Factors associated with nurse well-being in relation to electronic health record use: A systematic review

ObjectiveAlthough nurses comprise the largest group of health professionals and electronic health record (EHR) user base, it is unclear how EHR use has affected nurse well-being. This systematic review assesses the multivariable (ie, organizational, nurse, and health information technology [IT]) factors associated with EHR-related nurse well-being and identifies potential improvements recommended by frontline nurses.Materials and MethodsWe searched MEDLINE, Embase, CINAHL, PsycINFO, ProQuest, and Web of Science for literature reporting on EHR use, nurses, and well-being. A quality appraisal was conducted using a previously developed tool.ResultsOf 4583 articles, 12 met inclusion criteria. Two-thirds of the studies were deemed to have a moderate or low risk of bias. Overall, the studies primarily focused on nurse- and IT-level factors, with 1 study examining organizational characteristics. That study found worse nurse well-being was associated with EHRs compared with paper charts. Studies on nurse-level factors suggest that personal digital literacy is one modifiable factor to improving well-being. Additionally, EHRs with integrated displays were associated with improved well-being. Recommendations for improving EHRs suggested IT-, organization-, and policy-level solutions to address the complex nature of EHR-related nurse well-being.ConclusionsThe overarching finding from this synthesis reveals a critical need for multifaceted interventions that better organize, manage, and display information for clinicians to facilitate decision making. Our study also suggests that nurses have valuable insight into ways to reduce EHR-related burden. Future research is needed to test multicomponent interventions that address these complex factors and use participatory approaches to engage nurses in intervention development.     

Measures of electronic health record use in outpatient settings across vendors

Electronic health record (EHR) log data capture clinical workflows and are a rich source of information to understand variation in practice patterns. Variation in how EHRs are used to document and support care delivery is associated with clinical and operational outcomes, including measures of provider well-being and burnout. Standardized measures that describe EHR use would facilitate generalizability and cross-institution, cross-vendor research. Here, we describe the current state of outpatient EHR use measures offered by various EHR vendors, guided by our prior conceptual work that proposed seven core measures to describe EHR use. We evaluate these measures and other reporting options provided by vendors for maturity and similarity to previously proposed standardized measures. Working toward improved standardization of EHR use measures can enable and accelerate high-impact research on physician burnout and job satisfaction as well as organizational efficiency and patient health.     

Factors associated with difficult electronic health record implementation in office practice

Little is known about physicians' perception of the ease or difficulty of implementing electronic health records (EHR). This study identified factors related to the perceived difficulty of implementing EHR. 163 physicians completed surveys before and after the implementation of EHR in an externally funded pilot program in three Massachusetts communities. Ordinal hierarchical logistic regression was used to identify baseline factors that correlated with physicians' report of difficulty with EHR implementation. Compared with physicians with ownership stake in their practices, physician employees were less likely to describe EHR implementation as difficult (adjusted OR 0.5, 95% CI 0.3 to 1.0). Physicians who perceived their staff to be innovative were also less likely to view EHR implementation as difficult (adjusted OR 0.4, 95% CI 0.2 to 0.8). Physicians who own their practice may need more external support for EHR implementation than those who do not. Innovative clinical support staff may ease the EHR implementation process and contribute to its success.     

Harmonized patient-reported data elements in the electronic health record: supporting meaningful use by primary care action on health behaviors and key psychosocial factors

Background

Electronic health records (EHR) have the potential to improve patient care through efficient access to complete patient health information. This potential may not be reached because many of the most important determinants of health outcome are rarely included. Successful health promotion and disease prevention requires patient-reported data reflecting health behaviors and psychosocial issues. Furthermore, there is a need to harmonize this information across different EHR systems.

Methods

To fill this gap a three-phased process was used to conceptualize, identify and recommend patient-reported data elements on health behaviors and psychosocial factors for the EHR. Expert panels (n=13) identified candidate measures (phase 1) that were reviewed and rated by a wide range of health professionals (n=93) using the grid-enabled measures wiki social media platform (phase 2). Recommendations were finalized through a town hall meeting with key stakeholders including patients, providers, researchers, policy makers, and representatives from healthcare settings (phase 3).

Results

Nine key elements from three areas emerged as the initial critical patient-reported elements to incorporate systematically into EHR—health behaviors (eg, exercise), psychosocial issues (eg, distress), and patient-centered factors (eg, demographics). Recommendations were also made regarding the frequency of collection ranging from a single assessment (eg, demographic characteristics), to annual assessment (eg, health behaviors), or more frequent (eg, patient goals).

Conclusions

There was strong stakeholder support for this initiative reflecting the perceived value of incorporating patient-reported elements into EHR. The next steps will include testing the feasibility of incorporating these elements into the EHR across diverse primary care settings.     

Visualizing collaborative electronic health record usage for hospitalized patients with heart failure

Objective To visualize and describe collaborative electronic health record (EHR) usage for hospitalized patients with heart failure.Materials and methods We identified records of patients with heart failure and all associated healthcare provider record usage through queries of the Northwestern Medicine Enterprise Data Warehouse. We constructed a network by equating access and updates of a patient’s EHR to a provider-patient interaction. We then considered shared patient record access as the basis for a second network that we termed the provider collaboration network. We calculated network statistics, the modularity of provider interactions, and provider cliques.Results We identified 548 patient records accessed by 5113 healthcare providers in 2012. The provider collaboration network had 1504 nodes and 83 998 edges. We identified 7 major provider collaboration modules. Average clique size was 87.9 providers. We used a graph database to demonstrate an ad hoc query of our provider-patient network.Discussion Our analysis suggests a large number of healthcare providers across a wide variety of professions access records of patients with heart failure during their hospital stay. This shared record access tends to take place not only in a pairwise manner but also among large groups of providers.Conclusion EHRs encode valuable interactions, implicitly or explicitly, between patients and providers. Network analysis provided strong evidence of multidisciplinary record access of patients with heart failure across teams of 100+ providers. Further investigation may lead to clearer understanding of how record access information can be used to strategically guide care coordination for patients hospitalized for heart failure.     

Enabling adoption and use of new health information technology during implementation: Roles and strategies for internal and external support personnel

ObjectiveSuccessful technological implementations frequently involve individuals who serve as mediators between end users, management, and technology developers. The goal for this project was to evaluate the structure and activities of such mediators in a large-scale electronic health record implementation.Materials and MethodsField notes from observations taken during implementation beginning in November 2017 were analyzed qualitatively using a thematic analysis framework to examine the relationship between specific types of mediators and the type and level of support to end users.ResultsWe found that support personnel possessing both contextual knowledge of the institution’s workflow and training in the new technology were the most successful in mediation of adoption and use. Those that lacked context of either technology or institutional workflow often displayed barriers in communication, trust, and active problem solving.ConclusionsThese findings suggest that institutional investment in technology training and explicit programs to foster skills in mediation, including roles for professionals with career development opportunities, prior to implementation can be beneficial in easing the pain of system transition.     

Effects of electronic health record use on the exam room communication skills of resident physicians: a randomized within-subjects study

Background The effects of electronic health records (EHRs) on doctor–patient communication are unclear.Objective To evaluate the effects of EHR use compared with paper chart use, on novice physicians’ communication skills.Design Within-subjects randomized controlled trial using observed structured clinical examination methods to assess the impact of use of an EHR on communication.Setting A large academic internal medicine training program.Population First-year internal medicine residents.Intervention Residents interviewed, diagnosed, and initiated treatment of simulated patients using a paper chart or an EHR on a laptop computer. Video recordings of interviews were rated by three trained observers using the Four Habits scale.Results Thirty-two residents completed the study and had data available for review (61.5% of those enrolled in the residency program). In most skill areas in the Four Habits model, residents performed at least as well using the EHR and were statistically better in six of 23 skills areas (p<0.05). The overall average communication score was better when using an EHR: mean difference 0.254 (95% CI 0.05 to 0.45), p = 0.012, Cohen''s d of 0.47 (a moderate effect). Residents scoring poorly (>3 average score) with paper methods (n = 8) had clinically important improvement when using the EHR.Limitations This study was conducted in first-year residents in a training environment using simulated patients at a single institution.Conclusions Use of an EHR on a laptop computer appears to improve the ability of first-year residents to communicate with patients relative to using a paper chart.     

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS).Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement.Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information.Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.     

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.     

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.     

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

Background As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling.Objective The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation.Methods The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic.Results A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001).Conclusions A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.     

Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning

ObjectiveThe characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics.Materials and MethodsWe leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit.ResultsWe examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57 234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness.ConclusionsThe use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.     

Optimizing gender-affirming medical care through anatomical inventories,clinical decision support,and population health management in electronic health record systems

Recent advances in electronic health records and health information technology are providing new opportunities to improve the quality of care for transgender and gender diverse people, a population that experiences significant health disparities. This article recommends changes to electronic health record systems that have the potential to optimize gender-affirming care. Specifically, we discuss the importance of creating an anatomical inventory form that captures organ diversity, and of developing clinical decision support tools and population health management systems that consider each patient’s gender identity, sex assigned at birth, and anatomy.     

Practice and market factors associated with provider volume of health information exchange

ObjectiveTo assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use.Materials and MethodsProvider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016.ResultsProviders used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use.DiscussionHIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors.ConclusionThis furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.     

Brief communication: Facility characteristics associated with the use of electronic health records in residential care facilities

The integration of electronic health records (EHRs) across care settings including residential care facilities (RCFs) promises to reduce medical errors and improve coordination of services. Using data from the 2010 National Survey of Residential Care Facilities (n=2302), this study examines the association between facility structural characteristics and the use of EHRs in RCFs. Findings indicate that in 2010, only 3% of RCFs nationwide were using an EHR. However, 55% of RCFs reported using a computerized system for one or more (but not all) of the functionalities defined by a basic EHR. Ownership, chain membership, staffing levels, and facility size were significantly associated with the use of one or more core EHR functionalities. These findings suggest that facility characteristics may play an important role in the adoption of EHRs in RCFs.     

Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality.Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index.Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR.Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.     

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset

ObjectiveThe goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.Materials and MethodsThe National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test.ResultsOf the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units).DiscussionThe harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference.ConclusionThe pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.