Development and validation of claims-based algorithms for identifying hospitalized patients with COVID-19 and their severity in 2020 and 2021.

BACKGROUND: This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease (COVID-19) and the disease severity. METHODS: We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (ICD-10 code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals. RESULTS: Of the 75,711 total patients, number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods; 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%. CONCLUSIONS: The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

Case-Reported Data Management Methodology Using an RDF Data Model for Building a Multicenter Clinical Registry.

In multicenter clinical research, case-reported clinical data are managed for each research project. Participating institutions manage the mapping between standardized codes and in-house codes. To use the data extracted from electronic medical records in case report forms, it is necessary to pay attention to the gap in the semantic hierarchy. Managing mapping information between in-house and standardized codes is centralized in Resource Description Framework (RDF) stores. The relationship between standardized and in-house codes is described in RDF and stored in RDF stores. RESTful APIs for accessing RDF stores in SPARQL was developed and verified. The relationship between standardized codes and in-house codes of pharmaceuticals was expressed in RDF triples. As a +result of the operational verification of the implemented APIs, it was confirmed that data management with knowledge bases expressed in RDF graphs is possible. The ability to dynamically modify mapping definitions enables flexible data management and ease of operational restrictions.

[Introduction of computerized anesthesia-recording systems and construction of comprehensive medical information network for patients undergoing surgery in the University of Tokyo Hospital].

The computerized anesthesia-recording systems are expensive and the introduction of the systems takes time and requires huge effort. Generally speaking, the efficacy of the computerized anesthesia-recording systems on the anesthetic managements is focused on the ability to automatically input data from the monitors to the anesthetic records, and tends to be underestimated. However, once the computerized anesthesia-recording systems are integrated into the medical information network, several features, which definitely contribute to improve the quality of the anesthetic management, can be developed; for example, to prevent misidentification of patients, to prevent mistakes related to blood transfusion, and to protect patients' personal information. Here we describe our experiences of the introduction of the computerized anesthesia-recording systems and the construction of the comprehensive medical information network for patients undergoing surgery in The University of Tokyo Hospital. We also discuss possible efficacy of the comprehensive medical information network for patients during surgery under anesthetic managements.