Automating Treatment Summary Development Using Electronic Billing Information: A Pilot Study of Survivors of Head and Neck Cancer Academic Article Article uri icon

Overview

MeSH Major

  • Clinical Competence
  • Prostatectomy
  • Prostatic Neoplasms

abstract

  • PURPOSE:: Although the provision of a treatment summary (TS) is a quality indicator in oncology, routine delivery of TSs remains challenging. Automatic TS generation could facilitate use, but data on accuracy are lacking in complex cancers such as head and neck cancer (HNC). We developed and evaluated an electronic platform to automate TS generation for HNC. METHODS:: The algorithms autopopulated TSs using data from billing records and an institutional cancer registry. A nurse practitioner used the medical record to verify the accuracy of the information and made corrections electronically. Inaccurate and missing data were considered errors. We described and investigated reasons for errors in the automatically generated TSs. RESULTS:: We enrolled a heterogeneous population of 43 survivors of HNC. Using billing data, the information on primary site, lymph node status, radiation, and chemotherapy use was accurate in 93%, 95%, 93%, and 95% of patients, respectively. Billing data captured surgery accurately in 77% of patients; once an omitted billing code was identified, accuracy increased to 98%. Chemotherapies were captured in 90% of patients. Using the cancer registry, month and year of diagnosis were accurate in 91% of cases; stage was accurate in 28% of cases. Reprogramming the algorithm to ascertain clinical stage when pathologic stage was unavailable resulted in 100% accuracy. The algorithms inconsistently identified radiation receipt and treating physicians from billing data. CONCLUSION:: It is feasible to automatically and accurately generate most components of TSs for HNC using billing and cancer registry data, although clinical review is necessary in some cases.

publication date

  • January 2019

Research

keywords

  • Academic Article

Identity

Digital Object Identifier (DOI)

  • 10.1200/JOP.18.00022

PubMed ID

  • 30523752

Additional Document Info

start page

  • e84

end page

  • e90

volume

  • 15

number

  • 1