IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v11y2019i3d10.1007_s12561-019-09257-z.html
   My bibliography  Save this article

Risk-Adjusted Control Charts: Theory, Methods, and Applications in Health

Author

Listed:
  • Athanasios Sachlas

    (University of Piraeus
    Athens University of Economics and Business)

  • Sotirios Bersimis

    (University of Piraeus)

  • Stelios Psarakis

    (Athens University of Economics and Business)

Abstract

Control charts, the most popular tool of statistical process control, appeared in the literature to ensure that an industrial process is operating only with natural variability, i.e., under statistical control. In the last decades, control charts have been also widely used to assess the quality of non-industrial processes, such as medicine and public health. Mainly in the last two decades, a modification of standard and advanced control charts appeared in the bibliography to improve the monitoring mainly of medical processes. This is the risk-adjusted control charts which take into consideration the varying health conditions of the patients. These charts are used to monitor certain medical processes such as surgeries, mortality, and doctors’ experience. In this paper, we have tried to present all the risk-adjusted control charts presented in the literature appropriately categorized. The risk-adjusted charts have been grouped into three categories: control charts for continuous variables, control charts for attributes (non-continuous variables), time-weighted control charts. The application of risk-adjusted control charts in practical medical processes is also discussed. This review paper highlights the value of the risk-adjusted control charts.

Suggested Citation

  • Athanasios Sachlas & Sotirios Bersimis & Stelios Psarakis, 2019. "Risk-Adjusted Control Charts: Theory, Methods, and Applications in Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 630-658, December.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09257-z
    DOI: 10.1007/s12561-019-09257-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-019-09257-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-019-09257-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ramezan Khosravi & Mohammad Saleh Owlia & Mohammad Saber Fallahnezhad & Amirhossein Amiri, 2018. "Phase I risk-adjusted control charts for surgical data with ordinal outcomes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(18), pages 4422-4432, September.
    2. Charalambos Sismanidis & Martin Bland & Jan Poloniecki, 2003. "Properties of the Cumulative Risk-Adjusted Mortality (CRAM) Chart, Including the Number of Deaths Before a Doubling of the Death Rate is Detected," Medical Decision Making, , vol. 23(3), pages 242-251, May.
    3. Xu Tang & Fah F. Gan & Lingyun Zhang, 2015. "Risk-Adjusted Cumulative Sum Charting Procedure Based on Multiresponses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 16-26, March.
    4. Willem Albers, 2011. "Risk-Adjusted Control Charts for Health Care Monitoring," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2011, pages 1-16, October.
    5. Li Zeng, 2016. "Risk-Adjusted Performance Monitoring in Healthcare Quality Control," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Quality and Reliability Management and Its Applications, pages 27-45, Springer.
    6. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
    7. Hassan Assareh & Kerrie Mengersen, 2012. "Change Point Estimation in Monitoring Survival Time," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    8. Matthew J. Keefe & Christopher T. Franck & William H. Woodall, 2017. "Monitoring foreclosure rates with a spatially risk-adjusted Bernoulli CUSUM chart for concurrent observations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 325-341, January.
    9. Sotirios Bersimis & Athanasios Sachlas & Ross Sparks, 2017. "Performance Monitoring and Competence Assessment in Health Services," Methodology and Computing in Applied Probability, Springer, vol. 19(4), pages 1169-1190, December.
    10. J Lovegrove & C Sherlaw-Johnson & O Valencia & T Treasure & S Gallivan, 1999. "Monitoring the performance of cardiac surgeons," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(7), pages 684-689, July.
    11. A. Gandy & J. T. Kvaløy & A. Bottle & F. Zhou, 2010. "Risk-adjusted monitoring of time to event," Biometrika, Biometrika Trust, vol. 97(2), pages 375-388.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    2. Molly C. Klanderman & Kathryn B. Newhart & Tzahi Y. Cath & Amanda S. Hering, 2020. "Fault isolation for a complex decentralized waste water treatment facility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 931-951, August.
    3. Rena Jie Sun & John D. Kalbfleisch, 2013. "A Risk-Adjusted O–E CUSUM with Monitoring Bands for Monitoring Medical Outcomes," Biometrics, The International Biometric Society, vol. 69(1), pages 62-69, March.
    4. Aamir Saghir & Zhengyan Lin, 2014. "Control chart for monitoring multivariate COM-Poisson attributes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 200-214, January.
    5. Lei Yong Lee & Michael Boon Chong Khoo & Sin Yin Teh & Ming Ha Lee, 2015. "A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    6. Öhman, Mikael & Finne, Max & Holmström, Jan, 2015. "Measuring service outcomes for adaptive preventive maintenance," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 457-467.
    7. Gandy, Axel, 2012. "Performance monitoring of credit portfolios using survival analysis," International Journal of Forecasting, Elsevier, vol. 28(1), pages 139-144.
    8. Axel Gandy & Jan Terje Kvaløy, 2013. "Guaranteed Conditional Performance of Control Charts via Bootstrap Methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 647-668, December.
    9. Wen-An Yang, 2016. "Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 845-874, August.
    10. Pedro Veiga & Luis Mendes & Luis Lourenço, 2016. "A retrospective view of statistical quality control research and identification of emerging trends: a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 673-692, March.
    11. J P Oddoye & M A Yaghoobi & M Tamiz & D F Jones & P Schmidt, 2007. "A multi-objective model to determine efficient resource levels in a medical assessment unit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1563-1573, December.
    12. Messaoud, Amor & Weihs, Claus & Hering, Franz, 2008. "Detection of chatter vibration in a drilling process using multivariate control charts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3208-3219, February.
    13. Jianbo Li & Jiancheng Jiang & Xuejun Jiang & Lin Liu, 2018. "Risk-adjusted monitoring of surgical performance," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.
    14. Epprecht, Eugenio K. & Aparisi, Francisco & Ruiz, Omar & Veiga, Álvaro, 2013. "Reducing sampling costs in multivariate SPC with a double-dimension T2 control chart," International Journal of Production Economics, Elsevier, vol. 144(1), pages 90-104.
    15. Wenjuan Liang & Xiaolong Pu & Dongdong Xiang, 2017. "A distribution-free multivariate CUSUM control chart using dynamic control limits," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2075-2093, August.
    16. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    17. Bersimis, Sotiris & Panaretos, John & Psarakis, Stelios, 2005. "Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry," MPRA Paper 6397, University Library of Munich, Germany.
    18. Nishimura, Kazuya & Matsuura, Shun & Suzuki, Hideo, 2015. "Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 7-13.
    19. Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
    20. Athanasios C. Rakitzis & Demetrios L. Antzoulakos, 2011. "Chi-square Control Charts with Runs Rules," Methodology and Computing in Applied Probability, Springer, vol. 13(4), pages 657-669, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09257-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.