Miscoding: a threat to the hospital care system: how to detect it?
Faculty of Medicine and Health Sciences
Revue d'épidémiologie et de santé publique. - Paris
, p. 169-177
University of Antwerp
Background Artificially influencing the case mix of hospitals may have several deleterious consequences for the hospital care system. One distinguishes over-evaluation (up-coding) and under-evaluation (under-coding) of the case mix. Apart from its financial consequences, miscoding may cause a fracture in epidemiological time series and, by increasing artificially the severity of illness, may affect the assessment of the quality of hospital care, based on administrative data. Methods Fixed effects models were used to assess deviant coding behavior at the hospital level. To do so, we examined the linear evolution over time of characteristics such as length of stay and of 21 triggering conditions susceptible to increase the case mix of a stay. In case of deviant coding, these triggering conditions were checked to direct the audit towards fraud-suspected discharge abstracts. Hereto, a method consisting in comparing a single hospital's linear evolution over time with the national linear evolution over time was developed, using an interaction term between linear evolution over time and hospitals. To test this methodology, fraud-directed audits were carried out in addition to the usual, at random audits. Results Important inter-hospital differences in the linear evolution over time of several characteristics of Belgian hospitals were identified, as well as evidence not only of improving coding practices, but also of up-coding, fraudulent under-coding and of numerous coding errors without financial impact. The coding errors, ascertained in the at random audit, resulted in a wrongful gain for the faulty hospitals of 28.23 days in 258 stays, whereas in case of fraud-directed audits these figures amounted up to 642.68 days in 334 stays. Conclusion Fraud-directed audit may constitute a valuable tool in the quality assurance of administrative databases, improving their use in epidemiology and assessment of the quality of care.