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This work develops an EHR-based multimorbidity-weighted index (eMWI) using the OMOP Common Data Model and validates performance against established comorbidity measures. A 76-condition eMWI was built using physical-functioning-based disease weights, published condition algorithms, and ICD-code definitions. Using a large UC Health Data Warehouse cohort (adults 18+, 2012–2024), eMWI outperformed Charlson, Elixhauser, and simple disease count, showing a broader score range and improved differentiation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/developing-and-validating-novel-ehr-based-multimorbidity-weighted-index-across-comorbidity-measures-abstract-citation-id-igaf1223913/426225/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/developing-and-validating-novel-ehr-based-multimorbidity-weighted-index-across-comorbidity-measures-abstract-citation-id-igaf1223913/426225.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the paper develop and why?","Question",{"text":112,"@type":113},"It develops an EHR-based multimorbidity-weighted index (eMWI) to quantify both multimorbidity burden and physical functioning in EHR data, addressing gaps in existing tools.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the eMWI constructed for EHR use?",{"text":117,"@type":113},"The study creates a 76-condition eMWI by applying previously validated physical-functioning-based disease weights using published condition algorithms and ICD-code-based definitions for remaining conditions.",{"name":119,"@type":110,"acceptedAnswer":120},"How does eMWI perform compared with Charlson, Elixhauser, and disease count?",{"text":121,"@type":113},"eMWI yields higher multimorbidity values and a wider score range (0–91), with only 26% scoring 0, outperforming Charlson and Elixhauser and simple disease count for differentiation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},426225,1790815329,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","1168 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.3913  \nDEVELOPING AND VALIDATING NOVEL EHR-BASED  \nMULTIMORBIDITY-WEIGHTED INDEX ACROSS  \nCOMORBIDITY MEASURES  \nMelissa Wei1, Ashley Kang2, Alexandra Klomhaus2,  \nLucia Chen2, and Chi-Hong Tseng2, 1. UCLA and VA, Los Angeles, California, United States, 2. University of California, Los Angeles, Los Angeles, California, United States  \nMultimorbidity and physical functioning are important to measure for older adults, but tools capturing both are lacking in electronic health record (EHR) data.The multimorbidity-weighted index is weighted to physical functioning and has been validated for claims and EHR data but is not designed specifically for clinical data available in the EHR (medications, labs) . Herein, we develop an EHR-based multimorbidity-weighted index (eMWI) for use in EHR systems using the Observational Medical Outcomes Partnership (OMOP) Common Data Model and validate its performance against prior comorbidity measures. To develop our 76-condition eMWI, we applied our previously validated physical functioning-based disease weights to 1) published condition algorithms when available, and 2) chart review-validated ICD-code-based definitions for remaining conditions. Using a University of California Health Data Warehouse dataset of adults ≥18 years with ≥2 outpatient encounters, 2012-2024, we compared multimorbidity values and distributions for eMWI, Charlson, Elixhauser, and simple disease count. Our final cohort included 4,672,662 adults, 55.7% female, with mean±SD age  \n46.3±16.8. Patients had higher multimorbidity values (mean±SD) using eMWI (5.66±7.85) vs Charlson (1.11±1.92), Elixhauser (1 . 89±2 .51), and disease count (3±3 .55) . eMWI demonstrated superior performance in quantifying unique values of multimorbidity burden, exhibiting the widest range of scores (0-91) with only 26% having a score of 0. By comparison, Charlson scores ranged from 0-25 and 59% had scores of 0. We demonstrate that our newly developed eMWI can be used in EHR data for quantifying multimorbidity burden in comorbidity adjustment and risk stratification tools for adults across the lifespan, with advantages over prior measures.","cbCaicuTHPw5iyPh","https://ap.wps.com/l/cbCaicuTHPw5iyPh","pdf",1026566,"English","# Abstract\n## Aim and index development\n## Methods and cohort\n## Results and performance comparison\n## Implications for adjustment and risk tools","[{\"question\":\"What does the paper develop and why?\",\"answer\":\"It develops an EHR-based multimorbidity-weighted index (eMWI) to quantify both multimorbidity burden and physical functioning in EHR data, addressing gaps in existing tools.\"},{\"question\":\"How is the eMWI constructed for EHR use?\",\"answer\":\"The study creates a 76-condition eMWI by applying previously validated physical-functioning-based disease weights using published condition algorithms and ICD-code-based definitions for remaining conditions.\"},{\"question\":\"How does eMWI perform compared with Charlson, Elixhauser, and disease count?\",\"answer\":\"eMWI yields higher multimorbidity values and a wider score range (0–91), with only 26% scoring 0, outperforming Charlson and Elixhauser and simple disease count for differentiation.\"}]","DEVELOPING AND VALIDATING NOVEL EHR-BASED MULTIMORBIDITY-WEIGHTED INDEX ACROSS COMORBIDITY MEASURES - Abstract citation ID igaf122.3913 | PDF",1790639831]