scholarly journals MP02: The impact of adoption of an electronic health record on emergency physician work: a time motion study

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S42-S43
Author(s):  
S. Calder-Sprackman ◽  
G. Clapham ◽  
T. Kandiah ◽  
J. Choo-Foo ◽  
S. Aggarwal ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Patient Care ◽  
Emergency Physician ◽  
Chart Review ◽  
Direct Patient Care ◽  
Health Record ◽  
Physical Movement ◽  
Time Motion ◽  
Motion Study ◽  
The Impact

Introduction: Adoption of a new Electronic Health Record (EHR) can introduce radical changes in task allocation, work processes, and efficiency for providers. In June 2019, The Ottawa Hospital transitioned from a primarily paper based EHR to a comprehensive EHR (Epic) using a “big bang” approach. The objective of this study was to assess the impact of the transition to Epic on Emergency Physician (EP) work activities in a tertiary care academic Emergency Department (ED). Methods: We conducted a time motion study of EPs on shift in low acuity areas of our ED (CTAS 3-5). Fifteen EPs representing a spectrum of pre-Epic baseline workflow efficiencies were directly observed in real-time during two 4-hour sessions prior to EHR implementation (May 2019) and again in go live (August 2019). Trained observers performed continuous observation and measured times for the following EP tasks: chart review, direct patient care, documentation, physical movement, communication, teaching, handover, and other (including breaks). We compared time spent on tasks pre Epic and during go live and report mean times for the EP tasks per patient and per shift using two tailed t-test for comparison. Results: All physicians had a 17% decrease in patients seen after Epic implementation (2.72/hr vs 2.24/hr, p < 0.01). EPs spent the same amount of time per patient on direct patient care and chart review (direct patient care: 9min06sec/pt pre vs 8min56sec/pt go live, p = 0.77; chart review: 2min47sec/pt pre vs 2min50sec/pt go live, p = 0.88), however, documentation time increased (5min28sec/pt pre vs 7min12sec/pt go live, p < 0.01). Time spent on shift teaching learners increased but did not reach statistical significance (31min26sec/shift pre vs 36min21sec/shift go live, p = 0.39), and time spent on non-patient-specific activities – physical movement, handover, team communication, and other – did not change (50min49sec/shift pre vs 50min53sec/shift go live, p = 0.99). Conclusion: Implementation of Epic did not affect EP time with individual patients - there was no change in direct patient care or chart review. Documentation time increased and EP efficiency (patients seen per hr on shift) decreased after go live. Patient volumes cannot be adjusted in the ED therefore anticipating the EHR impact on EP workflow is critical for successful implementation. EDs may consider up staffing 20% during go live. Findings from this study can inform how to best support EDs nationally through transition to EHR.

scholarly journals The impact of adoption of an electronic health record on emergency physician work: A time motion study

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Samantha Calder‐Sprackman ◽  
Glenda Clapham ◽  
Trisha Kandiah ◽  
Jade Choo‐Foo ◽  
Simran Aggarwal ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Emergency Physician ◽  
Health Record ◽  
Time Motion ◽  
Motion Study ◽  
Electronic Health ◽  
The Impact ◽  
Physician Work

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

2021 ◽  
Vol 12 (03) ◽  
pp. 637-646
Author(s):  
Amrita Sinha ◽  
Tait D. Shanafelt ◽  
Mickey Trockel ◽  
Hanhan Wang ◽  
Christopher Sharp
Keyword(s):  
Electronic Health Record ◽  
Patient Care ◽  
Dominant Factor ◽  
Health Record ◽  
Occupational Burnout ◽  
Electronic Health ◽  
Perception Of Time ◽  
Professional Fulfillment ◽  
The Impact ◽  
Physician Work

Abstract 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.

Implementation of Computerized Provider Order Entry (CPOE) Does Not Impact Provider Work Time in An Inpatient Malignant Hematology Service

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4704-4704
Author(s):  
David A Hanauer ◽  
Sung W Choi ◽  
Robert W Beasley ◽  
Ronald B Hirschl ◽  
Douglas W Blayney
Keyword(s):  
Patient Care ◽  
Training Session ◽  
Direct Patient Care ◽  
Order Entry ◽  
Time And Motion Study ◽  
Time And Motion ◽  
Motion Study ◽  
Order Sets ◽  
Before And After ◽  
The Impact

Abstract No data are available concerning the impact of CPOE on inpatient leukemia and lymphoma care. CPOE may improve patient safety, reduce time between order entry and medication administration, and reduce medication and transcription errors. However, concerns have arisen about potential increased time required to enter electronic orders compared to handwritten orders. Our hypothesis was that CPOE would require more order-related time from caregivers, and reduce the amount of time for direct patient care. We studied the work patterns of three Physician Assistants (PAs) who worked under the supervision of faculty physicians, and were the exclusive inpatient care providers. The PA-staffed hematology service was chosen to minimize the impact of rotating house staff on our results. Faculty, who were not studied, entered the few chemotherapy orders necessary, while PAs entered orders for hydration, antibiotics, supportive care and other medications, and for consultations and diagnostic tests. The UMHS Institutional Review Board reviewed the study protocol and waived the requirement for patient informed consent. We performed a direct observation time and motion study pre- and post-implementation of a commercial CPOE system (Sunrise Clinical Manager™ 4.5, Eclipsys, Boca Raton, Florida) on one inpatient hematology service at the UMHS University Hospital. The same three PAs were shadowed pre- and post-implementation. We also closely matched morning and afternoon observation times in order to reduce variability in activities taking place at different times of the day. Prior to CPOE implementation the PAs had a 4 hour general training session and a 1 hour chemotherapy training session. Pre-built order sets were routinely used by the PAs. A portable tablet computer was used by an independent observer to record data, using a data entry interface containing 63 individual activity categories modified from the Time and Motion database under “IT Tools” at http://www.ahrq.gov. Data were grouped into subcategories for analysis. We grouped 12 activities as ordering-related (e.g. writing orders, writing forms, clarifying orders, etc.) We observed the same three PAs for 85.4 hours (over 2 weeks) pre, and for 75.8 hours (over 4 weeks) starting 3 months post-CPOE. Mean patient census was 11.3 per day pre- and 9.2 per day post implementation observation periods. Overall time for order-related activities was unchanged, requiring 7.7% of total time pre- and 8.1% of total time post-CPOE even though actual order writing took longer with CPOE compared to written (4.9% pre vs. 7.0% post). CPOE had almost no impact on direct patient care time (Figure), with PAs spending 38.2% total time on direct patient care pre-CPOE compared to 38.4% post. A minimal difference was also found with the overall total for indirect patient care activities (37.1% pre vs. 38.7% post). Our results suggest that using CPOE on a busy hematology inpatient service has minimal impact on time spent by trained PAs using standard order sets 3 months after implementation. The decision to adopt CPOE for a busy hematology service should not be based on the hypothesis that there will be a change in workflow or task organization. More study is needed to determine if CPOE for hematology patients results in a change in the quality of patient care or safety. Figure. Percentage of total time spent in 6 analysis categories both before and after implementation of a commercial CPOE system for an inpatient hematology service. These 6 categories represent 63 individual activities categories that were recorded in the time and motion study. Error bars represent 95% confidence intervals. Figure. Percentage of total time spent in 6 analysis categories both before and after implementation of a commercial CPOE system for an inpatient hematology service. These 6 categories represent 63 individual activities categories that were recorded in the time and motion study. Error bars represent 95% confidence intervals.

The impact of electronic health record implementation on emergency physician efficiency and patient throughput

Healthcare ◽  
2014 ◽  
Vol 2 (3) ◽  
pp. 201-204 ◽  
Author(s):  
Nicholas Risko ◽  
David Anderson ◽  
Bruce Golden ◽  
Edward Wasil ◽  
Fermin Barrueto ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Emergency Physician ◽  
Health Record ◽  
Patient Throughput ◽  
Electronic Health ◽  
The Impact

scholarly journals 91. Development of an Electronic Algorithm to Identify Inappropriate Antibiotic Prescribing for Pediatric Otitis Media

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S176-S177
Author(s):  
Ebbing Lautenbach ◽  
Jeffrey Gerber ◽  
Robert Grundmeier ◽  
Keith W Hamilton ◽  
Lauri Hicks ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Otitis Media ◽  
Chart Review ◽  
Antibiotic Use ◽  
Antibiotic Prescribing ◽  
Health Record ◽  
Test Characteristics ◽  
Academic Practices ◽  
Electronic Health ◽  
The Impact

Abstract Background Antibiotic stewardship (AS) interventions have primarily focused on acute care settings. The majority of antibiotic use, however, occurs in outpatients. The electronic health record (EHR) might provide an effective and efficient tool for outpatient AS. We aimed to develop and validate an electronic algorithm to identify inappropriate antibiotic use for pediatric outpatients with acute otitis media (AOM). Methods Within the Children’s Hospital of Philadelphia (CHOP) Care Network, we used ICD-10 diagnostic codes to identify patient encounters for AOM at any CHOP practice between 3/15/17 – 3/14/18. Exclusion criteria included underlying immunocompromising condition, comorbidities, and concurrent infections that might influence antibiotic use. We randomly selected 450 eligible subjects (150 each from academic practices, non-academic practices, and urgent care). Inappropriate antibiotic use based on CHOP and professional society guidelines were assessed via chart review and served as the basis for assessment of the electronic algorithm which was constructed using only data in the electronic health record (EHR). Criteria for appropriateness focused on the decision to prescribe, the choice of antibiotic, and duration of therapy. Results Of 450 subjects, median age was 2, 46% were female, and 88% were evaluated by a physician (vs. advanced practice provider). On chart review, the prescribing decision was correct in 438 (97%), of which 25 appropriately received no antibiotics. Of the 413 subjects who were appropriately prescribed an antibiotic, the choice of antibiotic was appropriate in 37 (9%). Finally, of the 413 patients who were appropriately treated, 412 (99.7%) received the correct duration. Test characteristics of the EHR algorithm (compared to chart review) are noted in the Table. Conclusion For children with AOM, an electronic algorithm for identification of inappropriate antibiotic prescribing is highly accurate. This algorithm can also highlight for which elements of prescribing the impact of an intervention might be greatest (i.e., choice of agent). Future work should validate this approach in other health systems and geographic regions and evaluate the impact of an audit and feedback intervention based on this tool. Table. Test Characteristics of Electronic Algorithm for Inappropriate Prescribing, Agent, and Duration Disclosures All Authors: No reported disclosures

scholarly journals Twilighted Homegrown Systems: The Experience of Six Traditional Electronic Health Record Developers in the Post–Meaningful Use Era

2020 ◽  
Vol 11 (02) ◽  
pp. 356-365
Author(s):  
Tiago K. Colicchio ◽  
James J. Cimino
Keyword(s):  
Electronic Health Record ◽  
Patient Care ◽  
Clinical Decision Support Systems ◽  
Meaningful Use ◽  
Direct Patient Care ◽  
Future Research ◽  
Health Record ◽  
Snowball Sampling ◽  
Convenience Sample ◽  
Electronic Health

Abstract Objectives This study aimed to understand if and how homegrown electronic health record (EHR) systems are used in the post–Meaningful Use (MU) era according to the experience of six traditional EHR developers. Methods We invited informatics leaders from a convenience sample of six health care organizations that have recently replaced their long used homegrown systems with commercial EHRs. Participants were asked to complete a written questionnaire with open-ended questions designed to explore if and how their homegrown system(s) is being used and maintained after adoption of a commercial EHR. We used snowball sampling to identify other potential respondents and institutions. Results Participants from all six organizations included in our initial sample completed the questionnaire and provided referrals to four other organizations; from these, two did not respond to our invitations and two had not yet replaced their system and were excluded. Two organizations (Columbia University and University of Alabama at Birmingham) still use their homegrown system for direct patient care and as a downtime system. Four organizations (Intermountain Healthcare, Partners Healthcare, Regenstrief Institute, and Vanderbilt University) kept their systems primarily to access historical data. All organizations reported the need to continue to develop or maintain local applications despite having adopted a commercial EHR. The most common applications developed include display and visualization tools and clinical decision support systems. Conclusion Homegrown EHR systems continue to be used for different purposes according to the experience of six traditional homegrown EHR developers. The annual cost to maintain these systems varies from $21,000 to over 1 million. The collective experience of these organizations indicates that commercial EHRs have not been able to provide all functionality needed for patient care and local applications are often developed for multiple purposes, which presents opportunities for future research and EHR development.

scholarly journals Primary care physician time utilization before and after implementation of an electronic health record: A time-motion study

2005 ◽  
Vol 38 (3) ◽  
pp. 176-188 ◽  
Author(s):  
Lisa Pizziferri ◽  
Anne F. Kittler ◽  
Lynn A. Volk ◽  
Melissa M. Honour ◽  
Sameer Gupta ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Primary Care Physician ◽  
Care Physician ◽  
Health Record ◽  
Time Motion ◽  
Motion Study ◽  
Before And After ◽  
Physician Time ◽  
Time Utilization

scholarly journals The physician assistant hospitalist: a time-motion study

2015 ◽  
Vol 4 (5) ◽  
pp. 61 ◽  
Author(s):  
Julia V. Beresford ◽  
Roderick S. Hooker
Keyword(s):  
Patient Care ◽  
Activity Patterns ◽  
A Priori ◽  
Univariate Analysis ◽  
Physician Assistants ◽  
Direct Patient Care ◽  
Patient Encounter ◽  
Time Motion ◽  
Motion Study ◽  
Adult Medicine

Objective: The role of hospital-based physician assistants (PAs) is in need of delineation. To learn more about their activities, an administrative research project compared the tasks of hospitalists. In this setting an MD-PA team managed the adult medicine ward each weekday while three MDs rotated shifts.Methods: A priori a survey of hospitalist activities was administered to four providers in a medium sized hospital (3 MDs and 1 PA). This was followed by time-motion documentation that involved shadowing each member of the MD-PA hospitalist teams over a three-month period. A univariate analysis of activity patterns (perceived and observed) assessed what was perceived and what actually occurred on the wards. The mean, standard deviation, and difference in means for each task were calculated.Results: In the survey the PA reported she spent one-half of her hospitalist workdays on direct patient care and the physicians spent less time on direct patient care. Physicians believed they spent half days on direct patient care and believed the PA spent less time on direct patient care than they did. In the time-motion study shadowing the four hospitalists separately what was observed was that the PA spent 18% of her workday on direct patient care and 54% on indirect patient care - primarily patient encounter documentation. The three physicians spent 15% of their workday on direct patient care and 54% on indirect patient care – primarily patient encounter documentation. In summary, the perception of what each provider thought they did and what they in fact did differed significantly when actually observed. All four hospitalists (regardless of team composition) spent less than 20% of their workday with patients, and the rest divided among documentation, examination and test results, hospital meetings, and breaks. Task activity was similar for all providers except MDs attended more administrative meetings than the PA.Conclusions: The perception that physicians have of PA roles and what a PA actually does has been a reoccurring observation. A lack of understanding of PA role delineation by physicians may contribute to employment reluctance.

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