Full Electronic Health Record Research Articles

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Vacation Days Taken, Work During Vacation, and Burnout Among US Physicians

Vacation has been shown to be an important restorative activity in the general population; less is known about physicians' vacation behaviors and their association with burnout and professional fulfillment. To examine the number of vacation days taken per year and the magnitude of physician work while on vacation and their association with physician burnout and professional fulfillment, by individual and organizational characteristics. This cross-sectional survey of US physicians was conducted between November 20, 2020, and March 23, 2021. Data analysis was performed from March to July 2023. Burnout was measured using the Maslach Burnout Index, and professional fulfillment was measured using the Stanford Professional Fulfillment Index. Number of vacation days taken in the last year, time spent working on patient care and other professional tasks per typical vacation day (ie, work on vacation), electronic health record (EHR) inbox coverage while on vacation, barriers to taking vacation, and standard demographics were collected. Among 3024 respondents, 1790 of 3004 (59.6%), took 15 or fewer days of vacation in the last year, with 597 of 3004 (19.9%) taking 5 or fewer days. The majority, 2104 respondents (70.4%), performed patient care-related tasks on vacation, with 988 of 2988 (33.1%) working 30 minutes or more on a typical vacation day. Less than one-half of physicians (1468 of 2991 physicians [49.1%]) reported having full EHR inbox coverage while on vacation. On multivariable analysis adjusting for personal and professional factors, concern about finding someone to cover clinical responsibilities (odds ratio [OR], 0.48 [95% CI, 0.35-0.65] for quite a bit; OR, 0.30 [95% CI, 0.21-0.43] for very much) and financial concerns (OR, 0.49 [95% CI, 0.36-0.66] for quite a bit; OR, 0.38 [95% CI, 0.27-0.54] for very much) were associated with decreased likelihood of taking more than 3 weeks of vacation per year. Taking more than 3 weeks of vacation per year (OR, 0.66 [95% CI, 0.45-0.98] for 16-20 days; OR, 0.59 [95% CI, 0.40-0.86] for >20 days vs none) and having full EHR inbox coverage while on vacation (OR, 0.74; 95% CI, 0.63-0.88) were associated with lower rates of burnout on multivariable analysis, whereas spending 30 minutes or longer per vacation day on patient-related work (OR, 1.58; 95% CI, 1.22-2.04 for 30-60 minutes; OR, 1.97; 95% CI, 1.41-2.77 for 60-90 minutes; OR, 1.92; 95% CI, 1.36-2.73 for >90 minutes) was associated with higher rates of burnout. In this cross-sectional study of 3024 physicians, the number of vacation days taken and performing patient-related work while on vacation were associated with physician burnout. System-level efforts to ensure physicians take adequate vacation and have coverage for clinical responsibilities, including EHR inbox, may reduce physician burnout.

Reimagining Patient Data Access for Researchers

ObjectivesWidespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients’ privacy rights. MethodsWe analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients’ data aggregators, and final aggregation across patients’ data. ResultsCreating new HEOR data systems requires new organizations and funding, while also protecting patients’ data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients’ data. New secondary data aggregators must combine individuals’ aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients’ data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. ConclusionsImportant steps remain uncompleted to achieve maximally available HEOR data while protecting patients’ privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people’s health.

Active feature elicitation: An unified framework.

We consider the problem of active feature elicitation in which, given some examples with all the features (say, the full Electronic Health Record), and many examples with some of the features (say, demographics), the goal is to identify the set of examples on which more information (say, lab tests) need to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose a classifier-independent, similarity metric-independent, general active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by four real clinical tasks, our extensive evaluation demonstrates the effectiveness of this approach. To demonstrate the generalization capabilities of the proposed approach, we consider different divergence metrics and classifiers and present consistent results across the domains.

Identifying problematic opioid use in electronic health record data: Are we looking in the right place?

To examine the value of data obtained outside of regular healthcare visits (clinical communications) to detect problematic opioid use in electronic health records (EHRs). A retrospective cohort study. Chronic pain patient records in a large academic medical center. We compared evidence for problematic opioid use in (1) clinic notes, (2) clinical communications, and (3) full EHR data. We analyzed keyword counts and calculated concordance and Cohen's κ between data sources. Evidence of problematic opioid use in EHR defined as none, some, or high. Twenty-six percent of records were discordant in determination of problematic opioid use between clinical communications and clinic notes. Of these, 54 percent detected more evidence in clinical communications, and 46 percent in clinic notes. Compared to full EHR review, clinic notes exhibited higher concordance (78 percent; κ = 0.619) than clinical communications (60 percent; κ = 0.290). Clinical communications are a valuable addition to opioid EHR research.

A STUDY ON ELECTRONIC HEALTH RECORDS

Electronic health records (EHRs) were introduced to enhance patient outcomes and care quality. The adoption of EHRs and patient outcomes were compared in this study. Using State Inpatient Databases connected to the 2011 American Hospital Association survey, we conducted an observational study. Six sizable, diversified states' worth of surgical and medical patients were included. We conducted univariate analyses and created hierarchical regression models that examined the relationships between mortality, readmission rates, and complications with the extent of EHR utilisation. Using a difference-in-differences methodology, we assessed the impact of EHR adoption on outcomes from 2008 to 2011. Patients seeking medical and surgical care went to hospitals with no EHR (3.5%), partial EHR (55.2%), and full EHR (41.3%) systems. Patients at hospitals with full EHR had the lowest rates of inpatient mortality, readmissions, and hospitalizations in univariate studies. Keywords: Electronic Health Records, Bar Coding, Speech Recognition, Patient Confidentiality.

763Validity of algorithms for identifying five chronic conditions in MedicineInsight, Australian national primary care data

Abstract Background MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. To support the trust placed in analyses of MedicineInsight data, additional evidence regarding the accuracy of the data is needed. Methods This study measures the validity of algorithms available in MedicineInsight that identify patients with depression, anxiety, asthma, type 2 diabetes and osteoporosis. Fifty practices met eligibility criteria regarding patient load and location, five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected. This age restriction increased the prevalence of the evaluated conditions, thereby optimising statistical power. Trained staff review the full EHR for these patients, including progress notes and correspondence, which are not available in MedicineInsight because they may contain identifiable information. Results With data collection almost complete, the target sample size will not be attainable. Power calculations indicate the current sample of 479 should provide adequate precision. For each condition of interest, the sensitivity, specificity, positive predictive value and negative predictive value of the algorithm is calculated. The full EHR review is the gold standard against which the algorithms are benchmarked. Conclusions The findings will indicate whether these algorithms demonstrate adequate accuracy to be used for research and decision-making. Key messages This additional understanding regarding the accuracy of MedicineInsight data will facilitate the interpretation of analyses of MedicineInsight data and guide improvements to the algorithms.

Body Composition and Genetic Lipodystrophy Risk Score Associate With Nonalcoholic Fatty Liver Disease and Liver Fibrosis.

Up to 25% of patients with nonalcoholic fatty liver disease (NAFLD) are not obese but may have a fat or muscle composition that predisposes them to NAFLD. Our aim was to determine whether body composition parameters associate with NAFLD and to identify genetic contributors to this association. This study included two cohorts. The first included 2,249 participants from the Framingham Heart Study who underwent a computed tomography scan to evaluate hepatic steatosis, dual‐energy x‐ray absorptiometry testing to assess body composition, and clinical examination. Body composition parameters were normalized to total body weight. A subset of participants underwent genotyping with an Affymetrix 550K single‐nucleotide polymorphism array. The second cohort, Michigan Genomics Initiative, included 19,239 individuals with genotyping on the Illumina HumanCoreExome v.12.1 array and full electronic health record data. Using sex‐stratified multivariable linear regression, greater central body fat associated with increased hepatic steatosis while greater lower extremity body fat associated with decreased hepatic steatosis. Greater appendicular lean mass was associated with decreased hepatic steatosis in men but not in women. A polygenic risk score for lipodystrophy (regional or global loss of adipose tissue) was associated with increased hepatic steatosis, increased liver fibrosis, and decreased lower extremity fat mass. Conclusion: Greater central body fat associated with increased hepatic steatosis, while greater lower extremity body fat and, in men, greater appendicular lean mass were associated with decreased hepatic steatosis. A genetic risk score for lipodystrophy was associated with NAFLD and liver fibrosis. Our results suggest that buffering of excess energy by peripheral fat and muscle may protect against NAFLD and liver fibrosis in the general population.

Electronic Health Records and Quality of Care

Electronic health records (EHRs) were implemented to improve quality of care and patient outcomes. This study assessed the relationship between EHR-adoption and patient outcomes.We performed an observational study using State Inpatient Databases linked to American Hospital Association survey, 2011. Surgical and medical patients from 6 large, diverse states were included. We performed univariate analyses and developed hierarchical regression models relating level of EHR utilization and mortality, readmission rates, and complications. We evaluated the effect of EHR adoption on outcomes in a difference-in-differences analysis, 2008 to 2011.Medical and surgical patients sought care at hospitals reporting no EHR (3.5%), partial EHR (55.2%), and full EHR systems (41.3%). In univariate analyses, patients at hospitals with full EHR had the lowest rates of inpatient mortality, readmissions, and Patient Safety Indicators followed by patients at hospitals with partial EHR and then patients at hospitals with no EHR (P < 0.05). However, these associations were not robust when accounting for other patient and hospital factors, and adoption of an EHR system was not associated with improved patient outcomes (P > 0.05).These results indicate that patients receiving medical and surgical care at hospitals with no EHR system have similar outcomes compared to patients seeking care at hospitals with a full EHR system, after controlling for important confounders.To date, we have not yet seen the promised benefits of EHR systems on patient outcomes in the inpatient setting. EHRs may play a smaller role than expected in patient outcomes and overall quality of care.

Changes to Workflow and Process Measures in the PICU During Transition From Semi to Full Electronic Health Record

©2016The Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study

ObjectivesAn estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records are incomplete and not recorded systematically across patients and practice locations, as is typically seen in practice. MethodsIn this cross-sectional, retrospective study, EHR data from 9948 US patients were used to develop a pre-screening tool to predict current DM2, using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, (2) a restricted EHR DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, hypertension). ResultsUsing a patient’s full EHR or restricted EHR was superior to using basic covariates alone for detecting individuals with diabetes (hierarchical X2 test, p<0.001). Migraines, depot medroxyprogesterone acetate, and cardiac dysrhythmias were associated negatively with DM2, while sexual and gender identity disorder diagnosis, viral and chlamydial infections, and herpes zoster were associated positively. Adding EHR phenotypes improved classification; the AUC for the full EHR Model, EHR DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest machine learning out-of-sample prediction, accuracy also was improved when using EHR phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity. ConclusionsEHR phenotyping resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the ROC curves. EHR phenotypes could more efficiently identify which patients do require, and don’t require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating EHR phenotype screening would identify an additional 400,000 patients with active, untreated diabetes compared to the conventional pre-screening models.

Modest Associations Between Electronic Health Record Use and Acute Myocardial Infarction Quality of Care and Outcomes: Results From the National Cardiovascular Data Registry.

In 2009, national legislation promoted wide-spread adoption of electronic health records (EHRs) across US hospitals; however, the association of EHR use with quality of care and outcomes after acute myocardial infarction (AMI) remains unclear. Data on EHR use were collected from the American Hospital Association Annual Surveys (2007-2010) and data on AMI care and outcomes from the National Cardiovascular Data Registry Acute Coronary Treatment and Interventions Outcomes Network Registry-Get With The Guidelines. Comparisons were made between patients treated at hospitals with fully implemented EHR (n=43 527), partially implemented EHR (n=72 029), and no EHR (n=9270). Overall EHR use increased from 82.1% (183/223) hospitals in 2007 to 99.3% (275/277) hospitals in 2010. Patients treated at hospitals with fully implemented EHRs had fewer heparin overdosing errors (45.7% versus 72.8%; P<0.01) and a higher likelihood of guideline-recommended care (adjusted odds ratio, 1.40 [confidence interval, 1.07-1.84]) compared with patients treated at hospitals with no EHR. In non-ST-segment-elevation AMI, fully implemented EHR use was associated with lower risk of major bleeding (adjusted odds ratio, 0.78 [confidence interval, 0.67-0.91]) and mortality (adjusted odds ratio, 0.82 [confidence interval, 0.69-0.97]) compared with no EHR. In ST-segment-elevation MI, outcomes did not significantly differ by EHR status. EHR use has risen to high levels among hospitals in the National Cardiovascular Data Registry. EHR use was associated with less frequent heparin overdosing and modestly greater adherence to acute MI guideline-recommended therapies. In non-ST-segment-elevation MI, slightly lower adjusted risk of major bleeding and mortality were seen in hospitals implemented with full EHRs; however, in ST-segment-elevation MI, differences in outcomes were not seen.

Changes to Workflow and Process Measures in the PICU During Transition From Semi to Full Electronic Health Record.

Studies showing the changes in workflow during transition from semi to full electronic medical records are lacking. This objective study is to identify the changes in workflow in the PICU during transition from semi to full electronic health record. Prospective observational study. Children's Hospital of Wisconsin Institutional Review Board waived the need for approval so this study was institutional review board exempt. This study measured clinical workflow variables at a 72-bed PICU during different phases of transition to a full electronic health record, which occurred on November 4, 2012. Phases of electronic health record transition were defined as follows: pre-electronic health record (baseline data prior to transition to full electronic health record), transition phase (3 wk after electronic health record), and stabilization (6 mo after electronic health record). Data were analyzed for the three phases using Mann-Whitney U test with a two-sided p value of less than 0.05 considered significant. Seventy-two bed PICU. All patients in the PICU were included during the study periods. Five hundred and sixty-four patients with 2,355 patient days were evaluated in the three phases. Duration of rounds decreased from a median of 9 minutes per patient pre--electronic health record to 7 minutes per patient post electronic health record. Time to final note decreased from 2.06 days pre--electronic health record to 0.5 days post electronic health record. Time to first medication administration after admission also decreased from 33 minutes pre--electronic health record and 7 minutes post electronic health record. Time to Time to medication reconciliation was significantly higher pre-electronic health record than post electronic health record and percent of medication reconciliation completion was significantly lower pre--electronic health record than post electronic health record and percent of medication reconciliation completion was significantly higher pre--electronic health record than. There was no significant change in time between placement of discharge order and physical transfer from the unit [corrected].changes clinical workflow in a PICU with decreased duration of rounds, time to final note, time to medication administration, and time to medication reconciliation completion. There was no change in the duration from medical to physical transfer.

Impact of Electronic Documentation on Pap Screening Rates in an Urban Health Center

Providers and non-physician staff in primary care settings have reported barriers to full electronic health record (EHR) utilization. This study evaluates the effectiveness of EHR use for accurately documenting cervical cancer screening in a community healthcare setting, and proposes strategies to improve documentation. An electronic query generated data on average-risk patients aged 21-64 who had a medical visit at Fenway Health in 2012 and were overdue for a Papanicolaou (Pap) test according to the 2012 American Society for Colposcopy and Cervical Pathology guidelines. We then conducted a manual review of these records to determine the accuracy of EHR documentation. Of a total 5,279 patients, the electronic query classified 2,982 (56.5%) as up-to-date (UTD) for a Pap and 2,297 patients (43.5%) as overdue. Upon manual review, 65 (2.2%) patients thought to be UTD were actually overdue. Of those 2,297 patients classified by the query as overdue, 816 (35.5%) were reclassified as UTD due to evidence of a recent Pap in their chart that was not extractable by electronic query and 208 (9.1%) were ineligible for a Pap; only 1,272 patients (55.4%) of the 2,297 classified by the query were truly overdue. The cervical cancer screening rate indicated by electronic query was 56.5 %; after manual review, the adjusted rate was 73.6%. Overall, 1,090 patients (20.6%) were misclassified by the query. Inefficient EHR use can have serious implications for clinical practice and performance measures. Primary care practices need to develop mechanisms to capture outside medical records and create a team-based approach to facilitate accurate EHR documentation.

Lean implementation of electronic health records (EHR).

211 Background: The Palo Alto Medical Foundation (PAMF), an integrated multispecialty physician group and one of five regions of Sutter Health in Northern California, is converting from an electronic chemotherapy ordering and administration system, Intellidose, to Epic Beacon to achieve full EHR integration. As chemotherapy workflows are complex with multiple interactions between providers and high potential for patient harm, we adopted a novel approach of incorporating Lean process improvement principles in advance of our Beacon deployment to improve the design of our software build and increase operational safety and efficiency. Methods: Two multi-disciplinary teams including oncologists, pharmacists, nurses, administrative and technical support staff were deployed; a Sutter Beacon team responsible for technical design and build of the Sutter Beacon standard and a second PAMF team to evaluate workflow variation among PAMF chemotherapy infusion centers, identify best practices, engage the Sutter Beacon Build team around system design and institute rapid cycle process improvement in partnership with the PAMF Lean Promotion Office. Results: Visio maps of current state workflows were constructed to reflect oncologist, pharmacy and nursing workflows from medication ordering to chemotherapy administration and billing submission. Two Rapid Process Improvement Workshops designed future state workflows and identified key areas requiring re-engineering. Lean rapid cycle improvement strategies will be leveraged to improve clinical workflows prior to Beacon implementation. To support the 40% of infusion treatments that are for non-oncology patients, a second process was designed as an important patient safety initiative to avoid discordant nursing administration workflows. Conclusions: Successful design, build and implementation of EHR require achieving consensus-driven work processes that maximize patient safety, eliminate workflow redundancies and maximizes the value-added component. It requires coordination between the EHR technical build team who design the system and the implementation team who integrate it with re-engineered workflows. Lean methodology provides the conceptual framework to achieve a successful EHR deployment.

Transitioning from a computerized provider order entry and paper documentation system to an electronic health record: Expectations and experiences of hospital staff

ObjectivesTo examine healthcare worker's perceptions, expectations, and experiences regarding how work processes, patient-related safety, and care were affected when a quaternary care center transitioned from one computerized provider order entry (CPOE) system to a full electronic health record (EHR). MethodsThe I-SEE survey was administered prior to and 1-year after transition in systems. The construct validity and reliability of the survey was assessed within the current population and also compared to previously published results. Pre- and 1-year post-implementation scale means were compared within and across time periods. ResultsThe majority of respondents were nurses and personnel working in the acute care setting. Because a confirmatory factor analysis indicated a lack of fit of our data to the I-SEE survey's 5-factor structure, we conducted an exploratory factor analysis that resulted in a 7-factor structure which showed better reliability and validity. Mean scores for each factor indicated that attitudes and expectations were mostly positive and score trends over time were positive or neutral. Nurses generally had less positive attitudes about the transition than non-nursing respondents, although the difference diminished after implementation. ConclusionsFindings demonstrate that the majority of responding staff were generally positive about transitioning from CPOE system to a full electronic health record (EHR) and understood the goals of doing so, with overall improved ratings over time. In addition, the I-SEE survey, when modified based on our population, was useful for assessing patient care and safety related expectations and experiences during the transition from one CPOE system to an EHR.

Physician Specialty and Variations in Adoption of Electronic Health Records

Efforts to promote adoption of electronic health records (EHRs) have focused on primary care physicians, who are now expected to exchange data electronically with other providers, including specialists. However, the variation of EHR adoption among specialists is underexplored. We conducted a retrospective cross-sectional study to determine the association between physician specialty and the prevalence of EHR adoption, and a retrospective serial cross-sectional study to determine the association of physician specialty and the rate of EHR adoption over time. We used the 2005-2009 National Ambulatory Medical Care Survey. We considered fourteen specialties, and four definitions of EHR adoption (any EHR, basic EHR, full EHR, and a novel definition of EHR sophistication). We used multivariable logistic regression, and adjusted for several covariates (geography, practice characteristics, revenue characteristics, physician degree). Physician specialty was significantly associated with EHR adoption, regardless of the EHR definition, after adjusting for covariates. Psychiatrists, dermatologists, pediatricians, ophthalmologists, and general surgeons were significantly less likely to adopt EHRs, compared to the reference group of family medicine/general practitioners. After adjustment for covariates, these specialties were 44-94% less likely to adopt EHRs than the reference group. EHR adoption increased in all specialties, by approximately 40% per year. The rate of EHR adoption over time did not significantly vary by specialty. Although EHR adoption is increasing in all specialties, adoption varies widely by specialty. In order to insure each individual's network of providers can electronically share data, widespread adoption of EHRs is needed across all specialties.

Computers in the examination room and the electronic health record: physicians' perceived impact on clinical encounters before and after full installation and implementation

We compared physicians' self-reported attitudes and behaviours regarding electronic health record (EHR) use before and after installation of computers in patient examination rooms and transition to full implementation of an EHR in a family medicine training practice to identify anticipated and observed effects these changes would have on physicians' practices and clinical encounters. We conducted two individual qualitative interviews with family physicians. The first interview was before and second interview was 8 months later after full implementation of an EHR and computer installation in the examination rooms. Data were analysed through project team discussions and subsequent coding with qualitative analysis software. At the first interviews, physicians frequently expressed concerns about the potential negative effect of the EHR on quality of care and physician-patient interaction, adequacy of their skills in EHR use and privacy and confidentiality concerns. Nevertheless, most physicians also anticipated multiple benefits, including improved accessibility of patient data and online health information. In the second interviews, physicians reported that their concerns did not persist. Many anticipated benefits were realized, appearing to facilitate collaborative physician-patient relationships. Physicians reported a greater teaching role with patients and sharing online medical information and treatment plan decisions. Before computer installation and full EHR implementation, physicians expressed concerns about the impact of computer use on patient care. After installation and implementation, however, many concerns were mitigated. Using computers in the examination rooms to document and access patients' records along with online medical information and decision-making tools appears to contribute to improved physician-patient communication and collaboration.

341: Change in Staff Opinions After Initiation of Emergency Department Full Electronic Health Record

341: Change in Staff Opinions After Initiation of Emergency Department Full Electronic Health Record