electronic health record systems can be successfully adopted to achieve long-term reductions in FGF rates. The authors describe the implementation of an electronic health record-based clinical decision support tool aimed at reducing FGF and evaluate the effectiveness of this intervention in achieving sustained reductions in FGF rates and volatile anesthetic consumption. METHODS: On August 29, 2018, we implemented a decision support tool within the Epic Anesthesia Information Management System (AIMS) to alert providers of high FGF (>0.7 L/min for desflurane and >1 L/min for sevoflurane) during maintenance of anesthesia. July 22, 2015, to July 10, 2018, served as our baseline period before the intervention. The intervention period spanned from August 29, 2018, to December 31, 2019. Our primary outcomes were mean FGF (L/min) and volatile agent consumption (mL/MAC-h). Because a simple comparison of 2 time periods may result in false conclusions due to underlying trends independent of the intervention, we performed segmented regression of the interrupted time series to assess the change in level at the start of the intervention and the differences in slopes before and after the intervention. The analysis was also adjusted for potential confounding variables. Data included 44,899 cases using sevoflurane preintervention with 26,911 cases postintervention, and 17,472 cases using desflurane with 1185 cases postintervention. RESULTS: Segmented regression of the interrupted times series demonstrated a decrease in mean FGF by 0.6 L/min (95% CI, 0.6-0.6 L/min; P < .0001) for sevoflurane and 0.2 L/min (95% CI, 0.2-0.3 L/min; P < .0001) for desflurane immediately after implementation of the intervention. For sevoflurane, mL/MAC-h decreased by 3.8 mL/MAC-h (95% CI, 3.6-4.1 mL/MAC-h; P < .0001) after implementation of the intervention and decreased by 4.1 mL/MAC-h (95% CI, 2.6-5.6 mL/MAC-h; P < .0001) for desflurane. Slopes for both FGF and mL/MAC-h in the postintervention period were statistically less negative than the preintervention slopes (P < .0001 for sevoflurane and P < .01 for desflurane). CONCLUSIONS: A commercial AIMS-based decision support tool can be adopted to change provider FGF management patterns and reduce volatile anesthetic consumption in a sustainable fashion.


Assuntos
Anestésicos Inalatórios , Sistemas de Apoio a Decisões Clínicas , Isoflurano , Éteres Metílicos , Sevoflurano , Desflurano , Registros Eletrônicos de Saúde , Anestesia por Inalação
9.
BMC Bioinformatics ; 24(1): 3, 2023 Jan 03.
Artigo em Inglês | MEDLINE | ID: mdl-36597033

RESUMO

PURPOSE: The objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger. DESIGN/METHODOLOGY/APPROACH: In the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track . FINDINGS: The experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature. ORIGINALITY/VALUE: This research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Medicina de Precisão , Reprodutibilidade dos Testes , Algoritmos , Aprendizado de Máquina
10.
Artif Intell Med ; 135: 102439, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36628797

RESUMO

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.


Assuntos
COVID-19 , Overdose de Opiáceos , Humanos , COVID-19/epidemiologia , Registros Eletrônicos de Saúde , Aprendizado de Máquina , Redes Neurais de Computação , Pandemias , Sistemas de Apoio a Decisões Clínicas
11.
Trials ; 24(1): 24, 2023 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-36635747

RESUMO

BACKGROUND: Osteoarthritis (OA) affects 20% of the adult Danish population, and the financial burden to society amounts to DKK 4.6 billion annually. Research suggests that up to 75% of surgical patients could have postponed an operation and managed with physical training. ERVIN.2 is an artificial intelligence (AI)-based clinical support system that addresses this problem by enhancing patient involvement in decisions concerning surgical knee and hip replacement. However, the clinical outcomes and cost-effectiveness of using such a system are scantily documented. OBJECTIVE: The primary objective is to investigate whether the usual care is non-inferior to ERVIN.2 supported care. The second objective is to determine if ERVIN.2 enhances clinical decision support and whether ERVIN.2 supported care is cost-effective. METHODS: This study used a single-centre, non-inferiority, randomised controlled in a two-arm parallel-group design. The study will be reported in compliance with CONSORT guidelines. The control group receives the usual care. As an add-on, the intervention group have access to baseline scores and predicted Oxford hip/knee scores and HRQoL for both the surgical and the non-surgical trajectory. A cost-utility analysis will be conducted alongside the trial using a hospital perspective, a 1-year time horizon and effects estimated using EQ-5D-3L. Results will be presented as cost per QALY gain. DISCUSSION: This study will bring knowledge about whether ERVIN.2 enhances clinical decision support, clinical effects, and cost-effectiveness of the AI system. The study design will not allow for the blinding of surgeons. TRIAL REGISTRATION: ClinicalTrials.gov NCT04332055 . Registered on 2 April 2020.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Osteoartrite , Adulto , Humanos , Inteligência Artificial , Articulação do Joelho/cirurgia , Análise Custo-Benefício , Ensaios Clínicos Controlados Aleatórios como Assunto
12.
BMC Med Inform Decis Mak ; 23(1): 5, 2023 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-36627624

RESUMO

BACKGROUND: Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because real-world EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use. METHODS: We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rule-based algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative. RESULTS: Evaluation of system adoption was performed by a mixed methods approach applying The Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system. CONCLUSIONS: Our combined machine learning and rule-based approach benefits system performance, efficiency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Hipersensibilidade , Humanos , Registros Eletrônicos de Saúde , Aprendizado de Máquina , Hospitais
13.
Cien Saude Colet ; 28(1): 181-196, 2023 Jan.
Artigo em Português, Inglês | MEDLINE | ID: mdl-36629563

RESUMO

This article seeks to identify and discuss evidence-informed options to address the judicialization of health. The Supporting Policy Relevant Reviews and Trials Tools were used to define the problem and the search strategy, which was carried out in the following databases: PubMed, Health Systems Evidence, Campbell, Cochrane Collaboration, Rx for Change Database, and PDQ-Evidence. Selection and assessment of methodological quality was performed by two independent reviewers. The results were presented in a narrative synthesis. This study selected 19 systematic reviews that pointed out four strategies to address the judicialization of health in Brazil: 1) Rapid response service, 2) Continuous education program, 3) Mediation service between the parties involved, and 4) Adoption of a computer-based, online decision-making support tool and patient-mediated interventions. This study therefore presented and characterized four options that can be considered to address the judicialization of health. The implementation of these options must ensure the participation of different actors, reflecting on different contexts and the impact on the health system. The availability of human and financial resources and the training of teams are critical points for the successful implementation of the options.


A fim de identificar e discutir opções informadas por evidências para abordar a judicialização da saúde no Brasil, utilizou-se as Ferramentas SUPPORT (Supporting Policy Relevant Reviews and Trials). A busca foi realizada nas bases PubMed; Health Systems Evidence; Campbell Collaboration; Cochrane Library; Rx for Change Database e PDQ-Evidence. A seleção e avaliação da qualidade metodológica foi feita por dois revisores independentes. Os resultados foram apresentados numa síntese narrativa. Dezenove revisões sistemáticas apontam quatro opções: 1) Serviço de respostas rápidas; 2) Programa de educação permanente; 3) Serviço de mediação entre as partes envolvidas; e 4) Adoção de ferramenta online (baseada em computador) de suporte à decisão e de intervenções mediadas por pacientes. Conclusões: Apresentamos e caracterizamos quatro opções que podem ser consideradas para abordar a judicialização da saúde. A implementação dessas opções deve garantir a participação de diferentes atores, refletindo sobre variados contextos. Recursos humanos e financeiros, capacitação das equipes, são os principais pontos críticos.


Assuntos
Política de Saúde , Saúde Pública , Humanos , Brasil , Saúde Pública/legislação & jurisprudência , Negociação , Tomada de Decisões , Sistemas de Apoio a Decisões Clínicas
14.
BMC Health Serv Res ; 23(1): 30, 2023 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-36639801

RESUMO

BACKGROUND: Electronic decision-making support systems (CDSSs) can support clinicians to make evidence-based, rational clinical decisions about patient management and have been effectively implemented in high-income settings. Integrated Management of Childhood Illness (IMCI) uses clinical algorithms to provide guidelines for management of sick children in primary health care clinics and is widely implemented in low income countries. A CDSS based on IMCI (eIMCI) was developed in South Africa. METHODS: We undertook a mixed methods study to prospectively explore experiences of implementation from the perspective of newly-trained eIMCI practitioners. eIMCI uptake was monitored throughout implementation. In-depth interviews (IDIs) were conducted with selected participants before and after training, after mentoring, and after 6 months implementation. Participants were then invited to participate in focus group discussions (FGDs) to provide further insights into barriers to eIMCI implementation. RESULTS: We conducted 36 IDIs with 9 participants between October 2020 and May 2021, and three FGDs with 11 participants in October 2021. Most participants spoke positively about eIMCI reporting that it was well received in the clinics, was simple to use, and improved the quality of clinical assessments. However, uptake of eIMCI across participating clinics was poor. Challenges reported included lack of computer skills which made simple tasks, like logging in or entering patient details, time consuming. Technical support was provided, but was time consuming to access so that eIMCI was sometimes unavailable. Other challenges included heavy workloads, and the perception that eIMCI took longer and disrupted participant's work. Poor alignment between recording requirements of eIMCI and other clinic programmes increased participant's administrative workload. All these factors were a disincentive to eIMCI uptake, frequently leading participants to revert to paper IMCI which was quicker and where they felt more confident. CONCLUSION: Despite the potential of CDSSs to increase adherence to guidelines and improve clinical management and prescribing practices in resource constrained settings where clinical support is scarce, they have not been widely implemented. Careful attention should be paid to the work environment, work flow and skills of health workers prior to implementation, and ongoing health system support is required if health workers are to adopt these approaches (350).


Assuntos
Sistemas de Apoio a Decisões Clínicas , Enfermeiras e Enfermeiros , Telemedicina , Criança , Humanos , África do Sul , Atenção Primária à Saúde
15.
BMJ Paediatr Open ; 7(1)2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36697035

RESUMO

BACKGROUND: Drug dosing errors are among the most frequent causes of preventable harm in paediatrics. Due to the complexity of paediatric pharmacotherapy and the working conditions in healthcare, it is not surprising that human factor is a well-described source of error. Thus, a clinical decision support system (CDSS) that supports healthcare professionals (HCP) during the dose prescribing step provides a promising strategy for error prevention. METHODS: The aim of the trial was to simulate the dose derivation step during the prescribing process. HCPs were asked to derive dosages for 18 hypothetical patient cases. We compared the CDSS PEDeDose, which provides a built-in dose calculator to the Summary of Product Characteristics (SmPC) used together with a pocket calculator in a randomised within-subject trial. We assessed the number of dose calculation errors and the time needed for calculation. Additionally, the effect of PEDeDose without using the built-in calculator but with a pocket calculator instead was assessed. RESULTS: A total of 52 HCPs participated in the trial. The OR for an erroneous dosage using the CDSS as compared with the SmPC with pocket calculator was 0.08 (95% CI 0.02 to 0.36, p<0.001). Thus, the odds of an error were 12 times higher while using the SmPC. Furthermore, there was a 45% (95% CI 39% to 51%, p<0.001) time reduction when the dosage was derived using the CDSS. The exploratory analysis revealed that using only PEDeDose but without the built-in calculator did not substantially reduce errors. CONCLUSION: Our results provide robust evidence that the use of the CDSS is safer and more efficient than manual dose derivation in paediatrics. Interestingly, only consulting a dosing database was not sufficient to substantially reduce errors. We are confident the CDSS PEDeDose ensures a higher safety and speeds up the prescribing process in practice.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Humanos , Criança , Erros de Medicação/prevenção & controle , Prescrições de Medicamentos , Instalações de Saúde , Pessoal de Saúde
16.
Ann Fam Med ; 21(1): 57-69, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36690490

RESUMO

PURPOSE: To identify and quantify the barriers and facilitators to the use of clinical decision support systems (CDSSs) by primary care professionals (PCPs). METHODS: A mixed-methods systematic review was conducted using a sequential synthesis design. PubMed/MEDLINE, PsycInfo, Embase, CINAHL, and the Cochrane library were searched in July 2021. Studies that evaluated CDSSs providing recommendations to PCPs and intended for use during a consultation were included. We excluded CDSSs used only by patients, described as concepts or prototypes, used with simulated cases, and decision supports not considered as CDSSs. A framework synthesis was performed according to the HOT-fit framework (Human, Organizational, Technology, Net Benefits), then a quantitative synthesis evaluated the impact of the HOT-fit categories on CDSS use. RESULTS: A total of 48 studies evaluating 45 CDSSs were included, and 186 main barriers or facilitators were identified. Qualitatively, barriers and facilitators were classified as human (eg, perceived usefulness), organizational (eg, disruption of usual workflow), and technological (eg, CDSS user-friendliness), with explanatory elements. The greatest barrier to using CDSSs was an increased workload. Quantitatively, the human and organizational factors had negative impacts on CDSS use, whereas the technological factor had a neutral impact and the net benefits dimension a positive impact. CONCLUSIONS: Our findings emphasize the need for CDSS developers to better address human and organizational issues, in addition to technological challenges. We inferred core CDSS features covering these 3 factors, expected to improve their usability in primary care.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Humanos , Pessoal de Saúde , Tecnologia , Atenção Primária à Saúde
17.
Res Social Adm Pharm ; 19(1): 144-154, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36088188

RESUMO

BACKGROUND: Computerized decision support systems (CDSSs) help hospital-based clinical pharmacists to perform medication reviews and so are promising tools for improving medication safety. However, their poor usability can reduce effectiveness and acceptability. OBJECTIVES: To evaluate the usability and perceived usefulness of a CDSS for medication review by hospital-based pharmacists and to draw up guidelines on improving its usability. METHODS: We performed a convergent, parallel evaluation. Firstly, three researchers conducted a heuristic evaluation of the CDSS. Secondly, clinical pharmacists who use the CDSS filled out the Usefulness, Satisfaction and Ease of Use (USE) questionnaire. Lastly, semi-structured interviews with the pharmacists enabled us to understand their opinions and experiences. The results of the heuristic evaluation were used to identify potential improvements in the CDSS. We performed a statistical analysis of the USE questionnaire data. Interviews were analyzed based on the unified theory of acceptance and use of technology (UTAUT), together with a task-technology fit model. The results generated by these three approaches were compared in order to determine convergences and divergences, identify challenges related to the usability and usefulness of the CDSS, and draw up guidelines for its improvement. RESULTS: Forty-seven usability problems were discovered; they variously concerned the graphical user interface, the pharmacists' needs, and the medication review model implemented in the CDSS. Only the "usefulness" dimension of the USE was not scored positively. All the UTAUT dimensions and the task-technology fit dimension emerged in the interviews. Cross-comparisons of the results from the three approaches led to the identification of four challenges and the corresponding formulation of 23 guidelines. CONCLUSIONS: The guidelines developed here should help to improve the design and acceptability of CDSSs. Hence, CDSSs will be able to assist clinical pharmacists more fully with their medication reviews and help to further improve patient safety.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Farmacêuticos , Humanos , Revisão de Medicamentos , Hospitais , Segurança do Paciente
18.
Pediatr Blood Cancer ; 70(1): e30070, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36326111

RESUMO

BACKGROUND: The Children's Oncology Group Long-Term Follow-Up Guidelines provide exposure-based risks and recommendations for late effects screening of survivors of childhood cancer. Passport for Care (PFC) is a web-based clinical decision support tool for generating a personalized survivorship care plan (SCP) derived from the Guidelines and user-entered exposures. We assessed PFC clinician user practices and perceptions of PFC impact on clinic workflow, guidelines application, and survivor shared decision-making. PROCEDURE: A 35-item REDCap survey was emailed to all PFC users (n = 936) in 146 current and former PFC user clinics. Anonymous responses were permitted. Results were summarized and compared with a 2012 survey. RESULTS: Data were available from 148 respondents representing 64 out of 146 PFC user clinics (minimum clinic response rate 44%, excluding 49 anonymous responses). Generation of a personalized SCP was the most common application of PFC, followed by determination of surveillance recommendations and use as a survivor database. Twenty-five respondents (17%) felt data entry was a significant or insurmountable barrier to PFC application. Sixty-nine percent of respondents attributed PFC with a very high/high impact on guidelines adherence in their clinical practice, compared with 40% who attributed PFC with having a significant impact on adherence in 2012 (p < .001). CONCLUSION: The survey results provide valuable insights on patterns of SCP delivery and Survivor Clinic workflow. User-perceived benefits to PFC included facilitating clinician ability to follow guidelines recommendations in clinical practice. Importantly, some barriers to resource utilization were also identified, suggesting a need for user-informed adaptations to further improve uptake.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Neoplasias , Criança , Humanos , Sobrevivência , Sobreviventes , Neoplasias/terapia , Internet
19.
J Alzheimers Dis ; 91(2): 895-909, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36502329

RESUMO

BACKGROUND: The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. OBJECTIVE: In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. METHODS: The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic. RESULTS: The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine. CONCLUSION: The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Sistemas de Apoio a Decisões Clínicas , Humanos , Doença de Alzheimer/diagnóstico , Doença de Alzheimer/psicologia , Disfunção Cognitiva/diagnóstico , Disfunção Cognitiva/psicologia , Memória de Curto Prazo , Rememoração Mental , Testes Neuropsicológicos
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