scholarly journals “EMERGing” Electronic Health Record Data Metrics: Insights and Implications for Assessing Residents' Clinical Performance in Emergency Medicine

2020 ◽  
Author(s):  
Stefanie S. Sebok‐Syer ◽  
Lisa Shepherd ◽  
Allison McConnell ◽  
Adam M. Dukelow ◽  
Robert Sedran ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Electronic Health Record ◽  
Clinical Performance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

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 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

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

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