{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:49:47Z","timestamp":1782989387877,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>This study evaluates diagnostic coding consistency across sequential hospitalizations using a longitudinal analysis of the CMS Inpatient Dataset with more than five million records. By transforming ICD-10-CM codes into CCS categories, the research tracks 9 chronic and 7 acute conditions using State Transition Matrices to identify patterns: Continuous, Fading, Intermittent Gap, and Late Onset. Significant diagnostic gaps were observed. Diabetes and Multiple Myeloma showed \u2018Intermittent Gap\u2019 rates of approximately 14%. Critically, for most chronic conditions, the \u2018Intermittent Gap\u2019 pattern correlated with the highest hospital Length of Stay, suggesting that missed documentation may obscure patient complexity. While some gaps stem from coding regulations or clinical transitions (such as remission or combination coding) the prevalence of intermittent patterns indicates systemic coding failures, with possible clinical coordination implications.<\/jats:p>","DOI":"10.3233\/shti260799","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:23Z","timestamp":1782986363000},"source":"Crossref","is-referenced-by-count":0,"title":["Pattern Analysis of Comorbidity Undercoding and Its Association with Hospital Length of Stay"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2951-4350","authenticated-orcid":false,"given":"Dimitrios","family":"Zikos","sequence":"first","affiliation":[{"name":"Texas Tech University Health Sciences Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8120-8449","authenticated-orcid":false,"given":"Philip","family":"Eappen","sequence":"additional","affiliation":[{"name":"Cape Breton University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260799","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:23Z","timestamp":1782986363000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260799"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260799","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}