Electronic Health Record Platform Research Articles

Exploring Therapists’ Experiences Identifying Cognitive Impairments in Post-Acute Care

Aims Understanding therapy providers’ experiences with cognitive screening, assessment, and documentation can provide guidance on improving care quality. Methods Semi-structured interviews (n = 18) were conducted with therapy providers across disciplines and post-acute settings. Thematic analysis was used to identify themes. Results Six over-arching themes emerged pertaining to cognitive screening and assessment. (1) Therapists routinely conducted screening through task performance and informal observation. (2) Documentation habits varied due to multiple conflicting goals and lack of guidelines. (3) Therapists’ approach was honed on the job through clinical experience and trial and error. (4) Patient diagnosis, emerging medical conditions, and goals most strongly influenced therapists’ approach. (5) Roles and responsibilities were implicit. (6) The end goal was determining patient safety. Conclusion All therapy providers frequently screened for cognition, yet documentation varied widely. To increase consistent delivery and documentation of cognitive screening and assessment, future studies can examine strategies to streamline electronic health record platforms.

Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review.

Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome.

To develop an electronic health record (EHR)-based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types. We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS. Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with <2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system. We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS.

Novel Electronic Health Records-Based Consultation Workflow Improves Time to Operating Room for Vascular Surgery Patients in an Acute Setting

Inefficient clinical workflows can have downstream effects of increased costs, poor resource utilization, and worse patient outcomes. The surgical consultation process can be complex with unclear communication, potentially delaying care for patients requiring time-sensitive intervention in an acute setting. A novel electronic health records (EHR)-based workflow was implemented to improve the consultation process. After implementation, we assessed the impact of this initiative in patients requiring vascular surgery consultation. An EHR-driven consultation workflow was implemented at a single institution, standardizing the process across all consulting services. This order-initiated workflow automated notification to clinicians of consult requests, communication of patient data, patient addition to consultants' lists, and tracking consult completion. Preimplementation (1/1/2020-1/31/2022) and postimplementation (2/1/2022-12/4/2022) vascular surgery consultation cohorts were compared to evaluate the impact of this initiative on timeliness of care. There were 554 inpatient vascular surgery consultations (255 preimplementation and 299 postimplementation); 45 and 76 consults required surgery before and after implementation, respectively. The novel workflow resulted in placement of a consult note 32min faster than preimplementation (preimplementation: 462min, postimplementation: 430min, P=0.001) for all vascular surgery consults. Furthermore, vascular surgery patients with ASA class III or IV status requiring an urgent or emergent operation were transported to the operating room 63.3% faster after implementation of the workflow (preimplementation: 284min, postimplementation: 180min, P=0.02). There were no differences in procedure duration, postoperative disposition, or intraoperative complication rates. We implemented a novel workflow utilizing the EHR to standardize and automate the consultation process in the acute inpatient setting. This institutional initiative significantly improved timeliness of care for vascular surgery patients, including decreased time to operation. Innovations such as this can be further disseminated across shared EHR platforms across institutions, representing a powerful tool to increase the value of care in vascular surgery and healthcare overall.

Measurement Strategies for The Joint Commission Health Care Disparities Standard: Defining Hospital-Based Requirements—Part 1

Measurement Strategies for The Joint Commission Health Care Disparities Standard: Defining Hospital-Based Requirements—Part 1

A Cross-sectional Survey to Assess Awareness of Syndromic Surveillance by Clinicians Practicing Emergency Medicine: An Opportunity for Education and Collaboration.

Syndromic surveillance (SyS) is an important public health tool using de-identified healthcare discharge data from emergency department (ED) and urgent care settings to rapidly identify new health threats and provide insight into current community well-being. While SyS is directly fed by clinical documentation such as chief complaint or discharge diagnosis, the degree to which clinicians are aware their documentation directly influences public health investigations is unknown. The primary objective of this study was to evaluate the degree to which clinicians practicing in Kansas EDs or urgent care settings were aware that certain de-identified aspects of their documentation are used in public health surveillance and to identify barriers to improved data representation. We distributed an anonymous survey August-November 2021 to clinicians practicing at least part time in emergency or urgent care settings in Kansas. We then compared responses from emergency medicine (EM)-trained physicians to non-EM trained physicians. Descriptive statistics were used for analysis. A total of 189 respondents across 41 Kansas counties responded to the survey. Of those surveyed, 132 (83%) were unaware of SyS. Knowledge did not differ significantly by specialty, practice setting, urban region, age, nor by experience level. Respondents were unaware of which aspects of their documentation were visible to public health entities, or how quickly records were retrievable. When asked about improving documentation for SyS, lack of clinician awareness (71.5%) was perceived as a greater barrier than electronic health record platform usability or time available to document (61% and 59%, respectively). This survey suggests that most practitioners in EM have not heard of SyS and are unaware of the invaluable role certain aspects of their documentation play in public health. Critical information that would be captured and coded into a key syndrome is often missing, but clinicians are unaware of what types of information may be most useful in their documentation, and where to document that information. Lack of knowledge or awareness was identified by clinicians as the single greatest barrier to enhancing surveillance data quality. Increased awareness of this important tool may lead to enhanced utility for timely and impactful surveillance through improved data quality and collaboration between EM practitioners and public health.

Disseminating child abuse clinical decision support among commercial electronic health records: Effects on clinical practice.

The use of electronic health record (EHR)-embedded child abuse clinical decision support (CA-CDS) may help decrease morbidity from child maltreatment. We previously reported on the development of CA-CDS in Epic and Allscripts. The objective of this study was to implement CA-CDS into Epic and Allscripts and determine its effects on identification, evaluation, and reporting of suspected child maltreatment. After a preimplementation period, CA-CDS was implemented at University of Wisconsin (Epic) and Northwell Health (Allscripts). Providers were surveyed before the go-live and 4 months later. Outcomes included the proportion of children who triggered the CA-CDS system, had a positive Child Abuse Screen (CAS) and/or were reported to Child Protective Services (CPS). At University of Wisconsin (UW), 3.5% of children in the implementation period triggered the system. The CAS was positive in 1.8% of children. The proportion of children reported to CPS increased from 0.6% to 0.9%. There was rapid uptake of the abuse order set.At Northwell Health (NW), 1.9% of children in the implementation period triggered the system. The CAS was positive in 1% of children. The child abuse order set was rarely used. Preimplementation, providers at both sites were similar in desire to have CA-CDS system and perception of CDS in general. After implementation, UW providers had a positive perception of the CA-CDS system, while NW providers had a negative perception. CA-CDS was able to be implemented in 2 different EHRs with differing effects on clinical care and provider feedback. At UW, the site with higher uptake of the CA-CDS system, the proportion of children who triggered the system and the rate of positive CAS was similar to previous studies and there was an increase in the proportion of cases of suspected abuse identified as measured by reports to CPS. Our data demonstrate how local environment, end-users' opinions, and limitations in the EHR platform can impact the success of implementation. When disseminating CA-CDS into different hospital systems and different EHRs, it is critical to recognize how limitations in the functionality of the EHR can impact the success of implementation. The importance of collecting, interpreting, and responding to provider feedback is of critical importance particularly with CDS related to child maltreatment.

Variation in Family Physicians' Experiences Across Different Electronic Health Record Platforms: a Descriptive Study.

Electronic health records (EHRs) have been connected to excessive workload and physician burnout. Little is known about variation in physician experience with different EHRs, however. To analyze variation in reported usability and satisfaction across EHRs. Internet-based survey available between December 2021 and October 2022 integrated into American Board of Family Medicine (ABFM) certification process. ABFM-certified family physicians who use an EHR with at least 50 total responding physicians. Self-reported experience of EHR usability and satisfaction. We analyzed the responses of 3358 physicians who used one of nine EHRs. Epic, athenahealth, and Practice Fusion were rated significantly higher across six measures of usability. Overall, between 10 and 30% reported being very satisfied with their EHR, and another 32 to 40% report being somewhat satisfied. Physicians who use athenahealth or Epic were most likely to be very satisfied, while physicians using Allscripts, Cerner, or Greenway were the least likely to be very satisfied. EHR-specific factors were the greatest overall influence on variation in satisfaction: they explained 48% of variation in the probability of being very satisfied with Epic, 46% with eClinical Works, 14% with athenahealth, and 49% with Cerner. Meaningful differences exist in physician-reported usability and overall satisfaction with EHRs, largely explained by EHR-specific factors. User-centric design and implementation, and robust ongoing evaluation are needed to reduce physician burden and ensure excellent experience with EHRs.

Staying Informed of Best Evidence to Guide Practice

Staying Informed of Best Evidence to Guide Practice

Oncologist-driven development of an electronic health record (EHR) clinical data visualization tool for prostate cancer.

267 Background: Clinical information management is a burdensome process for oncology providers owing to the complexity of modern cancer data. The results that a clinician needs to review are often spread across several areas within the EHR, making it cumbersome to sense a broad overview of patient status. The purpose of this study is to describe a framework for oncologist-driven development of a data visualization tool to trend cancer biomarkers and assess the feasibility of this tool to query and display EHR data relevant for the treatment of prostate cancer. Methods: A clinical sponsor is selected to identify data elements necessary to make treatment decisions for patients receiving therapy for prostate cancer and to provide rapid feedback for the clinical tool interface. The commercial EHR database is queried to determine identifying codes for relevant laboratory tests, medications, and procedures. Data elements are assembled using the EHR platform for clinical synopsis and the clinical tool is made available to five genitourinary medical oncologists for initial pilot. Results: Oral prostate cancer medications were queried based on medication therapeutic and pharmaceutical class. In addition, androgen deprivation therapy (ADT) injections were separately identified based on route of administration. Prostate specific antigen (PSA) and testosterone result values were queried using laboratory base and/or common name codes. However, many duplicate entities are found varying by hospital/laboratory site and test assay. The assembled clinical data visualization tool can overlay temporal trends in PSA and testosterone over medication start/stop dates to convey treatment response and signs of early medication resistance. The clinician also has the option to overlay vital signs or other laboratory information to visualize treatment related adverse events (ex. weight gain related to ADT, anemia related to PARP inhibitor therapy, etc.) Lastly, the tool can also highlight the dates of the patient’s last imaging tests to allow clinicians to determine if the patient is due for any follow up imaging. Conclusions: The EHR can support novel data visualization tools for cancer biomarkers that can reasonably support clinical workflows. Development requires an intimate knowledge of EHR data but may still be limited by duplicate or erroneous codes for laboratory results. This issue may be addressed by using standard nomenclature for laboratory results such as LOINC codes but is not currently supported by the commercial EHR. Future work in this area will include formal usability testing from the perspective of oncology providers and patients.

Implementing a Care Plan System in a Community Hospital Electronic Health Record.

Nursing care plans, the result of the nursing process, are important because they guide quality patient care, define the nurse's role in patient treatment, and support patient specific nursing interventions. Nurse leaders at a community hospital identified that 70% of hospitalized patients shared the same three nursing problems and three-day resolution target dates. This quality improvement project involved the implementation of a nursing care plan system within the organization's existing electronic health record platform that supported the development of individualized plans. The implementation included a) integration of care plan activities into the nursing workflow, b) adoption of a standardized nursing language to provide structure nursing care plan data capture and reporting, c) configuration of the electronic health record to recommend patient data-driven care plan problems based on nursing assessment documentation; and d) trended care plan data to identify problem patterns within the patient population. A postimplementation audit indicated a 130% improvement in achieving individualized care plans within 24 hours of admission.

The unfulfilled promises of electronic health records.

We provide a brief update on the current evidence on electronic health records' benefits, risks, and potential harms through a rapid narrative review. Many of the promised benefits of electronic health records have not yet been realised. Electronic health records are often not user-friendly. To enhance their potential, electronic health record platforms should be continuously evaluated and enhanced by carefully considering feedback from all stakeholders.

Improving Patient Access to the My HealtheVet Electronic Patient Portal for Veterans.

The US Department of Veterans Affairs My HealtheVet (MHV) patient portal is a secure online tool that provides patients access to personal health information. Although facilitators exist to encourage veteran registration, barriers to both adoption and use among veterans persist. This quality improvement project sought to improve veteran access to MHV. Using Plan-Do-Study-Act (PDSA) methodology, we identified barriers to registration, evaluated processes for enrollment, and integrated a process improvement champion into a rural primary care clinic workflow. After 3 PDSA cycles, the integration of new processes resulted in increased enrollment and engagement with MHV. Fourteen veterans registered for MHV at the point-of-care in a 3-month time frame. Use of a connected electronic health record platform and implementation of an MHV champion in the outpatient primary care setting improved rural veteran access to personal health information. Audit and feedback on processes that provide access to health information is an important strategy to narrow the gap between veterans who access patient portals and those who do not.

Automatic Estimation of the Most Likely Drug Combination in Electronic Health Records Using the Smooth Algorithm: Development and Validation Study.

Since the use of electronic health records (EHRs) in an automated way, pharmacovigilance or pharmacoepidemiology studies have been used to characterize the therapy using different algorithms. Although progress has been made in this area for monotherapy, with combinations of 2 or more drugs the challenge to characterize the treatment increases significantly, and more research is needed. The goal of the research was to develop and describe a novel algorithm that automatically returns the most likely therapy of one drug or combinations of 2 or more drugs over time. We used the Information System for Research in Primary Care as our reference EHR platform for the smooth algorithm development. The algorithm was inspired by statistical methods based on moving averages and depends on a parameter Wt, a flexible window that determines the level of smoothing. The effect of Wt was evaluated in a simulation study on the same data set with different window lengths. To understand the algorithm performance in a clinical or pharmacological perspective, we conducted a validation study. We designed 4 pharmacological scenarios and asked 4 independent professionals to compare a traditional method against the smooth algorithm. Data from the simulation and validation studies were then analyzed. The Wt parameter had an impact over the raw data. As we increased the window length, more patient were modified and the number of smoothed patients augmented, although we rarely observed changes of more than 5% of the total data. In the validation study, significant differences were obtained in the performance of the smooth algorithm over the traditional method. These differences were consistent across pharmacological scenarios. The smooth algorithm is an automated approach that standardizes, simplifies, and improves data processing in drug exposition studies using EHRs. This algorithm can be generalized to almost any pharmacological medication and model the drug exposure to facilitate the detection of treatment switches, discontinuations, and terminations throughout the study period.

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for healthcare systems. Many devices on the EHR network utilize little or no encryption, which makes data in transit vulnerable to exploitative attacks, such as Man-in-the-Middle and other filtration methods. Recently, watermarking algorithms as an efficient response to these requirements is in the underline. In this paper, we present a robust watermarking method conceived as part of an Electronic Health Record platform. In this method a chaotic encryption and blind medical image watermarking technique was incorporated into the system as an authorization mechanism to ensure confidentiality and integrity of electronic health information. We present a hybrid watermarking method based on a combination of discrete wavelet transform (DWT), hessenberg Decomposition (HD), Singular value decomposition (SVD) and an original chaos crypto system based on the Arnold Transform (AT) of Singular Value Decomposition. In order to spread the robustness of our algorithm and provide additional security, an improved SVD-AT embedding and extraction procedure has been used to scramble the EHR data in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. In extraction process, the stored used plain image is obtained from the trusted authority server to complete the verification process. The receiver should compare the unsigned watermark with the extracted watermark again. The verification can be done before clinical procedures and diagnosis. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.

Development and testing of data infrastructure in the American College of Emergency Physicians' Clinical Emergency Data Registry for opioid-related research.

ObjectivePrior research has identified gaps in the capacity of electronic health records (EHRs) to capture the intricacies of opioid‐related conditions. We sought to enhance the opioid data infrastructure within the American College of Emergency Physicians’ Clinical Emergency Data Registry (CEDR), the largest national emergency medicine registry, through data mapping, validity testing, and feasibility assessment.MethodsWe compared the CEDR data dictionary to opioid common data elements identified through prior environmental scans of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Validity and feasibility assessments of CEDR opioid‐related data were conducted through the following steps: (1) electronic extraction of CEDR data meeting criteria for an opioid‐related emergency care visit, (2) manual chart review assessing the quality of the extracted data, (3) completion of feasibility scorecards, and (4) qualitative interviews with physician reviewers and informatics personnel.ResultsWe identified several data gaps in the CEDR data dictionary when compared with prior environmental scans including urine drug testing, opioid medication, and social history data elements. Validity testing demonstrated correct or partially correct data for >90% of most extracted CEDR data elements. Factors affecting validity included lack of standardization, data incorrectness, and poor delimitation between emergency department (ED) versus hospital care. Feasibility testing highlighted low‐to‐moderate feasibility of date and social history data elements, significant EHR platform variation, and inconsistency in the extraction of common national data standards (eg, Logical Observation Identifiers Names and Codes, International Classification of Diseases, Tenth Revision codes).ConclusionsWe found that high‐priority data elements needed for opioid‐related research and clinical quality measurement, such as demographics, medications, and diagnoses, are both valid and can be feasibly captured in a national clinical quality registry. Future work should focus on implementing structured data collection tools, such as standardized documentation templates and adhering to data standards within the EHR that would better characterize ED‐specific care for opioid use disorder and related research.

Use of a Cirrhosis Admission Order Set Improves Adherence to Quality Metrics and May Decrease Hospital Length of Stay.

Quality metrics for inpatient cirrhosis management have been created to improve processes of care. We aimed to improve adherence to quality metrics by creating a novel clinical decision support (CDS) tool in the electronic health record (EHR). We developed and piloted an alert system in the EHR that directs providers to a cirrhosis order set for patients who have a known diagnosis of cirrhosis or are likely to have cirrhosis. Adherence to process measures and outcomes when the CDS was used were compared with baseline performance before the implementation of the CDS. The use of the order set resulted in a significant increase in adherence to process measures such as diagnostic paracentesis (29.6%-51.1%), low-sodium diet (34.3%-77.8%), and social work involvement (36.6%-88.9%) ( P < 0.001 for all). There were also significant decreases in both intensive care and hospital lengths of stay ( P < 0.001) as well as in-hospital development of infection ( P = 0.002). There was no difference in hospital readmissions at 30 or 90 days between the groups ( P = 0.897, P = 0.640). The use of CDS in EHR-based interventions improves adherence to quality metrics for patients with cirrhosis and could easily be shared by institutions through EHR platforms. Further studies and larger sample sizes are needed to better understand its impact on additional outcome measures.

Unicare – A Electronic Health Record Platform

Abstract: Ever since the advent of the internet i.e interconnected computer technology, nearly every sector has joined the bandwagon and has shifted towards a centralized, robust and a highly organized database. Furthermore, the advancements in newly emerging fields like big data, data mining and warehousing has made it possible to obtain crucial and valuable insights from the enormous amounts of data being saved in various databases. As a result, several sectors have invested in having their own data analysis systems to gain better understanding of their works and possible improvements they can make in their current workings. Healthcare however seems to have been a bit behind in making optimum use of these tools and methodologies. Electronic health records carry significant potential in improving the quality of healthcare through easy access to information, precisely by helping to make prudent prognoses and thus saving the patient's time and finances. Unicare is a seamless electronic health records platform designed to address this issue with an aim of having a centralized health care portal, accessible to both the medical experts and patients. Medical professionals can access the patient’s history and make informed decisions regarding their ailments.

OpenSAFELY: Representativeness of electronic health record platform OpenSAFELY-TPP data compared to the population of England

Background: Since its inception in March 2020, data from the OpenSAFELY-TPP electronic health record platform has been used for more than 20 studies relating to the global COVID-19 emergency. OpenSAFELY-TPP data is derived from practices in England using SystmOne software, and has been used for the majority of these studies. We set out to investigate the representativeness of OpenSAFELY-TPP data by comparing it to national population estimates. Methods: With the approval of NHS England, we describe the age, sex, Index of Multiple Deprivation and ethnicity of the OpenSAFELY-TPP population compared to national estimates from the Office for National Statistics. The five leading causes of death occurring between the 1st January 2020 and the 31st December 2020 were also compared to deaths registered in England during the same period. Results: Despite regional variations, TPP is largely representative of the general population of England in terms of IMD (all within 1.1 percentage points), age, sex (within 0.1 percentage points), ethnicity and causes of death. The proportion of the five leading causes of death is broadly similar to those reported by ONS (all within 1 percentage point). Conclusions: Data made available via OpenSAFELY-TPP is broadly representative of the English population. Users of OpenSAFELY must consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research. Although the coverage of TPP practices varies regionally across England, TPP registered patients are generally representative of the English population as a whole in terms of key demographic characteristics.

OpenSAFELY: Representativeness of electronic health record platform OpenSAFELY-TPP data compared to the population of England.

Background: Since its inception in March 2020, data from the OpenSAFELY-TPP electronic health record platform has been used for more than 20 studies relating to the global COVID-19 emergency. OpenSAFELY-TPP data is derived from practices in England using SystmOne software, and has been used for the majority of these studies. We set out to investigate the representativeness of OpenSAFELY-TPP data by comparing it to national population estimates. Methods:With the approval of NHS England, we describe the age, sex, Index of Multiple Deprivation and ethnicity of the OpenSAFELY-TPP population compared to national estimates from the Office for National Statistics. The five leading causes of death occurring between the 1st January 2020 and the 31st December 2020 were also compared to deaths registered in England during the same period. Results: Despite regional variations, TPP is largely representative of the general population of England in terms of IMD (all within 1.1 percentage points), age, sex (within 0.1 percentage points), ethnicity and causes of death. The proportion of the five leading causes of death is broadly similar to those reported by ONS (all within 1 percentage point). Conclusions: Data made available via OpenSAFELY-TPP is broadly representative of the English population. Users of OpenSAFELY must consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research. Although the coverage of TPP practices varies regionally across England, TPP registered patients are generally representative of the English population as a whole in terms of key demographic characteristics.