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OBJECTIVE: This study aimed to determine whether clinically integrated Breastfeeding Peer Counseling (ci-BPC) added to usual lactation care reduces disparities in breastfeeding intensity and duration for Black and Hispanic/Latine participants. STUDY DESIGN: This study is a pragmatic, randomized control trial (RCT) of ci-BPC care at two ci-BPC-naïve obstetrical hospital facilities in the greater Chicago area. Participants will include 720 patients delivering at Hospital Site 1 and Hospital Site 2 who will be recruited from eight prenatal care sites during midpregnancy. Participants must be English or Spanish speaking, planning to parent their child, and have no exposure to ci-BPC care prior to enrollment. Randomization will be stratified by race and ethnicity to create three analytic groups: Black, Hispanic/Latine, and other races. RESULTS: The primary outcome will be breastfeeding duration. Additional outcomes will include the proportion of breastmilk feeds during the delivery admission, at 6-week postdelivery, and at 6-month postdelivery. A process evaluation will be conducted to understand implementation outcomes, facilitators, and barriers to inform replication and scaling of the innovative ci-BPC model. CONCLUSION: This research will produce findings of relevance to perinatal patients and their families, the vast majority of whom desire to provide breastmilk to their infants and require support to succeed with their feeding goals. As the largest RCT of ci-BPC in the United States to date, this research will improve the quality of evidence available regarding the effectiveness of ci-BPC at reducing disparities. These findings will help patients and stakeholders determine the benefits of accepting and adopting the program and inform policies focused on improving perinatal care and reducing maternal/child health disparities. This study is registered with Clinical Trial (identifier: NCT05441709). KEY POINTS: · Ci-BPC can promote racial breastfeeding equity.. · Ci-BPC has not been tested as a generalized lactation strategy in prior trials and is underused.. · This RCT will identify if ci-BPC can reduce breastfeeding disparities for Black and Hispanic patients..
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BACKGROUND: Prior studies of early antibiotic use and growth have shown mixed results, primarily on cross-sectional outcomes. This study examined the effect of oral antibiotics before age 24 months on growth trajectory at age 2-5 years. METHODS: We captured oral antibiotic prescriptions and anthropometrics from electronic health records through PCORnet, for children with ≥1 height and weight at 0-12 months of age, ≥1 at 12-30 months, and ≥2 between 25 and 72 months. Prescriptions were grouped into episodes by time and by antimicrobial spectrum. Longitudinal rate regression was used to assess differences in growth rate from 25 to 72 months of age. Models were adjusted for sex, race/ethnicity, steroid use, diagnosed asthma, complex chronic conditions, and infections. RESULTS: 430,376 children from 29 health U.S. systems were included, with 58% receiving antibiotics before 24 months. Exposure to any antibiotic was associated with an average 0.7% (95% CI 0.5, 0.9, p < 0.0001) greater rate of weight gain, corresponding to 0.05 kg additional weight. The estimated effect was slightly greater for narrow-spectrum (0.8% [0.6, 1.1]) than broad-spectrum (0.6% [0.3, 0.8], p < 0.0001) drugs. There was a small dose response relationship between the number of antibiotic episodes and weight gain. CONCLUSION: Oral antibiotic use prior to 24 months of age was associated with very small changes in average growth rate at ages 2-5 years. The small effect size is unlikely to affect individual prescribing decisions, though it may reflect a biologic effect that can combine with others.
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Antibacterianos , Estatura , Antibacterianos/uso terapêutico , Criança , Pré-Escolar , Estudos Transversais , Humanos , Lactente , Prescrições , Aumento de PesoRESUMO
Objective: Asthma exacerbations are associated with significant morbidity, mortality, and cost. Accurately identifying asthma patients at risk for exacerbation is essential. We sought to develop a risk prediction tool based on routinely collected data from electronic health records (EHRs).Methods: From a repository of EHRs data, we extracted structured data for gender, race, ethnicity, smoking status, use of asthma medications, environmental allergy testing BMI status, and Asthma Control Test scores (ACT). A subgroup of this population of patients with asthma that had available prescription fill data was identified, which formed the primary population for analysis. Asthma exacerbation was defined as asthma-related hospitalization, urgent/emergent visit or oral steroid use over a 12-month period. Univariable and multivariable statistical analysis was completed to identify factors associated with exacerbation. We developed and tested a risk prediction model based on the multivariable analysis.Results: We identified 37,675 patients with asthma. Of those, 1,787 patients with asthma and fill data were identified, and 979 (54.8%) of them experienced an exacerbation. In the multivariable analysis, smoking (OR = 1.69, CI: 1.08-2.64), allergy testing (OR = 2.40, CI: 1.54-3.73), obesity (OR = 1.66, CI: 1.29-2.12), and ACT score reflecting uncontrolled asthma (OR = 1.66, CI: 1.10-2.29) were associated with increased risk of exacerbation. The area-under-the-curve (AUC) of our model in a combined derivation and validation cohort was 0.67.Conclusion: Despite use of rigorous methodology, we were unable to produce a predictive model with an acceptable degree of accuracy and AUC to be clinically useful.
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Antiasmáticos/administração & dosagem , Asma/diagnóstico , Hospitalização/estatística & dados numéricos , Exacerbação dos Sintomas , Administração por Inalação , Administração Oral , Adulto , Asma/tratamento farmacológico , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Volume Expiratório Forçado , Glucocorticoides/administração & dosagem , Humanos , Hipersensibilidade/epidemiologia , Modelos Logísticos , Masculino , Obesidade/epidemiologia , Curva ROC , Medição de Risco/métodos , Fatores de Risco , Índice de Gravidade de Doença , Fumar/epidemiologia , Inquéritos e QuestionáriosRESUMO
Meta-analyses are becoming increasingly popular, especially in the fields of cardiovascular disease prevention and treatment. They are often considered to be a reliable source of evidence for making healthcare decisions. Unfortunately, problems among meta-analyses such as the misapplication and misinterpretation of statistical methods and tests are long-standing and widespread. The purposes of this statement are to review key steps in the development of a meta-analysis and to provide recommendations that will be useful for carrying out meta-analyses and for readers and journal editors, who must interpret the findings and gauge methodological quality. To make the statement practical and accessible, detailed descriptions of statistical methods have been omitted. Based on a survey of cardiovascular meta-analyses, published literature on methodology, expert consultation, and consensus among the writing group, key recommendations are provided. Recommendations reinforce several current practices, including protocol registration; comprehensive search strategies; methods for data extraction and abstraction; methods for identifying, measuring, and dealing with heterogeneity; and statistical methods for pooling results. Other practices should be discontinued, including the use of levels of evidence and evidence hierarchies to gauge the value and impact of different study designs (including meta-analyses) and the use of structured tools to assess the quality of studies to be included in a meta-analysis. We also recommend choosing a pooling model for conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodological similarities among studies to be included, rather than the results of a test for statistical heterogeneity.
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Cardiopatias/prevenção & controle , Cardiopatias/terapia , American Heart Association , Feminino , Humanos , Masculino , Estados UnidosRESUMO
The problems of poor or biased information and of misleading health and well-being advice on the Internet have been extensively documented. The recent decision by the Internet Corporation for Assigned Names and Numbers to authorize a large number of new generic, top-level domains, including some with a clear connection to health or healthcare, presents an opportunity to bring some order to this chaotic situation. In the case of the most general of these domains, ".health," experts advance a compelling argument in favor of some degree of content oversight and control. On the opposing side, advocates for an unrestricted and open Internet counter that this taken-for-granted principle is too valuable to be compromised, and that, once lost, it may never be recovered. We advance and provide evidence for a proposal to bridge the credibility gap in online health information by providing provenance information for websites in the .health domain.
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Informação de Saúde ao Consumidor/ética , Troca de Informação em Saúde/ética , Disseminação de Informação , Internet/ética , Educação de Pacientes como Assunto/ética , HumanosRESUMO
The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a major health system in the United States to reduce Heart Failure (HF)-related hospitalizations and readmissions using wearable biosensors to collect physiological data over a 30-day period to determine decompensation risk among HF patients. The alerts produced, coupled with electronic patient-reported outcomes, are utilized daily by the home health team, and escalated to the heart failure team as needed, for proactive actions. Limited research has examined anticipating the implementation and workflow challenges of such complex CRPM studies such as resource planning and staffing decisions that leverage the recorded data to drive clinical preparedness and operational efficiency. This preliminary analysis applies discrete event simulation modeling to the Cascade-HF protocol using pilot data from a soft launch to assess workload of the clinical team, evaluate escalation patterns and provide decision support recommendations to enable scale-up for all post-discharge patients.
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Insuficiência Cardíaca , Alta do Paciente , Humanos , Assistência ao Convalescente , Fluxo de Trabalho , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/terapia , Monitorização FisiológicaRESUMO
BACKGROUND: Inefficient electronic health record (EHR) usage increases the documentation burden on physicians and other providers, which increases cognitive load and contributes to provider burnout. Studies show that EHR efficiency sessions, optimization sprints, reduce burnout using a resource-intense five-person team. We implemented sprint-inspired one-on-one post-go-live efficiency training sessions (mini-sprints) as a more economical training option directed at providers. OBJECTIVES: We evaluated a post-go-live mini-sprint intervention to assess provider satisfaction and efficiency. METHODS: NorthShore University HealthSystem implemented one-on-one provider-to-provider mini-sprint sessions to optimize provider workflow within the EHR platform. The physician informaticist completed a 9-point checklist of efficiency tips with physician trainees covering schedule organization, chart review, speed buttons, billing, note personalization/optimization, preference lists, quick actions, and quick tips. We collected postsession survey data assessing for net promoter score (NPS) and open-ended feedback. We conducted financial analysis of pre- and post-mini-sprint efficiency levels and financial data. RESULTS: Seventy-six sessions were conducted with 32 primary care physicians, 28 specialty physicians, and 16 nonphysician providers within primary care and other areas. Thirty-seven physicians completed the postsession survey. The average NPS for the completed mini-sprint sessions was 97. The proficiency score had a median of 6.12 (Interquartile range (IQR): 4.71-7.64) before training, and a median of 7.10 (IQR: 6.25-8.49) after training. Financial data analysis indicates that higher level billing codes were used at a greater frequency post-mini-sprint. The revenue increase 12 months post-mini-sprint was $213,234, leading to a return of $75,559.50 for 40 providers, or $1,888.98 per provider in a 12-month period. CONCLUSION: Our data show that mini-sprint sessions were effective in optimizing efficiency within the EHR platform. Financial analysis demonstrates that this type of training program is sustainable and pays for itself. There was high satisfaction with the mini-sprint training modality, and feedback indicated an interest in further mini-sprint training sessions for physicians and nonphysician staff.
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Registros Eletrônicos de Saúde , Humanos , Satisfação Pessoal , MédicosRESUMO
Background: This research delves into the confluence of racial disparities and health inequities among individuals with disabilities, with a focus on those contending with both diabetes and visual impairment. Methods: Utilizing data from the TriNetX Research Network, which includes electronic medical records of roughly 115 million patients from 83 anonymous healthcare organizations, this study employs a directed acyclic graph (DAG) to pinpoint confounders and augment interpretation. We identified patients with visual impairments using ICD-10 codes, deliberately excluding diabetes-related ophthalmology complications. Our approach involved multiple race-stratified analyses, comparing co-morbidities like chronic pulmonary disease in visually impaired patients against their counterparts. We assessed healthcare access disparities by examining the frequency of annual visits, instances of two or more A1c measurements, and glomerular filtration rate (GFR) measurements. Additionally, we evaluated diabetes outcomes by comparing the risk ratio of uncontrolled diabetes (A1c > 9.0) and chronic kidney disease in patients with and without visual impairments. Results: The incidence of diabetes was substantially higher (nearly double) in individuals with visual impairments across White, Asian, and African American populations. Higher rates of chronic kidney disease were observed in visually impaired individuals, with a risk ratio of 1.79 for African American, 2.27 for White, and non-significant for the Asian group. A statistically significant difference in the risk ratio for uncontrolled diabetes was found only in the White cohort (0.843). White individuals without visual impairments were more likely to receive two A1c tests, a trend not significant in other racial groups. African Americans with visual impairments had a higher rate of glomerular filtration rate testing. However, White individuals with visual impairments were less likely to undergo GFR testing, indicating a disparity in kidney health monitoring. This pattern of disparity was not observed in the Asian cohort. Conclusions: This study uncovers pronounced disparities in diabetes incidence and management among individuals with visual impairments, particularly among White, Asian, and African American groups. Our DAG analysis illuminates the intricate interplay between SDoH, healthcare access, and frequency of crucial diabetes monitoring practices, highlighting visual impairment as both a medical and social issue.
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[This corrects the article DOI: 10.1017/cts.2023.670.].
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OBJECTIVES: To review publications in the field of Human Factors and Organisational Issues (HF&OI) in the year 2022 and to assess major contributions to the subject. METHOD: A bibliographic search was conducted following refinement of standardized queries used in previous years. Sources used were PubMed, Web of Science, and referral via references from other papers. The search was carried out in January 2023, and (using the PubMed article type inclusion functionality) included clinical trials, meta-analyses, randomized controlled trials, reviews, case reports, classical articles, clinical studies, observational studies (including veterinary), comparative studies, and pragmatic clinical trials. RESULTS: Among the 520 returned papers published in 2022 in the various areas of HF&OI, the full review process selected two best papers from among 10 finalists. As in previous years, topics showed development including increased use of Artificial Intelligence (AI) and digital health tools, advancement of methodological frameworks for implementation and evaluation as well as design, and trials of specific digital tools. CONCLUSIONS: Recent literature in HF&OI continues to focus on both theoretical advances and practical deployment, with focus on areas of patient-facing digital health, methods for design and evaluation, and attention to implementation barriers.
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Inteligência Artificial , HumanosRESUMO
INTRODUCTION: Penicillin allergy is the most commonly reported drug allergy in the United States. Patients labeled with penicillin allergy are at risk of receiving broad-spectrum antibiotics for surgical site infection prophylaxis, which can lead to increased antibiotic resistance, higher morbidity, suboptimal antibiotic therapy, and higher medical costs. This study aimed to determine the true prevalence of penicillin allergy among surgical patients and to decrease the unnecessary use of broad-spectrum antibiotics. METHODS: A retrospective chart review was performed of patients who underwent urogynecologic surgery in 2017. In 2018, a quality initiative was started, and all patients reporting penicillin allergies were offered antibiotic allergy testing as part of their preoperative testing. RESULTS: In 2017, 15% of patients reported penicillin allergy and 52% of them received surgical prophylaxis with broad-spectrum antibiotics. In 2018, 463 patients underwent surgery, 55 of whom reported penicillin allergy and were offered penicillin allergy testing. 35 (64%) agreed to proceed with testing, and of those tested, 33 (94%) tested negative for penicillin allergy. CONCLUSIONS: 94% of patients with stated penicillin allergy who consented to allergy testing proved to have negative test. Penicillin allergy testing should be considered as part of preoperative management.
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Hipersensibilidade a Drogas , Hipersensibilidade , Humanos , Penicilinas/efeitos adversos , Estudos Retrospectivos , Antibacterianos/efeitos adversos , Hipersensibilidade a Drogas/diagnóstico , Hipersensibilidade a Drogas/tratamento farmacológico , Hipersensibilidade a Drogas/epidemiologia , Hipersensibilidade/tratamento farmacológicoRESUMO
Introduction: While data repositories are well-established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied. Methods: We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology. Results: All 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine-readable formats, include implementation guidelines, and adhere to standards to convey Responsibility. Repositories report having Technology functions that enable end-users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability, less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision-making. Conclusion: It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution.
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Objectives: Introduce the CDS-Sandbox, a cloud-based virtual machine created to facilitate Clinical Decision Support (CDS) developers and implementers in the use of FHIR- and CQL-based open-source tools and technologies for building and testing CDS artifacts. Materials and Methods: The CDS-Sandbox includes components that enable workflows for authoring and testing CDS artifacts. Two workshops at the 2020 and 2021 AMIA Annual Symposia were conducted to demonstrate the use of the open-source CDS tools. Results: The CDS-Sandbox successfully integrated the use of open-source CDS tools. Both workshops were well attended. Participants demonstrated use and understanding of the workshop materials and provided positive feedback after the workshops. Discussion: The CDS-Sandbox and publicly available tutorial materials facilitated an understanding of the leading-edge open-source CDS infrastructure components. Conclusion: The CDS-Sandbox supports integrated use of the key CDS open-source tools that may be used to introduce CDS concepts and practice to the clinical informatics community.
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Background: Pulmonary fibrosis is characterized by lung parenchymal destruction and can increase morbidity and mortality. Pulmonary fibrosis commonly occurs following hospitalization for SARS-CoV-2 infection. As there are medications that modify pulmonary fibrosis risk, we investigated whether distinct pharmacotherapies (amiodarone, cancer chemotherapy, corticosteroids, and rituximab) are associated with differences in post-COVID-19 pulmonary fibrosis incidence. Methods: We used the National COVID-19 Cohort Collaboration (N3C) Data Enclave, which aggregates and harmonizes COVID-19 data across the United States, to assess pulmonary fibrosis incidence documented at least 60 days after COVID-19 diagnosis among adults hospitalized between January 1st, 2020 and July 6th, 2022 without pre-existing pulmonary fibrosis. We used propensity scores to match pre-COVID-19 drug-exposed and unexposed cohorts (1:1) based on covariates with known influence on pulmonary fibrosis incidence, and estimated the association of drug exposure with risk for post-COVID-19 pulmonary fibrosis. Sensitivity analyses considered pulmonary fibrosis incidence documented at least 30- or 90-days post-hospitalization and pulmonary fibrosis incidence in the COVID-19-negative N3C population. Findings: Among 5,923,394 patients with COVID-19, we analyzed 452,951 hospitalized adults, among whom pulmonary fibrosis incidence was 1.1 per 100-person-years. 277,984 hospitalized adults with COVID-19 were included in our primary analysis, among whom all drug exposed cohorts were well-matched to unexposed cohorts (standardized mean differences <0.1). The post-COVID-19 pulmonary fibrosis incidence rate ratio (IRR) was 2.5 (95% CI 1.2-5.1, P = 0.01) for rituximab, 1.6 (95% CI 1.3-2.0, P < 0.0001) for chemotherapy, and 1.2 (95% CI 1.0-1.3, P = 0.02) for corticosteroids. Amiodarone exposure had no significant association with post-COVID-19 pulmonary fibrosis (IRR = 0.8, 95% CI 0.6-1.1, P = 0.24). In sensitivity analyses, pre-COVID-19 corticosteroid use was not consistently associated with post-COVID-19 pulmonary fibrosis. In the COVID-19 negative hospitalized population (n = 1,240,461), pulmonary fibrosis incidence was lower overall (0.6 per 100-person-years) and for patients exposed to all four drugs. Interpretation: Recent rituximab or cancer chemotherapy before COVID-19 infection in hospitalized patients is associated with increased risk for post-COVID-19 pulmonary fibrosis. Funding: The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v1.2-2020-08-25b supported by NIHK23HL146942, NIHK08HL150291, NIHK23HL148387, NIHUL1TR002389, NCATSU24 TR002306, and a SECURED grant from the Walder Foundation/Center for Healthcare Delivery Science and Innovation, University of Chicago. WFP received a grant from the Greenwall Foundation. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource (https://doi.org/10.1093/jamia/ocaa196).
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Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on "hyper-local" data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.
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BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.
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COVID-19 , Síndrome de COVID-19 Pós-Aguda , Humanos , Progressão da Doença , SARS-CoV-2RESUMO
OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021. METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams. RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section. CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.
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COVID-19 , Informática Médica , Mídias Sociais , Humanos , Registros Eletrônicos de Saúde , MEDLINERESUMO
Developing clinical quality champions is an important strategy for improving health care quality. The NorthShore Quality and Patient Safety Fellowship was a yearlong program for practicing physicians devoting 4 hours/wk to a didactic curriculum and quality practicum. Thirty-seven clinicians completed the Fellowship from 2011 to 2018. Sixty percent of graduates reported a significant impact on their quality-related career trajectory, with 44% of early graduates and 64% of recent graduates reporting a new quality role or responsibility as a result of the Fellowship. Fifty-four percent of practicum projects were adopted or adapted by the organization. The Fellowship has been an effective framework to identify and train future quality champions and has led to further quality leadership opportunities for many graduates. Evolution of the Fellowship aligned practicum projects with organizational quality priorities. This curricular framework may be useful for other organizations that seek to develop quality champions among practicing physicians.
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Bolsas de Estudo , Segurança do Paciente , Currículo , Humanos , Liderança , Qualidade da Assistência à Saúde , Inquéritos e QuestionáriosRESUMO
Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.