Abstract
Some countries have adopted the diagnosis-related groups (DRG) system to pay hospitals according to the number and complexity of patients they treat. Translating diseases and procedures into medical codes based on international standards such as ICD-9-CM or ICD-10-CM/PCS is at the core of the DRG systems. However, certain types of coding errors undermine this system, namely, upcoding, in which data is manipulated by deliberately using medical codes that increase patient’s complexity, resulting in higher reimbursements. In this sense, ensuring data credibility in the context of upcoding is critical for an effectively functioning DRG system. We developed a method to measure data credibility in the context of upcoding through a case study using data on pneumonia-related hospitalizations from six public hospitals in Portugal. Frequencies of codes representing pneumonia-related diagnosis and comorbidities were compared between hospitals and support vector machine models to predict DRGs were employed to verify whether codes with discrepant frequencies were related to upcoding. Data were considered not credible if codes with discrepant frequencies were responsible for increasing DRG complexity. Six pneumonia-related diagnoses and fifteen comorbidities presented a higher-than-expected frequency in at least one hospital and a link between increased DRG complexity, and these targeted codes was found. However, overall credibility was very high for nearly all conditions, except for renal disease, which presented the highest percentage of potential upcoding. The main contribution of this paper is a generic and reproducible method that can be employed to monitor data credibility in the context of upcoding in DRG databases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Administração Central do Sistema de Saúde (2019). Benchmarking hospitais - grupos e instituições. http://benchmarking.acss.min-saude.pt/BH_Enquadramento/GrupoInstituicoes, .
Administração Central do Sistema de Saúde (2014). Agrupador de GDH All Patient Refined DRG. http://www2.acss.min-saude.pt/Portals/0/CN22.pdf. .
Aelvoet, W., Terryn, N., Windey, F., Redivo, M., van Sprundel, M., & Faes, C. (2009). Miscoding: A threat to the hospital care system. How to detect it? Revue d’epidemiologie et de sante publique, 57(3), 169–177.
Aiello, F. A., & Roddy, S. P. (2017). Inpatient coding and the diagnosis-related groups. Journal of Vascular Surgery, 66(5), 1621–1623.
Alonso, V., Santos, J. V., Pinto, M., Ferreira, J., Lema, I., Lopes, F., & Freitas, A. (2019). Health records as the basis of clinical coding: Is the quality adequate? A qualitative study of medical coders' perceptions. Health Information Management Journal. https://doi.org/10.1177/1833358319826351.
Averill, R. F., McCullough, E. C., Goldfield, N. I., Hughes, J. S., Bonazelli, J., Bentley, L. (2013). 3M APR-DRG classification system methodology overview, version 31. 3M health information systems. https://www.hcup-us.ahrq.gov/db/nation/nis/grp031_aprdrg_meth_ovrview.pdf. .
Barros, P. P., & Braun, G. (2017). Upcoding in a national health service: The evidence from Portugal. Health Economics, 26(5), 600–618.
Carter, G. M., Newhouse, J. P., & Relles, D. A. (1990). How much change in the case mix in-dex is DRG creep? Journal of Health Economics, 9(4), 411–428.
Carter, G. M., Newhouse, J. P., & Relles, D. A. (1991). Has DRG creep crept up? Decomposing the case mix index change between 1987 and 1988. Santa Monica, California: RAND Corporation.
Centers for Medicare and Medicaid Services. (2014). International classification of diseases. Clinical Modification: Ninth Revision https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html. .
Centers for Medicare and Medicaid Services. (2019). International classification of diseases. Clinical Modification: Tenth Revision https://www.cms.gov/Medicare/Coding/ICD10/index.html. .
Chong, W. F., Ding, Y. Y., & Heng, B. H. (2011). A comparison of comorbidities obtained from hospital administrative data and medical charts in older patients with pneumonia. BMC Health Services Research, 11, 105.
Chu, A., Ahn, H., Halwan, B., Kalmin, B., Artifon, E. L., Barkun, A., Lagoudakis, M. G., & Kumar, A. (2008). A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artificial Intelligence in Medicine, 42(3), 247–259.
Dafny, L. S. (2005). How do hospitals respond to price changes? American Economic Review, 95(5), 1525–1547.
Di Giacomo, M., Piacenza, M., Siciliani, L., & Turati, G. (2017). Do public hospitals respond to changes in DRG price regulation? The case of birth deliveries in the Italian NHS. Health Economics, 26, 23–37.
Feder, S. L. (2018). Data quality in electronic health records research: Quality domains and assessment methods. Western Journal of Nursing Research, 40(5), 753–766.
Freitas A., Lema I., da Costa-Pereira A. (2016) Comorbidity coding trends in hospital administrative databases. In: Rocha Á., Correia a., Adeli H., Reis L., Mendonça Teixeira M. (eds), New Advances in Information Systems and Technologies. Advances in intelligent systems and computing, vol 445. Springer, Cham.
Goodpasture, H., Nguyen-Dang, C., Lee, T. H., Ghazarian, P. G., & Fulton, M. A. (2004). Miscoding as a cause of elevated simple pneumonia mortality. The Joint Commission Journal on Quality and Safety, 30(6), 335–341.
Hebert, P. L., McBean, A. M., & Kane, R. L. (2005). Explaining trends in hospitalizations for pneumonia and influenza in the elderly. Medical Care Research and Review, 62(5), 560–582.
Hsia, D. C. (1990). Accuracy of Medicare reimbursement for cardiac arrest. Journal of the American Medical Association, 264(1), 59–62.
Hsia, D. C., Ahern, C. A., Ritchie, B. P., Moscoe, L. M., & Krushat, W. M. (1992). Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988. Journal of the American Medical Association, 268(7), 896–899.
ISO/IEC 25012 (2006). ISO/IEC 25012: Software product quality – Data quality model. https://iso25000.com/index.php/en/iso-25000-standards/iso-25012.
Januleviciute, J., Askildsen, J. E., Kaarboe, O., Siciliani, L., & Sutton, M. (2016). How do hospitals respond to price changes? Evidence from Norway. Health Economics, 25(5), 620–636.
Jarman, B., Gault, S., Alves, B., Hider, A., Dolan, S., Cook, A., Hurwitz, B., & Iezzoni, L. I. (1999). Explaining differences in English hospital death rates using routinely collected data. British Medical Journal, 318(7197), 1515–1520.
Lau, E. C., Mowat, F. S., Kelsh, M. A., Legg, J. C., Engel-Nitz, N. M., Watson, H. N., Collins, H. L., Nordyke, R. J., & Whyte, J. L. (2011). Use of electronic medical records (EMR) for oncology outcomes research: Assessing the comparability of EMR information to patient registry and health claims data. Clinical Epidemiology, 3, 259–272.
Lungen, M., & Lauterbach, K. W. (2000). Upcoding: A risk for the use of diagnosis-related groups. Deutsche Medizinische Wochenschrift, 125(28-29), 852–856.
Luo, W., & Gallagher, M. (2010). Unsupervised DRG upcoding detection in healthcare databases. In 2010 IEEE International Conference on Data Mining Workshops, Sydney, NSW (pp. 600–605).
Mathauer, I., & Wittenbecher, F. (2013). Hospital payment systems based on diagnosis-related groups: Experiences in low- and middle-income countries. Bulletin of the World Health Organization, 91(10), 746–756.
Ministério da Saúde. (2017). Portaria n.o 207/2017 - Diário da República n.o 132/2017, série i de 2017-07-11. http://www.acss.min-saude.pt/wp-content/uploads/2016/12/Portaria_207_2017-1.pdf. .
Pimenta D., Souza J., Caballero I., Freitas A. (2019) Toward the measure of credibility of hospital administrative datasets in the context of DRG classification. In: Piattini M., Rupino da Cunha P., García Rodríguez de Guzmán I., Pérez-Castillo R. (eds) Quality of Information and Communications Technology. QUATIC 2019. Communications in Computer and Information Science, vol 1010. Springer, Cham.
Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization. https://pdfs.semanticscholar.org/d1fa/8485ad749d51e7470d801bc1931706597601.pdf. Accessed 22 October 2019.
Pongpirul, K., & Robinson, C. (2013). Hospital manipulations in the DRG system: A systematic scoping review. Asian Biomedicine, 7, 301–310.
Psaty, B. M., Boineau, R., Kuller, L. H., & Luepker, R. V. (1999). The potential costs of upcoding for heart failure in the United States. The American Journal of Cardiology, 84(108–9), A9.
Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005). Coding algorithms for defining comorbidities in icd-9-cm and icd-10 administrative data. Medical Care, 43(11), 1130–1139.
Rea, S., Bailey, K. R., Pathak, J., Haug, P. J. (2013). Bias in recording of body mass index data in the electronic health record. AMIA Joint Summits on Translational Science Proceedings. AMIA Summit on Translational Science, 2013:214-218.
Reid, B., Allen, C., & McIntosh, J. (2005). Investigation of leukaemia and lymphoma ar-drgs at a Sydney teaching hospital. Health Information Management, 34(2), 34–39.
Reid, B., Palmer, G., & Aisbett, C. (2000). Under-coding in Australia limits the performance of drg groupers. Health Information Management, 29(3), 113–117.
Scott, I., Youlden, D., & Coory, M. (2004). Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care? BMJ Quality & Safety, 13(1), 32–39.
Silverman, E., & Skinner, J. (2004). Medicare upcoding and hospital ownership. Journal of Health Economics, 23(2), 369–389.
Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd international conference on computing for sustainable global development (INDIACom), New Delhi (pp. 1310–1315).
Sjoding, M. W., Iwashyna, T. J., Dimick, J. B., & Cooke, C. R. (2015). Gaming hospital-level pneumonia 30-day mortality and readmission measures by legitimate changes to diagnostic coding. Critical Care Medicine, 43(5), 989–995.
Souza, J., Santos, J. V., Lopes, F., Viana, J., & Freitas, A. (2018). Miscoding alerts within hospital datasets: An unsupervised machine learning approach. In A. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Trends and advances in information systems and technologies, Advances in intelligent systems and computing, vol (Vol. 746, pp. 1198–1207). Cham p: Springer.
Spangler, W. E., May, J. H., Strum, D. P., & Vargas, L. G. (2002). A data mining approach to characterizing medical code usage patterns. Journal of Medical Systems, 26(3), 255–275.
Strong, D. M., Lee, Y. W., Wang, R. Y., Strong, D., Lee, Y. W., & Wang, R. (1997). 10 potholes in the road to information quality. IEEE Computer, 30, 38–46.
Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer-Verlag.
Verplancke, T., Van Looy, S., Benoit, D., Vansteelandt, S., Depuydt, P., De Turck, F., & Decruyenaere, J. (2008). Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Medical Informatics and Decision Making, 8, 56.
Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144–151.
Yates, D., Moore, D., & McCabe, G. (1999). The practice of statistics. New York: Freeman.
Acknowledgments
The authors would like to thank the Central Authority for Health Services, I.P. (ACSS) for providing access to the data. We would also like to thank to project GEMA: Generation and Evaluation of Models for Data Quality (Ref.: SBPLY/17/180501/000293) and the Master Program in Medical Informatics of the Faculty of Medicine and Faculty of Sciences of the University of Porto for financial support. Finally, we thank the project ECLIPSE (RTI2018–094283-B-C31), co-funded by the Spanish Ministry of Science, Innovation and Universities and Fundo Europeu de Desenvolvimento Regional (FEDER) funds.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Souza, J., Pimenta, D., Caballero, I. et al. Measuring data credibility and medical coding: a case study using a nationwide Portuguese inpatient database. Software Qual J 28, 1043–1061 (2020). https://doi.org/10.1007/s11219-020-09504-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11219-020-09504-3