Background Falls in acute care settings threaten patients’ safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. Objective The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. Methods As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). Results In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. Conclusions To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

OBJECTIVES The aim of this study was to evaluate the real-world incidence of endophthalmitis after intravitreal anti-vascular endothelial growth factor (VEGF) injections using data from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). METHODS Patients with endophthalmitis that developed within 6 weeks after intravitreal anti-VEGF injections were identified in 3 large OMOP CDM databases. RESULTS We identified 23,490 patients who received 128,123 intravitreal anti-VEGF injections. The incidence rates of endophthalmitis were 15.75 per 10,000 patients and 2.97 per 10,000 injections. The incidence rates of endophthalmitis for bevacizumab, ranibizumab, and aflibercept (per 10,000 injections) were 3.64, 1.39, and 0.76, respectively. The annual incidence has remained below 5.00 per 10,000 injections since 2011 despite the increasing number of intravitreal anti-VEGF injections. Bevacizumab presented a higher incidence rate for endophthalmitis than ranibizumab and aflibercept (incidence rate ratio, 3.17; p=0.021). CONCLUSIONS The incidence of endophthalmitis after intravitreal anti-VEGF injections has stabilized since 2011 despite the explosive increase in anti-VEGF injections. The off-label use of bevacizumab accounted for its disproportionately high incidence of endophthalmitis. The OMOP CDM, which includes off-label uses, laboratory data, and a scalable standardized database, could provide a novel strategy to reveal real-world evidence, especially in ophthalmology.

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BACKGROUND Falls in acute care settings threaten patients’ safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. OBJECTIVE The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. METHODS As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). RESULTS In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. CONCLUSIONS To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

BACKGROUND A common data model (CDM) helps to standardize electronic health record (EHR) data and eases the analysis of outcomes for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free-text–based pathology reports into the CDM. There are few use cases of representing cancer data in CDM. OBJECTIVE In this study, we aimed to construct a colon-cancer–related pathological CDM database with natural language processing (NLP) for a research platform that could utilize both clinical and omics data. The essential text entities from the pathology reports were extracted, standardized, and converted to the OMOP CDM to utilize the pathology data in cancer research. METHODS We extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics (OHDSI) vocabularies, and built database and defined relations for the CDM tables. Major clinical entities were extracted through NLP on immunochemistry tests, molecular genetic tests, and surgical pathology reports of colon-cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using a regular expression based on Python. Unstructured data, text that does not have a particular pattern, was handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the logical observation identifiers names and codes (LOINC) and the systematized nomenclature of medicine (SNOMED) standard terminologies recommended by OMOP CDM. The database-table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse. RESULTS We examined and standardized 1,848 immunochemistry test reports, 3,890 molecular genetic test reports, and 12,352 surgical pathology reports (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: 1) NOTE_NLP, 2) MEASUREMENT, 3) CONDITION_OCCURRENCE, 4) SPECIMEN, and 5) FACT_RELATIONSHIP of specimen with condition and measurement. CONCLUSIONS This study was aimed at preparing CDM data for the research platform to take advantage of all the omics clinical and patient data at Seoul National University Bundang Hospital (SNUBH) for colon-cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.