scholarly journals The Effect of Access to Electronic Health Records on Throughput Efficiency and Imaging Utilization in the Emergency Department

2017 ◽  
Vol 53 (2) ◽  
pp. 787-802 ◽  
Author(s):  
Matthew M. Knepper ◽  
Edward M. Castillo ◽  
Theodore C. Chan ◽  
David A. Guss
Keyword(s):  
Emergency Department ◽  
Electronic Health Records ◽  
Health Records ◽  
Imaging Utilization ◽  
Electronic Health

scholarly journals Electronic health records in ambulances: the ERA multiple-methods study

2020 ◽  
Vol 8 (10) ◽  
pp. 1-140
Author(s):  
Alison Porter ◽  
Anisha Badshah ◽  
Sarah Black ◽  
David Fitzpatrick ◽  
Robert Harris-Mayes ◽  
...  
Keyword(s):  
Emergency Department ◽  
Health Services ◽  
Electronic Health Records ◽  
Case Studies ◽  
Routine Data ◽  
Health Economy ◽  
Multiple Methods ◽  
Health Records ◽  
Electronic Health ◽  
Future Work

Background Ambulance services have a vital role in the shift towards the delivery of health care outside hospitals, when this is better for patients, by offering alternatives to transfer to the emergency department. The introduction of information technology in ambulance services to electronically capture, interpret, store and transfer patient data can support out-of-hospital care. Objective We aimed to understand how electronic health records can be most effectively implemented in a pre-hospital context in order to support a safe and effective shift from acute to community-based care, and how their potential benefits can be maximised. Design and setting We carried out a study using multiple methods and with four work packages: (1) a rapid literature review; (2) a telephone survey of all 13 freestanding UK ambulance services; (3) detailed case studies examining electronic health record use through qualitative methods and analysis of routine data in four selected sites consisting of UK ambulance services and their associated health economies; and (4) a knowledge-sharing workshop. Results We found limited literature on electronic health records. Only half of the UK ambulance services had electronic health records in use at the time of data collection, with considerable variation in hardware and software and some reversion to use of paper records as services transitioned between systems. The case studies found that the ambulance services’ electronic health records were in a state of change. Not all patient contacts resulted in the generation of electronic health records. Ambulance clinicians were dealing with partial or unclear information, which may not fit comfortably with the electronic health records. Ambulance clinicians continued to use indirect data input approaches (such as first writing on a glove) even when using electronic health records. The primary function of electronic health records in all services seemed to be as a store for patient data. There was, as yet, limited evidence of electronic health records’ full potential being realised to transfer information, support decision-making or change patient care. Limitations Limitations included the difficulty of obtaining sets of matching routine data for analysis, difficulties of attributing any change in practice to electronic health records within a complex system and the rapidly changing environment, which means that some of our observations may no longer reflect reality. Conclusions Realising all the benefits of electronic health records requires engagement with other parts of the local health economy and dealing with variations between providers and the challenges of interoperability. Clinicians and data managers, and those working in different parts of the health economy, are likely to want very different things from a data set and need to be presented with only the information that they need. Future work There is scope for future work analysing ambulance service routine data sets, qualitative work to examine transfer of information at the emergency department and patients’ perspectives on record-keeping, and to develop and evaluate feedback to clinicians based on patient records. Study registration This study is registered as Health and Care Research Wales Clinical Research Portfolio 34166. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.

102GERIATRIC VULNERABILITY IN OLDER EMERGENCY DEPARTMENT PATIENTS ACCORDING TO ELECTRONIC HEALTH RECORDS

2019 ◽  
Vol 48 (Supplement_1) ◽  
pp. i27-i30
Author(s):  
L C Blomaard ◽  
B Korpershoek ◽  
J A Lucke ◽  
J de Gelder ◽  
J Gussekloo ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health ◽  
Emergency Department Patients

scholarly journals Predicting Acute Kidney Injury: A Machine Learning Approach Using Electronic Health Records

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 386
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Amit X. Garg ◽  
Eric McArthur
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Acute Kidney Injury ◽  
Electronic Health Records ◽  
Prediction Models ◽  
Kidney Injury ◽  
Machine Learning Techniques ◽  
Health Records ◽  
Mortality And Morbidity ◽  
Electronic Health

Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.

Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden

2021 ◽  
Vol 12 (05) ◽  
pp. 1002-1013
Author(s):  
Amanda J. Moy ◽  
Lucy Aaron ◽  
Kenrick D. Cato ◽  
Jessica M. Schwartz ◽  
Jonathan Elias ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Records ◽  
Average Duration ◽  
Clinical Information ◽  
Future Research ◽  
Clinical Workflow ◽  
Health Records ◽  
Time Motion ◽  
Electronic Health ◽  
The Impact

Abstract Background The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions. Objectives The aim of the study is to understand multitasking and task fragmentation in the clinical workflow among ED clinicians using clinical information systems (CIS) through time-motion study (TMS) data, and inform their applications to more robust and generalizable measures of CIS-related documentation burden. Methods Using TMS data collected among 15 clinicians in the ED, we investigated the role of documentation burden, multitasking (i.e., performing physical and communication tasks concurrently), and workflow fragmentation in the ED. We focused on CIS-related tasks, including EHRs. Results We captured 5,061 tasks and 877 communications in 741 locations within the ED. Of the 58.7 total hours observed, 44.7% were spent on CIS-related tasks; nearly all CIS-related tasks focused on data-viewing and data-entering. Over one-fifth of CIS-related task time was spent on multitasking. The mean average duration among multitasked CIS-related tasks was shorter than non-multitasked CIS-related tasks (20.7 s vs. 30.1 s). Clinicians experienced 1.4 ± 0.9 task switches/min, which increased by one-third when multitasking. Although multitasking was associated with a significant increase in the average duration among data-entering tasks, there was no significant effect on data-viewing tasks. When engaged in CIS-related task switches, clinicians were more likely to return to the same CIS-related task at higher proportions while multitasking versus not multitasking. Conclusion Multitasking and workflow fragmentation may play a significant role in EHR documentation among ED clinicians, particularly among data-entering tasks. Understanding where and when multitasking and workflow fragmentation occurs is a crucial step to assessing potentially burdensome clinician tasks and mitigating risks to patient safety. These findings may guide future research on developing more scalable and generalizable measures of CIS-related documentation burden that do not necessitate direct observation techniques (e.g., EHR log files).

Leveraging Large-scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation (Preprint)

2021 ◽  
Author(s):  
Nan Liu ◽  
Feng Xie ◽  
Fahad Javaid Siddiqui ◽  
Andrew Fu Wah Ho ◽  
Bibhas Chakraborty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Electronic Health Records ◽  
Large Scale ◽  
Black Box ◽  
Learning Tools ◽  
Health Records ◽  
Interpretable Machine Learning ◽  
Ed Visits ◽  
Electronic Health

BACKGROUND There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer wait times. The triage process plays a crucial role in assessing and stratifying patients' risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. OBJECTIVE In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHR) and machine learning. METHODS To achieve this objective, we will conduct a retrospective, single-centre study based on a large, longitudinal dataset obtained from the EHR of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit, inpatient death, among others. With pre-identified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning-based AutoScore to develop three SERT scores. These three scores can be used at different times in the ED, i.e., upon arrival, during the ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. The receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. RESULTS The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. CONCLUSIONS The SERT scoring system proposed in this study will be unique and innovative due to its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools.

scholarly journals Validation of Los Angeles County Department of Public Health Respiratory Syndrome using Electronic Health Records

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Kelsey OYong ◽  
Emily Kajita ◽  
Patricia Araki ◽  
Monica Luarca ◽  
Bessie Hwang
Keyword(s):  
Emergency Department ◽  
Los Angeles ◽  
Electronic Health Records ◽  
Syndromic Surveillance ◽  
Los Angeles County ◽  
Surveillance Systems ◽  
Health Records ◽  
High K ◽  
Chief Complaints ◽  
Electronic Health

In Los Angeles County, emergency department data is collected from hospitals and classified into syndromes based on chief complaints. To validate this respiratory syndromic surveillance categorization, chief complaint data were compared to discharge diagnoses extracted from electronic health records from one hospital emergency department in Los Angeles County during one week in January 2013. The agreement between syndrome classification and discharge diagnosis for respiratory reports is high (k=0.75), though over 25% of diagnosis data were missing. Further validation of additional syndromes is needed. Electronic health records are valuable sources of data and can enhance the validity of syndromic surveillance systems.

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