Dataset Information

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.

ABSTRACT: To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools.We used the coded data and clinical notes from the 24,506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources.We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%).Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools.

SUBMITTER: Abhyankar S

PROVIDER: S-EPMC4147606 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature

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Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.

Abhyankar Swapna S Demner-Fushman Dina D Callaghan Fiona M FM McDonald Clement J CJ

Journal of the American Medical Informatics Association : JAMIA 20140102 5

<h4>Objective</h4>To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools.<h4>Methods</h4>We used the coded data and clinical notes from the 24,506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations ...[more]

PMID: 24384230

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Project description:BackgroundElectronic health records (EHRs) have yet to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information.ObjectiveIt is unclear how accurately EHR data reflect patients' lived experience of SDOH. The manual process of retrieving SDOH information from clinical notes is time-consuming and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data: PatientExploreR and Electronic Medical Record Search Engine (EMERSE).MethodsWe included adult patients (≥18 years of age) receiving primary care for their diabetes at the University of California, San Francisco (UCSF), from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC.ResultsWe included 4283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information from patient clinical notes on problems related to social connectionsisolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress.ConclusionsWe discovered more patient information related to SDOH in unstructured data than in structured data. The application of this technique and further investment in similar user-friendly tools and infrastructure to extract SDOH information from unstructured data may help to identify the range of social conditions that influence patients' disease experiences and inform clinical decision-making.

Dataset Information

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.

Publications

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.

Similar Datasets

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