Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability.

Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS.

Advanced Care Planning Content Encoding with Natural Language Processing.

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.

Health Information Technology Use among Chronic Disease Patients: An Analysis of the United States Health Information National Trends Survey.

Background: Chronic disease is the leading cause of mortality in the United States. Health information technology (HIT) tools show promise for improving disease management.

Objectives: This study aims to understand the following: (1) how self-perceptions of health compare between those with and without disease; (2) how HIT usage varies between chronic disease profiles (diabetes, hypertension, cardiovascular disease, pulmonary disease, depression, cancer, and comorbidities); (3) how HIT trends have changed in the past 6 years; and (4) the likelihood that a given chronic disease patient uses specific HIT tools.

Rapid implementation of a COVID-19 remote patient monitoring program.

Objective: The study sought to evaluate early lessons from a remote patient monitoring engagement and education technology solution for patients with coronavirus disease 2019 (COVID-19) symptoms.

Materials And Methods: A COVID-19-specific remote patient monitoring solution (GetWell Loop) was offered to patients with COVID-19 symptoms. The program engaged patients and provided educational materials and the opportunity to share concerns.

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

Background: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness.

Predictors of elderly mortality after trauma: A novel outcome score.

Introduction: Elderly trauma patients are at high risk for mortality, even when presenting with minor injuries. Previous prognostic models are poorly used because of their reliance on elements unavailable during the index hospitalization. The purpose of this study was to develop a predictive algorithm to accurately estimate in-hospital mortality using easily available metrics.

Using word embeddings to expand terminology of dietary supplements on clinical notes.

Objective: The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes.

Methods: Word embedding models (ie, word2vec and GloVe) trained on clinical notes were used to predefine a list of top 40 semantically related terms for each of 14 commonly used DS. Each list was further evaluated by experts to generate semantically similar terms.

Electronic Progress Note Reading Patterns: An Eye Tracking Analysis.

This study used eye-tracking to understand how the order of note sections influences the way physicians read electronic progress notes. Participants (n = 7) wore an eye-tracking device while reviewing progress notes for four patient cases and then provided a verbal summary. We reviewed and analyzed verbal summaries and eye tracking recordings.

Named Entity Recognition in Prehospital Trauma Care.

Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy.

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.

Challenges and Opportunities to Improve the Clinician Experience Reviewing Electronic Progress Notes.

Background: High-quality clinical notes are essential to effective clinical communication. However, electronic clinical notes are often long, difficult to review, and contain information that is potentially extraneous or out of date. Additionally, many clinicians write electronic clinical notes using customized templates, resulting in notes with significant variability in structure.

Characterizing Functional Health Status of Surgical Patients in Clinical Notes.

Functional health status is an important factor not only for determining overall health, but also for measuring risks of adverse events. Our hypothesis is that important functional status data is contained in clinical notes. We found that several categories of phrases related to functional status including diagnoses, activity and care assessments, physical exam, functional scores, assistive equipment, symptoms, and surgical history were important factors.

Comparing Existing Resources to Represent Dietary Supplements.

Dietary supplements, often considered as food, are widely consumed despite of limited knowledge around their safety/efficacy and any well-established regulatory policies, unlike their drug counterparts. Informatics methods may be useful in filling this knowledge gap, however, the lack of standardized representation of DS hinders this progress. In this pilot study, five electronic DS resources, i.

Usability Evaluation of an Unstructured Clinical Document Query Tool for Researchers.

Natural Language Processing - Patient Information Extraction for Researchers (NLP-PIER) was developed for clinical researchers for self-service Natural Language Processing (NLP) queries with clinical notes. This study was to conduct a user-centered analysis with clinical researchers to gain insight into NLP-PIER's usability and to gain an understanding of the needs of clinical researchers when using an application for searching clinical notes. Clinical researcher participants (n=11) completed tasks using the system's two existing search interfaces and completed a set of surveys and an exit interview.

Residence, Living Situation, and Living Conditions Information Documentation in Clinical Practice.

Social determinants of health (SDOH) have an important role in diagnosis, prevention, health outcomes, and quality of life. Currently, SDOH information in electronic health record (EHR) systems is often contained in unstructured text. The objective of this study is to examine an important subset of SDOH documentation for Residence, Living Situation and Living Conditions in an enterprise EHR informed by previous model representations.

Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation.

As individuals age, there is potential for dramatic changes in the social and behavioral determinants that affect health status and outcomes. The importance of these determinants has been increasingly recognized in clinical decision-making. We sought to characterize how social and behavioral health determinants vary in different demographic groups using a previously established schema of 28 social history types through both manual analysis and automated topic analysis of social documentation in the electronic health record across the population of an entire integrated healthcare system.

Usability Evaluation of NLP-PIER: A Clinical Document Search Engine for Researchers.

NLP-PIER (Natural Language Processing - Patient Information Extraction for Research) is a self-service platform with a search engine for clinical researchers to perform natural language processing (NLP) queries using clinical notes. We conducted user-centered testing of NLP-PIER's usability to inform future design decisions. Quantitative and qualitative data were analyzed.

Assessing the Representation of Occupation Information in Free-Text Clinical Documents Across Multiple Sources.

There has been increasing recognition of the key role of social determinants like occupation on health. Given the relatively poor understanding of occupation information in electronic health records (EHRs), we sought to characterize occupation information within free-text clinical document sources. From six distinct clinical sources, 868 total occupation-related sentences were identified for the study corpus.

Representation of occupational information across resources and validation of the occupational data for health model.

Reports by the National Academy of Medicine and leading public health organizations advocate including occupational information as part of an individual's social context. Given recent National Academy of Medicine recommendations on occupation-related data in the electronic health record, there is a critical need for improved representation. The National Institute for Occupational Safety and Health has developed an Occupational Data for Health (ODH) model, currently in draft format.

Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.

The electronic health record (EHR) provides an opportunity for improved use of clinical documentation including leveraging tobacco use information by clinicians and researchers. In this study, we investigated the content, consistency, and completeness of tobacco use data from structured and unstructured sources in the EHR. A natural language process (NLP) pipeline was utilized to extract details about tobacco use from clinical notes and free-text tobacco use comments within the social history module of an EHR system.

Automated Extraction of Substance Use Information from Clinical Texts.

Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g.