Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Original article

Vol. 148 No. 4950 (2018)

Diagnostic diversity – an indicator of institutional and regional healthcare quality

  • Martin Brutsche
  • Frank Rassouli
  • Harald Gallion
  • Sanjay Kalra
  • Veronique L Roger
  • Florent Baty
DOI
https://doi.org/10.4414/smw.2018.14691
Cite this as:
Swiss Med Wkly. 2018;148:w14691
Published
04.01.2019

Summary

AIM

Our aim was to estimate the diagnostic performance of institutions and healthcare regions from a nationwide hospitalisation database.

METHODS

The Shannon diversity index was used as an indicator of diagnostic performance based on the International Classification of Disease, 10th revision, German Modification (ICD-10-GM codes). The dataset included a total of 9,325,326 hospitalisation cases from 2009 to 2015 and was provided by the Swiss Federal Office for Statistics. A total of 16,435 diagnostic items from the ICD-10-GM codes were taken as the basis for the calculation of the diagnostic diversity index (DDI). Numerical simulations were performed to evaluate the effect of misdiagnoses in the DDI. We arbitrarily defined the minimum clinically important difference (MCID) as 10% misdiagnoses. The R statistical software was used for all analyses.

RESULTS

Diagnostic performance of institutions and healthcare regions as measured by the DDI were strongly associated with caseload and number of inhabitants, respectively. A caseload of >7217 hospitalisations per year for institutions and a population size >363,522 for healthcare regions were indicators of an acceptable diagnostic performance. Among hospitals, there was notable heterogeneity of diagnostic diversity, which was strongly associated with caseload. Application of misdiagnosis-thresholds within each ICD-10-GM category allowed classification of hospitals in four distinct groups: high-volume hospitals with an all-over comprehensive diagnostic performance; high- to mid-volume hospitals with extensive to relevant basic diagnostic performance in most categories; low-volume specialised hospitals with a high diagnostic performance in a single category; and low-volume hospitals with inadequate diagnostic performance in all categories. The diagnostic diversity observed in the 26 Swiss healthcare regions showed relevant heterogeneity, an association with ICD-10-GM code utilisation, and was strongly associated with the size of the healthcare region. The limited diagnostic performance in small healthcare regions was partially, but not fully, compensated for by consumption of health services outside of their own healthcare region.

CONCLUSION

Calculation of the DDI from ICD-10 codes is easy and complements the information derived from other quality indicators as it sheds a light on the fitness of the institutionalised interplay between primary and specialised medical inpatient care.

References

  1. WHO – Global Reference List of 100 Core Health Indicators. http://www.who.int/healthinfo/indicators/2015/en/; 2015.
  2. Ng N, Hakimi M, Santosa A, Byass P, Wilopo SA, Wall S. Is self-rated health an independent index for mortality among older people in Indonesia? PLoS One. 2012;7(4):e35308. doi:.https://doi.org/10.1371/journal.pone.0035308
  3. WHO - World Health Statistics. 2016. http://www.who.int/gho/publications/world_health_statistics/en/; 2016.
  4. Amato L, Colais P, Davoli M, Ferroni E, Fusco D, Minozzi S, et al. [Volume and health outcomes: evidence from systematic reviews and from evaluation of Italian hospital data]. Epidemiol Prev. 2013;37(2-3, Suppl 2):1–100. Article in Italian.
  5. Keygnaert IGA, Ivanova O. What is the Evidence on the Reduction of Inequalities in Accessibility and Quality of Maternal Health Care Delivery for Migrants? A Review of the Existing Evidence in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2016.
  6. Christianson JB, Shaw BW, Greene J, Scanlon DP. Reporting provider performance: what can be learned from the experience of multi-stakeholder community coalitions? Am J Manag Care. 2016;22(12, Suppl):s382–92.
  7. Shi Y, Scanlon DP, Kang R, McHugh M, Greene J, Christianson JB, et al. The longitudinal impact of Aligning Forces for Quality on measures of population health, quality and experience of care, and cost of care. Am J Manag Care. 2016;22(12, Suppl):s373–81.
  8. McNutt R, Johnson TJ, Odwazny R, Remmich Z, Skarupski K, Meurer S, et al. Change in MS-DRG assignment and hospital reimbursement as a result of Centers for Medicare & Medicaid changes in payment for hospital-acquired conditions: is it coding or quality? Qual Manag Health Care. 2010;19(1):17–24. doi:.https://doi.org/10.1097/QMH.0b013e3181ccbd07
  9. Ketelaar NA, Faber MJ, Flottorp S, Rygh LH, Deane KH, Eccles MP. Public release of performance data in changing the behaviour of healthcare consumers, professionals or organisations. Cochrane Database Syst Rev. 2011;(11):CD004538.
  10. Faber M, Bosch M, Wollersheim H, Leatherman S, Grol R. Public reporting in health care: how do consumers use quality-of-care information? A systematic review. Med Care. 2009;47(1):1–8. doi:.https://doi.org/10.1097/MLR.0b013e3181808bb5
  11. Totten AM, Wagner J, Tiwari A, O’Haire C, Griffin J, Walker M. Closing the quality gap: revisiting the state of the science (vol. 5: public reporting as a quality improvement strategy). Evid Rep Technol Assess (Full Rep). 2012;(208.5):1–645.
  12. Schuster F, Ostermann T, Emcke T, Schuster R. Analysis of Age and Gender Structures for ICD-10 Diagnoses in Outpatient Treatment Using Shannon’s Entropy. Stud Health Technol Inform. 2017;243:52–6.
  13. Bell AV. Diagnostic diversity: the role of social class in diagnostic experiences of infertility. Sociol Health Illn. 2014;36(4):516–30. doi:.https://doi.org/10.1111/1467-9566.12083
  14. Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013;369(5):448–57. doi:.https://doi.org/10.1056/NEJMra1201534
  15. Khullar D, Jena AB. Reducing prognostic errors: a new imperative in quality healthcare. BMJ. 2016;352:i1417. doi:.https://doi.org/10.1136/bmj.i1417
  16. O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005;40(5 Pt 2):1620–39. doi:.https://doi.org/10.1111/j.1475-6773.2005.00444.x
  17. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014;23(9):727–31. doi:.https://doi.org/10.1136/bmjqs-2013-002627
  18. Euro Health Consumer index 2015. http://www.healthpowerhouse.com/publications/euro-health-consumer-index-2015/; 2016.
  19. Baty F, Putora PM, Isenring B, Blum T, Brutsche M. Comorbidities and burden of COPD: a population based case-control study. PLoS One. 2013;8(5):e63285. doi:.https://doi.org/10.1371/journal.pone.0063285
  20. Pohle S, Baty F, Brutsche M. In-Hospital Disease Burden of Sarcoidosis in Switzerland from 2002 to 2012. PLoS One. 2016;11(3):e0151940. doi:.https://doi.org/10.1371/journal.pone.0151940
  21. ICD-10-GM. http://www.who.int/classifications/icd/en; 2017.
  22. Swiss-DRG Kodierleitfaden. 2017. https://www.bfs.admin.ch/bfs/de/home/statistiken/gesundheit/nomenklaturen/medkk/instrumente-medizinische-kodierung.assetdetail.1000136.html; 2017.
  23. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379–423, 623–56. doi:.https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  24. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available: https://www.R-project.org/
  25. Dray S, Dufour AB. The ade4 package: implementing the duality diagram for ecologists. J Stat Softw. 2007;22(4):1–20. doi:. https://doi.org/10.18637/jss.v022.i04
  26. Jari Oksanen F, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5-2. 2018; https://CRAN.R-project.org/package=vegan
  27. BFS-Spitalstatistik. 2015. https://www.admin.ch/gov/de/start/dokumentation/medienmitteilungen.msg-id-64676.html; 2015.
  28. BFS-Soziale Sicherheit. 2016. https://www.admin.ch/gov/de/start/dokumentation/medienmitteilungen.msg-id-64985.html; 2016.
  29. Hoffman GE, Schenker M, Wilke MH. Impact of laboratory testing on DRG coding and reimbursement--results of a data base research. Clin Lab. 2002;48(5-6):327–33.
  30. Bjertnaes OA, Sjetne IS, Iversen HH. Overall patient satisfaction with hospitals: effects of patient-reported experiences and fulfilment of expectations. BMJ Qual Saf. 2012;21(1):39–46. doi:.https://doi.org/10.1136/bmjqs-2011-000137

Most read articles by the same author(s)