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Protocol for Analysis of Root Causes of Problems Affecting the Quality of the Diagnosis Related Group-Based Hospital Data: A Rapid Review and Delphi Process

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Trends and Innovations in Information Systems and Technologies (WorldCIST 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1159))

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Abstract

The Diagnosis Related Group-based administrative hospital database is an important tool for hospital financing in several health systems. It is also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of these databases is of utmost importance for the exploitation of the data. To prevent or to solve these problems, it is paramount to identify the root causes of the lack of data quality. In order to do this process easier, data quality analysts could benefit from having a catalog of potential root causes to explore. Unfortunately, literature has not covered this concern extensively, what motivated us to research. This paper presents a protocol for the analysis of root causes that may affect the quality of these data. The proposed protocol includes two stages: (1) a systematic review to extract root causes from the scientific literature, and (2) a Delphi technique to analyze the relevance of root causes and to map them into data quality dimensions.

Moreover, we provide a pilot study (rapid review) based on a systematic review covering papers published during 2018 and 2019. This aimed to establish proof of concept of the proposed methodology and to examine its feasibility. Eighteen studies were included for data charting, rendering 70 root causes. The Ishikawa framework provided a meaningful way to represent the root causes. Most of these causes were associated with professional knowledge, education and processes performed in the data collection, analysis or treatment.

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References

  1. Busse, R., Geissler, A., Aaviksoo, A., Cots, F., Häkkinen, U., Kobel, C., Mateus, C., Or, Z., O’Reilly, J., Serdén, L.: Diagnosis related groups in Europe: moving towards transparency, efficiency, and quality in hospitals? BMJ 346, f3197 (2013)

    Article  Google Scholar 

  2. Alonso, V., Santos, J.V., Pinto, M., Ferreira, J., Lema, I., Lopes, F., Freitas, A.: Health records as the basis of clinical coding: is the quality adequate? A qualitative study of medical coders’ perceptions. Health Inf. Manag. J. 49(1), 28–37 (2019). 1833358319826351

    Google Scholar 

  3. Rabia, L., Amarouche, I.A., Bey, K.B.: Rule-based approach for detecting dirty data in discharge summaries. In: 2018 International Symposium on Programming and Systems (ISPS): 2018, pp. 1–6. IEEE (2018)

    Google Scholar 

  4. Alonso, V., Santos, J.V., Pinto, M., Ferreira, J., Lema, I., Lopes, F., Freitas, A.: Problems and barriers in the transition to ICD-10-CM/PCS: a qualitative study of medical coders’ perceptions. In: World Conference on Information Systems and Technologies, 2019, pp. 72–82. Springer, Cham (2019)

    Google Scholar 

  5. Miguel, A., Marques, B., Freitas, A., Lopes, F., Azevedo, L., Pereira, A.C.: Detection of adverse drug reactions using hospital databases—a nationwide study in Portugal. Pharmacoepidemiol. Drug Saf. 22(8), 907–913 (2013)

    Article  Google Scholar 

  6. Santos, J.V., Lisboa, C., Lanna, C., Costa-Pereira, A., Freitas, A.: Hospitalisations with hidradenitis suppurativa: an increasing problem that deserves closer attention. Dermatology 232(5), 613–618 (2016)

    Article  Google Scholar 

  7. Santos, J.V., Pereira, J., Pinto, R., Castro, P.M., Azevedo, E., Freitas, A.: Atrial fibrillation as an ischemic stroke clinical and economic burden modifier: a 15-year nationwide study. Value Health 20(8), 1083–1091 (2017)

    Article  Google Scholar 

  8. Freitas, A., Gaspar, J., Rocha, N., Marreiros, G., Costa-Pereira, A.: Quality in hospital administrative databases. Appl. Math. Inf. Sci. 8, 1–6 (2014)

    Article  Google Scholar 

  9. Lobo, M.F., Azzone, V., Azevedo, L.F., Melica, B., Freitas, A., Bacelar-Nicolau, L., Rocha-Gonçalves, F.N., Nisa, C., Teixeira-Pinto, A., Pereira-Miguel, J.: A comparison of in-hospital acute myocardial infarction management between Portugal and the United States: 2000–2010. Int. J. Qual. Health Care 29(5), 669–678 (2017)

    Article  Google Scholar 

  10. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

  11. Souza, J., Santos, J.V., Canedo, V.B., Betanzos, A., Alves, D., Freitas, A.: Importance of coding co-morbidities for APR-DRG assignment: focus on cardiovascular and respiratory diseases. Health Inf. Manag. J. (2019). 1833358319840575

    Google Scholar 

  12. Arendt, L.H., Ernst, A., Lindhard, M.S., Jønsson, A.A., Henriksen, T.B., Olsen, J., Thorup, J., Olsen, L.H., Ramlau-Hansen, C.H.: Accuracy of the hypospadias diagnoses and surgical treatment registrations in the Danish National Patient Register. Clin. Epidemiol. 9, 483 (2017)

    Article  Google Scholar 

  13. Assareh, H., Achat, H.M., Levesque, J.-F.: Accuracy of inter-hospital transfer information in Australian hospital administrative databases. Health Informat. J. 25(3), 960–972 (2017). 1460458217730866

    Article  Google Scholar 

  14. Gaspar, J., Lopes, F., Freitas, A.: Detection of Inconsistencies in Hospital Data Coding. In: HEALTHINF: 2012, 189–194 (2012)

    Google Scholar 

  15. Marques, B., Sousa-Pinto, B., Silva-Costa, T., Lopes, F., Freitas, A.: Detection of adverse events through hospital administrative data. In: World Conference on Information Systems and Technologies: 2017, pp. 825–834. Springer, Cham (2017)

    Google Scholar 

  16. Strong, D.M., Lee, Y.W., Wang, R.Y.: 10 potholes in the road to information quality. Computer 30(8), 38–46 (1997)

    Article  Google Scholar 

  17. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A.: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 4, 1 (2015)

    Article  Google Scholar 

  18. Aiello, F.A., Judelson, D.R., Durgin, J.M., Doucet, D.R., Simons, J.P., Durocher, D.M., Flahive, J.M., Schanzer, A.: A physician-led initiative to improve clinical documentation results in improved health care documentation, case mix index, and increased contribution margin. J. Vasc. Surg. 68(5), 1524–1532 (2018)

    Article  Google Scholar 

  19. Bastani, H., Goh, J., Bayati, M.: Evidence of upcoding in pay-for-performance programs. Manag. Sci. 65(3), 1042–1060 (2018)

    Article  Google Scholar 

  20. Carter, G.M., Newhouse, J.P., Relles, D.A.: How much change in the case mix index is DRG creep? J. Health Economics 9(4), 411–428 (1990)

    Article  Google Scholar 

  21. Rayan QCRI. https://rayyan.qcri.org/welcome

  22. McMillan, S.S., King, M., Tully, M.P.: How to use the nominal group and Delphi techniques. Int. J. Clin. Pharm. 38(3), 655–662 (2016)

    Google Scholar 

  23. Boulkedid, R., Abdoul, H., Loustau, M., Sibony, O., Alberti, C.: Using and reporting the Delphi method for selecting healthcare quality indicators: a systematic review. PLoS ONE 6(6), e20476 (2011)

    Article  Google Scholar 

  24. Ayub, S., Scali, S.T., Richter, J., Huber, T.S., Beck, A.W., Fatima, J., Berceli, S.A., Upchurch, G.R., Arnaoutakis, D., Back, M.R.: Financial implications of coding inaccuracies in patients undergoing elective endovascular abdominal aortic aneurysm repair. J. Vasc. Surg. 69(1), 210–218 (2019)

    Article  Google Scholar 

  25. Cohen, S., Jannot, A.-S., Iserin, L., Bonnet, D., Burgun, A., Escudié, J.-B.: Accuracy of claim data in the identification and classification of adults with congenital heart diseases in electronic medical records. Arch. Cardiovasc. Dis. 112(1), 31–43 (2019)

    Article  Google Scholar 

  26. Doktorchik, C., Lu, M., Quan, H., Ringham, C., Eastwood, C.: A qualitative evaluation of clinically coded data quality from health information manager perspectives. Health Inf. Manag. J. (2019), 1833358319855031

    Google Scholar 

  27. Fermaut, M., Fauconnier, A., Brossard, A., Razafimamonjy, J., Fritel, X., Serfaty, A.: Detection of complicated ectopic pregnancies in the hospital discharge database: a validation study. PLoS ONE 14(6), e0217674 (2019)

    Article  Google Scholar 

  28. Hay, P., Wilton, K., Barker, J., Mortley, J., Cumerlato, M.: The importance of clinical documentation improvement for Australian hospitals. Health Inf. Manag. J. 49(1), 69–73 (2019). 1833358319854185

    Google Scholar 

  29. Howard, R., Reddy, R.M.: Coding discrepancies between medical student and physician documentation. J. Surg. Educ. 75(5), 1230–1235 (2018)

    Article  Google Scholar 

  30. Joos, C., Lawrence, K., Jones, A.E., Johnson, S.A., Witt, D.M.: Accuracy of ICD-10 codes for identifying hospitalizations for acute anticoagulation therapy-related bleeding events. Thromb. Res. 181, 71–76 (2019)

    Article  Google Scholar 

  31. Kessler, B.A., Catalino, M.P., Jordan, J.D.: Reducing the reported mortality index within a neurocritical care unit through documentation and coding accuracy. World Neurosurg. 133, e819–e827 (2019)

    Article  Google Scholar 

  32. Khan, A., Ramsey, K., Ballard, C., Armstrong, E., Burchill, L.J., Menashe, V., Pantely, G., Broberg, C.S.: Limited accuracy of administrative data for the identification and classification of adult congenital heart disease. J. Am. Heart Assoc. 7(2), e007378 (2018)

    Article  Google Scholar 

  33. Mercier, F., Laplace, N., Mitmaker, E.J., Colin, C., Kraimps, J.-L., Sebag, F., Bourdy, S., Duclos, A., Lifante, J.-C., CATHY Study Group: Unexpected discrepancies in hospital administrative databases can impact the accuracy of monitoring thyroid surgery outcomes in France. PLoS ONE 13(12), e0208416 (2018)

    Google Scholar 

  34. Resslar, M.A., Ivanitskaya, L.V., Perez III, M.A., Zikos, D.: Sources of variability in hospital administrative data: clinical coding of postoperative ileus. Health Inf. Manag. J. 48(2), 101–108 (2019)

    Google Scholar 

  35. Rodenberg, H., Shay, L., Sheffield, K., Dange, Y.: The Expanding Role of Clinical Documentation Improvement Programs in Research and Analytics. Perspect. Health Inf. Manag. 16(Winter) (2019)

    Google Scholar 

  36. Tsopra, R., Peckham, D., Beirne, P., Rodger, K., Callister, M., White, H., Jais, J.-P., Ghosh, D., Whitaker, P., Clifton, I.J.: The impact of three discharge coding methods on the accuracy of diagnostic coding and hospital reimbursement for inpatient medical care. Int. J. Med. Informat. 115, 35–42 (2018)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the support given by the Project “POCI-01-0145-FEDER-030766” (1st.IndiQare - Quality indicators in primary health care: validation and implementation of quality indicators as an assessment and comparison tool), funded by Fundação para a Ciência e a Tecnologia (FCT) and co-funded by Fundo de Desenvolvimento Regional (FEDER) through Operacional Competitividade e Internacionalização (COMPETE 2020); and the Project GEMA: Generation and Evaluation of Models for Data Quality (Ref.: SBPLY/17/180501/000293).

Author information

Authors and Affiliations

  1. Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal

    M. F. Lobo, M. Oliveira, A. R. Oliveira, J. V. Santos, F. Lopes, A. Ramalho, J. Souza, J. Viana & A. Freitas

  2. Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal

    M. F. Lobo, J. V. Santos, V. Alonso, F. Lopes, A. Ramalho, J. Souza, J. Viana & A. Freitas

  3. Public Health Unit, AceS Grande Porto VIII – Espinho/Gaia, ARS Norte, Porto, Portugal

    J. V. Santos

  4. Institute of Information Systems and Technologies (ITSI), University of Castilla-La Mancha, Ciudad Real, Castilla-La Mancha, Ciudad Real, Spain

    I. Caballero

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  1. M. F. Lobo
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  2. M. Oliveira
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  3. A. R. Oliveira
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  4. J. V. Santos
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  5. V. Alonso
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  6. F. Lopes
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  7. A. Ramalho
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  8. J. Souza
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  9. J. Viana
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  10. I. Caballero
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  11. A. Freitas
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Corresponding author

Correspondence to M. F. Lobo .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. FEUP, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata, Italy

    Sandra Costanzo

  5. Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro

    Irena Orovic

  6. Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Lobo, M.F. et al. (2020). Protocol for Analysis of Root Causes of Problems Affecting the Quality of the Diagnosis Related Group-Based Hospital Data: A Rapid Review and Delphi Process. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_10

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