Assessment of Electronic Health Record Use Between US and Non-US Health Systems

Holmgren, Anders; Downing, N. Lance; Bates, David W.; Shanafelt, Tait D.; Milstein, Arnold; Sharp, Christopher; Cutler, David M.; Huckman, Robert S.; Schulman, Kevin A.

doi:10.1001/jamainternmed.2020.7071

Cited by 91 publications

(104 citation statements)

References 43 publications

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“…Finally, we only included physician participants practicing in the United States. Given known differences in EHR use and regulatory structures in other countries, these results might differ if examined in other healthcare systems [ 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis

Greenberg

Otun

Nasraddin

et al. 2021

BMC Med Inform Decis Mak

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Background Current management of children with minor head trauma (MHT) and intracranial injuries is not evidence-based and may place some children at risk of harm. Evidence-based electronic clinical decision support (CDS) for management of these children may improve patient safety and decrease resource use. To guide these efforts, we evaluated the sociotechnical environment impacting the implementation of electronic CDS, including workflow and communication, institutional culture, and hardware and software infrastructure, among other factors. Methods Between March and May, 2020 semi-structured qualitative focus group interviews were conducted to identify sociotechnical influences on CDS implementation. Physicians from neurosurgery, emergency medicine, critical care, and pediatric general surgery were included, along with information technology specialists. Participants were recruited from nine health centers in the United States. Focus group transcripts were coded and analyzed using thematic analysis. The final themes were then cross-referenced with previously defined sociotechnical dimensions. Results We included 28 physicians and four information technology specialists in seven focus groups (median five participants per group). Five physicians were trainees and 10 had administrative leadership positions. Through inductive thematic analysis, we identified five primary themes: (1) clinical impact; (2) stakeholders and users; (3) tool content; (4) clinical practice integration; and (5) post-implementation evaluation measures. Participants generally supported using CDS to determine an appropriate level-of-care for these children. However, some had mixed feelings regarding how the tool could best be used by different specialties (e.g. use by neurosurgeons versus non-neurosurgeons). Feedback from the interviews helped refine the tool content and also highlighted potential technical and workflow barriers to address prior to implementation. Conclusions We identified key factors impacting the implementation of electronic CDS for children with MHT and intracranial injuries. These results have informed our implementation strategy and may also serve as a template for future efforts to implement health information technology in a multidisciplinary, emergency setting.

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Section: Discussionmentioning

confidence: 99%

Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis

Greenberg

Otun

Nasraddin

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…[5][6][7][8][9] Clinicians in the United States spend significantly more time per day actively using the EHR and performing four clinical activities: notes, orders, in-basket messages, and clinical review, a reflection of this documentation burden. 10,11 Time spent on EHR has been identified as an important unit of measure of attenuated efficiency in use of health care provider clinical time, which is a high-cost and limited resource and may be associated with reduced patient satisfaction. 12,13 Time motion studies and EHR log records have indicated an increase in time allocated for desktop medicine with physicians spending nearly 2 hours in the EHR and on other desk work for every hour of direct patient care.…”

Section: Background and Significancementioning

confidence: 99%

“…12,13 Time motion studies and EHR log records have indicated an increase in time allocated for desktop medicine with physicians spending nearly 2 hours in the EHR and on other desk work for every hour of direct patient care. 11,[14][15][16][17][18] This perhaps contributes to overflow of EHR work into the physician's nonclinical and personal time, negatively affecting work-life balance. 19 There is accumulating evidence indicating the association of EHR use after work hours with occupational distress including burnout.…”

Section: Background and Significancementioning

confidence: 99%

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

et al. 2021

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Background 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. Objective The objective of our study was to measure the association between CLOC metrics and validated measures of physician burnout and professional fulfillment. Methods 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. Results 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. Conclusion 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.

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“…Growing deployments of Electronic Health Records (EHRs) systems have established large practice-based longitudinal patient records causing an increase in the volume of unstructured data (80%) in the currently available health care records [ 1 ]. Inefficient and ineffective use of EHRs due to overwhelming volume has led to physician burnout (70% of clinicians reported health information technology [HIT]-related stress) due to increased workload during their limited (e.g.,15–20 min) clinic visit [ 2 – 5 ]. The application of Artificial Intelligence (AI) to health care may potentially address these challenges through AI-assisted clinical decision support (CDS) tools.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

et al. 2021

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Rationale Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. Objectives To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). Methods This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Measurements Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Main results Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374–1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2–5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3–15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82–1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. Conclusions While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians’ burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. Trial registration ClinicalTrials.gov Identifier: NCT02865967.

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Assessment of Electronic Health Record Use Between US and Non-US Health Systems

Cited by 91 publications

References 43 publications

Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis

Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis

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

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

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