"Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review".
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ABSTRACT:
SUBMITTER: Wark K
PROVIDER: S-EPMC8276667 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background Social determinants of health (SDOH) affect around 70% of health outcomes. However, it is not clear how to integrate SDOH into clinical practice and health care policy. This quality improvement project engaged stakeholders to identify SDOH factors relevant in an Alaska Native/American Indian health system and how to integrate SDOH data into electronic health records (EHRs). Methods The authors utilized an internal steering committee of clinical leadership; conducted focus groups with patients, practitioners, administrative staff, and clinical leaders; developed programmatic workgroups to engage with the health system; and coordinated with allied health systems. Results The Steering Committee members prioritized uses of SDOH data. Focus groups grounded work in local community values and refined SDOH subdomains. Workgroups developed data visualizations, such as EHR dashboards, to automate data collection for reporting and assess performance metrics. External stakeholders helped innovate ways to utilize SDOH data through community partnerships and advocacy work. Stakeholders liked how the holistic approach of SDOH looks at whole-person wellness and how it can improve patient-practitioner relationships and reduce health disparities. They were concerned about outdated SDOH data and how some sensitive SDOH could lead to unanticipated harms. Leaders emphasized developing an actionable, strengths-based SDOH framework. Conclusions Many initiatives call for integrating SDOH into health care and EHRs. Engaging diverse audiences helps guide the work. This engagement may be particularly helpful for minority-serving health systems. SDOH data collection can be stigmatizing for patients. Stakeholder engagement can mitigate that by identifying which SDOH data elements to prioritize, and how to utilize them.
| S-EPMC9761288 | biostudies-literature
Project description:ObjectiveSocial determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.Materials and methodsA broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.ResultsSmoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).ConclusionNLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
| S-EPMC8633615 | biostudies-literature
Project description:Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH mentions (macro-F1 0.71), and Flan-T5 XXL for adverse SDoH mentions (macro-F1 0.70). Adding LLM-generated synthetic data to training varied across models and architecture, but improved the performance of smaller Flan-T5 models (delta F1 + 0.12 to +0.23). Our best-fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models in the zero- and few-shot setting, except GPT4 with 10-shot prompting for adverse SDoH. Fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p < 0.05). Our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. These results demonstrate the potential of LLMs in improving real-world evidence on SDoH and assisting in identifying patients who could benefit from resource support.
| S-EPMC10781957 | biostudies-literature
Project description:ObjectiveThis integrative review identifies and analyzes the extant literature to examine the integration of social determinants of health (SDoH) domains into electronic health records (EHRs), their impact on risk prediction, and the specific outcomes and SDoH domains that have been tracked.Materials and methodsIn accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a literature search in the PubMed, CINAHL, Cochrane, EMBASE, and PsycINFO databases for English language studies published until March 2020 that examined SDoH domains in the context of EHRs.ResultsOur search strategy identified 71 unique studies that are directly related to the research questions. 75% of the included studies were published since 2017, and 68% were U.S.-based. 79% of the reviewed articles integrated SDoH information from external data sources into EHRs, and the rest of them extracted SDoH information from unstructured clinical notes in the EHRs. We found that all but 1 study using external area-level SDoH data reported minimum contribution to performance improvement in the predictive models. In contrast, studies that incorporated individual-level SDoH data reported improved predictive performance of various outcomes such as service referrals, medication adherence, and risk of 30-day readmission. We also found little consensus on the SDoH measures used in the literature and current screening tools.ConclusionsThe literature provides early and rapidly growing evidence that integrating individual-level SDoH into EHRs can assist in risk assessment and predicting healthcare utilization and health outcomes, which further motivates efforts to collect and standardize patient-level SDoH information.
| S-EPMC7671639 | biostudies-literature
Project description:IntroductionEffective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training.Methods and analysisThe Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised.Ethics and disseminationSince no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.
| S-EPMC7422637 | biostudies-literature
Project description:Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.
| S-EPMC9198008 | biostudies-literature
Project description:BackgroundThere are several social cognitive theories (SCTs) and models that support platform design in electronic health (eHealth) promotion trials. The rationale for this scoping review was to determine how social design features (informational aid, expressive support, gaming, and tailored content) are used to promote self-efficacy, engagement, knowledge, and behavior change.ObjectiveThis study aimed to review a broad spectrum of digital health interventions in the literature seeking trials that use SCTs for the design of eHealth applications.MethodsThe author conducted a systematic scoping review of 161 Web-based health interventions from published randomized clinical trials using 1 or more tools to address the social cognitive determinants in their website design from January 2006 to April 2016. An iterative approach was used in the selection of studies and data extraction. The studies were analyzed for quality and coded for type of social design features employed.ResultsExpressive interaction tools were found in 48.6% (54/111) of studies categorized as a strong recommendation by the Joanna Briggs Institute criteria. Overall, less than half of the studies addressed participant social support and motivational needs (43.8%). The vast majority of studies (100%) relied on the use of the Web for delivery of informational aid and tailored content for the individual participant (75.9%).ConclusionsThis review fills a research gap by linking social theory to Web strategy to improve the impact and sustainability of eHealth interventions. A Digital Health Intervention Model was developed to provide a framework to enhance future Web-based health intervention design and execution.
| S-EPMC6676794 | biostudies-literature
Project description:ObjectiveThis paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness.Population studied5 313 965 Veterans who at least had two primary care visits between 2008 and 2016.Study designThe dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set.Principal findingsSeparate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4- to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77.ConclusionIn predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes.Implications for practicePolicies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH.
| S-EPMC7518826 | biostudies-literature
Project description:The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10-15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; "middleware" products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
| S-EPMC5081968 | biostudies-literature
Project description:BackgroundPersonal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR.ObjectiveThis scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services.MethodsWe searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query.ResultsOf the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction.ConclusionsThe findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.
| S-EPMC9832357 | biostudies-literature