Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-17T12:35:39.310Z Has data issue: false hasContentIssue false

Chapter 5 - Uncertainties and Their Impact on Healthcare Decisions

Published online by Cambridge University Press:  13 July 2023

Ramalingam Shanmugam
Affiliation:
Texas State University, San Marcos
Get access

Summary

First, let us examine the role of uncertainty in scientific inquiries in general and in healthcare decision-making in particular. Weurlander (2020) warns physicians to be careful to deal with uncertainty before making decisions to treat patients. Koffman et al. (2020) discuss reasons for involving uncertainty in healthcare, especially with respect to the COVID-19 pandemic. Uncertainty is not easily defined because of inadequate, incomplete, and ambiguous information.

Many occurrences in personal and professional life exhibit patterns of complete unpredictability – climate, disease outbreaks, financial volatility, natural disasters. Especially in healthcare, a specified outcome might be seen or missing. This vagueness is framed as uncertainty and raises fundamental challenges. Understanding how uncertainties appear is perhaps the beginning of solving this issue. Like an atom can be decomposed to its constituent parts of electrons, neutrons, and protons, the probability of uncertainty can be decomposed to its axioms. Refer to Camio et al. (2019) and Scoones (2019) for more discussion of how uncertainty is identified and illustrated.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Selected References

Agresti, A. (2003). Categorical Data Analysis (Vol. 482). Hoboken, NJ: Wiley.Google Scholar
Albatineh, A. N., Niewiadomska-Bugaj, M., & Mihalko, D. (2006). On similarity indices and correction for chance agreement. Journal of Classification, 23(2), 301313.Google Scholar
Alemi, F., & Gustafson, D. H. (2007). Decision Analysis for Healthcare Managers. Chicago, IL: Health Administration Press.Google Scholar
Anderson, O. D. (1988) How many Venn diagrams are there? International Journal of Mathematical Education in Science and Technology, 19(2), 299305.Google Scholar
Ben-Naim, A. (2008). Statistical Thermodynamics Based on Information: A Farewell to Entropy. Singapore: World Scientific Press.CrossRefGoogle Scholar
Camio, A. C., Esparza, L., & Mallor Giménez, F. (2019). The problem of the last bed: Contextualization and a new simulation framework for analyzing physician decisions. Omega, 96, 102120. https://doi.org/10.1016/j.omega.2019.102120.Google Scholar
Carnap, R. (1962). Logical Foundations of Probability. Chicago, IL: University of Chicago Press.Google Scholar
Christensen, D. (1999). Measuring confirmation. Journal of Philosophy, 96(9), 437461.Google Scholar
Congdon, P. (2005). Bayesian Models for Categorical Data. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Crupi, V., Festa, R., & Buttasi, C. (2009). Towards a grammar of Bayesian confirmation. In EPSA Epistemology and Methodology of Science, edited by Suárez, M., Dorato, M., & Rédei, M. (pp. 7393). Dordrecht: Springer.Google Scholar
Dimitrakakis, C., & Ortner, R. Decision Making under Uncertainty and Reinforcement Learning. www.cse.chalmers.se/~chrdimi/downloads/book.pdf.Google Scholar
Feuerman, M., & Miller, A. R. (2005). The kappa statistic as a function of sensitivity and specificity. International Journal of Mathematical Education in Science and Technology, 36(5), 517527.CrossRefGoogle Scholar
Fitelson, B. (1999). The plurality of Bayesian measures of confirmation and the problem of measure sensitivity. Philosophy of Science, 66, S362S378.Google Scholar
Fitelson, B. (2001). A Bayesian account of independent evidence with applications. Philosophy of Science, 68(S3), S123S140.CrossRefGoogle Scholar
Forthofer, R. N., & Lehnen, R. G. (1981). Rank correlation methods. In Public Program Analysis (pp. 146163). Boston, MA: Springer.CrossRefGoogle Scholar
Francom, F. , Chuang-Stein, C. , & Landis, J. R. (1989). A log-linear model for ordinal data to characterize differential change among treatments. Statistics in Medicine, 8(5), 571582.Google Scholar
Gökalp, E. (2017). “Modelling and Solving Healthcare Decision Making Problems under Uncertainty (Doctoral dissertation, University of Warwick).Google Scholar
Good, I. J. (1960). Weight of evidence, corroboration, explanatory power, information, and the utility of experiments. Journal of the Royal Statistical Society: Series B (Methodological), 22(2), 319331.Google Scholar
Good, I. J. (1983). Good Thinking: The Foundations of Probability and Its Applications. Minneapolis: University of Minnesota Press.Google Scholar
Jerak-Zuiderent, S. (2012). Certain uncertainties: Modes of patient safety in healthcare. Social Studies of Science, 42(5), 732752.Google Scholar
Joarder, A. H., & al-Sabah, W. S. (2002). The dependence structure of conditional probabilities in a contingency table. International Journal of Mathematical Education in Science and Technology, 33(3), 475480. https://doi.org/10.1080/002073902760047986.CrossRefGoogle Scholar
Kachapova, F., & Kachapov, I. (2012). Students’ misconceptions about random variables. International Journal of Mathematical Education in Science and Technology, 43(7), 963971.Google Scholar
Kemney, J. G., & Oppenheim, P. (1952). Degree of factual support. Philosophy of Science, 19(4), 307324.CrossRefGoogle Scholar
Koffman, J., Gross, J., Etkind, S. N., & Selman, L. (2020). Uncertainty and COVID-19: How are we to respond? Journal of the Royal Society of Medicine, 113(6), 211216.CrossRefGoogle ScholarPubMed
Lisk, D. R. (1993). Stroke risk factors in an African population: A report from Sierra Leone. Stroke, 24(1), 139141.CrossRefGoogle Scholar
Lu, C., & Zelterman, D. (2002). Statistical inference for familial disease clusters. Biometrics, 58, 481491.Google Scholar
Marukatat, R. (2009). On the selection of meaningful association rules. In Data Mining and Knowledge Discovery in Real Life Applications, edited by Ponce, J. & Karahoca, A. (pp. 7588). London: IntechOpen. https://doi.org/10.5772/6442.Google Scholar
Mendenhall, W. M., Million, R. R., Sharkey, D. E., & Cassisi, N. J. (1984). Stage T3 squamous cell carcinoma of the glottic larynx treated with surgery and/or radiation therapy. International Journal of Radiation Oncology Biology Physics, 10(3), 357363.Google Scholar
Milne, P. (1996). log [p (h/eb)/p (h/b)] is the one true measure of confirmation. Philosophy of Science, 63(1), 2126.Google Scholar
Mortimer, H. (1988). The Logic of Induction. Paramus, N. J., Prentice Hall.Google Scholar
Nozick, R. (1981). Philosophical Explanations. Cambridge, MA: Harvard University Press.Google Scholar
Rogers, W. A., & Walker, M. J. (2016). Fragility, uncertainty, and healthcare. Theoretical Medicine and Bioethics, 37(1), 7183.CrossRefGoogle ScholarPubMed
Schippers, M. (2014). Probabilistic measures of coherence: From adequacy constraints towards pluralism. Synthese, 191(16), 38213845.Google Scholar
Scoones, I. (2019). What is uncertainty and why does it matter? STEPS working paper 105. Brighton: ESRC STEPS Centre.Google Scholar
Shanmugam, R. (2008). Double anchored syllogisms for medical scenarios. Journal of Statistics and Applications, 3, 253277.Google Scholar
Shanmugam, R. (2010). A diagnostic methodology for hazy data with “borderline” cases. Journal of Medical Systems, 34(2), 161177.CrossRefGoogle ScholarPubMed
Shanmugam, R. (2012). Intervened 2-tier Poisson distribution for understanding hospital site infectivity. International Journal of Research in Nursing, 3(1), 814.Google Scholar
Shanmugam, R. (2013a). Alternate to traditional goodness of fit test with illustration using service duration to patients in hospitals. International Journal of Statistics and Economics, 11(2), 3143.Google Scholar
Shanmugam, R. (2013b). Odds to quicken reporting already delayed cases: acquired immune deficiency syndrome incidences are illustrated. International Journal of Research in Nursing, 4(1), 110.CrossRefGoogle Scholar
Shanmugam, R. (2015). Never, once, and repeated illness: A geometric view for insights and interpretations. International Journal of Research in Medical Sciences, 3(6), 13361346.Google Scholar
Shanmugam, R., & Radhakrishnan, R. (2011). Incidence jump rate reveals over/under dispersion in count data. International Journal of Data Analysis and Information Systems, 3(1), 18.Google Scholar
Shogenji, T. (1999). Is coherence truth conducive? Analysis, 59(4), 338345.CrossRefGoogle Scholar
Skogen, M. D., Ji, R. , Akimova, A. et al. (2021). Disclosing the truth: Are models better than observations? Marine Ecology Progress Series, 680, 713.Google Scholar
Van der Bles, A. M., Van der Linden, S., Freeman, A. L. et al. (2019). Communicating uncertainty about facts, numbers, and science. Royal Society of Open Science, 6(5), 181870.Google Scholar
Wang, M. Y., & Park, T. (2020). A brief tour of Bayesian sampling methods. Bayesian Inference on Complicated Data, 4(8). books.google.com.Google Scholar
Wermuth, N. (1976). 9th International Biometrics Conference (Vol. 1). Boston, MA: Biometric Society.Google Scholar
Weurlander, M. (2020). Becoming a physician involves learning to manage uncertainty and learning how to fail. Medical Education, 54(9), 776778.Google Scholar
WickwireJr., E. M., Burke, R. S., Brown, S. A., Parker, J. D., & May, R. K. (2008). Psychometric evaluation of the national opinion research center DSM-IV screen for gambling problems (NODS). American Journal on Addictions, 17(5), 392395.Google Scholar
Willows, R., Reynard, N., Meadowcroft, I., & Connell, R. (2003). Climate adaptation: Risk, uncertainty and decision-making. UKCIP Technical Report. UK Climate Impacts Programme.Google Scholar
Zhang, Y. Y. (2019). The Bayesian posterior estimators under six loss functions for unrestricted and restricted rarameter spaces. In Bayesian Inference on Complicated Data. London: Intech Open.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×