Ambulatory Electronic Health Record Research Articles

The user-centered design and development of a childhood and adolescent obesity Electronic Health Record tool, a mixed-methods study

BackgroundChildhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.ObjectivesThe objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.MethodsWe utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.ResultsA total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.ConclusionMore research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.

Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules' identification. Our approach is applied on a sub-cohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain.

Pilot Results from the Ambulatory Electronic Health Record (EHR) Evaluation Tool: Lessons Learned

Pilot Results from the Ambulatory Electronic Health Record (EHR) Evaluation Tool: Lessons Learned

How Safe are Outpatient Electronic Health Records? An Evaluation of Medication-Related Decision Support using the Ambulatory Electronic Health Record Evaluation Tool.

The purpose of the Ambulatory Electronic Health Record (EHR) Evaluation Tool is to provide outpatient clinics with an assessment that they can use to measure the ability of the EHR system to detect and prevent common prescriber errors. The tool consists of a medication safety test and a medication reconciliation module. The goal of this study was to perform a broad evaluation of outpatient medication-related decision support using the Ambulatory EHR Evaluation Tool. We performed a cross-sectional study with 10 outpatient clinics using the Ambulatory EHR Evaluation Tool. For the medication safety test, clinics were provided test patients and associated medication test orders to enter in their EHR, where they recorded any advice or information they received. Once finished, clinics received an overall percentage score of unsafe orders detected and individual order category scores. For the medication reconciliation module, clinics were asked to electronically reconcile two medication lists, where modifications were made by adding and removing medications and changing the dosage of select medications. For the medication safety test, the mean overall score was 57%, with the highest score being 70%, and the lowest score being 40%. Clinics performed well in the drug allergy (100%), drug dose daily (85%), and inappropriate medication combinations (74%) order categories. Order categories with the lowest performance were drug laboratory (10%) and drug monitoring (3%). Most clinics (90%) scored a 0% in at least one order category. For the medication reconciliation module, only one clinic (10%) could reconcile medication lists electronically; however, there was no clinical decision support available that checked for drug interactions. We evaluated a sample of ambulatory practices around their medication-related decision support and found that advanced capabilities within these systems have yet to be widely implemented. The tool was practical to use and identified substantial opportunities for improvement in outpatient medication safety.

Long COVID Incidence in a Large US Ambulatory Electronic Health Record System.

Estimates of the prevalence of long-term symptoms of coronavirus disease 2019 (COVID-19), referred to as long COVID, vary widely. This retrospective cohort study describes the incidence of long COVID symptoms 12-20 weeks postdiagnosis in a US ambulatory care setting and identifies potential risk factors. We identified patients with and without a diagnosis of or positive test for COVID-19 between January 1, 2020, and March 13, 2022, in the Veradigm (Veradigm LLC, Chicago, Illinois) electronic health record database. We captured data on patient demographic characteristics, clinical characteristics, and COVID-19 comorbidity in the 12-month baseline period. We compared long COVID symptoms between matched cases and controls 12-20 weeks after the index date (COVID-19 diagnosis date (cases) or median visit date (controls)). Multivariable logistic regression was used to examine associations between baseline COVID-19 comorbid conditions and long COVID symptoms. Among 916,894 patients with COVID-19, 14.8% had at least 1 long COVID symptom in the 12-20 weeks postindex as compared with 2.9% of patients without documented COVID-19. Commonly reported symptoms were joint stiffness (4.5%), cough (3.0%), and fatigue (2.7%). Among patients with COVID-19, the adjusted odds of long COVID symptoms were significantly higher among patients with a baseline COVID-19 comorbid condition (odds ratio = 1.91, 95% confidence interval: 1.88, 1.95). In particular, prior diagnosis of cognitive disorder, transient ischemic attack, hypertension, or obesity was associated with higher odds of long COVID symptoms.

Children's rates of BMI change during pre-pandemic and two COVID-19 pandemic periods, IQVIA Ambulatory Electronic Medical Record, January 2018 Through November 2021.

Many US youth experienced accelerated weight gain during the early COVID-19 pandemic. Using an ambulatory electronic health record data set, the authors compared children's rates of BMI change in three periods: pre-pandemic (January 2018-February 2020), early pandemic (March-December 2020), and later pandemic (January-November 2021). This study used mixed-effects models to examine differences in rates of change in BMI, weight, and obesity prevalence among the three periods. Covariates included time as a continuous variable, a variable indicating in which period each BMI was taken, sex, age, and initial BMI category. In a longitudinal cohort of 241,600 children aged 2 through 19 years with ≥4 BMI measurements, the monthly rates of BMI change (kilograms per meters squared) were 0.056 (95% CI: 0.056-0.057) in the pre-pandemic period, 0.104 (95% CI: 0.102-0.106) in the early pandemic, and 0.035 (95% CI: 0.033-0.036) in the later pandemic. The estimated prevalence of obesity in this cohort was 22.5% by November 2021. In this large, geographically diverse cohort of US youth, accelerated rates of BMI change observed during 2020 were largely attenuated in 2021. Positive rates indicate continued weight gain rather than loss, albeit at a slower rate. Childhood obesity prevalence remained high, which raises concern about long-term consequences of excess weight and underscores the importance of healthy lifestyle interventions.

The COVID-19 Pandemic and Access to Selected Ambulatory Care Services Among Populations With Severely Uncontrolled Diabetes and Hypertension in Massachusetts.

The outbreak of COVID-19 in Massachusetts may have reduced ambulatory care access. Our study aimed to quantify this impact among populations with severely uncontrolled diabetes and hypertension; these populations are at greatest risk for adverse outcomes caused by disruptions in care. We analyzed multidisciplinary ambulatory electronic health record data from MDPHnet. We established 3 cohorts of patients with severely uncontrolled diabetes and 3 cohorts of patients with severely uncontrolled hypertension using 2017, 2018, and 2019 data, then followed each cohort through the subsequent 15 months. For the diabetes cohorts, we generated quarterly counts of glycated hemoglobin A1c (HbA1c) tests. For the hypertension cohorts, we generated monthly counts of blood pressure measurements. Finally, we assessed telehealth use among the 2019 diabetes and hypertension cohorts from January 2020 through March 2021. HbA1c testing and blood pressure monitoring dropped considerably during the pandemic compared with previous years. In the 2019 diabetes cohort, HbA1c measurements declined from 44.0% in January-March 2020 (baseline) to 15.9% in April-June 2020 and was 11.8 percentage points below baseline in January-March 2021. In the 2019 hypertension cohort, blood pressure measurements declined from 40.0% in January 2020 to 4.5% in April 2020 and was 23.5 percentage points below baseline in March 2021. Telehealth use increased precipitously during the pandemic but was not uniform across subpopulations. Access to selected diabetes and hypertension services declined sharply during the pandemic among populations with severely uncontrolled disease. Although telehealth is an important strategy, ensuring equity in access is essential. Telehealth hybrid models can also minimize disruptions in care.

Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use.

ObjectiveThe COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.Materials and MethodsWe use EHR meta-data from ambulatory care clinicians in 366 health systems using the Epic EHR system in the United States from December 2019 to December 2020. We used descriptive statistics for clinician EHR use including active-use time across clinical activities, time after-hours, and messages received. Multivariable regression to evaluate total and after-hours EHR work adjusting for daily volume and organizational characteristics, and to evaluate the association between messages and EHR time.ResultsClinician time spent in the EHR per day dropped at the onset of the pandemic but had recovered to higher than prepandemic levels by July 2020. Time spent actively working in the EHR after-hours showed similar trends. These differences persisted in multivariable models. In-Basket messages received increased compared with prepandemic levels, with the largest increase coming from messages from patients, which increased to 157% of the prepandemic average. Each additional patient message was associated with a 2.32-min increase in EHR time per day (P < .001).DiscussionClinicians spent more total and after-hours time in the EHR in the latter half of 2020 compared with the prepandemic period. This was partially driven by increased time in Clinical Review and In-Basket messaging.ConclusionsReimbursement models and workflows for the post-COVID era should account for these demands on clinician time that occur outside the traditional visit.

Real-World Diagnosis and Treatment of Diabetic Kidney Disease.

People with type2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD) have increased morbidity and mortality risk. Angiotensin-converting enzyme inhibitors (ACEi) or angiotensinII receptor blockers (ARB) are recommended to slow kidney function decline in DKD. This representative, real-world data analysis of patients with T2DM was performed to detect onset of DKD and determine methods and timing of DKD diagnosis and time to initiation of ACEi/ARB therapy. Patients diagnosed with T2DM before January1, 2016 who developed DKD between January1, 2017 and June30, 2019 were identified from a longitudinal ambulatory electronic health record (EHR) dataset (Veradigm Inc). Each record was analyzed using the CLinical INTelligence engine (CLINT™, HealthPals, Inc.) to identify delays and gaps in diagnosing DKD. DKD was diagnosed through two reduced estimated glomerular filtration rate (eGFR; < 60mL/min/1.73m2) measurements at least 90days apart, a single elevated urine albumin-to-creatinine ratio (UACR; > 30mg/g) measurement, or ICD-9/10 diagnosis codes for DKD and/or albuminuria. Time to diagnose (TTD), time to treat (TTT), and diagnosis to treatment time were assessed. Of 6,499,409 patients with T2DM before January 2016, 245,978 developed DKD between January1, 2017 and June30, 2019. In this DKD cohort, ca. 50% were first identified through EHR diagnosis and ca. 50% by UACR or eGFR lab-based diagnosis. In patients who had UACR/eGFR assessed, more than 90% exhibited DKD-level results on the first diagnostic test. Average TTD after eGFR labs was 2years; average TTT with ACEi/ARB was 6-9months after DKD lab evidence. The majority of patients who developed DKD received ACEi/ARB therapy 6-7months after diagnosis. In a contemporary, large national cohort of patients with T2DM, progression to DKD was common but likely underrepresented. The low rate of DKD-screening labs, along with sizable delays in diagnosis of DKD and initiation of ACEi/ARB therapy, indicates that many patients who progress to DKD are not being properly treated.

Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis.

ObjectiveTo derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.Materials and MethodsA cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.ResultsFive out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours.ConclusionsFor every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

Novel Nonproprietary Measures of Ambulatory Electronic Health Record Use Associated with Physician Work Exhaustion.

Accumulating evidence indicates an association between physician electronic health record (EHR) use after work hours and occupational distress including burnout. These studies are based on either physician perception of time spent in EHR through surveys which may be prone to bias or by utilizing vendor-defined EHR use measures which often rely on proprietary algorithms that may not take into account variation in physician's schedules which may underestimate time spent on the EHR outside of scheduled clinic time. The Stanford team developed and refined a nonproprietary EHR use algorithm to track the number of hours a physician spends logged into the EHR and calculates the Clinician Logged-in Outside Clinic (CLOC) time, the number of hours spent by a physician on the EHR outside of allocated time for patient care. The objective of our study was to measure the association between CLOC metrics and validated measures of physician burnout and professional fulfillment. Physicians from adult outpatient Internal Medicine, Neurology, Dermatology, Hematology, Oncology, Rheumatology, and Endocrinology departments who logged more than 8 hours of scheduled clinic time per week and answered the annual wellness survey administered in Spring 2019 were included in the analysis. We observed a statistically significant positive correlation between CLOC ratio (defined as the ratio of CLOC time to allocated time for patient care) and work exhaustion (Pearson's r = 0.14; p = 0.04), but not interpersonal disengagement, burnout, or professional fulfillment. The CLOC metrics are potential objective EHR activity-based markers associated with physician work exhaustion. Our results suggest that the impact of time spent on EHR, while associated with exhaustion, does not appear to be a dominant factor driving the high rates of occupational burnout in physicians.

Online Supplement to “Categorization of Medication Safety Errors in Ambulatory Electronic Health Records”

This supplementary material has been provided by the authors to give readers additional information about their work.

Categorization of Medication Safety Errors in Ambulatory Electronic Health Records

Preventable medication errors continue to affect the quality and consistency in the delivery of care. While numerous studies on medication safety have been performed in the inpatient setting, a review of ambulatory patient safety by the American Medical Association found that medication safety errors were the most frequent safety problems in the outpatient arena. The leading cause of ambulatory safety problems, adverse drug events (ADEs), are common, with estimates of more than 2 million ADEs each year in the ambulatory Medicare population alone, and these events are frequently preventable. We conducted an environmental scan that allowed us to create our own categorization schema of medication safety errors in electronic healthcare records (EHRs) found in the outpatient setting and observed which of these were additionally supported in the literature. This study combines data from the California Hospital Patient Safety Organization (CHPSO), with several key articles in the area of medication errors in the EHR era. Method: To best utilize the various EHR ambulatory medication events submitted into CHPSO’s database, we chose to create a framework to bucket the near misses or adverse events (AEs) submitted to the database. This newly created categorization scheme was based on our own drafted categorization labels of events, after a high-level review, and from two leading articles on physician order entry. Additionally, we conducted a literature review of computerized provider order entry (CPOE) medication errors in the ambulatory setting. Within the newly created categorization scheme, we organized the articles based on issues addressed so we could see areas that were supported by the literature and what still needed to be researched. Results: We initially screened the CHPSO database for ambulatory safety events and found 25,417 events. Based on those events, an initial review was completed, and 19,242 events were found in the “Medication or Other Substance” and “Other” categories, in which the EHR appeared to have been a potential contributing factor. This review identified a subset of 2,236 events that were then reviewed. One hundred events were randomly selected for further review to identify common categories. The most common categories in which errors occurred were orders in order sets and plans (n=12) and orders crossing or not crossing encounters (n=12), incorrect order placed on correct patient (n=10), orders missing (n=8), standing orders (n=8), manual data entry errors (n=6), and future orders (n=6). Conclusion: There were several common themes seen in this analysis of ambulatory medication safety errors related to the EHR. Common among them were incorrect orders consisting of examples such as dose errors or ordering the wrong medication. The manual data entry errors consisted of height or weight being entered incorrectly or entering the wrong diagnostic codes. Lastly, different sources of medication safety information demonstrate a diversity of errors in ambulatory medication safety. This confirms the importance of considering more than one source when attempting to comprehensively describe ambulatory medication safety errors.

The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned.

Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug-drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

Early use of erenumab in US real-world practice

Background: Erenumab, a monoclonal antibody (mAb) developed to block the calcitonin gene-related peptide (CGRP) receptor, is approved for the prevention of migraine in adults. This retrospective observational study sought to describe early real-world use of erenumab. Methods: This study used the Practice Fusion ambulatory Electronic Health Record database, which represents approximately 6% of ambulatory care among primary care and specialist practices in the United States. Among migraine patients initiating erenumab, demographics, migraine type, comorbidities, and prior treatments were assessed during the 12-month baseline period. Treatment patterns including changes in acute and preventive medications, switches to other CGRP mAbs (fremanezumab and galcanezumab), and for erenumab, changes in dose and adherence were examined among patients with 6 months of follow-up. Results: Of 3,336 patients identified (85.9% female; mean age = 49.1 years), approximately 40% had documentation of chronic migraine. Common comorbidities included non-migraine headache, anxiety, depression, and hypertension. Most patients (76.3%) initiated on the 70 mg dose of erenumab. Among 1,638 patients included in the treatment pattern analysis, 53.1% used acute medications and 55.7% used other non-specific preventive migraine medications during follow-up, reductions of 9.8% and 10.2%, respectively, over the same period of time before index. Switching to fremanezumab and galcanezumab were observed in 12.2% and 13.8% of patients, respectively. The mean proportion of days covered by erenumab at 6 months was 79%. Dosage of erenumab increased (from 70 mg to 140 mg) in 13.0% and decreased (from 140 mg to 70 mg) in 24.9% of patients. Conclusion: This early real-world study showed high adherence among erenumab users. This combined with observed reductions in previously used acute and preventive medications are suggestive of erenumab’s benefit.

National Trends in Daily Ambulatory Electronic Health Record Use by Otolaryngologists.

Since their development in the 1970s electronic health records (EHRs) are now nearly ubiquitous. This study aims to characterize the daily interactions of otolaryngology providers with EHRs. This study was a cross-sectional review of provider efficiency profile (PEP) data, as collected by a major EHR vendor. Participating institutions had 6 months of de-identified PEP data reviewed starting January 1, 2019. PEP data is generated for providers with scheduled patients, both attendings and advanced practice providers (APPs). Time metrics are recorded when a provider is interacting with the EHR including a 5-second time-out for inactivity. Data on 269 otolaryngologists and 29 APPs from 10 institutions were evaluated. On scheduled ambulatory clinic days attendings spent 70 ± 36 (mean ± standard deviation) min interacting in the EHR versus 108 ± 46 min for APPs. Of the daily EHR time, mean time in notes, clinical review, in basket, orders, and schedule were 30.1 ± 19.4, 9.6 ± 6.1, 7.3 ± 5.8, and 5.8 ± 7.6 min, respectively. Per patient visit, median (interquartile range) time in notes, clinical review, and orders were 3.19 (2.2-4.9), 1.14 (0.63-1.8), and 0.70 (0.47-1.05) min, respectively. Mean progress note length was 4638 ± 2143 characters. Otolaryngology providers spend a meaningful portion of their clinic day interacting with the EHR. PEP data may provide means to target interventions and a metric to measure the impact of those interventions on provider EHR efficiency. 3 Laryngoscope, 131:975-981, 2021.

Sociodemographic disparities in the management of type 2 diabetes in the United States

Objective To examine the potential sociodemographic disparities in type 2 diabetes (T2D) management and care among US adult individuals, after controlling for clinical and behavioral factors. Methods This was a retrospective cohort study of individuals with T2D (N = 4552) from a linked database of the National Health and Wellness Survey and a large US ambulatory electronic health record (EHR) database. This study period was between 1 January 2015 and 31 December 2018 and individuals were followed up for at least 6 months through EHR after the completion of the survey. The sociodemographic characteristics included gender, race, ethnicity, marital status, education, employment status, household income, insurance status, and geographic region. The independent variables included testing and control of HbA1c, blood pressure (BP), and low-density lipoprotein-cholesterol (LDL-C); hypoglycemia, emergency room (ER) visits, and all-cause hospitalization. Multivariable analyses were conducted using generalized linear models. Results The percentage of uncontrolled HbA1c was 38.6%. With clinical and behavioral characteristics adjusted, individuals living in the Northeast region had 30% higher odds of having HbA1c testing than those who lived in the South. Blacks and Asians were less likely to have HbA1c control than Whites. Uninsured individuals had a lower likelihood of receiving HbA1c, BP, or LDL-C testing compared with commercial insurers. Individuals with low income were more likely to have higher ER visits and hospitalizations. Conclusion Potential sociodemographic disparities exist in T2D management and care in the US, indicating the needs for improvement in healthcare access, educational and behavioral programs, as well as disease and treatment management in these subgroups.

1168 Time Spent on Ambulatory Electronic Health Records Outside of Scheduled Work Hours by Gastroenterology Physicians

INTRODUCTION: The introduction of electronic health records (EHR) has been linked to physician burnout. This study aims to quantify the time gastroenterology physicians spend on ambulatory EHR outside of scheduled work hours. METHODS: Data was collected for time spent in ambulatory EHR for the eight-week period 02/03/2019 - 03/30/2019. Cumulative scheduled time was calculated for each physician. Hospital-based clinical encounters and time spent in hospital-based EHR were excluded from analysis. Ratios of ambulatory EHR time to scheduled time were calculated. We also analyzed data on physician FTE status, post-fellowship experience, new patient visit time interval, and correlated these data with the aforementioned ratio. P-values &lt; 0.05 were considered significant. This study was reviewed by our Institutional Review Board, and a waiver was obtained. RESULTS: Data was available for 41 providers; 18 providers were excluded (5 non-physicians, 3 with FTE status &lt; 0.2, 2 pediatric gastroenterologists, 8 without available scheduled time data). Data were analyzed for 23 gastroenterology physicians with 7,373 ambulatory encounters (3465 office visits, 310 office-based procedures, and 3598 ASC) across 4 sites. The average weekly time spent in EHR was 41.30 ± 11.73 hours, and the average weekly scheduled time was 24.48 ± 7.64 hours. Correspondingly, the average ratio of EHR time to scheduled time was determined to be 1.78 ± 0.70. Part-time and full-time physicians were also compared. There was a greater proportion of males in the full-time group rather than the part-time group (90.91% vs. 58.33%, P = 0.076). In addition, there was a trend towards a higher ratio of EHR time to scheduled time for part-time physicians (1.99 ± 0.88 vs. 1.56 ± 0.36), although this was not statistically significant (P = 0.15). The only statistically significant predictor of ratio was FTE status, treated as a continuous variable; the slope of the linear regression model was -3.06 ± 0.83 (P = 0.001) and the corresponding R2 was 0.39. Neither experience, in years after fellowship, nor new patient visit interval, were correlated with ratio. CONCLUSION: Our data show that gastroenterology physicians spend significant time outside of their scheduled time on EHR-related tasks. More studies are needed to better understand the distribution of time across different tasks; targeted countermeasures, such automation and utilization of non-physician staff, may help reduce the burden of EHR time and, correspondingly, the risk for physician burnout.

PDB105 HEMOGLOBIN A1C (HBA1C) TESTING FREQUENCY AND HEALTH-RELATED QUALITY OF LIFE (HRQOL) IN PATIENTS WITH TYPE 2 DIABETES: AN ELECTRONIC HEALTH RECORDS LINKED WITH PATIENT-REPORTED OUTCOMES STUDY

Frequency of HbA1c testing is associated with type 2 diabetes (T2D) management and control, but limited data exists on the relationship between HbA1c testing and patient-reported measures such as HRQoL. This study used patient-reported survey data linked with laboratory tests from electronic health records (EHR) to assess differences in HRQoL associated with HbA1c testing frequency. Patient-Centered-Research (PaCeR) US data (2015-2018; N=345,184), consisting of nationally-representative patient-reported survey data, were linked with a large US ambulatory EHR database (2015-2018; N=50 million+) in a HIPAA-compliant linking methodology. The study population consisted of linked respondents who were diagnosed with T2D and had at least one HbA1c test result in the EHR within six months of survey participation. Study participants were grouped by HbA1c testing frequency (1-2 tests/year, 3-4 tests/year, 5+ tests/year) and compared on sociodemographic and HRQoL measures using two-sample t-tests and chi-square tests. HRQoL measures included SF-36v2 (mental and physical component summary scores [MCS and PCS] and SF-6D health utility index). Of 492 total T2D patients who met the study criteria [mean age = 61.3 years, 54.7% female, 85.6% White, 38.8% uncontrolled HbA1c (≥ 7%)], 58.1% had 1-2 HbA1c tests/year, 26.6% had 3-4 HbA1c tests/year, and 15.2% had 5+ HbA1c tests/year. T2D patients with 1-2 HbA1c tests/year had significantly lower MCS compared to those with 3-4 HbA1c tests/year (mean = 48.8 vs. 51.8, p = 0.01). Vitality (mean = 47.6 vs. 50.0, p = 0.04) and mental health (mean = 48.6 vs. 52.1, p < 0.005) domain scores were also lower among patients with 1-2 HbA1c tests/year compared to patients with 3-4 HbA1c tests/year. There were no differences in HRQoL between the 1-2 tests/year and 5+ tests/year groups. Linking patient-reported outcomes with EHR data facilitated analysis of HbA1c testing with HRQoL. Results showed that less frequent HbA1c testing was associated with lower MCS.

2017 AHA/ACC Key Data Elements and Definitions for Ambulatory Electronic Health Records in Pediatric and Congenital Cardiology: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards.

The American College of Cardiology (ACC) and the American Heart Association (AHA) support their members’ goal to improve the care of patients with cardiovascular disease through professional education, research, and development of guidelines and standards and by fostering policies that support optimal patient outcomes. The ACC and AHA recognize the importance of the use of clinical data standards for patient management, assessment of outcomes, and conduct of research, and the importance of defining the processes and outcomes of clinical care, whether in randomized trials, observational studies, registries, or quality improvement initiatives. Clinical data standards strive to define and standardize data relevant to clinical concepts, with the primary goal of facilitating uniform data collection by providing a platform of clinical terms with corresponding definitions and data elements. Broad agreement on a …