--- what is cohort identification and why is it important ? - Traditional methods, and computable phenotypes.
Diagnosis codes alone are not sufficient in developing optimal patient identification algorithms Using additional clinical data elements beyond diagnosis codes can often be better predictors of disease-specific outcomes
Accurate Cohort identification is the key for conducting any research What are the challemges of cohort identification
More accurate than methods relying on diagnosis billing code data alone Less prone to instances of mis-coding Using multiple clinical elements improves accuracy of case identification (stronger evidence)
CP Using multiple clinical elements improves accuracy of case identification Allows for more complex patient identification Can use an algorithmic approach to patient identification with multiple inclusion and exclusion criteria Can aid in new patient identification Patients may meet clinical criteria for conditions but not yet identified by clinician or have no documented diagnosis code in the HER Automatic identification of patients with hypertension and obesity based on biometric data or by disease-specific medications Integration into EHRs to offer clinical decision support to alert providers to undiagnosed conditions Sources: diagnosis codes, procedure codes, laboratory test results and biometric data Phenotypes developed with domain experts who understand the clinical phenotype and how it is represented in EHR data Clinical informaticians identify and extract the information
Describe higher level workflow. Based on development of at least 3 different computable phenotypes in PEDSnet. For three different chronic conditions. : type 2 diabetes, crohn’s disease and glomerular disease. We will use Crohn;s disease as a running example for this talk.
Endo.... Would expect a higher odds ratio Ever recieved one of those exams....
All exposures in one equation. New model --- one dummy for has CD medications. And -- one dummy --- 1,2,3,4 or more.
Average 89% in the first graph and 79% in the second graph Alogrithm version Most specific algorithm, For first graphs – did they shift to UC diagnosis. ? At chart review level. Look at patients who we are losing from one graph to another. Take Chris’s feedback.
Developing Computable Phenotype for Chronic Diseases using PEDSnet
Designing Computable Phenotypes for Chronic
Diseases using PEDSnet
Ritu Khare, PhD
The Children’s Hospital of Philadelphia
November 16 2016
• Cohort identification
• Certain conditions or features
• Key in pediatric research
• Traditional practice
• Chart abstractions
• Administrative data (billing code)
• Computable Phenotype
• EHR-based, pragmatic
• Multiple clinical elements
• e.g. eMERGE network
Conclusions and Lessons Learned
• patient-level chart reviews are expensive
• several possibilities of internal validation
• Data quality issues
• sites with no GI providers or care sites!
• sites with no patients on Crohn’s disease medications!
• Algorithm Selection
• known data quality issues
• internal validation findings
• sensitivity vs. specificity balance