Literature DB >> 9760096

Automated coding of injuries from autopsy reports.

L Riddick1, W B Long, W S Copes, D M Dove, W J Sacco.   

Abstract

Medical examiners have a unique database about trauma victims, many, if not most, of whom died at the scene or in transit to a hospital and who, thus, never had their injuries documented by trauma surgeons and so never entered into a local or regional trauma registry. These trauma registries have assisted in assessing the magnitude of traumatic injuries in the community and in evaluating the community's emergency medical systems. Without information about those who are dead at the scene or who die in transit, these trauma registries are incomplete and the evaluations based on them inaccurate. The data about the 50% of trauma victims who never enter the medical system are lacking in these registries. Such information is present in the death investigation and autopsy reports in the various medical examiner/coroner offices in the country. To access this important information more easily in trauma registries, an expert computer system was developed. This pilot study presents the results of using that system to gather medical examiner data. Injury descriptions were abstracted from autopsy reports of 50 consecutive nonhospitalized persons fatally injured in Mobile County, Alabama and its environs. Injury descriptions for all cases were successfully coded in International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) and the Abbreviated Injury Scale (AIS-90) by an expert system. For some cases the expert system "requested" and received clarifying information, all of which was present in the medical records. This research demonstrates the feasibility of gathering accurate and consistent information on the estimated 50% of trauma deaths who do not reach a hospital and who are not included in acute care registries. Without data on such patients, our evaluation of trauma systems is incomplete and resources directed at prevention and treatment may be misapplied.

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Year:  1998        PMID: 9760096     DOI: 10.1097/00000433-199809000-00015

Source DB:  PubMed          Journal:  Am J Forensic Med Pathol        ISSN: 0195-7910            Impact factor:   0.921


  2 in total

1.  Trauma patients without a trauma diagnosis: the data gap at a level one trauma center.

Authors:  James M Whedon; Gwen Fulton; Charles H Herr; Friedrich M von Recklinghausen
Journal:  J Trauma       Date:  2009-10

2.  Comparison and interpretability of machine learning models to predict severity of chest injury.

Authors:  Sujay Kulshrestha; Dmitriy Dligach; Cara Joyce; Richard Gonzalez; Ann P O'Rourke; Joshua M Glazer; Anne Stey; Jacqueline M Kruser; Matthew M Churpek; Majid Afshar
Journal:  JAMIA Open       Date:  2021-03-01
  2 in total

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