[Undercoding more common than overcoding].

BACKGROUND: As a result of information about coding practice in a regional hospital, the Norwegian Board of Health carried out a special supervision of ear, nose and throat (ENT) departments and neurological departments during the autumn of 2003 and the spring of 2004. The aim was to investigate whether there was accordance between information in the patient records and the chosen diagnoses and codes. MATERIAL AND METHOD: Three hospitals were examined. Information in 100 selected patient records from each department was compared with diagnoses and codes. The codes reported to the Norwegian Patient Register (NPR) from two hospitals were compared with codes in the patient records. RESULTS: Incorrect diagnoses had been systematically recorded in one ENT department. Incorrect codes had been recorded in all the departments, especially the neurological departments. Many inconsistencies between codes in the patient records and those reported to NPR were found. INTERPRETATION: Wrong diagnoses were seldom made, but many diagnoses were unprecise because only ICD-10 diagnoses were used. The rules of coding hospital stays were frequently not used. Many of the codes reported to NPR had been altered, without medical basis and without being noted in the patients' records.

Diagnosis-Related Groups↗

PubMed2005

Linked analysis for definition of nurse advice line syndrome groups, and comparison to encounters.

INTRODUCTION: Nurse advice call centers are a potentially important source of data for syndromic surveillance purposes. For this reason, researchers at Johns Hopkins University Applied Physics Laboratory and Kaiser Permanente of the Mid-Atlantic States (KPMAS) have been collaborating to develop methods to use this data within the ESSENCE II Syndromic Surveillance System in the National Capital Region. OBJECTIVE: The objective of this report is to present a general method for finding syndrome groups in data sources that can be linked to physician encounters and to determine effective advice call syndrome groups for use with KPMAS advice data. METHODS: Advice calls are linked to physician encounters and stratified by patient age. They are placed in groups according to a maximum positive predictive value criterion. The groups are evaluated by correlating the resulting syndrome time series against physician encounter data. RESULTS: Potentially useful advice syndrome groups are found for respiratory, lower gastrointestinal (GI), and total GI syndromes for each age stratum. CONCLUSION: The time series of the advice data for respiratory, lower GI, and upper GI syndromes accurately predict the physician encounter time series for the corresponding syndromes for each age stratum.

Diagnosis-Related Groups↗