Extracting social determinants of health from electronic health records using natural language processing: a systematic review

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.     

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records

ObjectiveDeveloping algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.Materials and MethodsPheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center''s BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. ResultsIn this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.ConclusionsPheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.     

Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality.Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index.Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR.Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.     

Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

ObjectiveTo develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).Materials and MethodsWe developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database.ResultsFor the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose.DiscussionOur algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm.ConclusionOur algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.     

CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

ObjectiveAccurate extraction of breast cancer patients’ phenotypes is important for clinical decision support and clinical research. This study developed and evaluated cancer domain pretrained CancerBERT models for extracting breast cancer phenotypes from clinical texts. We also investigated the effect of customized cancer-related vocabulary on the performance of CancerBERT models.Materials and MethodsA cancer-related corpus of breast cancer patients was extracted from the electronic health records of a local hospital. We annotated named entities in 200 pathology reports and 50 clinical notes for 8 cancer phenotypes for fine-tuning and evaluation. We kept pretraining the BlueBERT model on the cancer corpus with expanded vocabularies (using both term frequency-based and manually reviewed methods) to obtain CancerBERT models. The CancerBERT models were evaluated and compared with other baseline models on the cancer phenotype extraction task.ResultsAll CancerBERT models outperformed all other models on the cancer phenotyping NER task. Both CancerBERT models with customized vocabularies outperformed the CancerBERT with the original BERT vocabulary. The CancerBERT model with manually reviewed customized vocabulary achieved the best performance with macro F1 scores equal to 0.876 (95% CI, 0.873–0.879) and 0.904 (95% CI, 0.902–0.906) for exact match and lenient match, respectively.ConclusionsThe CancerBERT models were developed to extract the cancer phenotypes in clinical notes and pathology reports. The results validated that using customized vocabulary may further improve the performances of domain specific BERT models in clinical NLP tasks. The CancerBERT models developed in the study would further help clinical decision support.     

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning

ObjectiveThe United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.MethodsElectronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner’s Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.ResultsThe long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).ConclusionsLSTM–based sequential deep learning models can accurately predict OUD using a patient’s history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.     

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications.Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.     

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

ObjectiveTo facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.Material and MethodsWe used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features.ResultsWe observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 “other headache” clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2–8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles.DiscussionCostly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data.ConclusionWe establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.     

A global travelers' electronic health record template standard for personal health records

Tourism as well as international business travel creates health risks for individuals and populations both in host societies and home countries. One strategy to reduce health-related risks to travelers is to provide travelers and relevant caregivers timely, ongoing access to their own health information. Many websites offer health advice for travelers. For example, the WHO and US Department of State offer up-to-date health information about countries relevant to travel. However, little has been done to assure travelers that their medical information is available at the right place and time when the need might arise. Applications of Information and Communication Technology (ICT) utilizing mobile phones for health management are promising tools both for the delivery of healthcare services and the promotion of personal health. This paper describes the project developed by international informaticians under the umbrella of the International Medical Informatics Association. A template capable of becoming an international standard is proposed. This application is available free to anyone who is interested. Furthermore, its source code is made open.     

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Background Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data.Methods We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared.Results On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00).Conclusions Statistical NLP can accurately identify VTE from narrative radiology reports.     

RS纠删码在电子健康档案云存储中的应用

电子健康档案云存储成为研究热点,但云端存储节点的不可控特性使得其存储数据的安全性得不到保障,从而制约了电子健康档案云存储的进一步发展。利用RS纠删码编码将文件信息割裂并分片存储来防止局部存储节点数据块泄露导致的整个文件信息被窃取,利用其纠删能力实现损坏数据的恢复,为电子健康档案云存储数据安全性保障提供了一种新的解决方案。     

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

Objective Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.Methods We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin.Results Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules.Conclusion Significant improvements in the EHR certification and implementation procedures are necessary.     

对电子健康档案APP中虚拟社区建设的文献分析

梳理归纳了我国虚拟健康社区研究的相关文献，以SWOT矩阵分析电子健康档案APP中虚拟社区的发展战略。针对“健康湖州”手机客户端母子健康手册中的虚拟社区现状，结合矩阵分析要点，比对关键文献研究成果，提出相关发展建议。     

Enabling adoption and use of new health information technology during implementation: Roles and strategies for internal and external support personnel

ObjectiveSuccessful technological implementations frequently involve individuals who serve as mediators between end users, management, and technology developers. The goal for this project was to evaluate the structure and activities of such mediators in a large-scale electronic health record implementation.Materials and MethodsField notes from observations taken during implementation beginning in November 2017 were analyzed qualitatively using a thematic analysis framework to examine the relationship between specific types of mediators and the type and level of support to end users.ResultsWe found that support personnel possessing both contextual knowledge of the institution’s workflow and training in the new technology were the most successful in mediation of adoption and use. Those that lacked context of either technology or institutional workflow often displayed barriers in communication, trust, and active problem solving.ConclusionsThese findings suggest that institutional investment in technology training and explicit programs to foster skills in mediation, including roles for professionals with career development opportunities, prior to implementation can be beneficial in easing the pain of system transition.     

Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review

Background ObjectiveElectronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics.Materials and MethodsBased on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria.ResultsOf the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout.DiscussionStandard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden.ConclusionFurther research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.     

Use of unstructured text in prognostic clinical prediction models: a systematic review

ObjectiveThis systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance.Materials and MethodsWe searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.ResultsWe identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited.ConclusionThe use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.     

Organizational strategies for promoting patient and provider uptake of personal health records

Objective To investigate organizational strategies to promote personal health records (PHRs) adoption with a focus on patients with chronic disease.Methods Using semi-structured interviews and a web-based survey, we sampled US health delivery organizations which had implemented PHRs for at least 12 months, were recognized as PHR innovators, and had scored highly in national patient satisfaction surveys. Respondents had lead positions for clinical information systems or high-risk population management. Using grounded theory approach, thematic categories were derived from interviews and coupled with data from the survey.Results Interviews were conducted with 30 informants from 16 identified organizations. Organizational strategies were directed towards raising patient awareness via multimedia communications, and provider acceptance and uptake. Strategies for providers were grouped into six main themes: organizational vision, governance and policies, work process redesign, staff training, information technology (IT) support, and monitoring and incentives. Successful organizations actively communicated their vision, engaged leaders at all levels, had clear governance, planning, and protocols, set targets, and celebrated achievement. The most effective strategy for patient uptake was through health professional encouragement. No specific outreach efforts targeted patients with chronic disease. Registration and PHR activity was routinely measured but without reference to a denominator population or high risk subpopulations.Discussion and conclusion Successful PHR implementation represents a social change and operational project catalyzed by a technical solution. The key to clinician acceptance is making their work easier. However, organizations will likely not achieve the value they want from PHRs unless they target specific populations and monitor their uptake.     

Electronic health records and clinician burnout: A story of three eras

ObjectiveThe study sought to provide physicians, informaticians, and institutional policymakers with an introductory tutorial about the history of medical documentation, sources of clinician burnout, and opportunities to improve electronic health records (EHRs). We now have unprecedented opportunities in health care, with the promise of new cures, improved equity, greater sensitivity to social and behavioral determinants of health, and data-driven precision medicine all on the horizon. EHRs have succeeded in making many aspects of care safer and more reliable. Unfortunately, current limitations in EHR usability and problems with clinician burnout distract from these successes. A complex interplay of technology, policy, and healthcare delivery has contributed to our current frustrations with EHRs. Fortunately, there are opportunities to improve the EHR and health system. A stronger emphasis on improving the clinician’s experience through close collaboration by informaticians, clinicians, and vendors can combine with specific policy changes to address the causes of burnout.Target audienceThis tutorial is intended for clinicians, informaticians, policymakers, and regulators, who are essential participants in discussions focused on improving clinician burnout. Learners in biomedicine, regardless of clinical discipline, also may benefit from this primer and review.ScopeWe include (1) an overview of medical documentation from a historical perspective; (2) a summary of the forces converging over the past 20 years to develop and disseminate the modern EHR; and (3) future opportunities to improve EHR structure, function, user base, and time required to collect and extract information.     

Let the left hand know what the right is doing: a vision for care coordination and electronic health records

Despite the potential for electronic health records to help providers coordinate care, the current marketplace has failed to provide adequate solutions. Using a simple framework, we describe a vision of information technology capabilities that could substantially improve four care coordination activities: identifying collaborators, contacting collaborators, collaborating, and monitoring. Collaborators can include any individual clinician, caregiver, or provider organization involved in care for a given patient. This vision can be used to guide the development of care coordination tools and help policymakers track and promote their adoption.     

Electronic health records in small physician practices: availability,use, and perceived benefits

Objective

To examine variation in the adoption of electronic health record (EHR) functionalities and their use patterns, barriers to adoption, and perceived benefits by physician practice size.

Design

Mailed survey of a nationally representative random sample of practicing physicians identified from the Physician Masterfile of the American Medical Association.

Measurements

We measured, stratified by practice size: (1) availability of EHR functionalities, (2) functionality use, (3) barriers to the adoption and use of EHR, and (4) impact of the EHR on the practice and quality of patient care.

Results

With a response rate of 62%, we found that <2% of physicians in solo or two-physician (small) practices reported a fully functional EHR and 5% reported a basic EHR compared with 13% of physicians from 11+ group (largest group) practices with a fully functional system and 26% with a basic system. Between groups, a 21–46% difference in specific functionalities available was reported. Among adopters there were moderate to large differences in the use of the EHR systems. Financial barriers were more likely to be reported by smaller practices, along with concerns about future obsolescence. These differences were sizable (13–16%) and statistically significant (p<0.001). All adopters reported similar benefits.

Limitations

Although we have adjusted for response bias, influences may still exist.

Conclusion

Our study found that physicians in small practices have lower levels of EHR adoption and that these providers were less likely to use these systems. Ensuring that unique barriers are addressed will be critical to the widespread meaningful use of EHR systems among small practices.