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 Health System Implementation of a Tobacco Quitline eReferral

2019 ◽  
Vol 10 (04) ◽  
pp. 735-742 ◽  
Author(s):  
Eve Angeline Hood-Medland ◽  
Susan L. Stewart ◽  
Hien Nguyen ◽  
Mark Avdalovic ◽  
Scott MacDonald ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Best Practice ◽  
Health Record ◽  
System Implementation ◽  
Electronic Health Record Data ◽  
Outpatient Group ◽  
Primary Care Specialty ◽  
Electronic Health ◽  
Order Set

Abstract Background Proactive referrals through electronic orders (eReferrals) can increase patient connection with tobacco quitlines. More information is needed on “real-world” implementation of electronic health record tools to promote tobacco cessation while minimizing provider burden. Objectives This paper examines the health system implementation of an eReferral to a tobacco quitline without best practice alerts in primary care, specialty, and hospital settings in an academic health system. Methods This is a prospective implementation study of a health system tobacco eReferral to a state quitline that was completed with an approach to minimize provider cognitive burden. Data are drawn from electronic health record data at University of California, Davis Health Systems (March 2013–February 2016). Results Over 3 years, 16,083 encounters with smokers resulted in 1,137 eReferral orders (7.1%). Treatment reach was 1.6% for quitline services and 2.3% for outpatient group classes. While the group classes were offered to outpatient smokers, the eReferral order was included in an outpatient order set and eventually an automated inpatient discharge order set; no provider alerts were implemented. Referrals were sustained and doubled after inpatient order set implementation. Among all first time eReferral patients, 12.2% had a 6 to 12 month follow-up visit at which they were documented as nonsmoking. Conclusion This study demonstrates a quitline eReferral order can be successfully implemented and sustained with minimal promotion, without provider alerts and in conjunction with group classes. Reach and effectiveness were similar to previously described literature.

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 Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e017583 ◽  
Author(s):  
Kevin M Pantalone ◽  
Todd M Hobbs ◽  
Kevin M Chagin ◽  
Sheldon X Kong ◽  
Brian J Wells ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Cleveland Clinic ◽  
P Value ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Cross Sectional ◽  
Academic Medical Centre ◽  
Integrated Health ◽  
Electronic Health

ObjectiveTo determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m2.DesignThe electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested.SettingA large US integrated health system.ResultsA total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30–34.9, 34 152 (10.5%) had BMI 35–39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code.ConclusionsThis cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation.

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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