Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.

BackgroundThe aim of this study was to determine the validity of acute myocardial infarction (AMI) diagnosis coding in the National Health Insurance Research Database (NHIRD) by cross-comparisons of discharge diagnoses listed in the NHIRD with those in the medical records obtained from a medical center in Taiwan.MethodsThis was a cross-sectional study comparing records in the NHIRD and discharge notes in one medical center (DNMC) in the year 2008. Positive predictive values (PPVs) for AMI diagnoses were evaluated by reviewing the relevant clinical and laboratory data recorded in the discharge notes of the medical center. Agreement in comorbidities, cardiac procedures, and antiplatelet agent (aspirin or clopidogrel) prescriptions between the two databases was evaluated.ResultsWe matched 341 cases of AMI hospitalizations from the two databases, and 338 cases underwent complete chart review. Of these 338 AMI cases, 297 were confirmed with clinical and lab data, which yielded a PPV of 0.88. The consistency rate for coronary intervention, stenting, and antiplatelet prescription at admission was high, yielding a PPV over 0.90. The percentage of consistency in comorbidity diagnoses was 95.9% (324/338) among matched AMI cases.ConclusionsThe NHIRD appears to be a valid resource for population research in cardiovascular diseases.

Purpose The Chang Gung Research Database (CGRD), the largest multi‐institutional electronic medical records (EMR) collection in Taiwan, provides good access for researchers to efficiently use the standardized patient‐level data. This study evaluates the capacity and representativeness of the CGRD to promote secondary use of EMR data for clinical research with more accurate estimates. Methods The National Health Insurance Research Database (NHIRD) which covers over 99.9% of the Taiwanese population served as the comparator in this study. We compare the data components of the CGRD with the NHIRD, including records for health care facilities, patients, diagnoses, drugs, and procedures. Using the chi‐square test, we compared the distributions of age categories and sex of patients, and the rates of their health conditions between NHIRD and CGRD based on the year 2015. Results The CGRD contains more clinical information such as pathological and laboratory results than the NHIRD. The CGRD includes 6.1% of outpatients and 10.2% of hospitalized patients from the NHIRD. We found the CGRD includes more elderly outpatients (23.5% vs 12.5%) and pediatric inpatients (19.7% vs 14.4%) compared with the NHIRD. We found patients' sex distributions were similar between CGRD and NHIRD, but coverage rates of severe conditions, such as cancer, were higher than other health conditions in CGRD. Conclusions The CGRD could serve as the basis for accurate estimates in medical studies. However, researchers should pay special attention to selection biases since patients' characteristics from CGRD differ from those of the national database.

No abstract