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.

scholarly journals A Comparison of Fever Classified Chief Complaints and Diagnoses with Recorded Body Temperatures

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Patricia Araki ◽  
Emily Kajita ◽  
Kelsey OYong ◽  
Monica Z. Luarca ◽  
Bessie Hwang ◽  
...  
Keyword(s):  
Emergency Department ◽  
Los Angeles ◽  
Syndromic Surveillance ◽  
Los Angeles County ◽  
Body Temperatures ◽  
Emergency Department Data ◽  
Chief Complaints

In an effort to evaluate patient stated "fever" chief complaints and diagnoses utilizing emergency department data from the Los Angeles County Syndromic Surveillance project, each were compared with measured patient body temperatures in the fever range.

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 Syndromic Surveillance Using Ambulatory Electronic Health Records

2009 ◽  
Vol 16 (3) ◽  
pp. 354-361 ◽  
Author(s):  
G. Hripcsak ◽  
N. D. Soulakis ◽  
L. Li ◽  
F. P. Morrison ◽  
A. M. Lai ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Syndromic Surveillance ◽  
Health Records ◽  
Electronic Health

Implementing Electronic Health Records in the Emergency Department

2010 ◽  
Vol 38 (2) ◽  
pp. 257-263 ◽  
Author(s):  
Daniel A. Handel ◽  
Jeffrey L. Hackman
Keyword(s):  
Emergency Department ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

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.

scholarly journals Monitoring Heat-Related Illness through Syndromic Surveillance in Los Angeles County

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jimmy Duong ◽  
Michael Lim ◽  
Emily Kajita ◽  
Bessie Hwang
Keyword(s):  
Public Health ◽  
Los Angeles ◽  
Syndromic Surveillance ◽  
Los Angeles County ◽  
Correlation Coefficients ◽  
Temperature Data ◽  
Extreme Heat ◽  
Chief Complaints ◽  
Ed Visits ◽  
Council Of State

ObjectiveTo analyze Los Angeles County’s (LAC) extreme heat season in 2018 and evaluate the Council of State and Territorial Epidemiologists’ (CSTE) syndrome query for heat-related-illness (HRI) in Los Angeles County (LAC)IntroductionLAC experienced several days of record-breaking temperatures during the summer of 2018. Downtown Los Angeles temperatures soared to 108°F in July with an average daily maximum of 92°F. Extreme heat events such as these can pose major risks to human health. Syndromic surveillance can be a useful tool in providing near real-time surveillance of HRI. In 2014, a working group was formed within the CSTE Climate Change Subcommittee to define and analyze HRI. The workgroup’s goal was to provide guidance to public health professionals in adapting and implementing an HRI syndrome surveillance query. The Acute Communicable Disease Control Program’s (ACDC) Syndromic Surveillance Unit utilized CSTE’s HRI query to provide surveillance during the extreme heat season in 2018 in LAC. Additional modifications to the CSTE query were evaluated for potential improvements towards characterizing HRI trends.MethodsFrom May 1 to September 30, 2018, Emergency Department (ED) data were queried for cases using the CSTEs definition for HRI. The queries consisted of key word searches within the chief complaint (CC) data field, and, if available, the diagnosis data fields. The query was derived from the CSTE HRI query published in 20161. In addition, ACDC explored the utility of expanding the CSTE syndrome definition to include additional chief complaints commonly associated with HRI such as dehydration and syncope. Both queries were applied on all participating syndromic EDs in LAC alongside daily high temperature data trends. Local temperature data for downtown Los Angeles weather station KCQT were taken from the Weather Underground website. Spearman correlation coefficients were calculated for each query during the heat season. Similarly, both queries were also applied during colder months from October 1, 2017 to April 30, 2018 for comparison. Lastly, results for dehydration and syncope were independently assessed apart from other HRI query terms during both heat seasons and colder months.ResultsThe CSTE HRI query and the query with the added terms yielded 1,258 and 63,332 ED visits, respectively, during the heat season. On July 6, the maximum daily temperature peaked at 108 °F; the HRI and the query with the added terms yielded 136 and 618 ED visits, respectively. The HRI query and the HRI query with the added terms had a correlation coefficient of 0.714 (p <0.0001) and 0.427 (p <0.0001), respectively. During colder months, the CSTE HRI query and the query with the added terms yielded 377 and 86,008, respectively, with correlation coefficients of 0.342 (p < 0.0001) and 0.133 (p < 0.052). The syncope-only query saw no variation in HRI classified encounters throughout the heat season (mean: 328; min: 228; max: 404) or colder months (mean: 328; min: 261; max: 404) with correlation coefficients of 0.238 (p = 0.003) and 0.155 (p = 0.024), respectively. Similarly, the dehydration-only query saw no variation in HRI classified encounters throughout the heat season (mean: 96; min: 58; max: 258) or colder months (mean: 94; min: 60; max: 160) with correlation coefficients of 0.596 (p < 0.0001) and -0.016 (p = 0.822).ConclusionsThe CSTE HRI query proved to be a strong indicator for HRI, and the addition of terms associated with dehydration and syncope to the CSTE HRI query weakened the correlation with temperature. Compared to the original CSTE HRI query, the added terms yielded a 4934% increase in HRI classified encounters during the heat season; however, these were likely due to causes other than HRI -- adding the extra terms resulted in a weaker correlation with temperature. Additionally, the comparative analysis showed that, with the added terms, the volume of HRI encounters was larger during colder months than hotter months suggesting misclassification of non-HRI illnesses. Surveillance of HRI has proven to be difficult because many of the HRI symptoms are too commonly associated with non-HRI conditions which would explain the weaker correlations when adding additional chief complaints associated with HRI. In conclusion, the CSTE syndrome definition for HRI proved to be the most robust query for HRI during the heat season. Case counts of HRI are difficult due to symptom overlap with many other medical conditions. However, syndromic surveillance using the CSTE HRI query is useful for trend analysis in near real-time during heat events.References1. Council of State and Territorial Epidemiologists. Heat-Related Illness Syndrome Query: A Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. Version 1.0. 2016 Sep. 12 p. 

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
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