title: ANTEROCOD: actuarial survival curves applied to medical coding support for chronic diseases.
abstract: For the practitioner, choosing diagnosis codes is a non-intuitive operation. Mistakes are frequent, causing severe consequences on healthcare performance evaluation and funding. French physicians have to assign a code to all their activities and are frequently prone to these errors. Given that most of the time and particularly for chronic diseases indexed information is already available, we propose a tool named AnterOcod, in order to support the medical coding task. It suggests the list of most relevant plausible codes, predicted from the patient's earlier hospital stays, according to a set of previously utilized diagnosis codes. Our method applies the estimation of code reappearance rates, based on an equivalent approach to actuarial survival curves. Around 33% of the expected correct diagnosis codes were retrieved in this manner, after evaluating 998 discharge abstracts, significantly improving the coding task.
authors.affiliation: Institut Telecom; Telecom Bretagne, UEB; Dpt Image et Traitement de l'Information, Brest, France. Laurent.Lecornu@telecom-bretagne.eu
authors.forename: L
authors.initials: L
authors.lastname: Lecornu
authors.forename: C
authors.initials: C
authors.lastname: Le Guillou
authors.forename: F
authors.initials: F
authors.lastname: Le Saux
authors.forename: M
authors.initials: M
authors.lastname: Hubert
authors.forename: J
authors.initials: J
authors.lastname: Puentes
authors.forename: J M
authors.initials: JM
authors.lastname: Cauvin
mesh_terms: D000206:Actuarial Analysis / Q000379:methods*; D002908:Chronic Disease / Q000145:classification* / Q000401:mortality*; D059019:Clinical Coding / Q000379:methods* / Q000706:statistics & numerical data*; D005602:France / Q000453:epidemiology; D006801:Humans; D015995:Prevalence; D016019:Survival Analysis; D015996:Survival Rate