Lisa P Spees1, Karen Hicklin2, Michael C Adams3, Laura Farnan4, Jeannette T Bensen5, Donna B Gilleskie6, Jonathan S Berg7, Bradford C Powell7, Kristen Hassmiller Lich8. 1. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. Electronic address: lspees21@live.unc.edu. 2. Department of Industrial & Systems Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL. 3. Division of Pediatric Genetics & Metabolism, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. 4. Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. 5. Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC. 6. Department of Economics, College of Arts and Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC. 7. Department of Genetics, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. 8. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Abstract
PURPOSE: To better understand health care utilization and develop decision support tools, methods for identifying patients with suspected genetic diseases (GDs) are needed. Previous studies had identified inpatient-relevant International Classification of Diseases (ICD) codes that were possibly, probably, or definitely indicative of GDs. We assessed whether these codes identified GD-related inpatient, outpatient, and emergency department encounters among pediatric patients with suspected GDs from a previous study (the North Carolina Clinical Genomic Evaluation by Next-Generation Exome Sequencing [NCGENES] study). METHODS: Using the electronic medical records of 140 pediatric patients from the NCGENES study, we characterized the presence of ICD codes representing possible, probable, or definite GD-related diagnoses across encounter types. In addition, we examined codes from encounters for which initially no GD-related codes had been found and determined whether these codes were indicative of a GD. RESULTS: Among NCGENES patients with visits between 2014 and 2017, 92% of inpatient, 75% of emergency department, and 63% of outpatient encounters included ≥1 GD-related code. Encounters with highly specific (ie, definite) GD codes had fewer low-specificity GD codes than encounters with only low-specificity GD codes. We identified an additional 32 ICD-9 and 56 ICD-10 codes possibly indicative of a GD. CONCLUSION: Code-based strategies can be refined to assess health care utilization among pediatric patients and may contribute to a systematic approach to identify patients with suspected GDs.
PURPOSE: To better understand health care utilization and develop decision support tools, methods for identifying patients with suspected genetic diseases (GDs) are needed. Previous studies had identified inpatient-relevant International Classification of Diseases (ICD) codes that were possibly, probably, or definitely indicative of GDs. We assessed whether these codes identified GD-related inpatient, outpatient, and emergency department encounters among pediatric patients with suspected GDs from a previous study (the North Carolina Clinical Genomic Evaluation by Next-Generation Exome Sequencing [NCGENES] study). METHODS: Using the electronic medical records of 140 pediatric patients from the NCGENES study, we characterized the presence of ICD codes representing possible, probable, or definite GD-related diagnoses across encounter types. In addition, we examined codes from encounters for which initially no GD-related codes had been found and determined whether these codes were indicative of a GD. RESULTS: Among NCGENES patients with visits between 2014 and 2017, 92% of inpatient, 75% of emergency department, and 63% of outpatient encounters included ≥1 GD-related code. Encounters with highly specific (ie, definite) GD codes had fewer low-specificity GD codes than encounters with only low-specificity GD codes. We identified an additional 32 ICD-9 and 56 ICD-10 codes possibly indicative of a GD. CONCLUSION: Code-based strategies can be refined to assess health care utilization among pediatric patients and may contribute to a systematic approach to identify patients with suspected GDs.
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