Electronic Health Record Platform Research Articles

Development and integration of a clinical dashboard within a dental school setting.

To describe the development and integration of an electronic health record-driven, student dashboard that displays real-time data relative to the students' patient management and clinic experiences at the University of Illinois Chicago, College of Dentistry. Following development and implementation of the student dashboard, various objective metrics were evaluated to identify any improvements in the clinical patient management. A cross-sectional retrospective chart review was completed of the electronic health record (axiUm, Exan, Coquitlam, BC, Canada) from January 2019 to April 2022 evaluating four performance metrics: student lockouts, note/code violations, overdue active patients, and overdue recall patients. Descriptive statistics were analyzed. The Kolmogorov-Smirnov test was applied to assess the normal distribution of data. Data were analyzed by the Kruskal-Wallis tests for potential differences between pre-dashboard and post-dashboard implementation years with the mean overdue active/recall patient to student ratio variables. Mann-Whitney U-tests for between-groups comparisons with Bonferroni correction for multiple comparisons were performed (α=0.05). Descriptive statistics were performed to analyze the student utilization frequency of the dashboard. Post-implementation analysis indicated a slight decrease in the number of lockouts and note/code violation; and a statistically significant decrease in overdue active patients post-dashboard (P<0.001). On average, students accessed their dashboards 3.3 times a week. Implementation of a student dashboard through the electronic health record platform within an academic dental practice has the potential to assist students with patient management and is utilized regularly by the students.

Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study

BackgroundVariable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach. MethodsWe propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites). ResultsThe site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site. ConclusionsWe suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. Original trial registration numberNCT02475850

Developing an integrated clinical decision support system for the early identification and management of kidney disease—building cross-sectoral partnerships

BackgroundThe burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care.AimThis paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care.MethodsBuilding on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system’s design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation.ResultsWe established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events.ConclusionDisconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

Integrated Electronic Health Records (EHR) Platform: Enhancing Patient-Centric Healthcare Management

In contemporary healthcare systems, digitizing medical records has become imperative to streamline and enhance patient care. This research paper explores the development and implementation of a website that integrates patients' medical records, aiming to provide a comprehensive and patient-centric approach to healthcare management. The platform seeks to facilitate seamless information exchange among healthcare providers, improve treatment decision- making, and empower patients in managing their health.

Real world performance of the 21st Century Cures Act population-level application programming interface.

To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12000 resources/min. The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Sprint-inspired One-on-One Post-Go-Live Training Session (Mini-Sprint) Improves Provider Electronic Health Record Efficiency and Satisfaction.

Inefficient electronic health record (EHR) usage increases the documentation burden on physicians and other providers, which increases cognitive load and contributes to provider burnout. Studies show that EHR efficiency sessions, optimization sprints, reduce burnout using a resource-intense five-person team. We implemented sprint-inspired one-on-one post-go-live efficiency training sessions (mini-sprints) as a more economical training option directed at providers. We evaluated a post-go-live mini-sprint intervention to assess provider satisfaction and efficiency. NorthShore University HealthSystem implemented one-on-one provider-to-provider mini-sprint sessions to optimize provider workflow within the EHR platform. The physician informaticist completed a 9-point checklist of efficiency tips with physician trainees covering schedule organization, chart review, speed buttons, billing, note personalization/optimization, preference lists, quick actions, and quick tips. We collected postsession survey data assessing for net promoter score (NPS) and open-ended feedback. We conducted financial analysis of pre- and post-mini-sprint efficiency levels and financial data. Seventy-six sessions were conducted with 32 primary care physicians, 28 specialty physicians, and 16 nonphysician providers within primary care and other areas. Thirty-seven physicians completed the postsession survey. The average NPS for the completed mini-sprint sessions was 97. The proficiency score had a median of 6.12 (Interquartile range (IQR): 4.71-7.64) before training, and a median of 7.10 (IQR: 6.25-8.49) after training. Financial data analysis indicates that higher level billing codes were used at a greater frequency post-mini-sprint. The revenue increase 12 months post-mini-sprint was $213,234, leading to a return of $75,559.50 for 40 providers, or $1,888.98 per provider in a 12-month period. Our data show that mini-sprint sessions were effective in optimizing efficiency within the EHR platform. Financial analysis demonstrates that this type of training program is sustainable and pays for itself. There was high satisfaction with the mini-sprint training modality, and feedback indicated an interest in further mini-sprint training sessions for physicians and nonphysician staff.

OpenSAFELY: Measuring BMI in 22 million patients in England

Background Body mass index (BMI) has been identified as a risk factor for clinical outcomes in patients with COVID-19. Studies identifying this risk have used electronic health record (EHR) platforms in which clinical conditions must be properly identified. We set out to define and evaluate various methods of deriving BMI measurements in OpenSAFELY-TPP, an EHR platform that has been used in many studies relating to the COVID-19 pandemic. Methods With the approval of NHS England, we use routine clinical data from &gt;22 million patients in England to define four derivations of BMI. We compare the number of patients with each type of BMI measurement and the number of measurements themselves. We also examine the plausibility of each derivation by looking at the distribution of measurements and counting values out of the expected range. To evaluate how frequently the BMI derivations are recorded, we track the number of new measurements recorded over time and the average time between updates in patients with multiple measurements. Results Primary constraints in creating the optimal BMI derivation is coverage, accuracy, and computational complexity. BMI derivations calculated from height and weight contain a few extreme outliers that affect aggregated statistics. SNOMED-recorded BMI records are more accurate on average and offer better coverage across the population. The canonical OpenSAFELY definition – which uses calculated BMI as a first instance and SNOMED-recorded BMI if missing – offers the best coverage, but contains the same extreme outliers found in calculated BMI and is the most computationally expensive of all methods. Conclusions Across all derivations, some cleaning should be performed to drop implausible outliers. Using calculated BMI on its own does not offer the best coverage or accuracy. In choosing between SNOMED-recorded BMI and the current OpenSAFELY implementation, users should decide whether they would like to maximise computational efficiency or coverage.

Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Making the EHR Work for You-Modifications of an Electronic Health Record System to Improve Tracking and Management of Patients Receiving Outpatient Parenteral Antibiotic Therapy.

Managing the complex needs of outpatient parenteral antibiotic therapy (OPAT) patients is challenging and time-consuming. We describe development of multimodal interventions to facilitate patient management within an Epic® (Epic Systems Corporation)-based electronic health record (EHR) platform. During 2016-2018, a multidisciplinary team created several modifications in our local EHR to improve gaps in OPAT care, including shared note templates, shared patient lists, automatically triggered notifications, and comprehensive order sets. A SmartForm was created, allowing collection of discrete, self-contained extractable data about each OPAT episode. We reviewed OPAT episodes from January 2019 through December 2022. The multimodal EHR interventions culminated in the creation of a patient report, the "OPAT Monitoring View" collating OPAT-relevant data from multiple sections of the chart onto 1 screen display. This view is accessible both within the patient chart and from multiple list-based, in-basket, and snapshot-anchored preview functions in the EHR. Implementation of the EHR bundle facilitated management of 3402 OPAT episodes from 2019 to 2022 (850 episodes/year), about 50% higher than anticipated based on 540 OPAT courses in 2016. The OPAT EHR bundle allowed efficient (<3 hours) multidisciplinary rounds for management of 130-145 patients each week, streamlining of care transitions, and increasing staff satisfaction. Bundled multimodal modifications to the local EHR increased patient care efficiency and staff satisfaction and facilitated data collection to support a large OPAT program. These modifications apply commonly available EHR functionalities to OPAT care and could be adapted to other settings with different EHR platforms.

Study protocol for a case series: implementation and evaluation of an integrated mental and physical healthcare programme to screen for mental health symptoms in people with epilepsy

IntroductionThe prevalence of mental health symptoms in people with epilepsy (PWE) is elevated compared with that of the general population. These symptoms can negatively impact epilepsy management and patient outcomes...

Systematic symptom management in the IMPACT Consortium: rationale and design for 3 effectiveness-implementation trials.

Cancer and its treatment produce deleterious symptoms across the phases of care. Poorly controlled symptoms negatively affect quality of life and result in increased health-care needs and hospitalization. The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium was created to develop 3 large-scale, systematic symptom management systems, deployed through electronic health record platforms, and to test them in pragmatic, randomized, hybrid effectiveness and implementation trials. Here, we describe the IMPACT Consortium's conceptual framework, its organizational components, and plans for evaluation. The study designs and lessons learned are highlighted in the context of disruptions related to the COVID-19 pandemic.

Implementation of Social Isolation Screening and an Integrated Community Resource Referral Platform.

Screening and referral programs for social isolation and loneliness in older patients increased during the COVID-19 pandemic in primary care settings to mitigate associated adverse health outcomes. This study explores community health centers' experiences implementing a social isolation and loneliness screening program involving a community resource referral platform integrated into the electronic health record to support referrals. A formative mixed methods evaluation in 4 community health centers. Semistructured interviews, observation of implementation meetings, facilitated group discussions, surveys, and utilization data extracted from the electronic health record and community resource referral platform were collected and analyzed concurrently. Screening for social isolation and loneliness can heighten health center staff knowledge and prioritization of socially isolated older patients. Participants indicate using an integrated community resource referral platform may only be useful in certain circumstances, particularly for those located outside urban areas. The experiences of these health centers indicate that when implementing interventions to mitigate patients' social isolation and loneliness, it is necessary to consider other resource directories, needed adjustments to referral and documentation workflows, and potential impacts on patients and care teams. Screening older patients for social isolation could increase care team awareness of social risk; assistance related referral options should be considered carefully.

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.