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1.
BMC Bioinformatics ; 22(1): 104, 2021 Mar 02.
Artículo en Inglés | MEDLINE | ID: mdl-33653260

RESUMEN

BACKGROUND: VCF formatted files are the lingua franca of next-generation sequencing, whereas HL7 FHIR is emerging as a standard language for electronic health record interoperability. A growing number of FHIR-based clinical genomics applications are emerging. Here, we describe an open source utility for converting variants from VCF format into HL7 FHIR format. RESULTS: vcf2fhir converts VCF variants into a FHIR Genomics Diagnostic Report. Conversion translates each VCF row into a corresponding FHIR-formatted variant in the generated report. In scope are simple variants (SNVs, MNVs, Indels), along with zygosity and phase relationships, for autosomes, sex chromosomes, and mitochondrial DNA. Input parameters include VCF file and genome build ('GRCh37' or 'GRCh38'); and optionally a conversion region that indicates the region(s) to convert, a studied region that lists genomic regions studied by the lab, and a non-callable region that lists studied regions deemed uncallable by the lab. Conversion can be limited to a subset of VCF by supplying genomic coordinates of the conversion region(s). If studied and non-callable regions are also supplied, the output FHIR report will include 'region-studied' observations that detail which portions of the conversion region were studied, and of those studied regions, which portions were deemed uncallable. We illustrate the vcf2fhir utility via two case studies. The first, 'SMART Cancer Navigator', is a web application that offers clinical decision support by linking patient EHR information to cancerous gene variants. The second, 'Precision Genomics Integration Platform', intersects a patient's FHIR-formatted clinical and genomic data with knowledge bases in order to provide on-demand delivery of contextually relevant genomic findings and recommendations to the EHR. CONCLUSIONS: Experience to date shows that the vcf2fhir utility can be effectively woven into clinically useful genomic-EHR integration pipelines. Additional testing will be a critical step towards the clinical validation of this utility, enabling it to be integrated in a variety of real world data flow scenarios. For now, we propose the use of this utility primarily to accelerate FHIR Genomics understanding and to facilitate experimentation with further integration of genomics data into the EHR.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Genómica , Registros Electrónicos de Salud , Humanos , Bases del Conocimiento , Oncogenes
2.
Medicine (Baltimore) ; 100(13): e25276, 2021 Apr 02.
Artículo en Inglés | MEDLINE | ID: mdl-33787612

RESUMEN

ABSTRACT: Clinical information systems (CISs) that do not consider usability and safety could lead to harmful events. Therefore, we aimed to develop a safety and usability guideline of CISs that is comprehensive for both users and developers. And the guideline was categorized to apply actual clinical workflow and work environment.The guideline components were extracted through a systematic review of the articles published between 2000 and 2015, and existing CIS safety and/or usability design guidelines. The guideline components were categorized according to clinical workflow and types of user interface (UI). The contents of the guideline were evaluated and validated by experts with 3 specialties: medical informatics, patient safety, and human engineering.Total 1276 guideline components were extracted through article and guideline review. Of these, 464 guideline components were categorized according to 5 divisions of the clinical workflow: "Data identification and selection," "Document entry," "Order entry," "Clinical decision support and alert," and "Management". While 521 guideline components were categorized according to 4 divisions of UI: UIs related to information process steps, "Perception," "Recognition," "Control," and "Feedback". We developed a guideline draft with 219 detailed guidance for clinical task and 70 for UI. Overall appropriateness and comprehensiveness were proven to achieve more than 90% in experts' survey. However, there were significant differences among the groups of specialties in the judgment of appropriateness (P < .001) and comprehensiveness (P = .038).We developed and verified a safety and usability guideline for CIS that qualifies the requirements of both clinical workflows and usability issues. The developed guideline can be a practical tool to enhance the usability and safety of CISs. Further validation is required by applying the guideline for designing the actual CIS.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas/normas , Aplicaciones de la Informática Médica , Interfaz Usuario-Computador , Ergonomía , Humanos , Errores Médicos/prevención & control , Seguridad del Paciente , Flujo de Trabajo
3.
PLoS One ; 16(3): e0247773, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33705435

RESUMEN

BACKGROUND: The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments' (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients' triage and allocate resources for patients at risk. METHODS AND PRINCIPAL FINDINGS: From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97-4.50), dry cough (OR = 1.71; 95% CI: 1.39-2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67-2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56-0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68-0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. CONCLUSION AND MAIN SIGNIFICANCE: The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.


Asunto(s)
/diagnóstico , Sistemas de Apoyo a Decisiones Clínicas , Adulto , Anciano , Tos/diagnóstico , Disnea/diagnóstico , Femenino , Fiebre/diagnóstico , Cefalea/diagnóstico , Hospitales , Humanos , Masculino , Persona de Mediana Edad , Faringitis/diagnóstico , /aislamiento & purificación
4.
Nat Commun ; 12(1): 1880, 2021 03 25.
Artículo en Inglés | MEDLINE | ID: mdl-33767174

RESUMEN

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Interpretación de Imagen Asistida por Computador/métodos , Aprendizaje Automático , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Imagen de Cuerpo Entero/métodos , Conjuntos de Datos como Asunto , Fluorodesoxiglucosa F18 , Humanos , Comportamiento Multifuncional , Procesamiento de Lenguaje Natural
6.
Lancet Psychiatry ; 8(3): 202-214, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33571453

RESUMEN

BACKGROUND: The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression. METHODS: In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303. OUTCOMES: Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psychological distress than usual care plus attention control at 6 months (p=0·03), with a standardised mean difference (SMD) of -0·09 (95% CI -0·17 to -0·01). This reduction was also seen in the severe prognostic group (p=0·003), with a SMD of -0·26 (-0·43 to -0·09), but not in the minimal/mild group (p=0·73), with a SMD of 0·04 (-0·17 to 0·24). In the complier average causal effect analysis in the severe prognostic group, differences were larger among those who received some or all aspects of the intervention (SMD range -0·58 to -1·15). No serious adverse effects were recorded. INTERPRETATION: Prognosis-based matching of interventions reduces psychological distress in patients with anxiety or depressive symptoms, particularly in those with severe symptoms, and is associated with better outcomes when patients access the recommended treatment. Optimisation of the Link-me approach and implementation into routine practice could help reduce the burden of disease associated with common mental health conditions such as anxiety and depression. FUNDING: Australian Government Department of Health.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas/organización & administración , Servicios de Salud Mental/organización & administración , Atención Primaria de Salud/organización & administración , Estrés Psicológico/terapia , Adolescente , Adulto , Anciano , Ansiedad/terapia , Australia , Depresión/terapia , Femenino , Humanos , Modelos Lineales , Masculino , Salud Mental , Persona de Mediana Edad , Calidad de Vida , Índice de Severidad de la Enfermedad , Resultado del Tratamiento , Adulto Joven
7.
Semin Respir Crit Care Med ; 42(2): 308-315, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33548931

RESUMEN

Venous thromboembolism (VTE) is the leading preventable cause of death in hospitalized patients and data consistently show that acutely ill medical patients remain at increased risk for VTE-related morbidity and mortality in the post-hospital discharge period. Prescribing extended thromboprophylaxis for up to 45 days following an acute hospitalization in key patient subgroups that include more than one-quarter of hospitalized medically-ill patients represents a paradigm shift in the way hospital-based physicians think about VTE prevention. Advances in the field of primary thromboprophylaxis in acutely-ill medical patients using validated VTE and bleeding risk assessment models have established key patient subgroups at high risk of VTE and low risk of bleeding that may benefit from both in-hospital and extended thromboprophylaxis. The direct oral anticoagulants betrixaban and rivaroxaban are now U.S. Food and Drug Administration-approved for in-hospital and extended thromboprophylaxis in medically ill patients and provide net clinical benefit in these key subgroups. Coronavirus disease-2019 may predispose patients to VTE due to excessive inflammation, platelet activation, endothelial dysfunction, and hemostasis. The optimum preventive strategy for these patients requires further investigation. This article aims to review the latest concepts in predicting and preventing VTE and discuss the new era of extended thromboprophylaxis in hospitalized medically ill patients.


Asunto(s)
Anticoagulantes/uso terapéutico , Duración de la Terapia , Hospitalización , Embolia Pulmonar/prevención & control , Tromboembolia Venosa/prevención & control , Trombosis de la Vena/prevención & control , Benzamidas/uso terapéutico , /complicaciones , Cuidados Críticos , Sistemas de Apoyo a Decisiones Clínicas , Humanos , Informática Médica , Alta del Paciente , Embolia Pulmonar/etiología , Piridinas/uso terapéutico , Medición de Riesgo , Rivaroxabán/uso terapéutico , Tromboembolia Venosa/etiología , Trombosis de la Vena/etiología
8.
JAMA Netw Open ; 4(2): e2036344, 2021 02 01.
Artículo en Inglés | MEDLINE | ID: mdl-33560426

RESUMEN

Importance: Appendicitis is the most common pediatric surgical emergency. Efforts to improve efficiency and quality of care have increased reliance on computed tomography (CT) and ultrasonography (US) in children with suspected appendicitis. Objective: To evaluate the effectiveness of an electronic health record-linked clinical decision support intervention, AppyCDS, on diagnostic imaging, health care costs, and safety outcomes for patients with suspected appendicitis. Design, Setting, and Participants: In this parallel, cluster randomized trial, 17 community-based general emergency departments (EDs) in California, Minnesota, and Wisconsin were randomized to the AppyCDS intervention group or usual care (UC) group. Patients were aged 5 to 20 years, presenting for an ED visit with right-sided or diffuse abdominal pain lasting 5 days or less. We excluded pregnant patients, those with a prior appendectomy, those with selected comorbidities, and those with traumatic injuries. The trial was conducted from October 2016 to July 2019. Interventions: AppyCDS prompted data entry at the point of care to estimate appendicitis risk using the pediatric appendicitis risk calculator (pARC). Based on pARC estimates, AppyCDS recommended next steps in care. Main Outcomes and Measures: Primary outcomes were CT, US, or any imaging (CT or US) during the index ED visit. Safety outcomes were perforations, negative appendectomies, and missed appendicitis. Costs were a secondary outcome. Ratio of ratios (RORs) for primary and safety outcomes and differences by group in cost were used to evaluate effectiveness of the clinical decision support tool. Results: We enrolled 3161 patients at intervention EDs and 2779 patients at UC EDs. The mean age of patients was 11.9 (4.6) years and 2614 (44.0%) were boys or young men. RORs for CT (0.94; 95% CI, 0.75-1.19), US (0.98; 95% CI, 0.84-1.14), and any imaging (0.96; 95% CI, 0.86-1.07) did not differ by study group. In an exploratory analysis conducted in 1 health system, AppyCDS was associated with a reduction in any imaging (ROR, 0.82; 95% CI, 0.73- 0.93) for patients with pARC score of 15% or less and a reduction in CT (ROR, 0.58; 95% CI, 0.45-0.74) for patients with a pARC score of 16% to 50%. Perforations, negative appendectomies, and cases of missed appendicitis by study phase did not differ significantly by study group. Costs did not differ overall by study group. Conclusions and Relevance: In this study, AppyCDS was not associated with overall reductions in diagnostic imaging; exploratory analysis revealed more appropriate use of imaging in patients with a low pARC score. Trial Registration: ClinicalTrials.gov Identifier: NCT02633735.


Asunto(s)
Dolor Abdominal/diagnóstico , Apendicitis/diagnóstico , Sistemas de Apoyo a Decisiones Clínicas , Diagnóstico Erróneo/estadística & datos numéricos , Tomografía Computarizada por Rayos X/estadística & datos numéricos , Ultrasonografía/estadística & datos numéricos , Dolor Abdominal/etiología , Adolescente , Apendicectomía , Apendicitis/complicaciones , Apendicitis/diagnóstico por imagen , Apendicitis/cirugía , Niño , Preescolar , Servicio de Urgencia en Hospital , Femenino , Costos de la Atención en Salud/estadística & datos numéricos , Humanos , Masculino , Medición de Riesgo , Adulto Joven
9.
J Am Board Fam Med ; 34(Suppl): S127-S135, 2021 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-33622827

RESUMEN

PURPOSE: Develop and validate simple risk scores based on initial clinical data and no or minimal laboratory testing to predict mortality in hospitalized adults with COVID-19. METHODS: We gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. RESULTS: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive protein (CRP), and serum creatinine. The COVID-NoLab risk score includes age, respiratory rate, and oxygen saturation and identified risk groups with 0.8%, 11.4%, and 40.4% mortality in the validation group (AUROCC = 0.803). The COVID-SimpleLab score includes age, respiratory rate, oxygen saturation, WBC, CRP, serum creatinine, and comorbid asthma and identified risk groups with 1.0%, 9.1%, and 29.3% mortality in the validation group (AUROCC = 0.833). CONCLUSIONS: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.


Asunto(s)
/diagnóstico , Sistemas de Apoyo a Decisiones Clínicas/normas , Medición de Riesgo/métodos , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pandemias , Pronóstico , Factores de Riesgo , Estados Unidos/epidemiología
10.
Am J Cardiol ; 145: 1-11, 2021 04 15.
Artículo en Inglés | MEDLINE | ID: mdl-33454343

RESUMEN

The secondary prevention (SP) of coronary heart disease (CHD) has become a major public health and economic burden worldwide. In the United States, the prevalence of CHD has risen to 18 million, the incidence of recurrent myocardial infarctions (MI) remains high, and related healthcare costs are projected to double by 2035. In the last decade, practice guidelines and performance measures for the SP of CHD have increasingly emphasized evidence-based lifestyle (LS) interventions, including healthy dietary patterns, regular exercise, smoking cessation, weight management, depression screening, and enrollment in cardiac rehabilitation. However, data show large gaps in adherence to healthy LS behaviors and low rates of enrollment in cardiac rehabilitation in patients with established CHD. These gaps may be related, since behavior change interventions have not been well integrated into traditional ambulatory care models in the United States. The chronic care model, an evidence-based practice framework that incorporates clinical decision support, self-management support, team-care delivery and other strategies for delivering chronic care is well suited for both chronic CHD management and prevention interventions, including those related to behavior change. This article reviews the evidence base for LS interventions for the SP of CHD, discusses current gaps in adherence, and presents strategies for closing these gaps via evidence-based and emerging interventions that are conceptually aligned with the elements of the chronic care model.


Asunto(s)
Rehabilitación Cardiaca , Enfermedad Coronaria/prevención & control , Dieta , Ejercicio Físico , Cooperación del Paciente , Prevención Secundaria/métodos , Cese del Hábito de Fumar , Sistemas de Apoyo a Decisiones Clínicas , Prestación de Atención de Salud , Depresión/diagnóstico , Depresión/terapia , Dieta Mediterránea , Dieta Vegetariana , Enfoques Dietéticos para Detener la Hipertensión , Humanos , Estilo de Vida , Tamizaje Masivo , Atención Plena , Conducta de Reducción del Riesgo , Automanejo , Estrés Psicológico/terapia
11.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Artículo en Inglés | MEDLINE | ID: mdl-33404516

RESUMEN

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.


Asunto(s)
/diagnóstico , Sistemas de Apoyo a Decisiones Clínicas , Salud , Aprendizaje Automático , Neumonía Viral/diagnóstico , /diagnóstico por imagen , Diagnóstico Diferencial , Humanos , Persona de Mediana Edad , Neumonía Viral/diagnóstico por imagen , Máquina de Vectores de Soporte , Tomografía Computarizada por Rayos X
12.
PLoS One ; 16(1): e0245296, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33434221

RESUMEN

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.


Asunto(s)
/sangre , Quimiocina CXCL10/sangre , Sistemas de Apoyo a Decisiones Clínicas , Adulto , Anciano , Anciano de 80 o más Años , Biomarcadores/sangre , /terapia , Femenino , Humanos , Masculino , Persona de Mediana Edad , Guías de Práctica Clínica como Asunto
13.
Methods Mol Biol ; 2194: 45-59, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-32926361

RESUMEN

Clinical practice guidelines in oncology provide an evidence-based roadmap for most cancer care delivery but often lack directions for specific patient factors and disease conditions. Clinical pathways serve as a real-time clinical decision support system to translate guidelines to clinical practice. Pathways allow for the creation of a standardized, multidimensional roadmap for the continuum of care that can support clinical decision-making, maintain optimal outcomes, and limit unnecessary variation in cancer care. Here we describe the process to develop and implement clinical pathways in the electronic health record. This process includes building the appropriate foundation for a clinical pathways team with supports in the institutional ecosystem, creating visual representations of care paths, formalizing the pathway approval process, and translating clinical pathways into an electronic health record-integrated clinical decision support tool.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas/organización & administración , Prestación de Atención de Salud/métodos , Registros Electrónicos de Salud/organización & administración , Oncología Médica/métodos , Prestación de Atención de Salud/organización & administración , Humanos , Oncología Médica/organización & administración
14.
Ann Pharmacother ; 55(1): 123-126, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-32536291

RESUMEN

Acute care pharmacists play an integral role in identifying drug-drug interactions that may predispose patients to QT prolongation. Although most pharmacists are equipped with a baseline understanding of drug interactions and the risks of QTc prolongation, few understand the limitations of QTc calculation and interpretation. In this commentary, we put forth the notion that at times health care providers, including pharmacists, place an overemphasis on the QTc interval. In the context of using the QTc to guide pharmacotherapy decisions, unintended consequences may include a cascade of effects leading to delays in treatment, suboptimal medication selection, alert fatigue, and overutilization of resources.


Asunto(s)
Electrocardiografía/efectos de los fármacos , Síndrome de QT Prolongado/diagnóstico , Farmacéuticos/normas , Torsades de Pointes/prevención & control , Sistemas de Apoyo a Decisiones Clínicas , Interacciones Farmacológicas , Femenino , Humanos , Síndrome de QT Prolongado/inducido químicamente
15.
Sensors (Basel) ; 20(24)2020 Dec 11.
Artículo en Inglés | MEDLINE | ID: mdl-33322442

RESUMEN

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly's behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.


Asunto(s)
Actividades Cotidianas , Cuidadores , Sistemas de Apoyo a Decisiones Clínicas , Evaluación Geriátrica/métodos , Anciano , Femenino , Lógica Difusa , Humanos
16.
Lancet Digit Health ; 2(5): e250-e258, 2020 05.
Artículo en Inglés | MEDLINE | ID: mdl-33328057

RESUMEN

BACKGROUND: Acute diarrhoeal disease management often requires rehydration alone without antibiotics. However, non-indicated antibiotics are frequently ordered and this is an important driver of antimicrobial resistance. The mHealth Diarrhoea Management (mHDM) trial aimed to establish whether electronic decision support improves rehydration and antibiotic guideline adherence in resource-limited settings. METHODS: A cluster randomised controlled trial was done at ten district hospitals in Bangladesh. Inclusion criteria were patients aged 2 months or older with uncomplicated acute diarrhoea. Admission orders were observed without intervention in the pre-intervention period, followed by randomisation to electronic (rehydration calculator) or paper formatted WHO guidelines for the intervention period. The primary outcome was rate of intravenous fluid ordered as a binary variable. Generalised linear mixed-effect models, accounting for hospital clustering, served as the analytical framework; the analysis was intention to treat. The trial is registered with ClinicalTrials.gov (NCT03154229) and is completed. FINDINGS: From March 11 to Sept 10, 2018, 4975 patients (75·6%) of 6577 screened patients were enrolled. The intervention effect for the primary outcome showed no significant differences in rates of intravenous fluids ordered as a function of decision-support type. Intravenous fluid orders decreased by 0·9 percentage points for paper electronic decision support and 4·2 percentage points for electronic decision support, with a 4·2-point difference between decision-support types in the intervention period (paper 98·7% [95% CI 91·8-99·8] vs electronic 94·5% [72·2-99·1]; pinteraction=0·31). Adverse events such as complications and mortality events were uncommon and could not be statistically estimated. INTERPRETATION: Although intravenous fluid orders did not change, electronic decision support was associated with increases in the volume of intravenous fluid ordered and decreases in antibiotics ordered, which are consistent with WHO guidelines. FUNDING: US National Institutes of Health.


Asunto(s)
Toma de Decisiones Asistida por Computador , Sistemas de Apoyo a Decisiones Clínicas , Prestación de Atención de Salud , Diarrea/terapia , Fluidoterapia/métodos , Adhesión a Directriz , Administración Intravenosa , Adolescente , Adulto , Antibacterianos , Bangladesh , Niño , Preescolar , Prestación de Atención de Salud/normas , Electrónica , Femenino , Hospitales , Humanos , Lactante , Masculino , Papel , Prescripciones , Atención Primaria de Salud , Organización Mundial de la Salud , Adulto Joven
17.
Rev. cuba. salud pública ; 46(4): e2459, oct.-dic. 2020. tab, graf
Artículo en Español | LILACS, CUMED | ID: biblio-1156627

RESUMEN

Introducción: Los escenarios de convivencia de las diversas poblaciones son muy complejos, lo que contribuye con la propagación de enfermedades. Diagnosticar tempranamente enfermedades infecciosas representa una tarea fundamental para disminuir su propagación y evitar epidemias. Sin embargo, la inconsistencia en los datos de poblaciones y la imposibilidad de contar con un diagnóstico oportuno en muchos casos trae como consecuencia la proliferación de pandemias tales como la COVID-19. Objetivo: Desarrollar un sistema de apoyo al diagnóstico médico para COVID-19 a partir de la modelación de las relaciones causales de los criterios de diagnóstico, para conformar el mapa cognitivo difuso. Métodos: Para el desarrollo de la investigación se utilizaron métodos teóricos, empíricos y estadísticos, tales como: analítico-sintético, inductivo-deductivo, hipotético-deductivo, modelación. Como método empírico se utilizó la entrevista semiestructurada con la intención de recoger información que permitiera incluir contenidos no prescritos y precisar el conocimiento de los expertos sobre los principales indicadores para la toma de decisiones en el diagnóstico médico de la COVID-19. Resultados: El sistema funciona a través de un mapa cognitivo difuso para modelar las relaciones causales que representan la base de la inferencia. Se utilizan técnicas de inteligencia artificial como base al diagnóstico médico. Se presenta un ejemplo demostrativo para el diagnóstico médico de la COVID-19 en el que se modelan las relaciones causales de los diferentes conceptos que describen la enfermedad provocada. Conclusiones: El sistema diseñado constituye una herramienta viable de apoyo a la toma de decisiones en el diagnóstico médico de la COVID-19, que permite obtener criterios evaluativos a partir de la modelación de las relaciones causales, esto lo hace extensible a otros tipos de situaciones de emergencias sanitarias(AU)


Introduction: Different populations coexistence scenarios are very complex, which contributes to the spread of diseases. Diagnosing infectious diseases early is a critical task in reducing its spread and preventing epidemics. However, inconsistency in population data and the inability to have timely diagnosis in many cases result in the proliferation of pandemics such as COVID-19. Objective: Develop a support system for COVID-19 medical diagnostic from modeling causal relations of diagnostic criteria, to form the diffuse cognitive map. Methods: Theoretical, empirical and statistical methods were used for the development of the research, such as: analytical-synthetic, inductive-deductive, hypothetical-deductive, modeling. As an empirical method, the semi-structured interview was used with the intention of collecting information that would include unprescribed contents and require expert knowledge of the main indicators for decision-making in COVID-19 medical diagnosis. Results: The system works through a diffuse cognitive map to model causal relationships that represent the inference´s basis. Artificial intelligence techniques are used as a basis for medical diagnosis. A demonstrative example is presented for COVID-19 medical diagnosis in which are modelled the causal relations of the different concepts that the disease describes. Conclusions: The designed system is a viable support tool for decision-making in COVID-19 medical diagnosis, which allows to obtain evaluative criteria from the modelling of causal relations, and this makes it extendable to other types of health emergencies situations(AU)


Asunto(s)
Humanos , Masculino , Femenino , Enfermedades Transmisibles , Infecciones por Coronavirus/diagnóstico , Sistemas de Apoyo a Decisiones Clínicas/normas
18.
BMC Fam Pract ; 21(1): 271, 2020 12 18.
Artículo en Inglés | MEDLINE | ID: mdl-33339508

RESUMEN

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.


Asunto(s)
/prevención & control , Planificación en Desastres/organización & administración , Brotes de Enfermedades/prevención & control , Pautas de la Práctica en Medicina/organización & administración , Atención Primaria de Salud/organización & administración , /epidemiología , China , Sistemas de Apoyo a Decisiones Clínicas , Humanos , Difusión de la Información , Equipo de Protección Personal/estadística & datos numéricos , Salud Pública
19.
Artículo en Inglés | MEDLINE | ID: mdl-33371223

RESUMEN

This research was motivated by the nurses' decision-making process in the current emergency department (ED) triage process in the United States. It explores how continuous vital signs monitoring can be integrated into the ED. The article presents four shortcomings on current ED triage systems and proposes a new conceptual clinical decision support model that exploits the benefits of combining wireless wearable devices with Multi-Attribute Utility Theory to address those shortcomings. A literature review was conducted using various engineering and medical research databases, analyzing current practices and identifying potential improvement opportunities. The results from the literature review show that advancements in wireless wearable devices provide opportunities to enhance current ED processes by monitoring patients while they wait after triage and, therefore, reduce the risk of an adverse event. A dynamic mathematical decision support model to prioritize patients is presented, creating a feedback loop in the ED. The coupling of wearable devices (to collect data) with decision theory (to synthesize and organize the information) can assist in reducing sources of uncertainty inherent to ED systems. The authors also address the feasibility of the proposed conceptual model.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Servicio de Urgencia en Hospital , Dispositivos Electrónicos Vestibles , Teoría de las Decisiones , Humanos , Triaje , Estados Unidos
20.
Zhonghua Yi Xue Za Zhi ; 100(48): 3870-3873, 2020 Dec 29.
Artículo en Chino | MEDLINE | ID: mdl-33371633

RESUMEN

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.


Asunto(s)
Carcinoma Hepatocelular , Sistemas de Apoyo a Decisiones Clínicas , Neoplasias Hepáticas , Inteligencia Artificial , Carcinoma Hepatocelular/terapia , China , Humanos , Estimación de Kaplan-Meier , Neoplasias Hepáticas/terapia , Pronóstico , Estudios Retrospectivos
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