scholarly journals Algorithm to detect pediatric provider attention to high BMI and associated medical risk

2018 ◽  
Vol 26 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Christy B Turer ◽  
Celette S Skinner ◽  
Sarah E Barlow
Keyword(s):  
Body Mass Index ◽  
Electronic Health Record ◽  
Chart Review ◽  
Health Record ◽  
Medical Risk ◽  
Practice Behaviors ◽  
Diagnosis Codes ◽  
Manual Review ◽  
Clinician Practice ◽  
High Bmi

Abstract We developed and validated an algorithm that uses combinations of extractable electronic-health-record (EHR) indicators (diagnosis codes, orders for laboratories, medications, and referrals) that denote widely-recommended clinician practice behaviors: attention to overweight/obesity/body mass index alone (BMI Alone), with attention to hypertension/other comorbidities (BMI/Medical Risk), or neither (No Attention). Data inputs used for each EHR indicator were refined through iterative chart review to identify and resolve modifiable coding errors. Validation was performed through manual review of randomly selected visit encounters (n = 308) coded by the refined algorithm. Of 104 encounters coded as No Attention, 89.4% lacked any evidence (specificity) of attention to BMI/Medical Risk. Corresponding evidence (sensitivity) of attention to BMI Alone was identified in 96.0% (of 101 encounters coded as BMI Alone) and BMI/Medical Risk in 96.1% (of 103 encounters coded as BMI/Medical Risk). Our EHR data algorithm can validly determine provider attention to BMI alone, with Medical Risk, or neither.

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

scholarly journals Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

2019 ◽  
Vol 27 (3) ◽  
pp. 480-490 ◽  
Author(s):  
Adam Rule ◽  
Michael F Chiang ◽  
Michelle R Hribar
Keyword(s):  
Systematic Review ◽  
Access Control ◽  
Electronic Health Record ◽  
Chart Review ◽  
Observational Research ◽  
Clinical Activity ◽  
Health Record ◽  
Problem List ◽  
Inclusion Criteria ◽  
Electronic Health

Abstract Objective To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. Materials and Methods In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research. Results Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. Discussion While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis. Conclusion EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

scholarly journals Electronic Health Record Use among Ophthalmology Residents while on Call

2020 ◽  
Vol 12 (02) ◽  
pp. e143-e150
Author(s):  
Christopher P. Long ◽  
Ming Tai-Seale ◽  
Robert El-Kareh ◽  
Jeffrey E. Lee ◽  
Sally L. Baxter
Keyword(s):  
Internal Medicine ◽  
Electronic Health Record ◽  
Chart Review ◽  
Health Record ◽  
Physician Burnout ◽  
Single Institution ◽  
Clinical Exposure ◽  
Future Studies ◽  
After Hours ◽  
Electronic Health

Abstract Background As electronic health record (EHR) use becomes more widespread, detailed records of how users interact with the EHR, known as EHR audit logs, are being used to characterize the clinical workflows of physicians including residents. After-hours EHR use is of particular interest given its known association with physician burnout. Several studies have analyzed EHR audit logs for residents in other fields, such as internal medicine, but none thus far in ophthalmology. Here, we focused specifically on EHR use during on-call shifts outside of normal clinic hours. Methods In this retrospective study, we analyzed raw EHR audit log data from on-call shifts for 12 ophthalmology residents at a single institution over the course of a calendar year. Data were analyzed to characterize total time spent using the EHR, clinical volume, diagnoses of patients seen on call, and EHR tasks. Results Across all call shifts, the median and interquartile range (IQR) of the time spent logged into the EHR per shift were 88 and 131 minutes, respectively. The median (IQR) unique patient charts accessed per shift was 7 (9) patients. When standardized to per-hour measures, weekday evening shifts were the busiest call shifts with regard to both EHR use time and clinical volume. Total EHR use time and clinical volume were greatest in the summer months (July to September). Chart review comprised a majority (63.4%) of ophthalmology residents' on-call EHR activities. Conclusion In summary, EHR audit logs demonstrate substantial call burden for ophthalmology residents outside of regular clinic hours. These data and future studies can be used to further characterize the clinical exposure and call burden of ophthalmology residents and could potentially have broader implications in the fields of physician burnout and education policy.

scholarly journals Validation of diagnosis codes to identify side of colon in an electronic health record registry

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Patricia Luhn ◽  
Deborah Kuk ◽  
Gillis Carrigan ◽  
Nathan Nussbaum ◽  
Rachael Sorg ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Diagnosis Codes ◽  
Electronic Health

scholarly journals Model-Based Algorithms for Detecting Peripheral Artery Disease Using Administrative Data From an Electronic Health Record Data System: Algorithm Development Study

10.2196/18542 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e18542 ◽  
Author(s):  
Elizabeth Hope Weissler ◽  
Steven J Lippmann ◽  
Michelle M Smerek ◽  
Rachael A Ward ◽  
Aman Kansal ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Administrative Data ◽  
Peripheral Artery Disease ◽  
Health Record ◽  
Peripheral Artery ◽  
Model Based ◽  
Diagnosis Codes ◽  
Electronic Health ◽  
Artery Disease

Background Peripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes. Objective The aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system. Methods An initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance. Results The initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862. Conclusions The algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

2022 ◽  
pp. 0272989X2110699
Author(s):  
Louise B. Russell ◽  
Qian Huang ◽  
Yuqing Lin ◽  
Laurie A. Norton ◽  
Jingsan Zhu ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Ejection Fraction ◽  
Electronic Health Record ◽  
Body Mass ◽  
Hospital Admissions ◽  
Pragmatic Trial ◽  
Patient Characteristics ◽  
Health Record ◽  
State Data ◽  
Electronic Health

Introduction. Pragmatic clinical trials test interventions in patients representative of real-world medical practice and reduce data collection costs by using data recorded in the electronic health record (EHR) during usual care. We describe our experience using the EHR to measure the primary outcome of a pragmatic trial, hospital readmissions, and important clinical covariates. Methods. The trial enrolled patients recently discharged from the hospital for treatment of heart failure to test whether automated daily monitoring integrated into the EHR could reduce readmissions. The study team used data from the EHR and several data systems that drew on the EHR, supplemented by the hospital admissions files of three states. Results. Almost three-quarters of enrollees’ readmissions over the 12-mo trial period were captured by the EHRs of the study hospitals. State data, which took 7 mo to more than 2 y from first contact to receipt of first data, provided the remaining one-quarter. Considerable expertise was required to resolve differences between the 2 data sources. Common covariates used in trial analyses, such as weight and body mass index during the index hospital stay, were available for >97% of enrollees from the EHR. Ejection fraction, obtained from echocardiograms, was available for only 47.6% of enrollees within the 6-mo window that would likely be expected in a traditional trial. Discussion. In this trial, patient characteristics and outcomes were collected from existing EHR systems, but, as usual for EHRs, they could not be standardized for date or method of measurement and required substantial time and expertise to collect and curate. Hospital admissions, the primary trial outcome, required additional effort to locate and use supplementary sources of data. Highlights Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently. Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not. Acquiring essential supplementary data—in this trial, state data on hospital admission—can be a lengthy and difficult process.

QIM19-121: Improving Testing for Hepatitis B Before Treatment With Rituximab Through Automated Electronic Health Record Alert System: A Single Institutional Retrospective Review and Quality Improvement Project

2019 ◽  
Vol 17 (3.5) ◽  
pp. QIM19-121
Author(s):  
Sowmya Boddhula ◽  
Satish Kumar Boddhula ◽  
Bishesh Shrestha ◽  
Kelly Morris ◽  
Rosana Gnanajothy
Keyword(s):  
Hepatitis B ◽  
Electronic Health Record ◽  
Chart Review ◽  
Screening Tests ◽  
Health Record ◽  
Alert System ◽  
Improvement Project ◽  
Chronic Hepatitis B Virus ◽  
Electronic Health ◽  
Hbv Screening

Introduction: Individuals with chronic hepatitis B virus infection (HBV) or previous infection with HBV are at increased risk of HBV exacerbation or reactivation when they receive treatment with anti-CD20 monoclonal antibodies like rituximab (RTX). HBV screening and appropriate use of prophylactic antiviral therapy is recommended to prevent reactivation. A software program named Beacon Oncology was integrated into Epic, which creates an automated alert for HBV screening before starting first dose of chemotherapy with RTX and results the previously resulted HBV test results. Retrospective data analysis for screening was done after implementation of the software and its impact was assessed. Methods: We conducted retrospective chart review on screening for HBV before starting treatment with RTX before and after implementation of the electronic health record (EHR) alert system. Results: A baseline review (before software introduction) of 165 patients showed that only 40 (24%) had screening tests for HBV (hepatitis B surface antigen [HBsAg] and hepatitis B core antibody [anti-HBcAb]) before receiving rituximab. Following introduction of the automated electronic alert system, chart review for HBV testing rates among patients being initiated onto rituximab was performed. There was a marked increase in pre-rituximab testing for HBsAg from 24% to 88% and for anti-HBcAb from 24% to 76%. The remainder cases also had the HBV screening done but after the first dose of the RTX chemotherapy between 1.3 to 7.5 days. There was one patient identified as anti-HBcAb-positive after the implementation of the protocol. Conclusions: This retrospective single-institution study clearly indicates that simple strategies can markedly improve appropriate HBV screening. There was a more than 3-fold increase in HBV testing before the first dose of HBV after implementation of the EHR alert system. There has been increased use of EHR alert systems recently to improve implementation of clinical guidelines, and they have been shown to improve patient outcomes. In conclusion, an automated EHR alert directed toward screening for HBV before initiating RTX effectively increased the number of HBV screening tests completed, and similar protocols could be implemented to identify other at-risk patient groups.

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.

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