scholarly journals A Query Taxonomy Describes Performance of Patient-Level Retrieval from Electronic Health Record Data

2019 ◽  
Author(s):  
Steven R. Chamberlin ◽  
Steven D. Bedrick ◽  
Aaron M. Cohen ◽  
Yanshan Wang ◽  
Andrew Wen ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Strong Association ◽  
Test Collection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Complexity Measures ◽  
Patient Cohort ◽  
Patient Level ◽  
Cohort Identification ◽  
Electronic Health

AbstractPerformance of systems used for patient cohort identification with electronic health record (EHR) data is not well-characterized. The objective of this research was to evaluate factors that might affect information retrieval (IR) methods and to investigate the interplay between commonly used IR approaches and the characteristics of the cohort definition structure.We used an IR test collection containing 56 test patient cohort definitions, 100,000 patient records originating from an academic medical institution EHR data warehouse, and automated word-base query tasks, varying four parameters. Performance was measured using B-Pref. We then designed 59 taxonomy characteristics to classify the structure of the 56 topics. In addition, six topic complexity measures were derived from these characteristics for further evaluation using a beta regression simulation.We did not find a strong association between the 59 taxonomy characteristics and patient retrieval performance, but we did find strong performance associations with the six topic complexity measures created from these characteristics, and interactions between these measures and the automated query parameter settings.Some of the characteristics derived from a query taxonomy could lead to improved selection of approaches based on the structure of the topic of interest. Insights gained here will help guide future work to develop new methods for patient-level cohort discovery with EHR data.

scholarly journals Evaluation of Patient-Level Retrieval from Electronic Health Record Data for a Cohort Discovery Task

2019 ◽  
Author(s):  
Steven D. Bedrick ◽  
Aaron M. Cohen ◽  
Yanshan Wang ◽  
Andrew Wen ◽  
Sijia Liu ◽  
...  
Keyword(s):  
Test Collection ◽  
Electronic Health Record Data ◽  
Patient Cohort ◽  
Patient Level ◽  
Medical Centers ◽  
Academic Medical ◽  
Level Information ◽  
Electronic Health ◽  
Boolean Queries ◽  
Future Work

ABSTRACTObjectiveGrowing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well-understood. The objective of this research was to assess patient-level information retrieval (IR) methods using electronic health records (EHR) for different types of cohort definition retrieval.Materials and MethodsWe developed a test collection consisting of about 100,000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated IR tasks using word-based approaches were performed, varying four different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics.ResultsThe best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision, but were still not able to recall all relevant patients found by the automated queries.ConclusionWhile word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Insights gained in this preliminary analysis will help guide future work to develop new methods for patient-level cohort discovery with EHR data.

scholarly journals Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 395-404 ◽  
Author(s):  
Steven R Chamberlin ◽  
Steven D Bedrick ◽  
Aaron M Cohen ◽  
Yanshan Wang ◽  
Andrew Wen ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Test Collection ◽  
Electronic Health Record Data ◽  
Patient Cohort ◽  
Patient Level ◽  
Medical Centers ◽  
Level Information ◽  
Electronic Health ◽  
Boolean Queries ◽  
Information Retrieval Methods

Abstract Objective Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval. Materials and Methods We developed a test collection consisting of about 100 000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated information retrieval tasks using word-based approaches were performed, varying 4 different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. Results The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision but were still not able to recall all relevant patients found by the automated queries. Conclusion While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Future work will focus on using the test collection to develop and evaluate new approaches to query structure, weighting algorithms, and application of semantic methods.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

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 HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

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