Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography. - Folks RD; Savir-Baruch B; Garcia EV; Verdes L; Taylor AT; Folks, Russell D; Savir-Baruch, Bital; Garcia, Ernest V; Verdes, Liudmila; Taylor, Andrew T (Journal of Nuclear Medicine Technology, 2012)
Unlabelled: Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from (99m)Tc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system.Methods: For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians.Results: RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84).Conclusion: We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.