scholarly journals P105: Observational study of distribution of time and activities over the course of an emergency physician's shift

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S102-S103
Author(s):  
E. Feng ◽  
Z. Zia ◽  
C. Tong ◽  
N. Cornell
Keyword(s):  
Medical Information ◽  
Patient Flow ◽  
Second Phase ◽  
Time Motion ◽  
Patient Interaction ◽  
Physician Productivity ◽  
Motion Study ◽  
One Year ◽  
The Impact ◽  
Insight Into

Introduction: The growing scrutiny to improve Emergency Department (ED) wait times and patient flow have resulted in many efforts to increase efficiency and maximize patient throughput via systems improvements. This study investigates areas of efficiency improvement from the Emergency Physician (EP) perspective by examining EP workflow in a two phased observational time-motion study. In the initial phase, the distribution of time and activities of EPs were dissected to identify potential sources for streamlining to maximize physician productivity. The first phase was of the study was completed during the period immediately preceding the implementation of an Electronic Health Records (EHR). The second phase of the study will repeat the analysis one year post EHR implementation. This data will be dissected to again identify sources for streamlining in an EHR environment and to identify shifts in work flow from a paper-based system. Methods: An observational time motion study was conducted at St. Mary's Hospital ED, in Kitchener Ontario. An observer was paired with an EP for the duration of an 8 hour shift, to a total of 14 shifts in the first phase of the study. Nine task categories were measured concurrently with a stopwatch application on a tablet, along with the number of interruptions experienced by the EP. Means of each category were calculated and converted to percentages, representing the amount of time per 8 hour shift dedicated to each activity. The second phase will be repeated in Fall 2020, 1 year after EHR implementation. Results: A total of 14 shifts were observed, accounting for 112 hours of observation. EP's time was allocated amongst the following categories: direct patient interaction (40.8%), documentation (27.1%), reviewing patient results (18.4%), communicating with ED staff (7.63%), personal activities (5.7%), writing orders (5.1%), communicating with consultants (3.3%), teaching (1.7%) and medical information searches (1.3%). On average, EPs experienced 15.8 interruptions over the course of an 8 hour shift. Conclusion: In a paper charting system, the direct patient interaction accounts for the largest timeshare over the course of a given shift. However, the next two largest categories, documentation and reviewing patient data, both represent areas of potential streamlining via clerical improvements. Additionally, detailed measurements of EPs’ activities have proven feasible and provides the potential for future insight into the impact of EHR's on EP workflow.

Evaluation of the efficiency of an embedded palliative nurse practitioner in an oncology clinic.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 146-146
Author(s):  
Anne M. Walling ◽  
Sarah D'Ambruoso ◽  
Christopher Pietras ◽  
Jennifer Malin ◽  
Sara A. Hurvitz ◽  
...  
Keyword(s):  
Palliative Care ◽  
Nurse Practitioner ◽  
Care Delivery ◽  
Symptom Assessment ◽  
Fee For Service ◽  
Model Of Care ◽  
Time Motion ◽  
Motion Study ◽  
Embedded Model ◽  
One Year

146 Background: We embedded a palliative nurse practitioner in 2 oncologists’ clinics in March 2014 using a reproducible training program. After one year, patients with advanced cancer receiving care in the embedded model clinics, compared to other oncologists’ clinics, were more likely to have advance care planning performed and to die receiving hospice. In order to expand the intervention to other oncologists, we evaluated the efficiency in our model of care delivery. Methods: We reviewed overall caseload and a subset of patient encounters in depth to understand how much of the NP's practice could be conducted by other staff to inform alternate clinical models of palliative care delivery in an oncology clinic. Overall time spent and time spent per task (Symptom Assessment Basic (SAB), Symptom Assessment Medical (SAM), Symptom Assessment Psychosocial (SAP), Communication Basic (CB), Communication Complex (CC)) was recorded for 16 patient encounters. We also completed 3 days of time motion study in which a trained observer tracked the NP's daily activities from minute to minute. Results: After part-time clinical participation over the first year of the program, the NP had seen 68 initial consults and 141 follow-up consults, which were potentially billable. She also had 120 encounters where she met a patient with an oncologist and 158 email or phone encounters that were not billable. Mean duration of a visit was 56 minutes (range 40-70 minutes) and about half of this time was spent on symptom assessment and communication topics requiring an MD or NP (SAM and CC), whereas half of the time was spent on topics that potentially could be covered by an RN (SAB or CB) or an MSW (SAB, SAP, CB). Time motion study revealed that a significant amount of time was spent with email correspondence and talking with other providers for care coordination. Conclusions: Palliative care is time consuming and much of the work is not reimbursed by a traditional fee for service model. Approximately half of the time spent by the NP in our embedded program potentially could have been completed by an RN or other interdisciplinary staff with training in palliative care under supervision of a physician. We plan to add an RN case management component to our model of care delivery.

scholarly journals A Time Motion Study Evaluating the Impact of Geographic Cohorting of Hospitalists

2019 ◽  
Vol 15 (6) ◽  
Keyword(s):  
Multilevel Modeling ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Direct Care ◽  
Time Motion ◽  
Motion Study ◽  
Indirect Care ◽  
The Impact ◽  
Computer Interactions

BACKGROUND: Geographic cohorting (GCh) localizes hospitalists to a unit. Our objective was to compare the GCh and non-GCh workday. METHODS: In an academic, Midwestern hospital we observed hospitalists in GCh and non-GCh teams. Time in patient rooms was considered direct care; other locations were considered ‘indirect’ care. Geotracking identified time spent in each location and was obtained for 17 hospitalists. It was supplemented by in-person observation of four GCh and four non-GCh hospitalists for a workday each. Multilevel modeling was used to analyze associations between direct and indirect care time and team and workday characteristics. RESULTS: Geotracking yielded 10,522 direct care episodes. GCh was associated with longer durations of patient visits while increasing patient loads were associated with shorter visits. GCh, increasing patient loads, and increasing numbers of units visited were associated with increased indirect care time. In-person observations yielded 3,032 minutes of data. GCh hospitalists were observed spending 56% of the day in computer interactions vs non-GCh hospitalists (39%; P < .005). The percentage of time spent multitasking was 18% for GCh and 14% for non-GCh hospitalists (P > .05). Interruptions were pervasive, but the highest interruption rate of once every eight minutes in the afternoon was noted in the GCh group. CONCLUSION: GCh may have the potential to increase patient–hospitalist interactions but these gains may be attenuated if patient loads and the structure of cohorting are suboptimal. The hospitalist workday is cognitively intense. The interruptions noted may increase the time taken for time-intensive tasks like electronic medical record interactions.

Evaluating the Impact of Computer-Generated Rounding Reports on Physician Workflow in the Nursing Home: A Feasibility Time-Motion Study

2013 ◽  
Vol 14 (5) ◽  
pp. 358-362 ◽  
Author(s):  
Patrice T. Thorpe-Jamison ◽  
Colleen M. Culley ◽  
Subashan Perera ◽  
Steven M. Handler
Keyword(s):  
Nursing Home ◽  
Time Motion ◽  
Motion Study ◽  
The Impact

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.

A Time-Motion Study of ICU Workflow and the Impact of Strain*

2016 ◽  
Vol 44 (8) ◽  
pp. 1482-1489 ◽  
Author(s):  
Yosefa Hefter ◽  
Purnema Madahar ◽  
Lewis A. Eisen ◽  
Michelle N. Gong
Keyword(s):  
Time Motion ◽  
Motion Study ◽  
The Impact

Impact of a scribe program on patient throughput, physician productivity, and patient satisfaction in a community-based emergency department

2017 ◽  
Vol 25 (1) ◽  
pp. 216-224 ◽  
Author(s):  
Waqas Shuaib ◽  
John Hilmi ◽  
Joshua Caballero ◽  
Ijaz Rashid ◽  
Hashim Stanazai ◽  
...  
Keyword(s):  
Emergency Department ◽  
Patient Satisfaction ◽  
Prospective Trial ◽  
Secondary Outcome ◽  
Patient Throughput ◽  
Time Motion ◽  
Physician Productivity ◽  
Relative Value ◽  
Quasi Experimental ◽  
The Impact

Previous literature on the impact of scribe programs varies and has mostly been reported from academic institutions or other clinics. We report the implementation of the scribe program in the emergency room of a community hospital and its impact on patient throughput, physician productivity, and patient satisfaction. We performed a quasi-experimental, before-and-after study measuring patient throughput metrics, physician productivity, and patient satisfaction. The intervention measuring the scribe implementation was divided into pre- and post-implementation periods. Patient throughput metrics were (1) door-to-room time, (2) room-to-doc time, (3) door-to-doc time, (4) doc-to-disposition time, and (5) length of stay for discharged/admitted patients. Our secondary outcome was physician productivity, which was calculated by measuring total patients seen per hour and work relative value units per hour. Additionally, we calculated the time-motion analysis in minutes to measure the emergency department physician’s efficiency by recording the following: (1) chart preparation, (2) chart review, (3) doctor–patient interaction, (4) physical examination, and (5) post-visit documentation. Finally, we measured patient satisfaction as provided by Press Ganey surveys. Data analysis was conducted in 12,721 patient encounters in the pre-scribe cohort, and 13,598 patient encounters in the post-scribe cohort. All the patient throughput metrics were statistically significant (p < 0.0001). The patients per hour increased from 2.3 ± 0.3 pre-scribe to 3.2 ± 0.6 post-scribe cohorts (p < 0.001). Total work relative value units per hour increased from 241(3.1 ± 1.5 per hour) pre-scribe cohort to 336 (5.2 ± 1.4 per hour) post-scribe cohort (p < 0.001). The pre-scribe patient satisfaction was high and remained high in the post-scribe cohort. There was a significant increase in the clinician providing satisfactory feedback from the pre-scribe (3.9 ± 0.3) to the post-scribe (4.7 ± 0.1) cohorts (p < 0.01). We describe a prospective trial of medical scribe use in the emergency department setting to improve patient throughput, physician productivity, and patient satisfaction. We illustrate that scribe use in community emergency department is feasible and results in improvement in all three metrics

scholarly journals A Descriptive Time Inventory of Emergency Department Nursing Workflows

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Dana Skold ◽  
Alice Mitchell, MD
Keyword(s):  
Emergency Department ◽  
Patient Care ◽  
Patient Flow ◽  
Time Of Day ◽  
High Turnover ◽  
Time Motion ◽  
Operational Load ◽  
Nursing Activity ◽  
Level I ◽  
The Impact

Background: Previous emergency department (ED) process improvement efforts used probabilistic analytical or simulated models without considering the impact of specific tasks on ED patient flow and resource needs. We focus on the tasks and workflows that comprise nursing activity in an urban academic ED and level I trauma center receiving over 80,000 annual visits.  Experimental Design: Using a time/motion observational methodology, we create a minute-by-minute time inventory account of nursing tasks and workflows as observed through the activities of 35 nurses over 124.5 hours, representative of 24/7 patient care in 7 ED care areas. “Tasks” were defined as discrete, measurable, and consequential step(s) to accomplish a clinically meaningful goal (“workflow”). The task with highest cognitive demand for each minute of observation was recorded. We also tracked 12 discrete highest-acuity (“shock”) events and catalogued second-by-second observational accounts of each nurse diverted.   Results: Our data demonstrate significant variation in tasks based on time of day. We observed substantial operational load moving patients between care areas, with intake and discharge comprising 25% of nurse workflows. Downtime averaged 32%, with variation depending on care area. Downtime was highest (47%) with passive video monitoring of psychiatric care and lowest (22%) in high-turnover intake areas. Highest-acuity patient-care events result in significant and variable nurse diversion from other tasks averaging 1:03:29 in combined nursing effort.  Conclusion and Potential Impact: Movement of patients between care areas represents significant operational load. Interruptions and task preparation accounted for a surprising portion of activity.  Highest-acuity patient care events result in substantial and variable diversion of nurse care. 

scholarly journals Digital medicine

JAHR ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 211-223 ◽  
Author(s):  
Elena Grebenshchikova
Keyword(s):  
Medical Information ◽  
Digital Health ◽  
Medical Documentation ◽  
Medical Care System ◽  
Health Technologies ◽  
Patient Interaction ◽  
Digital Medicine ◽  
Patient Relationships ◽  
The Impact

The digital health industry is developing rapidly: many new subjects are involved in the field of medicine; new opportunities for distant medical services, diagnostics, monitoring of patients’ health, and conducting medical research are emerging; electronic medical documentation is being developed, global medical information databases are being formed, etc. At the same time, the format of doctor-patient relationships is being transformed and new issues and challenges arise that require ethical evaluation. I identified three areas of digital medicine and analyzed issues of confidentiality, informed consent, autonomy and equity in each case. The impact of digital health technologies on the ethical contexts of medicine is uneven: telemedicine possesses the smallest revolutionary potential, which changes the mechanisms of doctor-patient interaction and actualizes issues of cultural differences. mHealth technologies significantly affect patient autonomy and change ways of sharing medical information. Artificial Intelligence (AI) is diverse in medicine, it can depersonalize relationships in medicine, radically change ideas about the role of the doctor and patient, lead to a radical restructuring of the medical care system in the center of which will be the new model of patient interaction with automated medical agents and systems.

Sign in / Sign up

Export Citation Format

Share Document