Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.

2021 ◽  
Vol 39 (1) ◽  
pp. 55-65
Author(s):  
Jessica M. Goodman ◽  
Angela L. Lamson ◽  
Ray H. Hylock ◽  
Jakob F. Jensen ◽  
Theodore R. Delbridge
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Population Health ◽  
Health Data ◽  
Empirical Theory ◽  
Frequent User

scholarly journals Predicting hospitalizations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

2021 ◽  
Author(s):  
Seung Eun Yi ◽  
Vinyas Harish ◽  
Jahir M. Gutierrez ◽  
Mathieu Ravaut ◽  
Kathy Kornas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Ambulatory Care ◽  
Population Health ◽  
Health Planning ◽  
Health Data ◽  
Ambulatory Care Sensitive Conditions ◽  
Administrative Health Data ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

AbstractObjectiveTo predict older adults’ risk of avoidable hospitalization related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada.Design, Setting, and ParticipantsA retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years, between 2008 and 2017. The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period.Data sourcesAdministrative health data from Ontario, Canada obtained from the ICES Data Repository.Main outcome measuresRisk of hospitalizations due to ACSCs one year after the observation period.ResultsThe study used a total of 1,854,116 patients, split into train, validation, and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an AUC of 80.5% on the held-out test set, and the predictions were well-calibrated. When ranking the predictions made by the model, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalization. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalizations during the observation window, age, rural residence, and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in the province of Ontario, and especially the elevated risk in rural and marginalized regions.ConclusionsThis study aimed to predict the 1-year risk of hospitalization from a series of ambulatory-care sensitive conditions in seniors aged 65 to 74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalizations.

Integrating Population Health Data on Violence Into the Emergency Department

2018 ◽  
Vol 25 (3) ◽  
pp. 149-158 ◽  
Author(s):  
Michael N. Levas ◽  
Jennifer L. Hernandez-Meier ◽  
Sara Kohlbeck ◽  
Nancy Piotrowski ◽  
Stephen Hargarten
Keyword(s):  
Emergency Department ◽  
Population Health ◽  
Health Data

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

scholarly journals Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eyal Klang ◽  
Benjamin R. Kummer ◽  
Neha S. Dangayach ◽  
Amy Zhong ◽  
M. Arash Kia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Learning Model ◽  
Free Text ◽  
Combined Model ◽  
Text Data ◽  
Machine Learning Model ◽  
Record Data

AbstractEarly admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

scholarly journals Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 182-192
Author(s):  
Dianna M. Smith ◽  
Alison Heppenstall ◽  
Monique Campbell
Keyword(s):  
Population Health ◽  
Spatial Scales ◽  
Local Level ◽  
Health Data ◽  
Future Planning ◽  
Academic Researchers ◽  
Quality Health ◽  
Spatial Microsimulation ◽  
Dynamic Populations ◽  
Economic Studies

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

scholarly journals Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

2021 ◽  
Author(s):  
Eun-Seok Choi ◽  
Jae Ang Sim ◽  
Young Gon Na ◽  
Jong- Keun Seon ◽  
Hyun Dae Shin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Confidence Interval ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Inflammatory Arthritis ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Extreme Gradient Boosting ◽  
Wbc Count

Abstract Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

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