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Pesquisa : L01.313.500.750.300.190 [Categoria DeCS]
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[PMID]:28455596
[Au] Autor:Koposov R; Fossum S; Frodl T; Nytrø Ø; Leventhal B; Sourander A; Quaglini S; Molteni M; de la Iglesia Vayá M; Prokosch HU; Barbarini N; Milham MP; Castellanos FX; Skokauskas N
[Ad] Endereço:Regional Centre for Children and Youth Mental Health and Welfare, Northern Norway, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway.
[Ti] Título:Clinical decision support systems in child and adolescent psychiatry: a systematic review.
[So] Source:Eur Child Adolesc Psychiatry;26(11):1309-1317, 2017 Nov.
[Is] ISSN:1435-165X
[Cp] País de publicação:Germany
[La] Idioma:eng
[Ab] Resumo:Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.
[Mh] Termos MeSH primário: Psiquiatria do Adolescente/normas
Psiquiatria Infantil/normas
Sistemas de Apoio a Decisões Clínicas/normas
[Mh] Termos MeSH secundário: Adolescente
Criança
Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170430
[St] Status:MEDLINE
[do] DOI:10.1007/s00787-017-0992-0


  2 / 6401 MEDLINE  
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[PMID]:28453645
[Au] Autor:Song H; Adamson A; Mostaghimi A
[Ad] Endereço:Harvard Medical School, Boston, Massachusetts.
[Ti] Título:Medicare Part D Payments for Topical Steroids: Rising Costs and Potential Savings.
[So] Source:JAMA Dermatol;153(8):755-759, 2017 Aug 01.
[Is] ISSN:2168-6084
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Importance: Rising pharmaceutical costs in the United States are an increasing source of financial burden for payers and patients. Although topical steroids are among the most commonly prescribed medications in dermatology, there are limited data on steroid-related spending and utilization. Objective: To characterize Medicare and patient out-of-pocket costs for topical steroids, and to model potential savings that could result from substitution of the cheapest topical steroid from the corresponding potency class. Design, Setting, and Participants: This study was a retrospective cost analysis of the Medicare Part D Prescriber Public Use File, which details annual drug utilization and spending on both generic and branded drugs from 2011 to 2015 by Medicare Part D participants who filled prescriptions for topical steroids. Main Outcomes and Measures: Total and potential Medicare and out-of-pocket patient spending. Costs were adjusted for inflation and reported in 2015 dollars. Results: Medicare Part D expenditures on topical steroids between 2011 and 2015 were $2.3 billion. Patients' out-of-pocket spending for topical steroids over the same period was $333.7 million. The total annual spending increased from $237.6 million to $775.9 million, an increase of 226.5%. Patients' annual out-of-pocket spending increased from $41.4 million to $101.8 million, an increase of 145.9%. The total number of prescriptions were 7.7 million in 2011 and 10.6 million in 2015, an increase of 37.0%. Generic medication costs accounted for 97.8% of the total spending during this time period. The potential health care savings and out-of-pocket patient savings from substitution of the cheapest topical steroid within the corresponding potency class were $944.8 million and $66.6 million, respectively. Conclusions and Relevance: Most topical steroids prescribed were generic drugs. There has been a sharp increase in Medicare and out-of-pocket spending on topical steroids that is driven by higher costs for generics. Use of clinical decision support tools to enable substitution of the most affordable generic topical steroid from the corresponding potency class may reduce drug expenditures.
[Mh] Termos MeSH primário: Medicamentos Genéricos/administração & dosagem
Financiamento Pessoal/economia
Glucocorticoides/administração & dosagem
Medicare Part D/economia
[Mh] Termos MeSH secundário: Administração Tópica
Custo Compartilhado de Seguro/economia
Custos e Análise de Custo
Sistemas de Apoio a Decisões Clínicas
Dermatologia
Custos de Medicamentos
Medicamentos Genéricos/economia
Glucocorticoides/economia
Seres Humanos
Estudos Retrospectivos
Estados Unidos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Drugs, Generic); 0 (Glucocorticoids)
[Em] Mês de entrada:1708
[Cu] Atualização por classe:180228
[Lr] Data última revisão:
180228
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:170429
[St] Status:MEDLINE
[do] DOI:10.1001/jamadermatol.2017.1130


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[PMID]:28471111
[Au] Autor:Hao SR; Geng SC; Fan LX; Chen JJ; Zhang Q; Li LJ
[Ad] Endereço:State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China.
[Ti] Título:Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.
[So] Source:J Zhejiang Univ Sci B;18(5):393-401, 2017 May.
[Is] ISSN:1862-1783
[Cp] País de publicação:China
[La] Idioma:eng
[Ab] Resumo:Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
[Mh] Termos MeSH primário: Algoritmos
Gráficos por Computador
Diagnóstico por Computador/métodos
Icterícia/diagnóstico
Aprendizado de Máquina
Modelos Estatísticos
[Mh] Termos MeSH secundário: Teorema de Bayes
Causalidade
Simulação por Computador
Sistemas de Apoio a Decisões Clínicas
Seres Humanos
Icterícia/epidemiologia
Prevalência
Reprodutibilidade dos Testes
Sensibilidade e Especificidade
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180226
[Lr] Data última revisão:
180226
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170505
[St] Status:MEDLINE
[do] DOI:10.1631/jzus.B1600273


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[PMID]:29260222
[Au] Autor:Cabitza F; Rasoini R; Gensini GF
[Ad] Endereço:Department of Informatics, University of Milano-Bicocca, Milan, Italy.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems-Reply.
[So] Source:JAMA;318(23):2356-2357, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16635


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[PMID]:29260219
[Au] Autor:Licitra L; Trama A; Hosni H
[Ad] Endereço:Fondazione IRCCS Istituto Nazionale dei Tumori, University of Milan, Milan, Italy.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems.
[So] Source:JAMA;318(23):2354, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16627


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[PMID]:29260218
[Au] Autor:Lasko TA; Walsh CG; Malin B
[Ad] Endereço:Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems.
[So] Source:JAMA;318(23):2355, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16623


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[PMID]:29260217
[Au] Autor:Berner ES; Ozaydin B
[Ad] Endereço:Department of Health Services Administration, University of Alabama, Birmingham.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems.
[So] Source:JAMA;318(23):2353-2354, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16619


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[PMID]:29260216
[Au] Autor:Fogel AL; Kvedar JC
[Ad] Endereço:Stanford University School of Medicine, Stanford, California.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems.
[So] Source:JAMA;318(23):2356, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16615


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[PMID]:29260215
[Au] Autor:Huesch MD
[Ad] Endereço:Department of Radiology, Milton S. Hershey Medical Center, Hershey, Pennsylvania.
[Ti] Título:Benefits and Risks of Machine Learning Decision Support Systems.
[So] Source:JAMA;318(23):2355-2356, 2017 12 19.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Sistemas de Apoio a Decisões Clínicas
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Sistemas Especialistas
Seres Humanos
Medição de Risco
[Pt] Tipo de publicação:LETTER; COMMENT
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171222
[Lr] Data última revisão:
171222
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.16611


  10 / 6401 MEDLINE  
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[PMID]:29194338
[Au] Autor:Bristol AA; Nibbelink CW; Gephart SM; Carrington JM
[Ad] Endereço:School of Nursing, Loma Linda University, Loma Linda, California (Dr Bristol); and College of Nursing, The University of Arizona, Tucson (Drs Nibbelink, Gephart, and Carrington).
[Ti] Título:Nurses' Use of Positive Deviance When Encountering Electronic Health Records-Related Unintended Consequences.
[So] Source:Nurs Adm Q;42(1):E1-E11, 2018 Jan/Mar.
[Is] ISSN:1550-5103
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:As organizations adopt electronic health records (EHRs), nurses frequently encounter system barriers and difficulty performing role expectations. This article describes nurses' experiences with unintended consequences emerging from the use of an EHR. In some situations, nurses were positively deviant when encountering unintended consequences relating to EHRs to accomplish patient care or protect patient safety. Nurses engaged in work-arounds to provide patient care when the EHR did not meet their needs, sometimes in positively deviant ways. Qualitative data were collected from 5 open-ended questions at the end of a quantitative survey. Analysis included coding of responses and organization of processes in line with the triangle model, a human factors framework, to identify overarching themes. Five themes emerged: (1) User support after implementation of EHR; (2) User satisfaction with EHR; (3) Communication for patient care, quality, and safety; (4) Effort to complete tasks; and (5) Areas for improvement. Nurses' ability to adopt positive deviance as they experience unintended consequences offers opportunities for organizations to engage nursing perspectives in improving the EHR and engineer it to be more resilient to nursing work.
[Mh] Termos MeSH primário: Atitude Frente aos Computadores
Registros Eletrônicos de Saúde
Processo de Enfermagem
Resolução de Problemas
[Mh] Termos MeSH secundário: Adulto
Idoso
Sistemas de Apoio a Decisões Clínicas
Feminino
Seres Humanos
Masculino
Meia-Idade
Informática em Enfermagem
Pesquisa Qualitativa
Inquéritos e Questionários
Fluxo de Trabalho
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171218
[Lr] Data última revisão:
171218
[Sb] Subgrupo de revista:N
[Da] Data de entrada para processamento:171202
[St] Status:MEDLINE
[do] DOI:10.1097/NAQ.0000000000000264



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