Abstract
The purpose of this article is to prompt quality-of-life researchers to periodically ask themselves why they are doing what they are doing. I will give you my answer to this question, but also briefly review the historic events that lead to the development of the field. Examining this history should teach each of us that the field evolved from the application of a operations research perspective to the medical care system. As such, more attention should be given to the quality control potential of quality-of-life assessments. I suggest two approaches to this quality control task; the prevention of adverse events and the enhancement of the qualitative status of ill persons.
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Notes
An example of a preventative practice that is not widespread and would have a qualitative consequence and is assessing quality-of-life during a Phase I clinical trial.
Physicians engage in many activities that are designed to ensure quality control. Medical case conferences, in which specific cases are discussed, would be one example of a quality control effort. Another example is the procedure a Department of Surgery established to prevent “wrong site, wrong procedure, and wrong patient outcomes” [9]. Quality control activities, however, does not ensure that the patient has an optimal qualitative outcome. Thus, when I discuss quality control there are multiple levels of application.
Inspection of the history of operations research reveals a linage that can cite significant intellectual contributions from gambling, probability theory, and decision making. Thus, Gass and Assad’s [10] first reference is to Cardano (1501–1576; a physician, mathematician and gambler), but they also cite Bernoulli (1700–1782), Bayes (1702–1761), Bentham (1748–1832), Frederick Taylor (1856–1915), Pareto (1848–1923), and a host of others. These investigators developed the mathematical models that were applied to various decision-making settings, and these approaches were also common to economics analyses, especially those concerned with the efficient distribution and utilization of resources [11]. Thus, operations research has a strong connection to economics and decision analyses.
When operations research was applied to human welfare, the judgment of how an optimal outcome occurred became an issue, and the principles developed to ensure such an outcome became an issue. These and related issues are discussed more extensively in a large number of available texts on operations research [12, 13].
Paul Kind (Personal communication; October, 2005) has indicated that Rachel Rosser’s husband was an operations researcher and he felt that his background influenced the model that was developed.
An example of an application of operations research intervention that had obvious implications for patient quality-of-life [14] involved a drug (Alimta) that was being developed by the drug manufacturer Eli Lilly. Early trials (Phase I) of the anti-folate drug showed encouraging anti-tumor effects, but also that severe toxic side effects occurred. Multivariate regression analysis techniques commonly used by operations research helped determine that lowered foliate and vitamin B12 levels most likely caused the observed toxicities. Providing patients with appropriate nutritional supplements prevented the onset of these adverse effects and allowed the continued development of the drug. The drug is now approved by the FDA for treating lung cancer.
Abbreviations
- EQ-5D:
-
EuroQol-5 Dimensions
- HRQOL:
-
Health-related Quality-of-Life
References
Barofsky, I., & Sugarbaker, P. H. (1990). The cancer patient. In: Spilker B (ed) Quality of life assessment in clinical studies (pp. 419–439). New York NY: Raven.
Schlessinger, L., & Eddy, D. M. (2002). Archimedes: A new model of simulating health care systems—the mathematical formulation. Journal of Biomedical Informatics, 35, 37–50.
Fanshel, S., & Bush, J. W. (1970). A Health Status Index and its application to the health services outcomes. Operations Research, 18, 1021–1066.
Kaplan, R., & Anderson, J. P. (1990). The general health policy model: An integrated approach. In: Spilker B (ed). Quality of life assessments in clinical trials. New York NY: Ravens.
Torrance, G. W. (1976). Health status index models: A unified mathematical view. Management Science, 22, 990–1001.
Rosser, R., & Kind, P. (1978). A scale of valuations of states of illness: Is there a social consensus? International Journal of Epidemiology, 7, 347–358.
Heinemann, A. W., Fisher, W. P., Jr, & Gershon, R. (2006). Improving health care quality with outcomes management. Journal of prosthetics and orthotics, 18, 46–50.
Barofsky, I. (in press). Quality: Its definition and assessment as applied to the medically ill.
Michaels, R. K., Makary, M. A., Dahab, Y., Frassica, F. J., et al. (2007). Achieving the quality Forum’s “Never Events:” Prevention of wrong site, wrong procedure, and wrong patient operation. Annals of Surgery, 245, 526–532.
Gass, S. I., & Assad, A. A. (2005). An annotated timeline of operations research: An informal history. New York: Kluwer.
Baumol, W. J. (1965). Economic theory and operations analysis. Englewood Cliffs NJ: Prentice-Hall.
Montgomery, D. C. (2009). Introduction to statistical quality control. Hoboken NJ: Wiley.
Nahmias, S. (2008). Production and operations analysis. New York NY: McGraw-Hill Irwin.
Bio-medicine News. (2009). Cited on the web at: http://news.bio-medicine.org/medicine-news-3/Six-organizations. On 2/3/2009.
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Sections of this paper are the extractions from Chapters 1 and 8 from Barofsky [8]. They are reproduced by permission of the author and publisher.
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Barofsky, I. Why perform a quality or quality-of-life assessment?. Qual Life Res 21, 633–636 (2012). https://doi.org/10.1007/s11136-011-9962-z
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DOI: https://doi.org/10.1007/s11136-011-9962-z