scholarly journals P138: Using electronic health record data to assess emergency medicine trainees independent and interdependent performance: a qualitative perspective on measuring what matters

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S106-S106
Author(s):  
L. Shepherd ◽  
S. Sebok-Syer ◽  
L. Lingard ◽  
A. McConnell ◽  
R. Sedran ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Electronic Health Record ◽  
Performance Indicators ◽  
Clinical Performance ◽  
Turnaround Time ◽  
Health Record ◽  
Structured Interviews ◽  
Electronic Health Record Data ◽  
Number Of Patients ◽  
Electronic Health

Introduction: Competency-based medical education (CBME) affirms that trainees will receive timely assessments and effective feedback about their clinical performance, which has inevitably raised concerns about assessment burden. Therefore, we need ways of generating assessments that do not rely exclusively on faculty-produced reports. The main object of this research is to investigate how data already collected in the electronic health record (EHR) might be meaningfully and appropriately used for assessing emergency medicine (EM) trainees independent and interdependent clinical performance. This study represents the first step in exploring what EHR data might be utilized to monitor and assess trainees clinical performance Methods: Following constructivist grounded theory, individual semi-structured interviews were conducted with 10 EM faculty and 11 EM trainees, across all postgraduate years, to identify EHR performance indicators that represent EM trainees independent and interdependent clinical actions and decisions. Participants were presented with a list of performance indicators and asked to comment on how valuable each would be in assessing trainee performance. Data analysis employed constant comparative inductive methods and occured throughout data collection. Results: Participants created, refined, and eliminated performance indicators. Our main result is a catalogue of clinical performance indicators, described by our participants, as reflecting independent and/or interdependent EM trainee performance that are believed to be captured within the EHR. Such independent indicators include: number of patients seen (according to CTAS levels), turnaround time between when a patient is signed up for and first orders are made, number of narcotics prescribed. Meanwhile, interdependent indicators include, but are not limited to, length of stay, bounce-back rates, ordering practices, and time to fluids. Conclusion: Our findings document a process for developing EM trainee report cards that incorporate the perspectives of clinical faculty and trainees. Our work has important implications for distinguishing between independent and interdependent clinical performance indicators.

scholarly journals Leveraging health system telehealth and informatics infrastructure to create a continuum of services for COVID-19 screening, testing, and treatment

2020 ◽  
Vol 27 (12) ◽  
pp. 1871-1877 ◽  
Author(s):  
Dee Ford ◽  
Jillian B Harvey ◽  
James McElligott ◽  
Kathryn King ◽  
Kit N Simpson ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Biomedical Informatics ◽  
Rapid Expansion ◽  
Urgent Care ◽  
Multidisciplinary Teams ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Number Of Patients ◽  
Electronic Health

Abstract Objectives We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care. Materials and Methods Our health system deployed 4 COVID-19 telehealth programs and 4 biomedical informatics innovations to screen and care for COVID-19 patients. Using programmatic and electronic health record data, we describe the implementation and initial utilization. Results Through collaboration across multidisciplinary teams and strategic planning, 4 telehealth program initiatives have been deployed in response to COVID-19: virtual urgent care screening, remote patient monitoring for COVID-19–positive patients, continuous virtual monitoring to reduce workforce risk and utilization of personal protective equipment, and the transition of outpatient care to telehealth. Biomedical informatics was integral to our institutional response in supporting clinical care through new and reconfigured technologies. Through linking the telehealth systems and the electronic health record, we have the ability to monitor and track patients through a continuum of COVID-19 services. Discussion COVID-19 has facilitated the rapid expansion and utilization of telehealth and health informatics services. We anticipate that patients and providers will view enhanced telehealth services as an essential aspect of the healthcare system. Continuation of telehealth payment models at the federal and private levels will be a key factor in whether this new uptake is sustained. Conclusions There are substantial benefits in utilizing telehealth during the COVID-19, including the ability to rapidly scale the number of patients being screened and providing continuity of care.

scholarly journals Using Electronic Health Record Data to Assess Residents’ Clinical Performance in the Workplace

2019 ◽  
Vol 94 (6) ◽  
pp. 853-860 ◽  
Author(s):  
Stefanie S. Sebok-Syer ◽  
Mark Goldszmidt ◽  
Christopher J. Watling ◽  
Saad Chahine ◽  
Shannon L. Venance ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Clinical Performance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Patient-related Workload Prediction in the Emergency Department: A Big Data Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 33-36
Author(s):  
Xiaomei Wang ◽  
H. Joseph Blumenthal ◽  
Daniel Hoffman ◽  
Natalie Benda ◽  
Tracy Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Emergency Medicine ◽  
Big Data ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Workload Prediction ◽  
Multi Stage ◽  
Record Data ◽  
Electronic Health

This research is a first stage in developing a method for modeling the clinician workload associated with an emergency medicine patient in order to display workload for purposes of managing clinician workload and emergency department (ED) flow. We proposed a multi-stage approach of predicting patient-related drivers of clinician’s workload in the emergency department. We trained the model from one month of electronic health record data (EHR) records of an ED. The model predicts the amount of work that individual patients contribute to the workload of clinicians. It can potentially help to manage clinician workload by supporting the decision of assigning new patients.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
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