Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program

ObjectiveTo give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout.Materials and MethodsAll ambulatory providers were offered at least 1 one-on-one session with an “optimizer” focusing on filling gaps in EHR knowledge and lack of customization. Success was measured using pre- and post-surveys that consisted of validated tools and homegrown questions. Only participants who returned both surveys were included in our calculations.ResultsOut of 1155 eligible providers, 1010 participated in optimization sessions. Pre-survey return rate was 90% (1034/1155) and post-survey was 54% (541/1010). 451 participants completed both surveys. After completing their optimization sessions, respondents reported a 26% improvement in mean knowledge of EHR functionality (P < .01), a 19% increase in the mean efficiency in the EHR (P < .01), and a 17% decrease in mean after-hours EHR usage (P < .01). Of the 401 providers asked to rate their burnout, 32% reported feelings of burnout in the pre-survey compared to 23% in the post-survey (P < .01). Providers were also likely to recommend colleagues participate in the program, with a Net Promoter Score of 41.DiscussionIt is possible to improve provider efficiency and feelings of burnout with a personalized optimization program. We ascribe these improvements to the one-on-one nature of our program which provides both training as well as addressing the feeling of isolation many providers feel after implementation.ConclusionIt is possible to reduce burnout in ambulatory providers with personalized retraining designed to improve efficiency and knowledge of the EHR.     

Impact of a problem-oriented view on clinical data retrieval

ObjectiveThe electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).MethodsSimple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.ResultsWith POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001).DiscussionThe study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.ConclusionEHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.     

Clinical data sharing improves quality measurement and patient safety

ObjectiveAccurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and MethodsData were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.ResultsA total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.DiscussionThese results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.ConclusionsFederal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.     

Can decision support combat incompleteness and bias in routine primary care data?

ObjectiveRoutine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and MethodsWe used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.ResultsSupported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.ConclusionsWe provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.     

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.     

A visual representation of microbiological culture data improves comprehension: a randomized controlled trial

ObjectiveWhile the judicious use of antibiotics takes past microbiological culture results into consideration, this data’s typical format in the electronic health record (EHR) may be unwieldy when incorporated into clinical decision-making. We hypothesize that a visual representation of sensitivities may aid in their comprehension.Materials and MethodsA prospective parallel unblinded randomized controlled trial was undertaken at an academic urban tertiary care center. Providers managing emergency department (ED) patients receiving antibiotics and having previous culture sensitivity testing were included. Providers were randomly selected to use standard EHR functionality or a visual representation of patients’ past culture data as they answered questions about previous sensitivities. Concordance between provider responses and past cultures was assessed using the kappa statistic. Providers were surveyed about their decision-making and the usability of the tool using Likert scales.Results518 ED encounters were screened from 3/5/2018 to 9/30/18, with providers from 144 visits enrolled and analyzed in the intervention arm and 129 in the control arm. Providers using the visualization tool had a kappa of 0.69 (95% CI: 0.65–0.73) when asked about past culture results while the control group had a kappa of 0.16 (95% CI: 0.12–0.20). Providers using the tool expressed improved understanding of previous cultures and found the tool easy to use (P < .001). Secondary outcomes showed no differences in prescribing practices.ConclusionA visual representation of culture sensitivities improves comprehension when compared to standard text-based representations.     

Impact of event notification services on timely follow-up and rehospitalization among primary care patients at two Veterans Affairs Medical Centers

ObjectiveTo examine the effectiveness of event notification service (ENS) alerts on health care delivery processes and outcomes for older adults.Materials and methodsWe deployed ENS alerts in 2 Veterans Affairs (VA) medical centers using regional health information exchange (HIE) networks from March 2016 to December 2019. Alerts targeted VA-based primary care teams when older patients (aged 65+ years) were hospitalized or attended emergency departments (ED) outside the VA system. We employed a concurrent cohort study to compare postdischarge outcomes between patients whose providers received ENS alerts and those that did not (usual care). Outcome measures included: timely follow-up postdischarge (actual phone call within 7 days or an in-person primary care visit within 30 days) and all-cause inpatient or ED readmission within 30 days. Generalized linear mixed models, accounting for clustering by primary care team, were used to compare outcomes between groups.ResultsCompared to usual care, veterans whose primary care team received notification of non-VA acute care encounters were 4 times more likely to have phone contact within 7 days (AOR = 4.10, P < .001) and 2 times more likely to have an in-person visit within 30 days (AOR = 1.98, P = .007). There were no significant differences between groups in hospital or ED utilization within 30 days of index discharge (P = .057).DiscussionENS was associated with increased timely follow-up following non-VA acute care events, but there was no associated change in 30-day readmission rates. Optimization of ENS processes may be required to scale use and impact across health systems.ConclusionGiven the importance of ENS to the VA and other health systems, this study provides guidance for future research on ENS for improving care coordination and population outcomes.Trial RegistrationClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02689076","term_id":"NCT02689076"}}NCT02689076. “Regional Data Exchange to Improve Care for Veterans After Non-VA Hospitalization.” Registered February 23, 2016.     

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility

ObjectiveThe US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.Materials and MethodsIn this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic^® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.ResultsOver 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).DiscussionMissing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.ConclusionThe use of longitudinal EHR smoking data could improve lung cancer screening.     

Mobile health technology to improve maternal health awareness in tribal populations: mobile for mothers

Mobile health (mHealth) applications have the potential to improve health awareness. This study reports a quasi-controlled intervention to augment maternal health awareness among tribal pregnant mothers through the mHealth application. Households from 2 independent villages with similar socio-demographics in tribal regions of India were selected as intervention (Village A) and control group (Village B). The control group received government mandated programs through traditional means (orally), whereas the intervention group received the same education through mHealth utilization. Postintervention, awareness about tetanus injections and consumption of iron tablets was significantly (P < .001) improved in the intervention group by 55% and 58%, respectively. Awareness about hygiene significantly (P < .001) increased by 57.1%. In addition, mothers in the intervention group who recognized vaginal bleeding, severe abdominal pain, severe blurring of vision, or convulsions as danger signs during pregnancy significantly (P < .001) increased by 18.30%, 23.2%, 20.0%, and 4.90%, respectively. Our study indicates that despite the low literacy of users, mHealth intervention can improve maternal health awareness among tribal communities.     

The association between perceived electronic health record usability and professional burnout among US nurses

ObjectivesTo measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout.MethodsA cross-sectional survey of a random sample of US nurses was conducted in November 2017. EHR usability was measured with the System Usability Scale (SUS; range 0–100) and burnout with the Maslach Burnout Inventory.ResultsAmong the 86 858 nurses who were invited, 8638 (9.9%) completed the survey. The mean nurse-rated EHR SUS score was 57.6 (SD 16.3). A score of 57.6 is in the bottom 24% of scores across previous studies and categorized with a grade of “F.” On multivariable analysis adjusting for age, gender, race, ethnicity, relationship status, children, highest nursing-related degree, mean hours worked per week, years of nursing experience, advanced certification, and practice setting, nurse-rated EHR usability was associated with burnout with each 1 point more favorable SUS score and associated with a 2% lower odds of burnout (OR 0.98; 95% CI, 0.97–0.99; P < .001).ConclusionsNurses rated the usability of their current EHR in the low marginal range of acceptability using a standardized metric of technology usability. EHR usability and the odds of burnout were strongly associated with a dose-response relationship.     

Measuring time clinicians spend using EHRs in the inpatient setting: a national,mixed-methods study

ObjectiveTo understand hospitals’ use of EHR audit-log-based measures to address burden associated with inpatient EHR use.Materials and MethodsUsing mixed methods, we analyzed 2018 American Hospital Association Information Technology Supplement Survey data (n = 2864 hospitals; 64% response rate) to characterize measures used and provided by EHR vendors to track clinician time spent documenting. We interviewed staff from the top 3 EHR vendors that provided these measures. Multivariable analyses identified variation in use of the measures among hospitals with these 3 vendors.Results53% of hospitals reported using EHR data to track clinician time documenting, compared to 68% of the hospitals using the EHR from the top 3 vendors. Among hospitals with EHRs from these vendors, usage was significantly lower among rural hospitals and independent hospitals (P < .05). Two of these vendors provided measures of time spent doing specific tasks while the third measured an aggregate of auditable activities. Vendors varied in the underlying data used to create measures, measure specification, and data displays.DiscussionTools to track clinicians’ documentation time are becoming more available. The measures provided differ across vendors and disparities in use exist across hospitals. Increasing the specificity of standards underlying the data would support a common set of core measures making these measures more widely available.ConclusionAlthough half of US hospitals use measures of time spent in the EHR derived from EHR generated data, work remains to make such measures and analyses more broadly available to all hospitals and to increase its utility for national burden measurement.     

The impact of time spent on the electronic health record after work and of clerical work on burnout among clinical faculty

ObjectiveTo identify specific thresholds of daily electronic health record (EHR) time after work and daily clerical time burden associated with burnout in clinical faculty.Materials and MethodsWe administered an institution-wide survey to faculty in all departments at Mount Sinai Health System from November 2018 to February 2019. The Maslach Burnout Inventory and Mayo Well-Being Index assessed burnout. Demographics, possible confounding variables, and time spent on EHR work/clerical burden were assessed.ResultsOf 4156 eligible faculty members, 1781(42.9%) participated in the survey. After adjustment for background factors, EHR frustration (odds ratio [OR]=1.64–1.66), spending >90 minutes on EHR-outside the workday by self-report (OR = 1.41–1.90) and >1 hour of self-reported clerical work/day (OR = 1.39) were associated with burnout. Reporting that one’s practice unloads clerical burden (OR = 0.50–0.66) and higher resilience scores (OR = 0.77–0.84) were negatively associated with burnout.Spending >90 minutes/day on EHR-outside work (OR = 0.66–0.67) and >60 minutes/day on clerical work (OR = 0.54–0.58) was associated with decreased likelihood of satisfactory work–life integration (WLI) and professional satisfaction (PS). Greater meaning in work was associated with an increasedlikelihoodof achieving WLI (OR = 2.51) and PS (OR = 21.67).ConclusionResults suggest there are thresholds of excessive time on the EHR-outside the workday (>90 minutes) and overall clerical tasks (>60 minutes), above which clinical faculty may be at increased risk for burnout, as well as reduced WLI and PS, independent of demographic characteristics and clinical work hours. These thresholds of EHR and clerical burden may inform interventions aimed at mitigating this burden to reduce physician burnout.     

Physicians’ electronic inbox work patterns and factors associated with high inbox work duration

ObjectivesElectronic health record systems are increasingly used to send messages to physicians, but research on physicians’ inbox use patterns is limited. This study’s aims were to (1) quantify the time primary care physicians (PCPs) spend managing inboxes; (2) describe daily patterns of inbox use; (3) investigate which types of messages consume the most time; and (4) identify factors associated with inbox work duration.Materials and MethodsWe analyzed 1 month of electronic inbox data for 1275 PCPs in a large medical group and linked these data with physicians’ demographic data.ResultsPCPs spent an average of 52 minutes on inbox management on workdays, including 19 minutes (37%) outside work hours. Temporal patterns of electronic inbox use differed from other EHR functions such as charting. Patient-initiated messages (28%) and results (29%) accounted for the most inbox work time. PCPs with higher inbox work duration were more likely to be female (P < .001), have more patient encounters (P < .001), have older patients (P < .001), spend proportionally more time on patient messages (P < .001), and spend more time per message (P < .001). Compared with PCPs with the lowest duration of time on inbox work, PCPs with the highest duration had more message views per workday (200 vs 109; P < .001) and spent more time on the inbox outside work hours (30 minutes vs 9.7 minutes; P < .001).ConclusionsElectronic inbox work by PCPs requires roughly an hour per workday, much of which occurs outside scheduled work hours. Interventions to assist PCPs in handling patient-initiated messages and results may help alleviate inbox workload.     

The design and evaluation of a novel algorithm for automated preference card optimization

BackgroundInaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.MethodsThe algorithm cross-references the supplies used in each procedure and listed on each preference card and uses a time-series regression to estimate the likelihood that each quantity listed on the preference card is inaccurate. Algorithm performance was evaluated by measuring changes in direct costs between preference cards revised with the algorithm and preference cards that were not revised or revised without use of the algorithm. Results were evaluated with a difference-in-differences (DID) multivariate fixed-effects model of costs during an 8-month pre-intervention and a 15-month post-intervention period.ResultsThe accuracies of the quantities of 469 155 surgeon–procedure-specific items were estimated. Nurses used these estimates to revise 309 preference cards across eight surgical services corresponding to, respectively, 1777 and 3106 procedures in the pre- and post-intervention periods. The average direct cost of supplies per case decreased by 8.38% ($352, SD $6622) for the intervention group and increased by 13.21% ($405, SD $14 706) for the control group (P < .001). The DID analysis showed significant cost reductions only in the intervention group during the intervention period (P < .001).ConclusionThe optimization of preference cards with a variety of institution-specific, manually intensive approaches has led to cost savings. The automated algorithm presented here produced similar results that may be more readily reproducible.     

Evaluation of electronic health record-integrated digital health tools to engage hospitalized patients in discharge preparation

ObjectiveTo evaluate the effect of electronic health record (EHR)-integrated digital health tools comprised of a checklist and video on transitions-of-care outcomes for patients preparing for discharge.Materials and MethodsEnglish-speaking, general medicine patients (>18 years) hospitalized at least 24 hours at an academic medical center in Boston, MA were enrolled before and after implementation. A structured checklist and video were administered on a mobile device via a patient portal or web-based survey at least 24 hours prior to anticipated discharge. Checklist responses were available for clinicians to review in real time via an EHR-integrated safety dashboard. The primary outcome was patient activation at discharge assessed by patient activation (PAM)-13. Secondary outcomes included postdischarge patient activation, hospital operational metrics, healthcare resource utilization assessed by 30-day follow-up calls and administrative data and change in patient activation from discharge to 30 days postdischarge.ResultsOf 673 patients approached, 484 (71.9%) enrolled. The proportion of activated patients (PAM level 3 or 4) at discharge was nonsignificantly higher for the 234 postimplementation compared with the 245 preimplementation participants (59.8% vs 56.7%, adjusted OR 1.23 [0.38, 3.96], P = .73). Postimplementation participants reported 3.75 (3.02) concerns via the checklist. Mean length of stay was significantly higher for postimplementation compared with preimplementation participants (10.13 vs 6.21, P < .01). While there was no effect on postdischarge outcomes, there was a nonsignificant decrease in change in patient activation within participants from pre- to postimplementation (adjusted difference-in-difference of −16.1% (9.6), P = .09).ConclusionsEHR-integrated digital health tools to prepare patients for discharge did not significantly increase patient activation and was associated with a longer length of stay. While issues uncovered by the checklist may have encouraged patients to inquire about their discharge preparedness, other factors associated with patient activation and length of stay may explain our observations. We offer insights for using PAM-13 in context of real-world health-IT implementations.Trial RegistrationNIH US National Library of Medicine, {"type":"clinical-trial","attrs":{"text":"NCT03116074","term_id":"NCT03116074"}}NCT03116074, clinicaltrials.gov     

Optimizing the electronic health record: An inpatient sprint addresses provider burnout and improves electronic health record satisfaction

ObjectiveWe sought reduce electronic health record (EHR) burden on inpatient clinicians with a 2-week EHR optimization sprint.Materials and MethodsA team led by physician informaticists worked with 19 advanced practice providers (APPs) in 1 specialty unit. Over 2 weeks, the team delivered 21 EHR changes, and provided 39 one-on-one training sessions to APPs, with an average of 2.8 hours per provider. We measured Net Promoter Score, thriving metrics, and time spent in the EHR based on user log data.ResultsOf the 19 APPs, 18 completed 2 or more sessions. The EHR Net Promoter Score increased from 6 to 60 postsprint (1.0; 95% confidence interval, 0.3-1.8; P = .01). The NPS for the Sprint itself was 93, a very high rating. The 3-axis emotional thriving, emotional recovery, and emotional exhaustion metrics did not show a significant change. By user log data, time spent in the EHR did not show a significant decrease; however, 40% of the APPs responded that they spent less time in the EHR.ConclusionsThis inpatient sprint improved satisfaction with the EHR.