Dataset Information

The adoption of social determinants of health documentation in clinical settings.

ABSTRACT:

Objective

To understand the frequency of social determinants of health (SDOH) diagnosis codes (Z-codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care.

Data sources

EHR data and qualitative interviews with health care providers and stakeholders.

Study design

An explanatory sequential mixed methods design first examined the use of Z-codes within the EHR and qualitatively examined barriers to documenting SDOH. Data were integrated and interpreted using a joint display. This research was informed by the Framework for Dissemination and Utilization of Research for Health Care Policy and Practice.

Data collection/extraction methods

We queried EHR data for patients with a hemoglobin A1c > 5.7 between October 1, 2015 and September 1, 2020 (n = 118,215) to examine the use of Z-codes and demographics and outcomes for patients with and without social needs. Semi-structured interviews were conducted with 23 participants (n = 15 health care providers; n = 7 billing and compliance stakeholders). The interview questions sought to understand how factors at the innovation-, individual-, organizational-, and environmental-level influence SDOH documentation. We used thematic analysis to analyze interview data.

Principal findings

Patients with social needs were disproportionately older, female, Black, uninsured, living in low-income and high unemployment neighborhoods, and had a higher number of hospitalizations, obesity, prediabetes, and type 2 diabetes than those without a Z-code. Z-codes were not frequently used in the EHR (<1% of patients), and there was an overall lack of congruence between quantitative and qualitative results related to the prevalence of social needs. Providers faced barriers at multiple levels (e.g., individual-level: discomfort discussing social needs; organizational-level: limited time, competing priorities) for documenting SDOH and identified strategies to improve documentation.

Conclusions

Providers recognized the impact of SDOH on patient health and had positive perceptions of screening for and documenting social needs. Implementation strategies are needed to improve systematic documentation.

SUBMITTER: Kepper MM

PROVIDER: S-EPMC9836948 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The adoption of social determinants of health documentation in clinical settings.

Kepper Maura M MM Walsh-Bailey Callie C Prusaczyk Beth B Zhao Min M Herrick Cynthia C Foraker Randi R

Health services research 20220731 1

<h4>Objective</h4>To understand the frequency of social determinants of health (SDOH) diagnosis codes (Z-codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care.<h4>Data sources</h4>EHR data and qualitative interviews with health care providers and stakeholders.<h4>Study design</h4>An explanatory sequential mixed methods design first examined the use of Z-codes within the EHR a ...[more]

PMID: 35862115

Similar Datasets

Project description:BackgroundInterest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mobile health from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-US samples. Consequently, there is a limited understanding of what personal factors hold statistically significant relationships with digital health uptake. Moreover, this limits digital health communities' knowledge of equity along digital health use patterns.ObjectiveThis study aims to identify the social determinants of digital health tool adoption in Georgia.MethodsWeb-based survey respondents in Georgia 18 years or older were recruited from mTurk to answer primarily closed-ended questions within the following domains: participant demographics and health consumption background, telehealth, digital health education, prescription management tools, digital mental health services, and doctor finder tools. Participants spent around 15 to 20 minutes on a survey to provide demographic and personal health care consumption data. This data was analyzed with multivariate linear and logistic regressions to identify which of these determinants, if any, held statistically significant relationships with the total number of digital health tool categories adopted and which of these determinants had absolute relationships with specific categories.ResultsA total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent emergency room (ER) visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tool categories adopted. The separate logistic regressions exhibited substantial variability, with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of primary care provider visits in the past 12 months, number of nonurgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to the internet, difficulty accessing health care, usual source of care, status of primary care provider, and status of chronic condition all had at least one statistically significant relationship with the use of a specific digital health category.ConclusionsThe results demonstrate that persons who are socioeconomically disadvantaged may not adopt digital health tools at disproportionately higher rates. Instead, digital health tools may be adopted along social determinants of health, providing strong evidence for the digital health divide. The variability of digital health adoption necessitates investing in and building a common framework to increase mobile health access. With a common framework and a paradigm shift in the design, evaluation, and implementation strategies around digital health, disparities can be further mitigated and addressed. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.

Project description:BackgroundElectronic health records (EHRs) have yet to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information.ObjectiveIt is unclear how accurately EHR data reflect patients' lived experience of SDOH. The manual process of retrieving SDOH information from clinical notes is time-consuming and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data: PatientExploreR and Electronic Medical Record Search Engine (EMERSE).MethodsWe included adult patients (≥18 years of age) receiving primary care for their diabetes at the University of California, San Francisco (UCSF), from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC.ResultsWe included 4283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information from patient clinical notes on problems related to social connectionsisolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress.ConclusionsWe discovered more patient information related to SDOH in unstructured data than in structured data. The application of this technique and further investment in similar user-friendly tools and infrastructure to extract SDOH information from unstructured data may help to identify the range of social conditions that influence patients' disease experiences and inform clinical decision-making.

Project description:Social and environmental determinants of health (SEDH) data in the electronic health record (EHR) can be inaccurate and incomplete. Providers are in a unique position to impact this issue as they both obtain and enter this data, however, the variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses. This study explores whether providers' perceptions of clinical importance of SEDH or EHR usability influenced data entry by analyzing two relationships: (1) provider charting behavior and clinical consideration of SEDH and (2) provider charting behavior and ease of EHR use in charting. We performed a cross-sectional study using an 11-question electronic survey to assess self-reported practices related to clinical consideration of SEDH elements, EHR usability and SEDH documentation of all staff physicians, identified using administrative listserves, at Penn State Health Hershey Medical Center during September to October 2021. A total of 201 physicians responded to and completed the survey out of a possible 2,478 identified staff physicians (8.1% response rate). A five-point Likert scale from "never" to "always" assessed charting behavior and clinical consideration. Responses were dichotomized as consistent/inconsistent and vital/not vital respectively. EHR usability was assessed as "yes" or "no" responses. Fisher's exact tests assessed the relationship between charting behavior and clinical consideration and to compare charting practices between different SEDHs. Cumulative measures were constructed for consistent charting and ease of charting. A generalized linear mixed model (GLMM) compared SDH and EDH with respect to each cumulative measure and was quantified using odds ratios (OR) and 95% confidence intervals (CI). Our results show that provider documentation frequency of an SEDH is associated with perceived clinical utility as well as ease of charting and that providers were more likely to consistently chart on SDH versus EDH. Nuances in these relationships did exist with one notable example comparing the results of smoking (SDH) to infectious disease outbreaks (EDH). Despite similar percentages of physicians reporting that both smoking and infectious disease outbreaks are vital to care, differences in charting consistency and ease of charting between these two were seen. Taken as a whole, our results suggest that SEDH quality optimization efforts cannot consider physician perceptions and EHR usability as siloed entities and that EHR design should not be the only target for intervention. The associations found in this study provide a starting point to understand the complexity in how clinical utility and EHR usability influence charting consistency of each SEDH element, however, further research is needed to understand how these relationships intersect at various levels in the SEDH data optimization process.

Project description:ObjectiveExisting research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.Materials and methodsWe retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.ResultsWord frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.DiscussionOur findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.ConclusionSocial work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

Project description:BackgroundThe United Nations' Sustainable Development Goal #3.8 targets 'access to quality essential healthcare services'. Clinical practice guidelines are an important tool for ensuring quality of clinical care, but many challenges prevent their use in low-resource settings. Monitoring the use of guidelines relies on cumbersome clinical audits of paper records, and electronic systems face financial and other limitations. Here we describe a unique approach to generating digital data from paper using guideline-based templates, rubber stamps and mobile phones.InterventionThe Guidelines Adherence in Slums Project targeted ten private sector primary healthcare clinics serving informal settlements in Nairobi, Kenya. Each clinic was provided with rubber stamp templates to support documentation and management of commonly encountered outpatient conditions. Participatory design methods were used to customize templates to the workflows and infrastructure of each clinic. Rubber stamps were used to print templates into paper charts, providing clinicians with checklists for use during consultations. Templates used bubble format data entry, which could be digitized from images taken on mobile phones. Besides rubber stamp templates, the intervention included booklets of guideline compilations, one Android phone for digitizing images of templates, and one data feedback/continuing medical education session per clinic each month. In this paper we focus on the effect of the intervention on documentation of three non-communicable diseases in one clinic.MethodsSeventy charts of patients enrolled in the chronic disease program (hypertension/diabetes, n=867; chronic respiratory diseases, n=223) at one of the ten intervention clinics were sampled. Documentation of each individual patient encounter in the pre-intervention (January-March 2016) and post-intervention period (May-July) was scored for information in four dimensions - general data, patient assessment, testing, and management. Control criteria included information with no counterparts in templates (e.g. notes on presenting complaints, vital signs). Documentation scores for each patient were compared between both pre- and post-intervention periods and between encounters documented with and without templates (post-intervention only).ResultsThe total number of patient encounters in the pre-intervention (282) and post-intervention periods (264) did not differ. Mean documentation scores increased significantly in the post-intervention period on average by 21%, 24% and 17% for hypertension, diabetes and chronic respiratory diseases, respectively. Differences were greater (47%, 43% and 27%, respectively) when documentation with and without templates was compared. Changes between pre- vs.post-intervention, and with vs.without template, varied between individual dimensions of documentation. Overall, documentation improved more for general data and patient assessment than in testing or management.ConclusionThe use of templates improves paper-based documentation of patient care, a first step towards improving the quality of care. Rubber stamps provide a simple and low-cost method to print templates on demand. In combination with ubiquitously available mobile phones, information entered on paper can be easily and rapidly digitized. This 'frugal innovation' in m-Health can empower small, private sector facilities, where large numbers of urban patients seek healthcare, to generate digital data on routine outpatient care. These data can form the basis for evidence-based quality improvement efforts at large scale, and help deliver on the SDG promise of quality essential healthcare services for all.

Dataset Information

The adoption of social determinants of health documentation in clinical settings.

Objective

Data sources

Study design

Data collection/extraction methods

Principal findings

Conclusions

Publications

The adoption of social determinants of health documentation in clinical settings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets