Disagreement of ICD-10 Codes Between a Local Hospital Information System and a Cancer Registry

  • Published : 2015.02.04


Background: In the field of cancer, the ICD-10 coding convention is based on the site of a neoplasm in the body and usually ignores the morphology, thus the same code may be assigned to tumors of different morphologic types in an organ. Nowadays, all general (provincial) and center hospitals in Thailand are equipped with the hospital information system (HIS) database. Objective: This study aimed to find the characteristics and magnitude of agreement represented by the positive predictive value (PPV) of provisional cancer diagnoses in the HIS database in Pattani Hospital in Thailand in comparison with the final cancer diagnosis of the ICD-10 codes generated from a well established cancer registry in Songklanagarind Hospital, the medical school hospital of Prince of Songkla University. Materials and Methods: Data on cancer patients residing in Pattani province who visited Pattani Hospital from January 2007 to May 2011 were obtained from the HIS database. The ICD-10 codes of the HIS computer database of Pattani Hospital were compared against the ICD-10 codes of the same person recorded in the hospital-based cancer registry of Songklanagarind Hospital. The degree of agreement or positive predictive value (PPV) was calculated for each sex and for both sexes combined. Results: A total of 313 cases (15.9%) could be matched in the two databases. Some 222 cases, 109 males and 113 females, fulfilled the criteria of referral from Pattani to Songklanagarind Hospitals. Of 109 male cancer cases, 76 had the same ICD-10 codes in both hospitals, thus, the PPV was 69.7% (95%CI: 60.2-78.2%). Agreement in 76 out of 113 females gave a PPV of 67.3% (95%CI: 57.8-75.8%). The two percentages were found non-significant with Fisher's exact p-value of 0.773. The PPV for combined cases of both sexes was 68.5% (95%CI: 61.9-74.5%). Conclusions: Changes in final diagnosis in the referral system are common, thus the summary statistics of a hospital without full investigation facilities must be used with care, as the statistics are biased towards simple diseases able to be investigated by available facilities. A systematic feedback of patient information from a tertiary to a referring hospital should be considered to increase the accuracy of statistics and to improve the comprehensive care of cancer patients.


Supported by : National Science and Technology Development Agency


  1. Bangkok Medical Software. HOSxP. [accessed on 2014 October 1]. Available from:
  2. Fritz A, Percy C, Jack A, et al (2000). International Classification of Diseases for Oncology. 3rd ed., Geneva, Switzerland: World Health Organisation.
  3. George AM, Maddocks GB. (1997) Accuracy of diagnostic content of hospital activity analysis in infectious diseases. Br Med J, 1, 1332-4.
  4. Gibson N, Bridgman SA (1998). A novel method for the assessment of the accuracy of diagnostic codes in general surgery. Ann R Coll Surg Engl, 80, 293-6.
  5. Gologorsky Y, Knightly JJ, Lu Y, Chi JH, Groff MW (2014) Improving discharge data fidelity for use in large administrative databases. Neurosurg Focus, 36, 2.
  6. IARC (2013a). [Internet] CanReg5. [accessed on 2013 April 15]. Available from:
  7. IARC (2013b). [Internet] IARCcrgTools. [accessed on 2013 April 15]. Available from:
  8. Jensen OM, Parkin DM, McLennan R, Muir CS, Skeet RG (1991). Cancer registration: principles and methods. IARC scientific publications No. 95. Lyon, France: International agency for research on cancer.
  9. O'Malley KJ, Cook KF, Price MD, et al (2005). Measuring Diagnoses: ICD code accuracy. Health Serv Res, 40, 1620-39.
  10. R Core Team. (2013). A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  11. World Health Organisation (1992). ICD-10 Classifications of mental and behavioural disorder: clinical descriptions and diagnostic guidelines. geneva, Switzerland: World Health Organisation.