Clin Orthop Surg.  2017 Mar;9(1):96-100. 10.4055/cios.2017.9.1.96.

Survey of Preferences in Patients Scheduled for Carpal Tunnel Release Using Conjoint Analysis

• Kim WL
• Kim JS
• Lee JB
• Kim SH
• Min DU
• Park HY

• KMID: 2412310
• DOI: http://doi.org/10.4055/cios.2017.9.1.96

Abstract

BACKGROUND
This study aimed to investigate the preferences of patients scheduled for carpal tunnel release using conjoint analysis and also introduce an example of how to apply a conjoint analysis to the medical field. The use of conjoint analysis in this study is new to the field of orthopedic surgery.
METHODS
A total of 97 patients scheduled for carpal tunnel release completed the survey. The following four attributes were predefined: board certification status, distance from the patient's residency, medical costs, and waiting time for surgery. Two plausible levels for each attribute were assigned. Based on these attributes and levels, 16 scenarios were generated (2 × 2 × 2 × 2). We employed 8 scenarios using a fractional factorial design (orthogonal plan). Preferences for scenarios were then evaluated by ranking: patients were asked to list the 8 scenarios in their order of preference. Outcomes consisted of two results: the average importance of each attribute and the utility score.
RESULTS
The most important attribute was the physician's board certificate, followed by distance from the patient's residency to the hospital, waiting time, and costs. Utility estimate findings revealed that patients had a greater preference for a hand specialist than a general orthopedic surgeon.
CONCLUSIONS
Patients considered the physician's expertise as the most important factor when choosing a hospital for carpal tunnel release. This suggests that patients are increasingly seeking safety without complications as interest in medical malpractice has increased.

Keyword

Carpal tunnel syndrome; Patient preference

MeSH Terms

Adult
Aged
Aged, 80 and over
Appointments and Schedules
Carpal Tunnel Syndrome/*surgery
Certification/*statistics & numerical data
Clinical Competence/statistics & numerical data
Fees and Charges/statistics & numerical data
Female
Health Services Accessibility/statistics & numerical data
Humans
Male
Middle Aged
Orthopedics/*standards
Patient Preference/*statistics & numerical data
Statistics as Topic
Surveys and Questionnaires
Time Factors

Reference

1. Curry J. Understanding conjoint analysis in 15 minutes. Sequim, WA: Sawtooth Software;1996.

2. Waltzman JT, Scholz T, Evans GR. What patients look for when choosing a plastic surgeon: an assessment of patient preference by conjoint analysis. Ann Plast Surg. 2011; 66(6):643–647.

3. Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ. 2000; 320(7248):1530–1533.

4. Ryan M, McIntosh E, Dean T, Old P. Trade-offs between location and waiting times in the provision of health care: the case of elective surgery on the Isle of Wight. J Public Health Med. 2000; 22(2):202–210.

5. Silverman S, Calderon A, Kaw K, et al. Patient weighting of osteoporosis medication attributes across racial and ethnic groups: a study of osteoporosis medication preferences using conjoint analysis. Osteoporos Int. 2013; 24(7):2067–2077.

6. Byrne MM, Souchek J, Richardson M, Suarez-Almazor M. Racial/ethnic differences in preferences for total knee replacement surgery. J Clin Epidemiol. 2006; 59(10):1078–1086.

7. Shifflett GD, Dy CJ, Daluiski A. Carpal tunnel surgery: patient preferences and predictors for satisfaction. Patient Prefer Adherence. 2012; 6:685–689.

8. Bradley M. User's manual for SPEED version 2.1 stated preference experiment editor and designer. Hague: Hague Consulting Group;1991.

9. Koo LC, Tao FK, Yeung JH. Preferential segmentation of restaurant attributes through conjoint analysis. Int J Contemp Hosp Manag. 1999; 11(5):242–253.

10. Levy DS. Modern marketing research techniques and the property professional. Prop Manag. 1995; 13(3):33–40.

11. Pignone MP, Brenner AT, Hawley S, et al. Conjoint analysis versus rating and ranking for values elicitation and clarification in colorectal cancer screening. J Gen Intern Med. 2012; 27(1):45–50.

12. Vilikus O. Optimization of sample size and number of tasks per respondent in conjoint studies using simulated datasets. Stud Ekon Uniw Ekon Katowicach. 2012; (120):77–83.

13. Tang J, Vandale W, Weiner J. Sample planning for CBC models: our experience. In : Proceedings of the Sawtooth Software Conference; 2006 Mar 29-31; Delray Beach, FL. Sequim, WA: Sawtooth Software;2016. p. 111–116.