Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

Abstract Background The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events. Methods We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period. Results We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm. Conclusions Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

Download Full-text

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

10.21203/rs.2.11038/v2 ◽

2019 ◽

Author(s):

Timothy Bergquist ◽

Vikas Pejaver ◽

Noah Hammarlund ◽

Sean D. Mooney ◽

Stephen J. Mooney

Keyword(s):

Emergency Department ◽

Population Health ◽

Large Scale ◽

Traumatic Injury ◽

Enrichment Analysis ◽

Emergency Department Visits ◽

Cold Weather ◽

Average Proportion ◽

Electronic Health ◽

Environmental Events

Abstract Background The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events. Methods We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period. Results We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm. Conclusions Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

Download Full-text

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

10.21203/rs.2.11038/v4 ◽

2019 ◽

Author(s):

Timothy Bergquist ◽

Vikas Pejaver ◽

Noah Hammarlund ◽

Sean D. Mooney ◽

Stephen J. Mooney

Keyword(s):

Emergency Department ◽

Population Health ◽

Large Scale ◽

Traumatic Injury ◽

Enrichment Analysis ◽

Emergency Department Visits ◽

Cold Weather ◽

Average Proportion ◽

Electronic Health ◽

Environmental Events

Abstract Background The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events. Methods We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period. Results We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm. Conclusions Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

Download Full-text

PRS45 A Large-Scale Retrospective Study of Emergency Department Visits or Hospitalizations for Anaphylaxis among Patients with Employer- Sponsored Health Insurance

Value in Health ◽

10.1016/j.jval.2012.03.329 ◽

2012 ◽

Vol 15 (4) ◽

pp. A60

Author(s):

P. Landsman-blumberg ◽

W. Wei ◽

D. Douglas ◽

D.M. Smith ◽

C.A. Camargo

Keyword(s):

Emergency Department ◽

Health Insurance ◽

Retrospective Study ◽

Large Scale ◽

Emergency Department Visits

Download Full-text

Innovative Information Technology–Powered Population Health Care Management Improves Outcomes and Reduces Hospital Readmissions and Emergency Department Visits

The Joint Commission Journal on Quality and Patient Safety ◽

10.1016/j.jcjq.2017.04.002 ◽

2017 ◽

Vol 43 (7) ◽

pp. 330-337 ◽

Cited By ~ 1

Author(s):

Sharon Anderson ◽

Michele Campbell ◽

Donna Mahoney ◽

Ann Kathryn Muther ◽

Janice Nevin ◽

...

Keyword(s):

Health Care ◽

Emergency Department ◽

Information Technology ◽

Population Health ◽

Care Management ◽

Hospital Readmissions ◽

Health Care Management ◽

Emergency Department Visits

Download Full-text

Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

JCO Clinical Cancer Informatics ◽

10.1200/cci.21.00116 ◽

2021 ◽

pp. 1106-1126

Author(s):

Dylan J. Peterson ◽

Nicolai P. Ostberg ◽

Douglas W. Blayney ◽

James D. Brooks ◽

Tina Hernandez-Boussard

Keyword(s):

Machine Learning ◽

At Risk ◽

Emergency Department ◽

Emergency Department Visits ◽

Test Model ◽

Receiver Operator Curve ◽

Patients At Risk ◽

Electronic Health ◽

Chemotherapy Patients ◽

Operator Curve

PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

Download Full-text

Relationship between Traumatic Injury, Alcohol Consumption, and Previous Emergency Department Visits: Opportunity for Early Screening and Intervention?

Academic Emergency Medicine ◽

10.1197/j.aem.2004.02.140 ◽

2004 ◽

Vol 11 (5) ◽

pp. 544-544

Author(s):

J. M. Cukor

Keyword(s):

Emergency Department ◽

Alcohol Consumption ◽

Traumatic Injury ◽

Emergency Department Visits ◽

Early Screening

Download Full-text

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study

Journal of Medical Internet Research ◽

10.2196/25113 ◽

2021 ◽

Vol 23 (1) ◽

pp. e25113

Author(s):

Yen-Pin Chen ◽

Yuan-Hsun Lo ◽

Feipei Lai ◽

Chien-Hua Huang

Keyword(s):

Emergency Department ◽

Outcome Prediction ◽

Medical Information ◽

Emergency Department Visits ◽

Free Text ◽

Disease Concept ◽

Learning Methods ◽

Data Set ◽

Proposed Model ◽

Electronic Health

Background The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors’ treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience. Objective In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks. Methods We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model. Then, Deep InfoMax (DIM) and Simple Contrastive Learning of Visual Representations (SimCLR) were used for the unsupervised embedding of the disease concept. The pretrained disease concept-embedding model, named EDisease, was further finetuned to adapt to the critical care outcome prediction task. We evaluated the performance of embedding using t-distributed stochastic neighbor embedding (t-SNE) to perform dimension reduction for visualization. The performance of the finetuned predictive model was evaluated against published models using the area under the receiver operating characteristic (AUROC). Results The performance of our model on the outcome prediction had the highest AUROC of 0.876. In the ablation study, the use of a smaller data set or fewer unsupervised methods for pretraining deteriorated the prediction performance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model. Conclusions Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.

Download Full-text