Showing papers in "Journal of the American Medical Informatics Association in 2022"

TL;DR: Chatbots’ scalability, wide accessibility, ease of use, and fast information dissemination provide complementary functionality that augments public health workers in public health response activities, addressing capacity constraints, social distancing requirements, and misinformation.

TL;DR: In this article , the authors search PubMed/MEDLINE, Web of Knowledge, and Google Scholar to identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic.

TL;DR: Outcome imbalance is not a problem in itself, imbalance correction may even worsen model performance, and inaccurate probability estimates reduce the clinical utility of the model, because decisions about treatment are ill-informed.

TL;DR: The results validated that using customized vocabulary may further improve the performances of domain specific BERT models in clinical NLP tasks and developed and evaluated CancerBERT models to extract the cancer phenotypes in clinical notes and pathology reports.

TL;DR: Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening and addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screened using longitudinal smoking data.

TL;DR: In this article , the authors used multivariable Poisson models to estimate the relative risk of interoperable exchange based on the size of the practice accounting for other characteristics and the mediating role of three factors: Federal incentive programs designed to encourage health IT use, value-based care, and selection of electronic health record developer.

TL;DR: A bias evaluation checklist is introduced that allows model developers and health care providers a means to systematically appraise a model’s potential to introduce bias and offers a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.

TL;DR: In this paper , a cross-sectional study was conducted to characterize EHR smoking data issues and to propose an approach to address these issues using longitudinal smoking data, which enabled the identification of 49.4% more patients potentially eligible for lung cancer screening.

TL;DR: In this article , the authors discuss methods that pediatric primary care organizations can use to collect and document sexual orientation and gender identity information with children and adolescents in electronic health records, taking into consideration children's developmental stages, the role of caregivers, and the need to protect the privacy of this information.

TL;DR: It is proposed that researchers shift away from designing cNLP systems independent of clinical needs, in which cN LP tasks are ends in themselves, and toward systems that are directly guided by the needs of clinicians in realistic decision-making contexts—“tasks as needs.”

TL;DR: In this article , a cancer-related corpus of breast cancer patients was extracted from the electronic health records of a local hospital, and the CancerBERT models were evaluated and compared with other baseline models on the cancer phenotype extraction task.

TL;DR: To develop FHir's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

TL;DR: In this paper , the authors discuss how these changes may lead to confusion and conflict regarding use of health information, both within and across state lines, why current health information security practices may need to be reconsidered, and what policy options may be possible to protect individuals' health information.

TL;DR: Telehealth-delivered care for racial/ethnic minorities offers promise across a range of conditions and outcomes, particularly when delivered in the patient’s preferred language, however, telehealth may be problematic for some due to cost and limited digital and health literacy.

TL;DR: In this paper , a systematic review explored the use of telehealth consultations for people from racial/ethnic minority populations in relation to health outcomes, access to care, implementation facilitators and barriers, and satisfaction with care.

TL;DR: The use of unstructured clinical text data in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies, and a future focus on explainability and external validation of the developed models is suggested.

TL;DR: In this article , the authors developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes.

TL;DR: A governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care is described, which is currently managing 52 models.

TL;DR: In this article , the authors developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes.

TL;DR: BigMouth allows for assessing the oral health status of a diverse US patient population; provides rationale and evidence for new oral health care delivery modes; and embraces the specific oral health research education mission.

TL;DR: It is found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use.

TL;DR: In this paper , the authors developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to several dimensions of the diagnostic process based on notes review and recall of recent “at-risk” visits.

TL;DR: Methods that pediatric primary care organizations can use to collect and document sexual orientation and gender identity information with children and adolescents in electronic health records are discussed.

TL;DR: In this article , a review-level evidence on the challenges associated with accessibility of virtual care among underserved population groups and to identify strategies that can improve access to, uptake of, and engagement with virtual care for these populations is presented.

TL;DR: In this article , the authors identify the risk factors home healthcare clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors using directed content analysis to identify risk factors for deterioration.

TL;DR: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections as unique properties of centralized data are harnessed to enable unit inference.

TL;DR: In this paper , the authors conducted interviews and surveys with pragmatic clinical trials (PCTs) that used electronic health record (EHR) data to ascertain endpoints, and found that the most common concerns about EHR data were data reliability, missingness or incompleteness of EHR elements, variation in data quality across study sites, and presence of implausible or incorrect values.

TL;DR: In this article , the authors assess and compare electronic health record (EHR) documentation of chronic disease in problem lists and encounter diagnosis records among Community Health Center (CHC) patients.

TL;DR: The policy approaches suggested by the review are likely to be difficult to achieve in a given policy context, but essential to a more equitable future for virtual care.