IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v6y1986i1p2-11.html
   My bibliography  Save this article

How Physicians Use Clinical Information in Diagnosing Pulmonary Embolism

Author

Listed:
  • Robert S. Wigton
  • Vincent L. Hoellerich
  • Kashinath D. Patil

Abstract

To investigate what diagnostic strategies physicians adopt when the literature is unclear about the best use of diagnostic information, the authors examined how physicians weighted eight items of clinical information in diagnosing pulmonary embolism. Thirteen faculty mem bers, 23 house officers, and 19 students estimated the likelihood of pulmonary embolism and predicted whether they would begin treatment in 27 simulated cases. The vignettes were constructed using a fractional factorial design and analyzed using conjoint analysis. Overall the lung scan results (34.7%) and leg examination (19.0%) were weighted most heavily in making the diagnosis, whereas the leg examination (35.7%) was given the greatest weight when deciding about therapy. Weights given by groups at different levels of training were similar, but there was considerable variation within groups. Heterogeneity of diagnostic strategies did not appear to decrease in groups with more training and experience. Multi variate analysis of predictors of pulmonary embolism in 102 actual cases showed that al though lung scan results were important in both actual and simulated cases, heart rate accounted for the most variance in the actual cases but was hardly used in the physicians' strategies. There is considerable variation among physicians in how clinical information is used in diagnosing pulmonary embolism, and the variation may not decrease with increased experience. Key words: conjoint analysis, clinical diagnosis, diagnostic strategies, pulmonary embolism. (Med Decis Making 6:2-11, 1986)

Suggested Citation

  • Robert S. Wigton & Vincent L. Hoellerich & Kashinath D. Patil, 1986. "How Physicians Use Clinical Information in Diagnosing Pulmonary Embolism," Medical Decision Making, , vol. 6(1), pages 2-11, February.
  • Handle: RePEc:sae:medema:v:6:y:1986:i:1:p:2-11
    DOI: 10.1177/0272989X8600600102
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X8600600102
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X8600600102?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
    ---><---

    References listed on IDEAS

    as
    1. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Margaret M. Holmes & Marilyn L. Rothert, 1988. "On Estending Judgment Analysis to Clinical Trials," Medical Decision Making, , vol. 8(1), pages 39-39, February.
    2. Robert S. Wigton, 1988. "Use of Linear Models to Analyze Physicians' Decisions," Medical Decision Making, , vol. 8(4), pages 241-252, December.

    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. Winfried Steiner & Harald Hruschka, 2002. "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data," Review of Marketing Science Working Papers 1-4-1003, Berkeley Electronic Press.
    2. Merja Halme & Kari Linden & Kimmo Kääriä, 2009. "Patients’ Preferences for Generic and Branded Over-the-Counter Medicines," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 2(4), pages 243-255, December.
    3. Haaijer, Marinus E., 1996. "Predictions in conjoint choice experiments : the x-factor probit model," Research Report 96B22, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    4. Fusco, Elisa, 2023. "Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    5. Xue, Hong & Mainville, Denise Y. & You, Wen & Nayga, Rodolfo M., Jr., 2009. "Nutrition Knowledge, Sensory Characteristics and Consumers’ Willingness to Pay for Pasture-Fed Beef," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49277, Agricultural and Applied Economics Association.
    6. Barbara Baarsma, 2003. "The Valuation of the IJmeer Nature Reserve using Conjoint Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 25(3), pages 343-356, July.
    7. Kowalska-Pyzalska, Anna & Michalski, Rafał & Kott, Marek & Skowrońska-Szmer, Anna & Kott, Joanna, 2022. "Consumer preferences towards alternative fuel vehicles. Results from the conjoint analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    8. Kim, Junghun & Seung, Hyunchan & Lee, Jongsu & Ahn, Joongha, 2020. "Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions," Energy Economics, Elsevier, vol. 86(C).
    9. Horna, J. Daniela & Smale, Melinda & von Oppen, Matthias, 2005. "Private Participation In Agricultural Extension In Nigeria And Benin: Determining The Willingness To Pay For Information," 2005 Annual meeting, July 24-27, Providence, RI 19401, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. John Liechty & Duncan Fong & Eelko Huizingh & Arnaud Bruyn, 2008. "Hierarchical Bayesian conjoint models incorporating measurement uncertainty," Marketing Letters, Springer, vol. 19(2), pages 141-155, June.
    11. Christian P Theurer & Andranik Tumasjan & Isabell M Welpe, 2018. "Contextual work design and employee innovative work behavior: When does autonomy matter?," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-35, October.
    12. Kannika Thampanishvong, 2013. "Determinants of Flash Flood Evacuation Choices and Assessment of Preferences for Flash Flood Warning Channels: The Case of Thailand," EEPSEA Research Report rr2013034, Economy and Environment Program for Southeast Asia (EEPSEA), revised Mar 2013.
    13. Teichert, Thorsten Andreas, 1997. "Schätzgenauigkeit von Conjoint-Analysen," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 444, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    14. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    15. Poortinga, Wouter & Steg, Linda & Vlek, Charles & Wiersma, Gerwin, 2003. "Household preferences for energy-saving measures: A conjoint analysis," Journal of Economic Psychology, Elsevier, vol. 24(1), pages 49-64, February.
    16. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    17. Sell, Sandra & Lopatta, Kerstin & Hundsdoerfer, Jochen, 2010. "Der Einfluss der Besteuerung auf die Rechtsformwahl: Eine Conjoint-Analyse," Discussion Papers 2010/10, Free University Berlin, School of Business & Economics.
    18. Fraser, Iain & Balcombe, Kelvin & Williams, Louis & McSorley, Eugene, 2021. "Preference stability in discrete choice experiments. Some evidence using eye-tracking," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    19. Chan-Halbrendt, Catherine & Yu, Jin & Keung, Helen & Lin, Tun & Ferguson, Carol, 2006. "Guangzhou Buyers Preference for Premium Hawaiian Grown Product Gift Baskets," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 9(4), pages 1-15.
    20. Dutta, Goutam & Sankarshan Basu & John, Jose, 2008. "Development of Utility Function for Life Insurance Buyers in the Indian Market," IIMA Working Papers WP2008-12-05, Indian Institute of Management Ahmedabad, Research and Publication Department.

    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:sae:medema:v:6:y:1986:i:1:p:2-11. 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: SAGE Publications (email available below). General contact details of provider: .

    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.