Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 22.953
Filtrar
1.
BMC Health Serv Res ; 21(1): 626, 2021 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-34193125

RESUMO

BACKGROUND: The integration of Patient Reported Outcome Measures (PROMs) into clinical care presents many challenges for health systems. PROMs provide quantitative data regarding patient-reported health status. However, the most effective model for collecting PROMs has not been established. Therefore the purpose of this study is to report the development and preliminary evaluation of the standardized collection of PROMs within a department of orthopedic surgery at a large academic health center. METHODS: We utilized the Users' Guide to Integrating Patient-Reported Outcomes in Electronic Health Records by Gensheimer et al., 2018 as a framework to describe the development of PROMs collection initiative. We framed our initiative by operationalizing the three aspects of PROM collection development: Planning, Selection, and Engagement. Next, we performed a preliminary evaluation of our initiative by assessing the response rate of patients completing PROMs (no. of PROMs completed/no. of PROMs administered) across the entire department (18 clinics), ambulatory clinics only (14 clinics), and hospital-based clinics only (4 clinics). Lastly, we reported on the mean response rates for the top 5 and bottom 5 orthopaedic providers to describe the variability across providers. RESULTS: We described the development of a fully-integrated, population health based implementation strategy leveraging the existing resources of our local EHR to maximize clinical utility of PROMs and routine collection. We collected a large volume of PROMs over a 13 month period (n = 10,951) across 18 clinical sites, 7 clinical specialties and over 100 providers. The response rates varied across the department, ranging from 29 to 42%, depending on active status for the portal to the electronic health record (MyChart). The highest single provider mean response rate was 52%, and the lowest provider rate was 13%. Rates were similar between hospital-based (26%) and ambulatory clinics (29%). CONCLUSIONS: We found that our standardized PROMs collection initiative, informed by Gensheimer et al., achieved scope and scale, but faced challenges in achieving a high response rate commensurate with existing literature. However, most studies reported a targeted recruitment strategy within a narrow clinical population. Further research is needed to elucidate the trade-off between scalability and response rates in PROM collection initiatives.


Assuntos
Registros Eletrônicos de Saúde , Ortopedia , Humanos , Medidas de Resultados Relatados pelo Paciente , Estudos Retrospectivos
2.
Cien Saude Colet ; 26(6): 2131-2140, 2021 Jun.
Artigo em Português, Inglês | MEDLINE | ID: mdl-34231725

RESUMO

As part of the evaluability study of the implementation of the Electronic Patient Record (EPR) evaluation, the aim of this Systematic Review (SR) was to identify the evaluation domains to be addressed. This SR, aligned with the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) encompassed articles published from 2006 to 2019. The search was carried out in the electronic databases SciELO, Oasis IBICT, BVS Regional and Scopus. The search resulted in 1,178 articles, 42 of which met the inclusion criteria. Most studies used qualitative methods for the analyses. The publications took place between 2006 and 2019, with a concentration in 2017 with 9 (21%) articles published in that year. No studies were published in 2008 and 2009. Only 10 studies included the description, analysis or results related to the domains of implementation. The main domains in which the EPR was problematized were: underutilization; professionals' resistance to its use; emphasis on usability; and EPR as an information source. Despite the inclusion of all studies that covered the principles and guidelines of the National Humanization Policy (NHP), they are still incipient.


Assuntos
Registros Eletrônicos de Saúde , Políticas , Bases de Dados Factuais , Humanos
3.
PLoS One ; 16(7): e0252384, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34214101

RESUMO

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients' day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.


Assuntos
COVID-19/mortalidade , Pandemias , SARS-CoV-2 , Máquina de Vetores de Suporte , Adulto , Idoso , Idoso de 80 Anos ou mais , Comorbidade , Registros Eletrônicos de Saúde , Feminino , Previsões , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Teóricos , Risco , Índice de Gravidade de Doença , Avaliação de Sintomas , Triagem , Adulto Jovem
4.
Trials ; 22(1): 429, 2021 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-34225782

RESUMO

BACKGROUND: Routinely recorded data held in electronic health records can be used to inform the conduct of randomised controlled trials (RCTs). However, limitations with access and accuracy have been identified. OBJECTIVE: Using epilepsy as an exemplar condition, we assessed the attributes and agreement of routinely recorded data compared to data collected using case report forms in a UK RCT assessing antiepileptic drug treatments for individuals newly diagnosed with epilepsy. METHODS: The case study RCT is the Standard and New Antiepileptic Drugs II (SANAD II) trial, a pragmatic, UK multicentre RCT assessing the clinical and cost-effectiveness of antiepileptic drugs as treatments for epilepsy. Ninety-eight of 470 eligible participants provided consent for access to routinely recorded secondary care data that were retrieved from NHS Digital Hospital Episode Statistics (N=71) and primary and secondary care data from The Secure Anonymised Information Linkage Databank (N=27). We assessed data items relevant to the identification of individuals eligible for inclusion in SANAD II, baseline and follow-up visits. The attributes of routinely recorded data were assessed including the degree of missing data. The agreement between routinely recorded data and data collected on case report forms in SANAD II was assessed using calculation of Cohen's kappa for categorical data and construction of Bland-Altman plots for continuous data. RESULTS: There was a significant degree of missing data in the routine record for 15 of the 20 variables assessed, including all clinical variables. Agreement was poor for the majority of comparisons, including the assessments of seizure occurrence and adverse events. For example, only 23/62 (37%) participants had a date of first-ever seizure identified in routine datasets. Agreement was satisfactory for the date of prescription of antiepileptic drugs and episodes of healthcare resource use. CONCLUSIONS: There are currently significant limitations preventing the use of routinely recorded data for participant identification and assessment of clinical outcomes in epilepsy, and potentially other chronic conditions. Further research is urgently required to assess the attributes, agreement, additional benefits, cost-effectiveness and 'optimal mix' of routinely recorded data compared to data collected using standard methods such as case report forms at clinic visits for people with epilepsy. TRIAL REGISTRATION: Standard and New Antiepileptic Drugs II (SANAD II (EudraCT No: 2012-001884-64, registered 05/09/2012; ISRCTN Number: ISRCTN30294119 , registered 03/07/2012)).


Assuntos
Anticonvulsivantes , Epilepsia , Anticonvulsivantes/efeitos adversos , Registros Eletrônicos de Saúde , Epilepsia/diagnóstico , Epilepsia/tratamento farmacológico , Humanos , Convulsões/diagnóstico , Convulsões/tratamento farmacológico , Reino Unido
5.
BMC Med Inform Decis Mak ; 21(1): 207, 2021 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-34210320

RESUMO

BACKGROUND: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model's prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. METHODS: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. RESULTS: The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. CONCLUSIONS: Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.


Assuntos
Tomada de Decisão Clínica , Registros Eletrônicos de Saúde , Humanos , Modelos Logísticos , Singapura , Máquina de Vetores de Suporte
6.
BMC Health Serv Res ; 21(1): 686, 2021 Jul 11.
Artigo em Inglês | MEDLINE | ID: mdl-34247600

RESUMO

BACKGROUND: Scribes have been proposed as an intervention to decrease physician electronic health record (EHR) workload and improve clinical quality. We aimed to assess the impact of a scribe on clinical efficiency and quality in an academic internal medicine practice. METHODS: Six faculty physicians worked with one scribe at an urban academic general internal medicine clinic April through June 2017. Patient visits during the 3 months prior to intervention (baseline, n = 789), unscribed visits during the intervention (concurrent control, n = 605), and scribed visits (n = 579) were included in the study. Clinical efficiency outcomes included time to close encounter, patient time in clinic, and number of visits per clinic session. Quality outcomes included EHR note quality, rates of medication and immunization review, population of patient instructions, reconciliation of outside information, and completion of preventative health recommendations. RESULTS: Median time to close encounter (IQR) was lower for scribed visits [0.4 (4.8) days] compared to baseline and unscribed visits [1.2 (5.9) and 2.9 (5.4) days, both p < 0.001]. Scribed notes were more likely to have a clear history of present illness (HPI) [OR = 7.30 (2.35-22.7), p = 0.001] and sufficient HPI information [OR = 2.21 (1.13-4.35), p = 0.02] compared to unscribed notes. Physicians were more likely to review the medication list during scribed vs. baseline visits [OR = 1.70 (1.22-2.35), p = 0.002]. No differences were found in the number of visits per clinic session, patient time in clinic, completion of preventative health recommendations, or other outcomes. CONCLUSIONS: Working with a scribe in an academic internal medicine practice was associated with more timely documentation.


Assuntos
Documentação , Médicos , Eficiência , Registros Eletrônicos de Saúde , Humanos , Medicina Interna
7.
Sensors (Basel) ; 21(13)2021 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-34208900

RESUMO

NDN is one of the new emerging future internet architectures which brings up new solutions over today's internet architecture, facilitating content distribution, in-network caching, mobility support, and multicast forwarding. NDNs ubiquitous in-network caching allows consumers to access data directly from the intermediate router's cache. However, it opens content privacy problems since data packets replicated in the router are always accessible by every consumer. Sensitive contents in the routers should be protected and accessed only by authorized consumers. Although the content protection problem can be solved by applying an encryption-based access control policy, it still needs an efficient content distribution scheme with lower computational overhead and content retrieval time. We propose an efficient and secure content distribution (ES_CD), by combining symmetric encryption and identity-based proxy re-encryption. The analysis shows that our proposed scheme achieves content retrieval time reduction up to 20% for the cached contents in our network simulation environment and a slight computational overhead of less than 19 ms at the content producer and 9 ms at the consumer for 2 KB content. ES_CD provides content confidentiality and ensures only legitimate consumers can access the contents during a predefined time without requiring a trusted third party and keeping the content producer always online.


Assuntos
Segurança Computacional , Registros Eletrônicos de Saúde , Confidencialidade , Internet , Privacidade
8.
JAMA Netw Open ; 4(6): e2111621, 2021 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-34081139

RESUMO

Importance: The influence of the COVID-19 pandemic on fertility rates has been suggested in the lay press and anticipated based on documented decreases in fertility and pregnancy rates during previous major societal and economic shifts. Anticipatory planning for birth rates is important for health care systems and government agencies to accurately estimate size of economy and model working and/or aging populations. Objective: To use projection modeling based on electronic health care records in a large US university medical center to estimate changes in pregnancy and birth rates prior to and after the COVID-19 pandemic societal lockdowns. Design, Setting, and Participants: This cohort study included all pregnancy episodes within a single US academic health care system retrospectively from 2017 and modeled prospectively to 2021. Data were analyzed September 2021. Exposures: Pre- and post-COVID-19 pandemic societal shutdown measures. Main Outcomes and Measures: The primary outcome was number of new pregnancy episodes initiated within the health care system and use of those episodes to project birth volumes. Interrupted time series analysis was used to assess the degree to which COVID-19 societal changes may have factored into pregnancy episode volume. Potential reasons for the changes in volumes were compared with historical pregnancy volumes, including delays in starting prenatal care, interruptions in reproductive endocrinology and infertility services, and preterm birth rates. Results: This cohort study documented a steadily increasing number of pregnancy episodes over the study period, from 4100 pregnancies in 2017 to 4620 in 2020 (28 284 total pregnancies; median maternal [interquartile range] age, 30 [27-34] years; 18 728 [66.2%] White women, 3794 [13.4%] Black women; 2177 [7.7%] Asian women). A 14% reduction in pregnancy episode initiation was observed after the societal shutdown of the COVID-19 pandemic (risk ratio, 0.86; 95% CI, 0.79-0.92; P < .001). This decrease appeared to be due to a decrease in conceptions that followed the March 15 mandated COVID-19 pandemic societal shutdown. Prospective modeling of pregnancies currently suggests that a birth volume surge can be anticipated in summer 2021. Conclusions and Relevance: This cohort study using electronic medical record surveillance found an initial decline in births associated with the COVID-19 pandemic societal changes and an anticipated increase in birth volume. Future studies can further explore how pregnancy episode volume changes can be monitored and birth rates projected in real-time during major societal events.


Assuntos
Coeficiente de Natalidade , COVID-19 , Pandemias , Distanciamento Físico , Isolamento Social , Centros Médicos Acadêmicos , Adulto , Coeficiente de Natalidade/tendências , COVID-19/prevenção & controle , Grupos de Populações Continentais , Registros Eletrônicos de Saúde , Feminino , Fertilidade , Previsões , Humanos , Análise de Séries Temporais Interrompida , Gravidez , Estudos Prospectivos , Estudos Retrospectivos , SARS-CoV-2 , Estados Unidos , Universidades
10.
J Nurs Educ ; 60(6): 337-341, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34077320

RESUMO

BACKGROUND: As the largest health care workforce, nursing is positioned to improve the health of populations using health information technology (HIT). Nurse graduates often lack confidence using HIT in practice, specifically, the electronic health record (EHR). Nurse scholars endorse the use of an academic electronic health record (AEHR) in nursing programs to provide students a safe learning platform to build levels of confidence using an EHR. METHOD: A quality improvement project was completed to evaluate student learning outcomes, satisfaction, and sustainability of an AEHR. Using an interprofessional approach, nurse educators incorporated the Systems Life Cycle Model to adopt an AEHR in two prelicensure nursing programs. RESULTS: Students' levels of confidence using an EHR in clinical settings increased markedly. Satisfaction rates for using an AEHR were high. CONCLUSION: Integration of an AEHR in nursing education contributes to building a proficient nursing workforce confident in using HIT for health care quality. [J Nurs Educ. 2021;60(6):337-341.].


Assuntos
Educação em Enfermagem , Registros Eletrônicos de Saúde , Melhoria de Qualidade , Estudantes de Enfermagem , Educação em Enfermagem/métodos , Docentes de Enfermagem , Humanos , Estudantes de Enfermagem/psicologia
11.
Sci Rep ; 11(1): 12801, 2021 06 17.
Artigo em Inglês | MEDLINE | ID: mdl-34140592

RESUMO

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Aprendizado de Máquina , Admissão do Paciente , SARS-CoV-2 , Idoso , COVID-19/virologia , Registros Eletrônicos de Saúde , Feminino , Humanos , Índia/epidemiologia , Masculino , Pessoa de Meia-Idade , Prognóstico , Pontuação de Propensão , Modelos de Riscos Proporcionais , Estudos Prospectivos , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Triagem
12.
Medicine (Baltimore) ; 100(23): e26246, 2021 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-34115013

RESUMO

ABSTRACT: Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.


Assuntos
Previsões/métodos , Aprendizado de Máquina/normas , Pneumonia Associada à Ventilação Mecânica/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Boston , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Unidades de Terapia Intensiva/organização & administração , Unidades de Terapia Intensiva/estatística & dados numéricos , Aprendizado de Máquina/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Respiração Artificial/efeitos adversos , Estudos Retrospectivos , Sensibilidade e Especificidade
13.
J Cancer Res Ther ; 17(2): 547-550, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34121706

RESUMO

Purpose: Health emergency due to COVID-19 started in Uruguay on March 13, 2020; our mastology unit tried to ensure adequate oncological care, and protect patients from the virus infection and complications. Objective: To assess the health care activities in the "peak" of the pandemic during 3 months. Materials and Methods: we collected data from the electronic health record. Results: There were a total of 293 medical appointments from 131 patients (221 face-to-face), that decreased by 16.7% compared to the same period in 2019 (352 appointments). The medical appointments were scheduled to evaluate the continuity of systemic treatment or modifications (95 patients; 72.5%), follow-up (17; 12.9%), first-time consultation (12; 9.1%), and assess paraclinical studies (7; 5.3%). The patients were on hormone therapy (81 patients; 74%), chemotherapy (CT) (21; 19%), and anti-HER2 therapies (9; 8%). New twenty treatments were initiated. Of the 14 patients that were on adjuvant/neoadjuvant CT, 9 (64.3%) continued with the same regimen with the addition of prophylactic granulocyte-colony-stimulating factors (G-CSF), and 5 (35.7%), who were receiving weekly paclitaxel, continued the treatment with no changes. Of the seven patients that were on palliative CT, 2 (28.5%) continued the treatment with the addition of G-CSF, 3 (42.8%) continued with weekly capecitabine or paclitaxel with no treatment changes, and 2 (28.5%) changed their treatment regimen (a less myelosuppressive regimen was selected for one and due to progression of the disease in the other patient). The ninety patients who were receiving adjuvant, neoadjuvant, or palliative criteria hormone therapy and/or anti-HER2 therapies, continued the treatment with no changes. Conclusions: The evidence suggests that, although medical appointments decreased by approximately 17%, we could maintain healthcare activities, continued most of the treatments while the most modified was CT with G-CSF to avoid myelosuppression.


Assuntos
Neoplasias da Mama/tratamento farmacológico , COVID-19/epidemiologia , Continuidade da Assistência ao Paciente/estatística & dados numéricos , Atenção à Saúde/estatística & dados numéricos , Oncologia/estatística & dados numéricos , Adulto , Idoso , Idoso de 80 Anos ou mais , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Medula Óssea/efeitos dos fármacos , Neoplasias da Mama/complicações , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/imunologia , COVID-19/imunologia , COVID-19/prevenção & controle , COVID-19/transmissão , Controle de Doenças Transmissíveis/normas , Continuidade da Assistência ao Paciente/organização & administração , Atenção à Saúde/organização & administração , Atenção à Saúde/normas , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Fator Estimulador de Colônias de Granulócitos/administração & dosagem , Hematopoese/efeitos dos fármacos , Hematopoese/imunologia , Humanos , Oncologia/organização & administração , Oncologia/normas , Pessoa de Meia-Idade , Pandemias/prevenção & controle , Encaminhamento e Consulta/normas , Encaminhamento e Consulta/estatística & dados numéricos , Estudos Retrospectivos , Telemedicina/organização & administração , Telemedicina/normas , Telemedicina/estatística & dados numéricos , Triagem/organização & administração , Triagem/normas , Uruguai/epidemiologia
14.
Nat Commun ; 12(1): 3566, 2021 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-34117227

RESUMO

Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic health record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2.


Assuntos
COVID-19/epidemiologia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Pandemias , SARS-CoV-2/isolamento & purificação , São Francisco/epidemiologia , Estudos Soroepidemiológicos , Adulto Jovem
15.
BMJ Open ; 11(6): e046545, 2021 06 21.
Artigo em Inglês | MEDLINE | ID: mdl-34155074

RESUMO

OBJECTIVE: To examine the social determinants of influenza and pertussis vaccine uptake among pregnant women in England. DESIGN: Nationwide population-based cohort study. SETTING: The study used anonymised primary care data from the Clinical Practice Research Datalink and linked Hospital Episode Statistics secondary care data. PARTICIPANTS: Pregnant women eligible for pertussis (2012-2015, n=68 090) or influenza (2010/2011-2015/2016, n=152 132) vaccination in England. MAIN OUTCOME MEASURES: Influenza and pertussis vaccine uptake. RESULTS: Vaccine uptake was 67.3% for pertussis and 39.1% for influenza. Uptake of both vaccines varied by region, with the lowest uptakes in London and the North East. Lower vaccine uptake was associated with greater deprivation: almost 10% lower in the most deprived quintiles compared with the least deprived for influenza (34.5% vs 44.0%), and almost 20% lower for pertussis (57.7% vs 76.0%). Lower uptake for both vaccines was also associated with non-white ethnicity (lowest among women of black ethnicity), maternal age under 20 years and a greater number of children in the household. The associations between all social factors and vaccine uptake were broadly unchanged in fully adjusted models, suggesting the social determinants of uptake were largely independent of one another. Among 3111 women vaccinated against pertussis in their first eligible pregnancy and pregnant again, 1234 (40%) were not vaccinated in their second eligible pregnancy. CONCLUSIONS: Targeting promotional campaigns to pregnant women who are younger, of non-white ethnicity, with more children, living in areas of greater deprivation or the London or North East regions, has potential to reduce vaccine-preventable disease among infants and pregnant women, and to reduce health inequalities. Vaccination promotion needs to be sustained across successive pregnancies. Further research is needed into whether the effectiveness of vaccine promotion strategies may vary according to social factors.


Assuntos
Vacinas contra Influenza , Influenza Humana , Complicações Infecciosas na Gravidez , Coqueluche , Adulto , Criança , Estudos de Coortes , Registros Eletrônicos de Saúde , Inglaterra/epidemiologia , Feminino , Número de Gestações , Humanos , Influenza Humana/prevenção & controle , Londres , Vacina contra Coqueluche , Gravidez , Determinantes Sociais da Saúde , Vacinação , Coqueluche/prevenção & controle , Adulto Jovem
16.
Appl Clin Inform ; 12(3): 507-517, 2021 05.
Artigo em Inglês | MEDLINE | ID: covidwho-1254109

RESUMO

OBJECTIVES: This article investigates the association between changes in electronic health record (EHR) use during the coronavirus disease 2019 (COVID-19) pandemic on the rate of burnout, stress, posttraumatic stress disorder (PTSD), depression, and anxiety among physician trainees (residents and fellows). METHODS: A total of 222 (of 1,375, 16.2%) physician trainees from an academic medical center responded to a Web-based survey. We compared the physician trainees who reported that their EHR use increased versus those whose EHR use stayed the same or decreased on outcomes related to depression, anxiety, stress, PTSD, and burnout using univariable and multivariable models. We examined whether self-reported exposure to COVID-19 patients moderated these relationships. RESULTS: Physician trainees who reported increased use of EHR had higher burnout (adjusted mean, 1.48 [95% confidence interval [CI] 1.24, 1.71] vs. 1.05 [95% CI 0.93, 1.17]; p = 0.001) and were more likely to exhibit symptoms of PTSD (adjusted mean = 15.09 [95% CI 9.12, 21.05] vs. 9.36 [95% CI 7.38, 11.28]; p = 0.035). Physician trainees reporting increased EHR use outside of work were more likely to experience depression (adjusted mean, 8.37 [95% CI 5.68, 11.05] vs. 5.50 [95% CI 4.28, 6.72]; p = 0.035). Among physician trainees with increased EHR use, those exposed to COVID-19 patients had significantly higher burnout (2.04, p < 0.001) and depression scores (14.13, p = 0.003). CONCLUSION: Increased EHR use was associated with higher burnout, depression, and PTSD outcomes among physician trainees. Although preliminary, these findings have implications for creating systemic changes to manage the wellness and well-being of trainees.


Assuntos
COVID-19/epidemiologia , Educação Médica , Registros Eletrônicos de Saúde/estatística & dados numéricos , Saúde Mental/estatística & dados numéricos , Adulto , Esgotamento Profissional/epidemiologia , Feminino , Humanos , Masculino , Pandemias , Estresse Psicológico/epidemiologia
17.
JAMA Netw Open ; 4(6): e2111621, 2021 06 01.
Artigo em Inglês | MEDLINE | ID: covidwho-1251879

RESUMO

Importance: The influence of the COVID-19 pandemic on fertility rates has been suggested in the lay press and anticipated based on documented decreases in fertility and pregnancy rates during previous major societal and economic shifts. Anticipatory planning for birth rates is important for health care systems and government agencies to accurately estimate size of economy and model working and/or aging populations. Objective: To use projection modeling based on electronic health care records in a large US university medical center to estimate changes in pregnancy and birth rates prior to and after the COVID-19 pandemic societal lockdowns. Design, Setting, and Participants: This cohort study included all pregnancy episodes within a single US academic health care system retrospectively from 2017 and modeled prospectively to 2021. Data were analyzed September 2021. Exposures: Pre- and post-COVID-19 pandemic societal shutdown measures. Main Outcomes and Measures: The primary outcome was number of new pregnancy episodes initiated within the health care system and use of those episodes to project birth volumes. Interrupted time series analysis was used to assess the degree to which COVID-19 societal changes may have factored into pregnancy episode volume. Potential reasons for the changes in volumes were compared with historical pregnancy volumes, including delays in starting prenatal care, interruptions in reproductive endocrinology and infertility services, and preterm birth rates. Results: This cohort study documented a steadily increasing number of pregnancy episodes over the study period, from 4100 pregnancies in 2017 to 4620 in 2020 (28 284 total pregnancies; median maternal [interquartile range] age, 30 [27-34] years; 18 728 [66.2%] White women, 3794 [13.4%] Black women; 2177 [7.7%] Asian women). A 14% reduction in pregnancy episode initiation was observed after the societal shutdown of the COVID-19 pandemic (risk ratio, 0.86; 95% CI, 0.79-0.92; P < .001). This decrease appeared to be due to a decrease in conceptions that followed the March 15 mandated COVID-19 pandemic societal shutdown. Prospective modeling of pregnancies currently suggests that a birth volume surge can be anticipated in summer 2021. Conclusions and Relevance: This cohort study using electronic medical record surveillance found an initial decline in births associated with the COVID-19 pandemic societal changes and an anticipated increase in birth volume. Future studies can further explore how pregnancy episode volume changes can be monitored and birth rates projected in real-time during major societal events.


Assuntos
Coeficiente de Natalidade , COVID-19 , Pandemias , Distanciamento Físico , Isolamento Social , Centros Médicos Acadêmicos , Adulto , Coeficiente de Natalidade/tendências , COVID-19/prevenção & controle , Grupos de Populações Continentais , Registros Eletrônicos de Saúde , Feminino , Fertilidade , Previsões , Humanos , Análise de Séries Temporais Interrompida , Gravidez , Estudos Prospectivos , Estudos Retrospectivos , SARS-CoV-2 , Estados Unidos , Universidades
18.
Sci Rep ; 11(1): 12801, 2021 06 17.
Artigo em Inglês | MEDLINE | ID: covidwho-1275956

RESUMO

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Aprendizado de Máquina , Admissão do Paciente , SARS-CoV-2 , Idoso , COVID-19/virologia , Registros Eletrônicos de Saúde , Feminino , Humanos , Índia/epidemiologia , Masculino , Pessoa de Meia-Idade , Prognóstico , Pontuação de Propensão , Modelos de Riscos Proporcionais , Estudos Prospectivos , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Triagem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...