title: A comparison between physicians and computer algorithms for form CMS-2728 data reporting.
abstract: CMS-2728 form (Medical Evidence Report) assesses 23 comorbidities chosen to reflect poor outcomes and increased mortality risk. Previous studies questioned the validity of physician reporting on forms CMS-2728. We hypothesize that reporting of comorbidities by computer algorithms identifies more comorbidities than physician completion, and, therefore, is more reflective of underlying disease burden.
  
  
  
  We collected data from CMS-2728 forms for all 296 patients who had incident ESRD diagnosis and received chronic dialysis from 2005 through 2014 at Indiana University outpatient dialysis centers. We analyzed patients' data from electronic medical records systems that collated information from multiple health care sources. Previously utilized algorithms or natural language processing was used to extract data on 10 comorbidities for a period of up to 10 years prior to ESRD incidence. These algorithms incorporate billing codes, prescriptions, and other relevant elements. We compared the presence or unchecked status of these comorbidities on the forms to the presence or absence according to the algorithms.
  
  
  
  Computer algorithms had higher reporting of comorbidities compared to forms completion by physicians. This remained true when decreasing data span to one year and using only a single health center source. The algorithms determination was well accepted by a physician panel. Importantly, algorithms use significantly increased the expected deaths and lowered the standardized mortality ratios.
  
  
  
  Using computer algorithms showed superior identification of comorbidities for form CMS-2728 and altered standardized mortality ratios. Adapting similar algorithms in available EMR systems may offer more thorough evaluation of comorbidities and improve quality reporting.
authors.affiliation: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.|Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA.
authors.forename: Mohammed Said
authors.initials: MS
authors.lastname: Malas
authors.affiliation: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
authors.forename: Jay
authors.initials: J
authors.lastname: Wish
authors.affiliation: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
authors.forename: Ranjani
authors.initials: R
authors.lastname: Moorthi
authors.affiliation: Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA.
authors.forename: Shaun
authors.initials: S
authors.lastname: Grannis
authors.affiliation: Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Dexter
authors.affiliation: Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA.
authors.forename: Jon
authors.initials: J
authors.lastname: Duke
authors.affiliation: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.|Roudebush Veterans Administration Medical Center, Indianapolis, Indiana, USA.
authors.forename: Sharon
authors.initials: S
authors.lastname: Moe
mesh_terms: D000465:Algorithms; D014487:Centers for Medicare and Medicaid Services, U.S.; D015897:Comorbidity; D006801:Humans; D008297:Male; D010820:Physicians / Q000592:standards*; D006435:Renal Dialysis / Q000401:mortality / Q000592:standards*; D012107:Research Design / Q000592:standards*; D014481:United States
keywords: CMS-2728; Dialysis; comorbidities; informatics; mortality