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    Identifying Asthma-related Symptoms from Electronic Health Records within a Large Integrated Healthcare System: Hybrid Natural Language Processing Approach (Preprint)
    Fagen XieRobert S. ZeigerMary SaparudinSahar Al-SalmanEric J. PuttockWilliam CrawfordMichael SchätzStanley XuWilliam M. VollmerWansu Chen
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    Abstract:
    BACKGROUND Asthma-related symptoms are significant predictors of asthma exacerbation. Most of these symptoms are documented in clinical notes in free text format. Methods that can effectively capture the asthma-related symptoms from the unstructured data are lacking. OBJECTIVE The study aims to develop a natural language process (NLP) algorithm and process to identify symptoms associated with asthma from clinical notes within a large integrated healthcare system. METHODS We used unstructured data within two years prior to asthma diagnosis visits in 2013-2018 and 2021-2022 to identify four common asthma-related symptoms. Related terms and phrases were first compiled from publicly available resources and then recursively reviewed and enriched with inputs from clinicians and chart review. A rule-based NLP algorithm was first iteratively developed and refined via multiple rounds of chart review followed by adjudication, and then transformer-based deep learning algorithms were developed and validated using the same manually annotated datasets. Subsequently, a hybrid algorithm was generated by combining the rule-based and the transformer-based algorithms. Finally, the developed algorithms were implemented in all the study notes. RESULTS A total of 11,374,552 eligible study clinical notes with 128,211,793 sentences were retrieved. At least one symptom was identified in 1,663,450 (1.30%) sentences and 858,350 (7.55%) notes, respectively. Cough had the highest frequency at both sentence (1.07%) and note (5.81%) levels while chest tightness had the lowest one at both sentence (0.11%) and note (0.57%) levels. The frequencies of concomitant symptoms ranged from 0.03% to 0.38% at the sentence level and 0.10% to 1.85% at the note level. The validation of the hybrid algorithm against the annotated result of 1,600 clinical notes yielded a positive predictive value ranging from 96.53% (wheezing) to 97.42% (chest tightness) at the sentence level and 96.76% (wheezing) to 97.42% (chest tightness) at the note level, sensitivity ranged from 93.90% (dyspnea) to 95.95% (cough) at the sentence level and 96.00% (chest tightness) to 99.07% (cough) at the note level. The corresponding F1 scores of all four symptoms were > 0.95 at both sentence and note levels regardless of NLP algorithms. CONCLUSIONS The developed NLP algorithms could effectively capture asthma-related symptoms from unstructured notes. These algorithms could be utilized to examine asthma burden and prediction of asthma exacerbation.
    Keywords:
    Preprint
    Health records
    Electronic health record
    Topics:
    Machine Learning in Healthcare
    Topic Modeling
    Explainable Artificial Intelligence (XAI)
    Patients’ Use of Electronic Health Records Facilitates Patient-Centered Communication: Findings From the 2017 Health Information National Trends Survey (Preprint)
    Suhwoo AhnChul-joo LeeInhwan Bae
    BACKGROUND Patient-centered communication refers to interaction between patients and health professionals that considers patients’ preferences and empowers patients to contribute to their own care. Research suggests that patient-centered communication promotes patients’ satisfaction with care, trust in physicians, and competence in their abilities to manage their health. OBJECTIVE The study aims to explore the role of patients’ use of electronic health records (EHRs) in promoting patient-centered communication. Specifically, we investigated how health information efficacy mediates the association of EHR use with patient-centered communication and whether and how the relationship between EHR use and health information efficacy varies according to patients’ perceived social support levels. METHODS We conducted mediation and multigroup analyses using nationally representative data from the Health Information National Trends Survey 5 cycle 1 conducted in the United States (N=3285). Among respondents, we analyzed those who received care from health professionals over the previous year (2823/3285, 85.94%). RESULTS EHR use by patients was associated with high levels of health information efficacy (unstandardized coefficient=0.050, SE 0.024; <i>P</i>=.04). In turn, health information efficacy was positively related to patient-centered communication (unstandardized coefficient=0.154, SE 0.024; <i>P</i>&lt;.001). The indirect pathway from EHR use to patient-centered communication, mediated by health information efficacy, was statistically significant (unstandardized coefficient=0.008, SE 0.004; <i>P</i>=.04). Among patients with high social support (2349/2823, 83.21%), EHR use was not significantly associated with health information efficacy (unstandardized coefficient=0.038, SE 0.026; <i>P</i>=.15), although health information efficacy was linked to high levels of patient-centered communication (unstandardized coefficient=0.151, SE 0.030; <i>P</i>&lt;.001). The indirect relationship in this group was not significant (unstandardized coefficient=0.006, SE 0.004; <i>P</i>=.11). However, among those with low social support (474/2823, 16.79%), EHR use was positively associated with health information efficacy (unstandardized coefficient=0.155, SE 0.048; <i>P</i>=.001), which in turn relates to high levels of patient-centered communication (unstandardized coefficient=0.137, SE 0.050; <i>P</i>=.01). The indirect pathway was also significant (unstandardized coefficient=0.021, SE 0.010; <i>P</i>=.03). CONCLUSIONS Patients who use EHRs may build health information efficacy, which seems to promote communication between patients and health care providers. This indirect pathway was not detected among patients with high social support. However, among those with low social support, EHR use seems to enhance health information efficacy, which may in turn facilitate patient-centered communication. Given the nature of the dataset used, the findings of this study are more relevant to the United States than other contexts.
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    Using electronic health records for clinical trials: Where do we stand and where can we go?
    Canadian Medical Association Journal (2019)
    Kimberly A. Mc CordLars G. Hemkens
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    Development of a Novel Electronic Record System for Documentation and Efficient Workflow for Community Health Workers (Preprint)
    Harshdeep AcharyaKevin J. SykesTon Mirás NeiraAngela ScottChristina M. Pacheco
    BACKGROUND The COVID-19 pandemic added to the decades of evidence that public health institutions are routinely stretched beyond their capacity. Community health workers (CHWs) can be a crucial extension of public health resources to address health inequities, but systems to document CHW efforts are often fragmented and prone to unneeded redundancy, errors, and inefficiency. OBJECTIVE We sought to develop a more efficient data collection system for recording the wide range of community-based efforts performed by CHWs. METHODS The Communities Organizing to Promote Equity (COPE) project is an initiative to address health disparities across Kansas, in part, through the deployment of CHWs. Our team iteratively designed and refined the features of a novel data collection system for CHWs. Pilot tests with CHWs occurred over several months to ensure that the functionality supported their daily use. Following implementation of the database, procedures were set to sustain the collection of feedback from CHWs, community partners, and organizations with similar systems to continually modify the database to meet the needs of users. A continuous quality improvement process was conducted monthly to evaluate CHW performance; feedback was exchanged at team and individual levels regarding the continuous quality improvement results and opportunities for improvement. Further, a 15-item feedback survey was distributed to all 33 COPE CHWs and supervisors for assessing the feasibility of database features, accessibility, and overall satisfaction. RESULTS At launch, the database had 60 active users in 20 counties. Documented client interactions begin with needs assessments (modified versions of the Arizona Self-sufficiency Matrix and PRAPARE [Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences]) and continue with the longitudinal tracking of progress toward goals. A user-specific automated alerts-based dashboard displays clients needing follow-up and upcoming events. The database contains over 55,000 documented encounters across more than 5079 clients. Available resources from over 2500 community organizations have been documented. Survey data indicated that 84% (27/32) of the respondents considered the overall navigation of the database as very easy. The majority of the respondents indicated they were overall very satisfied (14/32, 44%) or satisfied (15/32, 48%) with the database. Open-ended responses indicated the database features, documentation of community organizations and visual confirmation of consent form and data storage on a Health Insurance Portability and Accountability Act–compliant record system, improved client engagement, enrollment processes, and identification of resources. CONCLUSIONS Our database extends beyond conventional electronic medical records and provides flexibility for ever-changing needs. The COPE database provides real-world data on CHW accomplishments, thereby improving the uniformity of data collection to enhance monitoring and evaluation. This database can serve as a model for community-based documentation systems and be adapted for use in other community settings.
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    Shared Decision Making Tools Implemented in the Electronic Health Record: A Scoping Review (Preprint)
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    BACKGROUND Shared decision making (SDM) is a model of patient-centered care that encourages patients and clinicians to work together to reach medical decisions by weighing the risks and benefits of various options within the context of the values and goals of the patient. Despite the interest in incorporating SDM into routine care, current research studies identify various obstacles that limit SDM adoption. These obstacles include technical integration issues, logistical and workflow challenges, and psychological impediments such as uncertainty and legacy belief systems, which continue to impede progress. Integrating SDM tools and processes into EHR systems is often a complex and challenging problem. OBJECTIVE We aimed to understand the integration and implementation characteristics of reported Shared Decision Making (SDM) interventions integrated into an electronic health record (EHR) system. METHODS We conducted a scoping review using Arksey and O’Malleys' methodologic framework with guidance from the Joanna Briggs Institute. RESULTS A total of 19 studies of 2153 were included in the final review. There is a high degree of variation across studies, including SDM definitions, standardized measures, technical integration, and implementation strategies. SDM tools that target established healthcare processes promoted use. Integrating SDM templates and tools into an EHR appeared to improve the outcomes for most studies. Most SDM interventions were designed for clinicians. Patient-specific goals and values were not included in several studies. The two most common study outcome measures were patient satisfaction and SDM tool use. CONCLUSIONS Understanding the approaches for presenting SDM tools directly into a clinician’s workflow within the EHR is a logical approach to promoting SDM into routine clinical practice. This review contributes to the literature by illuminating features of SDM tools that have been integrated into an EHR system. Standardization of SDM tools and processes is needed for consistency across SDM studies. Targeting accepted clinical processes may enhance the adoption and use of SDM tools. Future studies designed as randomized control trials are needed to expand the quality of the evidence base. Keeping the goals and values of the patient at the center of shared decision making interactions is a key area for future studies.
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