Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 8.394
Filtrar
1.
PLoS One ; 16(1): e0245296, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33434221

RESUMO

BACKGROUND: Treatment of severely ill COVID-19 patients requires simultaneous management of oxygenation and inflammation without compromising viral clearance. While multiple tools are available to aid oxygenation, data supporting immune biomarkers for monitoring the host-pathogen interaction across disease stages and for titrating immunomodulatory therapy is lacking. METHODS: In this single-center cohort study, we used an immunoassay platform that enables rapid and quantitative measurement of interferon γ-induced protein 10 (IP-10), a host protein involved in lung injury from virus-induced hyperinflammation. A dynamic clinical decision support protocol was followed to manage patients infected with severe acute respiratory syndrome coronavirus 2 and examine the potential utility of timely and serial measurements of IP-10 as tool in regulating inflammation. RESULTS: Overall, 502 IP-10 measurements were performed on 52 patients between 7 April and 10 May 2020, with 12 patients admitted to the intensive care unit. IP-10 levels correlated with COVID-19 severity scores and admission to the intensive care unit. Among patients in the intensive care unit, the number of days with IP-10 levels exceeding 1,000 pg/mL was associated with mortality. Administration of corticosteroid immunomodulatory therapy decreased IP-10 levels significantly. Only two patients presented with subsequent IP-10 flare-ups exceeding 1,000 pg/mL and died of COVID-19-related complications. CONCLUSIONS: Serial and readily available IP-10 measurements potentially represent an actionable aid in managing inflammation in COVID-19 patients and therapeutic decision-making. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04389645, retrospectively registered on May 15, 2020.


Assuntos
/sangue , Quimiocina CXCL10/sangue , Sistemas de Apoio a Decisões Clínicas , Adulto , Idoso , Idoso de 80 Anos ou mais , Biomarcadores/sangue , /terapia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Guias de Prática Clínica como Assunto
2.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-33404516

RESUMO

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Assuntos
/diagnóstico , Sistemas de Apoio a Decisões Clínicas , Saúde , Aprendizado de Máquina , Pneumonia Viral/diagnóstico , /diagnóstico por imagem , Diagnóstico Diferencial , Humanos , Pessoa de Meia-Idade , Pneumonia Viral/diagnóstico por imagem , Máquina de Vetores de Suporte , Tomografia Computadorizada por Raios X
3.
BMC Med Inform Decis Mak ; 20(1): 310, 2020 11 30.
Artigo em Inglês | MEDLINE | ID: mdl-33256715

RESUMO

BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. METHODS: Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the "Principles of Biomedical Ethics" by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. RESULTS: Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. CONCLUSIONS: To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.


Assuntos
Inteligência Artificial , Sistemas de Apoio a Decisões Clínicas , Assistência à Saúde , Instalações de Saúde , Humanos , Consentimento Livre e Esclarecido
4.
J Med Internet Res ; 22(12): e24478, 2020 12 16.
Artigo em Inglês | MEDLINE | ID: mdl-33301417

RESUMO

BACKGROUND: Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care. OBJECTIVE: The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model. METHODS: We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19-compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020. RESULTS: We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged <60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. CONCLUSIONS: Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.


Assuntos
/métodos , /epidemiologia , Sistemas de Apoio a Decisões Clínicas , Aprendizado de Máquina , Avaliação de Sintomas , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Benchmarking , California/epidemiologia , Comorbidade , Tosse , Feminino , Febre , Humanos , Masculino , Pessoa de Meia-Idade , Prevalência , Probabilidade , Risco
5.
BMJ Open ; 10(12): e041004, 2020 12 10.
Artigo em Inglês | MEDLINE | ID: mdl-33303453

RESUMO

OBJECTIVES: Rapid detection and surveillance of COVID-19 is essential to reducing spread of the virus. Inadequate screening capacity has hampered COVID-19 detection, while traditional infectious disease response has been delayed due to significant demands for healthcare resources, time and personnel. This study investigated whether an online health decision-support tool could supplement COVID-19 surveillance and detection in China and the USA. SETTING: Daily website traffic to Thermia was collected from China and the USA, and cross-correlation analyses were used to assess the designated lag time between the daily time series of Thermia sessions and COVID-19 case counts from 22 January to 23 April 2020. PARTICIPANTS: Thermia is a validated health decision-support tool that was modified to include content aimed at educating users about Centers for Disease Control and Prevention recommendations on COVID-19 symptoms. An advertising campaign was released on Microsoft Advertising to refer searches for COVID-19 symptoms to Thermia. RESULTS: The lead times observed for Thermia sessions to COVID-19 case reports was 3 days in China and 19 days in the USA. We found negative cross-correlation between the number of Thermia sessions and rates of influenza A and B, possibly due to the decreasing prevalence of influenza and the lack of specificity of the system for identification of COVID-19. CONCLUSION: This study suggests that early deployment of an online campaign and modified health decision-support tool may support identification of emerging infectious diseases like COVID-19. Researchers and public health officials should deploy web campaigns as early as possible in an epidemic to detect, identify and engage those potentially at risk to help prevent transmission of the disease.


Assuntos
/epidemiologia , Sistemas de Apoio a Decisões Clínicas , Promoção da Saúde , Internet , Vigilância da População/métodos , Publicidade , /prevenção & controle , China/epidemiologia , Diagnóstico Precoce , Humanos , Estados Unidos/epidemiologia
6.
BMC Fam Pract ; 21(1): 271, 2020 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-33339508

RESUMO

BACKGROUND: The new coronavirus pneumonia (NCP) caused by COVID-19 has affected more than 46 million people worldwide. In China, primary care has played a vital role during the COVID-19 outbreak, and it is important to examine the challenges faced by general practitioners (GPs). This study investigated the roles, preparedness and training needs of GPs in China in managing the NCP outbreak. Based on the outcomes of the study, we hope to take lessons and identify how GPs could be supported in delivering their gatekeeping roles and clinical duties in times of infectious disease outbreak. METHODS: An online survey on the official website of Shenzhen Continuing Education Center. It included questions on GPs' demographics, their awareness of COVID-19 and their preparedness in managing suspected cases of NCP, as well as referrals and their training needs. Conditional multi-variate logistic models were used to investigate the relationships between GPs' preparedness, situational confidence and anxiety. RESULTS: GPs' clinical practice was significantly affected. GPs endeavoured to answer a flood of COVID-19-related enquiries, while undertaking community preventive tasks. In addition to in-person consultations, GP promoted COVID-19 awareness and education through telephone consultations, physical posters and social media. Overall GPs in Shenzhen felt well supported with adequate Personal Protective Equipment (PPE) and resources from secondary care services. Higher levels of self-perceived preparedness (OR = 2.19; 95%CI, 1.04-4.61), lower level of anxiety (OR = 0.56; 95%CI, 0.29-1.09) and fewer perceived family worries (OR = 0.37; 95%CI, 0.12-1.12) were associated with better confidence in coping at work. CONCLUSIONS: Training and supporting GPs while reducing their (and their families') anxiety increase their confidence in delivering the important roles of gatekeeping in face of major disease outbreaks.


Assuntos
/prevenção & controle , Planejamento em Desastres/organização & administração , Surtos de Doenças/prevenção & controle , Padrões de Prática Médica/organização & administração , Atenção Primária à Saúde/organização & administração , /epidemiologia , China , Sistemas de Apoio a Decisões Clínicas , Humanos , Disseminação de Informação , Equipamento de Proteção Individual/estatística & dados numéricos , Saúde Pública
7.
Zhonghua Yi Xue Za Zhi ; 100(48): 3870-3873, 2020 Dec 29.
Artigo em Chinês | MEDLINE | ID: mdl-33371633

RESUMO

Objective: To apply artificial intelligence technology in clinical real-world data of patients with primary hepatocellular carcinoma, explore the precise treatment of disease and build up artificial intelligence-based clinical decision support system. Methods: A total of 5 642 patients with primary hepatocellular carcinoma admitted to West China Hospital from July 2004 to June 2016 with complete follow-up records were included in the study. A merged model composed of multiple sub-classifiers was adopted to calculate therapy recommendation coefficient, and receiver operator characteristic curve was analyzed. Survival risk and recurrence risk were predicted by DeepSurv algorithm, and Kaplan-Meier survival curves were further compared among low, middle and high risk groups. Siamese-Net was applied to find similar patients. Results: The Top-1 and Top-2 accuracy of therapy recommendation coefficient reached 82.36% and 94.13% respectively. In internal verification of West China Hospital, the above-mentioned value reached 95.10% in accordance with multi-disciplinary team results. The C-index derived from survival risk model was 0.735 (95%CI:0.70-0.77), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (P<0.001). Meanwhile, the C-index derived from recurrence risk model was 0.705 (95%CI:0.68-0.73), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (P<0.001). Conclusions: The artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma has can accurately make therapy recommendation and prognosis prediction for primary hepatocellular carcinoma.


Assuntos
Carcinoma Hepatocelular , Sistemas de Apoio a Decisões Clínicas , Neoplasias Hepáticas , Inteligência Artificial , Carcinoma Hepatocelular/terapia , China , Humanos , Estimativa de Kaplan-Meier , Neoplasias Hepáticas/terapia , Prognóstico , Estudos Retrospectivos
8.
BMC Med Inform Decis Mak ; 20(1): 325, 2020 12 10.
Artigo em Inglês | MEDLINE | ID: mdl-33302942

RESUMO

BACKGROUND: Healthcare is a rapidly expanding area of application for Artificial Intelligence (AI). Although there is considerable excitement about its potential, there are also substantial concerns about the negative impacts of these technologies. Since screening and diagnostic AI tools now have the potential to fundamentally change the healthcare landscape, it is important to understand how these tools are being represented to the public via the media. METHODS: Using a framing theory approach, we analysed how screening and diagnostic AI was represented in the media and the frequency with which media articles addressed the benefits and the ethical, legal, and social implications (ELSIs) of screening and diagnostic AI. RESULTS: All the media articles coded (n = 136) fit into at least one of three frames: social progress (n = 131), economic development (n = 59), and alternative perspectives (n = 9). Most of the articles were positively framed, with 135 of the articles discussing benefits of screening and diagnostic AI, and only 9 articles discussing the ethical, legal, and social implications. CONCLUSIONS: We found that media reporting of screening and diagnostic AI predominantly framed the technology as a source of social progress and economic development. Screening and diagnostic AI may be represented more positively in the mass media than AI in general. This represents an opportunity for health journalists to provide publics with deeper analysis of the ethical, legal, and social implications of screening and diagnostic AI, and to do so now before these technologies become firmly embedded in everyday healthcare delivery.


Assuntos
Inteligência Artificial , Assistência à Saúde/ética , Assistência à Saúde/normas , Meios de Comunicação de Massa , Programas de Rastreamento/métodos , Sistemas de Apoio a Decisões Clínicas , Assistência à Saúde/métodos , Ética , Instalações de Saúde , Humanos
9.
BMC Med Inform Decis Mak ; 20(1): 294, 2020 11 16.
Artigo em Inglês | MEDLINE | ID: mdl-33198720

RESUMO

BACKGROUND: Evidence-based information available at the point of care improves patient care outcomes. Online knowledge bases can increase the application of evidence-based medicine and influence patient outcome data which may be captured in quality registries. The aim of this study was to explore the effect of use of an online knowledge base on patient experiences and health care quality. METHODS: The study was conducted as a retrospective, observational study of 24 primary health care centers in Sweden exploring their use of an online knowledge base. Frequency of use was compared to patient outcomes in two national quality registries. A socio-economic Care Need Index was applied to assess whether the burden of care influenced the results from those quality registries. Non-parametric statistical methods and linear regression were used. RESULTS: Frequency of knowledge base use showed two groups: frequent and non-frequent users, with a significant use difference between the groups (p < 0.001). Outcome data showed significant higher values for all seven National Primary Care Patient Survey dimensions in the frequent compared to the non-frequent knowledge base users (p < 0.001), whereas 10 out of 11 parameters in the National Diabetes Register showed no differences between the groups (p > 0.05). Adjusting for Care Need Index had almost no effect on the outcomes for the groups. CONCLUSIONS: Frequent users of a national online knowledge base received higher ratings on patient experiences, but figures on health care quality in diabetes showed near to no correlation. The findings indicate that some effects may be attributed to the use of knowledge bases and requires a controlled evaluation.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Sistemas de Informação , Atenção Primária à Saúde , Qualidade da Assistência à Saúde , Humanos , Bases de Conhecimento , Estudos Retrospectivos , Suécia
10.
BMC Med Inform Decis Mak ; 20(1): 287, 2020 11 04.
Artigo em Inglês | MEDLINE | ID: mdl-33148237

RESUMO

BACKGROUND: Acute kidney injury (AKI) is common in hospitalized patients and is associated with poor patient outcomes and high costs of care. The implementation of clinical decision support tools within electronic medical record (EMR) could improve AKI care and outcomes. While clinical decision support tools have the potential to enhance recognition and management of AKI, there is limited description in the literature of how these tools were developed and whether they meet end-user expectations. METHODS: We developed and evaluated the content, acceptability, and usability of electronic clinical decision support tools for AKI care. Multi-component tools were developed within a hospital EMR (Sunrise Clinical Manager™, Allscripts Healthcare Solutions Inc.) currently deployed in Calgary, Alberta, and included: AKI stage alerts, AKI adverse medication warnings, AKI clinical summary dashboard, and an AKI order set. The clinical decision support was developed for use by multiple healthcare providers at the time and point of care on general medical and surgical units. Functional and usability testing for the alerts and clinical summary dashboard was conducted via in-person evaluation sessions, interviews, and surveys of care providers. Formal user acceptance testing with clinical end-users, including physicians and nursing staff, was conducted to evaluate the AKI order set. RESULTS: Considerations for appropriate deployment of both non-disruptive and interruptive functions was important to gain acceptability by clinicians. Functional testing and usability surveys for the alerts and clinical summary dashboard indicated that the tools were operating as desired and 74% (17/23) of surveyed healthcare providers reported that these tools were easy to use and could be learned quickly. Over three-quarters of providers (18/23) reported that they would utilize the tools in their practice. Three-quarters of the participants (13/17) in user acceptance testing agreed that recommendations within the order set were useful. Overall, 88% (15/17) believed that the order set would improve the care and management of AKI patients. CONCLUSIONS: Development and testing of EMR-based decision support tools for AKI with clinicians led to high acceptance by clinical end-users. Subsequent implementation within clinical environments will require end-user education and engagement in system-level initiatives to use the tools to improve care.


Assuntos
Lesão Renal Aguda/diagnóstico , Lesão Renal Aguda/terapia , Sistemas de Apoio a Decisões Clínicas , Registros Eletrônicos de Saúde , Alberta , Feminino , Hospitais , Humanos , Masculino
11.
JAMA Netw Open ; 3(11): e2025889, 2020 11 02.
Artigo em Inglês | MEDLINE | ID: mdl-33201236

RESUMO

Importance: Falls represent a leading cause of preventable injury in hospitals and a frequently reported serious adverse event. Hospitalization is associated with an increased risk for falls and serious injuries including hip fractures, subdural hematomas, or even death. Multifactorial strategies have been shown to reduce falls in acute care hospitals, but evidence for fall-related injury prevention in hospitals is lacking. Objective: To assess whether a fall-prevention tool kit that engages patients and families in the fall-prevention process throughout hospitalization is associated with reduced falls and injurious falls. Design, Setting, and Participants: This nonrandomized controlled trial using stepped wedge design was conducted between November 1, 2015, and October 31, 2018, in 14 medical units within 3 academic medical centers in Boston and New York City. All adult inpatients hospitalized in participating units were included in the analysis. Interventions: A nurse-led fall-prevention tool kit linking evidence-based preventive interventions to patient-specific fall risk factors and designed to integrate continuous patient and family engagement in the fall-prevention process. Main Outcomes and Measures: The primary outcome was the rate of patient falls per 1000 patient-days in targeted units during the study period. The secondary outcome was the rate of falls with injury per 1000 patient-days. Results: During the interrupted time series, 37 231 patients were evaluated, including 17 948 before the intervention (mean [SD] age, 60.56 [18.30] years; 9723 [54.17%] women) and 19 283 after the intervention (mean [SD] age, 60.92 [18.10] years; 10 325 [53.54%] women). There was an overall adjusted 15% reduction in falls after implementation of the fall-prevention tool kit compared with before implementation (2.92 vs 2.49 falls per 1000 patient-days [95% CI, 2.06-3.00 falls per 1000 patient-days]; adjusted rate ratio 0.85; 95% CI, 0.75-0.96; P = .01) and an adjusted 34% reduction in injurious falls (0.73 vs 0.48 injurious falls per 1000 patient-days [95% CI, 0.34-0.70 injurious falls per 1000 patient-days]; adjusted rate ratio, 0.66; 95% CI, 0.53-0.88; P = .003). Conclusions and Relevance: In this nonrandomized controlled trial, implementation of a fall-prevention tool kit was associated with a significant reduction in falls and related injuries. A patient-care team partnership appears to be beneficial for prevention of falls and fall-related injuries. Trial Registration: ClinicalTrials.gov Identifier: NCT02969343.


Assuntos
Acidentes por Quedas/prevenção & controle , Sistemas de Apoio a Decisões Clínicas , Hospitalização , Assistência Centrada no Paciente , Ferimentos e Lesões/prevenção & controle , Adulto , Idoso , Enfermagem Baseada em Evidências , Família , Feminino , Humanos , Análise de Séries Temporais Interrompida , Masculino , Pessoa de Meia-Idade , Participação do Paciente , Segurança do Paciente
12.
Rev Med Suisse ; 16(716): 2242-2247, 2020 Nov 25.
Artigo em Francês | MEDLINE | ID: mdl-33237640

RESUMO

Medication prescribing is a critical feature in the electronic health record (EHR). Computerized Clinical Decision Support (CCDS) for medication prescribing has the potential to improve quality of care, patient safety and reduce cost. However, its development, implementation, and maintenance in the clinical environment, are major challenges. We describe the basics of the CCDS in medication prescribing, the acquired experience of the last years at the Lausanne University Hospital (CHUV), and we expose the perspectives and future challenges in this domain.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Prescrições de Medicamentos , Registros Eletrônicos de Saúde , Segurança do Paciente , Humanos
13.
Stud Health Technol Inform ; 275: 1-5, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33227729

RESUMO

Though a preventable risk, the management of pressure ulcers (PUs) in nursing homes is not satisfactory due to inadequate prevention and complex care plans. PUs early detection and wound assessment require to know the patient condition and risk factors and to have a good knowledge of best practices. We built a guideline-based clinical decision support system (CDSS) for the prevention, the assessment, and the management of PUs. Clinical practice guidelines have been modeled as decision trees and formalized as IF-THEN rules to be triggered by electronic health record (EHR) data. From PU assessment yielded by the CDSS, we propose a synthetic visualization of PU current and previous stages as a gauge that illustrates the different stages of PU continuous evolution. This allows to display PU current and previous stages to inform health care professionals of PU updated assessment and support their evaluation of previously delivered care efficiency. The CDSS will be integrated in NETSoins nursing homes EHR where gauges for several health problems constitute a patient dashboard.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Lesão por Pressão , Registros Eletrônicos de Saúde , Pessoal de Saúde , Humanos , Casas de Saúde , Lesão por Pressão/diagnóstico , Lesão por Pressão/terapia
14.
Stud Health Technol Inform ; 275: 107-111, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33227750

RESUMO

How textual clinical practice guidelines are written may have an impact on how they are formalized and on the kind of recommendations issued by the clinical decision support systems (CDSSs) that implement them. Breast cancer guidelines are mostly centered on the description of the different recommended therapeutic modalities, represented as atomic recommendations, but seldom provide comprehensive plans that drive care delivery. The objective of this work is to implement a knowledge-based approach to develop a care plan builder (CPB) that works on atomic recommendations to build patient-centered care plans as sequences of chronologically ordered therapeutic steps. The CPB uses the atomic recommendations issued by the guideline-based decision support system (GL-DSS) of the DESIREE project. The domain knowledge is represented as the list of all care plans that apply to breast cancer patients. Scenarios are introduced to locate the patient on these theoretical care plans. The CPB has been evaluated on a sample of 99 solved clinical cases leading to an overall performance of 89,8%.


Assuntos
Neoplasias da Mama , Sistemas de Apoio a Decisões Clínicas , Neoplasias da Mama/terapia , Humanos , Bases de Conhecimento
15.
Stud Health Technol Inform ; 275: 177-181, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33227764

RESUMO

Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE.


Assuntos
Neoplasias da Mama , Sistemas de Apoio a Decisões Clínicas , Médicos , Neoplasias da Mama/terapia , França , Humanos , Bases de Conhecimento
16.
Stud Health Technol Inform ; 275: 117-121, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33227752

RESUMO

One of the most important challenges in the scenario of COVID-19 is to design and develop decision support systems that can help medical staff to identify a cohort of patients that is more likely to have worse clinical evolution. To achieve this objective it is necessary to work on collected data, pre-process them in order to obtain a consistent dataset and then extract the most relevant features with advanced statistical methods like principal component analysis. As preliminary results of this research, very influential features that emerged are the presence of cardiac and liver illnesses and the levels of some inflammatory parameters at the moment of diagnosis.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Análise de Dados , Sistemas de Apoio a Decisões Clínicas , Pandemias , Pneumonia Viral , Humanos
17.
JAMA Netw Open ; 3(11): e2023547, 2020 11 02.
Artigo em Inglês | MEDLINE | ID: mdl-33136133

RESUMO

Importance: Hospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be. Objective: To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures. Design, Setting, and Participants: In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020. Main Outcomes and Measures: Predicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk. Results: Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making. Conclusions and Relevance: The clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.


Assuntos
Infecções por Coronavirus , Tomada de Decisões , Sistemas de Apoio a Decisões Clínicas , Procedimentos Cirúrgicos Eletivos , Alocação de Recursos para a Atenção à Saúde , Hospitalização , Hospitais , Pandemias , Pneumonia Viral , Idoso , Betacoronavirus , Comorbidade , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Infecções por Coronavirus/virologia , Registros Eletrônicos de Saúde , Feminino , Humanos , Unidades de Terapia Intensiva , Tempo de Internação , Masculino , Pessoa de Meia-Idade , Alta do Paciente , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Pneumonia Viral/virologia , Respiração Artificial , Estudos Retrospectivos , Medição de Risco , Índice de Gravidade de Doença , Instituições de Cuidados Especializados de Enfermagem
18.
BMC Med Inform Decis Mak ; 20(1): 267, 2020 10 17.
Artigo em Inglês | MEDLINE | ID: mdl-33069228

RESUMO

BACKGROUND: Chronic pain has emerged as a disease in itself, affecting a growing number of people. Effective patient-provider communication is central to good pain management because pain can only be understood from the patient's perspective. We aimed to develop a user-centered tool to improve patient-provider communication about chronic pain and assess its feasibility in real-world settings in preparation for further evaluation and distribution. METHODS: To identify and prioritize patient treatment goals for chronic pain, strategies to improve patient-provider communication about chronic pain, and facilitate implementation of the tool, we conducted nominal group technique meetings and card sorting with patients with chronic pain and experienced providers (n = 12). These findings informed the design of the PainAPP tool. Usability and beta-testing with patients (n = 38) and their providers refined the tool and assessed its feasibility, acceptability, and preliminary impact. RESULTS: Formative work revealed that patients felt neither respected nor trusted by their providers and focused on transforming providers' negative attitudes towards them, whereas providers focused on gathering patient information. PainAPP incorporated areas prioritized by patients and providers: assessing patient treatment goals and preferences, functional abilities and pain, and providing patients tailored education and an overall summary that patients can share with providers. Beta-testing involved 38 patients and their providers. Half of PainAPP users shared their summaries with their providers. Patients rated PainAPP highly in all areas. All users would recommend it to others with chronic pain; nearly all trusted the information and said it helped them think about my treatment goals (94%), understand my chronic pain (82%), make the most of my next doctor's visit (82%), and not want to use opioids (73%). Beta-testing revealed challenges delivering the tool and summary report to patients and providers in a timely manner and obtaining provider feedback. CONCLUSIONS: PainAPP appears feasible for use, but further adaptation and testing is needed to assess its impact on patients and providers. TRIAL REGISTRATION: This study was approved by the University of New England Independent Review Board for the Protection of Human Subjects in Research (012616-019) and was registered with ClinicalTrials.gov (protocol ID: NCT03425266) prior to enrollment. The trial was prospectively registered and was approved on February 7, 2018.


Assuntos
Dor Crônica/diagnóstico , Comunicação , Tomada de Decisão Compartilhada , Sistemas de Apoio a Decisões Clínicas/normas , Manejo da Dor/métodos , Relações Profissional-Paciente , Dor Crônica/terapia , Inglaterra , Estudos de Viabilidade , Feminino , Humanos , Masculino
19.
BMC Med Inform Decis Mak ; 20(1): 276, 2020 10 27.
Artigo em Inglês | MEDLINE | ID: mdl-33109167

RESUMO

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. METHODS: Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. RESULTS: 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. CONCLUSIONS: The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Aprendizado de Máquina/normas , Sepse/diagnóstico , Algoritmos , Conjuntos de Dados como Assunto , Feminino , Previsões , Mortalidade Hospitalar , Humanos , Unidades de Terapia Intensiva , Masculino , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sepse/mortalidade , Índice de Gravidade de Doença , Fatores de Tempo , Tempo para o Tratamento
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA