The Risk and Consequences of Clinical Miscoding Due to Inadequate Medical Documentation: A Case Study of the Impact on Health Services Funding

Abstract

As coded clinical data are used in a variety of areas (e.g. health services funding, epidemiology, health sciences research), coding errors have the potential to produce far-reaching consequences. In this study the causes and consequences of miscoding were reviewed. In particular, the impact of miscoding due to inadequate medical documentation on hospital funding was examined. Appropriate reimbursement of hospital revenue in the casemix-based (output-based) funding system in the state of Victoria, Australia relies upon accurate, comprehensive, and timely clinical coding. In order to assess the reliability of these data in a Melbourne tertiary hospital, this study aimed to: (a) measure discrepancies in clinical code assignment; (b) identify resultant Diagnosis Related Group (DRG) changes; (c) identify revenue shifts associated with the DRG changes; (d) identify the underlying causes of coding error and DRG change; and (e) recommend strategies to address the aforementioned. An internal audit was conducted on 752 surgical inpatient discharges from the hospital within a six-month period. In a blind audit, each episode was re-coded. Comparisons were made between the original codes and the auditor-assigned codes, and coding errors were grouped and statistically analysed by categories. Changes in DRGs and weighted inlier-equivalent separations (WIES) were compared and analysed, and underlying factors were identified. Approximately 16% of the 752 cases audited reflected a DRG change, equating to a significant revenue increase of nearly AU$575,300. Fifty-six percent of DRG change cases were due to documentation issues. Incorrect selection or coding of the principal diagnosis accounted for a further 13% of the DRG changes, and missing additional diagnosis codes for 29%. The most significant of the factors underlying coding error and DRG change was poor quality of documentation. It was concluded that the auditing process plays a critical role in the identification of causes of coding inaccuracy and, thence, in the improvement of coding accuracy in routine disease and procedure classification and in securing proper financial reimbursement.

Variation in Prosthetic Joint Infection and treatment strategies during 4.5 years of follow-up after primary joint arthroplasty using administrative data of 41397 patients across Australian, European and United States hospitals

“…In the present study the average number of reported comorbidities in patients was clearly higher for US patients, most likely due to the financial incentives associated with coding [16], which may result in PJI rates in the US being underestimated after adjustment for these reported comorbidities. However, reimbursement of health services in Australia also depends on clinical coding [17], but did not result in a higher number of reported comorbidities, so that it does not seem likely that this will explain the results entirely. Another issue is the ability to correctly identify PJI from administrative data.…”

A retrospective cohort study assessing patient characteristics and the incidence of cardiovascular disease using linked routine primary and secondary care data

“…Studies have observed that variations in diagnostic criteria can affect estimates of disease prevalence,2 and the complexities of clinical coding systems for electronic healthcare records can lead to inconsistent data recording 3. This will lead to uncertainties with respect to disease prevalence and mortality,4 impact on clinical care, have additional health service implications such as affecting funding5 and potentially influence identification of patients for clinical trials. Previous studies have compared general practice coding and disease prevalence with other unlinked data sources, including paper notes 6 7.…”

An overview of electronic health information management systems quality assessment

“…-clinical data, electronic health records and clinical coding ( [12,23,33,37,44,45,50,52,58]) and definition of data and information quality in eHealth ( [9,20,24,36,53]) -the impact of data quality and information processing on medical records ( [3,4,18,22,26,27,28,31,35,42,48,55,57]) -socio-technical aspects, including clinicians perspectives on EHRs and eHealth ( [13,17,41,55]) and patients' experiences and perspectives on eHealth and medical encounters with ICT ( [2,15,38]) -the study of quality of care, health outcomes and the studies of impact of ICT on health organisations ( [5,10,11,21,25,29,30,32])…”

“…Data stored in Electronic Health Records (EHRs) can be used for a wide range of purposes: patient care, administrative management, billing, services and resources management and planning, quality of care and financial audits, quality improvement programmes, as well as public health and medical research and reporting. Consequently, poor quality electronic health data can have a wide range of negative impacts, potentially affecting any of the following: standards of care, patient outcomes, audits, public health data and the current medical evidence [12].…”