Abstract
Background:
As part of the process of developing a decision aid for carpal tunnel syndrome (CTS) according to the Ottawa Decision Support Framework, we were interested in the level of ‘decisional conflict’ of hand surgeons and patients with CTS. This study addresses the null hypothesis that there is no difference between surgeon and patient decisional conflict with respect to test and treatment options for CTS. Secondary analyses assess the impact of patient and physician demographics and the strength of the patient-physician relationship on decisional conflict.
Methods:
One-hundred-twenty-three observers of the Science of Variation Group (SOVG) and 84 patients with carpal tunnel syndrome completed a survey regarding the Decisional Conflict Scale. Patients also filled out the Pain Self-efficacy Questionnaire (PSEQ) and the Patient Doctor Relationship Questionnaire (PDRQ-9).
Results:
On average, patients had significantly greater decision conflict and scored higher on most subscales of the decisional conflict scale than hand surgeons. Factors associated with greater decision conflict were specific hand surgeon, less self-efficacy (confidence that one can achieve one’s goals in spite of pain), and higher PDRQ (relationship between patient and doctor). Surgeons from Europe have--on average--significantly more decision conflict than surgeons in the United States of America.
Conclusions:
Patients with CTS have more decision conflict than hand surgeons. Decision aids might help narrow this gap in decisional conflict.
Keywords: Carpal Tunnel Syndrome, Decision aids, Hand surgery, Shared decision makin
Introduction
In shared decision-making, health care professionals provide diagnosis, prognosis, and treatment options and the patient expresses his or her preferences and values (1). Decisional conflict--defined as a state of uncertainty about the course of action to be taken—can occur either when there is debate regarding optimal management or when the patient has poorly defined or biased preferences (2-4). Decision aids help clarify areas of debate and help patients determine their preferences when there is more than one option.
Potential sources of decision conflict in the management of carpal tunnel syndrome (CTS) include the role of electrodiagnostic testing; the role of corticosteroid injection; the role of surgery for immeasurable nerve pathology; and the role of surgery for severe CTS with static numbness and atrophy. As part of the process of developing a decision aid for CTS according to the Ottawa Decision Support Framework, we were interested in the level of ‘decisional conflict’ of patients with CTS and among hand surgeons.
This study addresses the null hypothesis that there is no difference between surgeon and patient decision conflict with respect to test and treatment options for CTS. Secondary analyses assess the impact of patient and physician demographics and the strength of the patient-physician relationship on decision conflict.
Material and Methods
Using an IRB approved protocol; we surveyed observers of the Science of Variation Group (SOVG) and all new English speaking, 18 years or older and not pregnant patients with carpal tunnel syndrome visiting the orthopaedic hand and upper extremity outpatient offices between May 2012 and April 2013. The research assistant described the study in detail and informed consent was obtained.
All hand surgeons participating in the Science of Variation Group (SOVG) were asked to complete the survey and 123 surgeons completed it [Table 1]. Incentives, other than acknowledgement as part of the SOVG were not provided. Ninety-one patients were enrolled, but 1 patient was excluded due to computer illiteracy and 6 patients declined participation. The mean age of the 84 patients that completed the study was 55 years (SD= 16, range 20- 90 years) and 29 patients (35%) were men [Table 2].
Table 1.
Demographic informationt of the observers
| Parameters | n | (%) |
|---|---|---|
| Sex | ||
| Man | 92 | 89.3 |
| Woman | 11 | 10.7 |
| Location of practice | ||
| Asia | 2 | 1.9 |
| Canada | 1 | 1.0 |
| Europe | 6 | 5.8 |
| United Kingdom | 2 | 1.9 |
| United States of America | 84 | 81.6 |
| Other | 8 | 7.8 |
| Years In practice | ||
| 0-5 | 34 | 33.0 |
| 6-10 | 23 | 22.3 |
| 11-20 | 26 | 25.2 |
| 21-30 | 20 | 19.4 |
| Supervise | ||
| Yes | 77 | 74.8 |
| No | 26 | 25.2 |
| Specialization | ||
| Hand surgeons | 102 | 99.0 |
| Other | 1 | 1.0 |
Table 2.
Demographic information of the patients (n= 84)
| Parameter | Mean | SD | Range |
|---|---|---|---|
| Age (y) | 55 | 16 | 20 - 90 |
| Education (y of School, n=84) | 15 | 2.9 | 1-22 |
| Number | % | ||
| Sex | |||
| Man | 29 | 35 | |
| Woman | 55 | 65 | |
| Marital status | |||
| Single | 14 | 17 | |
| Living with partner | 3 | 3.6 | |
| Married | 50 | 60 | |
| Separated/Divorced | 11 | 13 | |
| Widowed | 6 | 7.2 | |
| Work status (n=81) | |||
| Working full time | 40 | 49 | |
| Working part time | 8 | 9.9 | |
| Homemaker | 3 | 3.7 | |
| Retired | 16 | 20 | |
| Unemployed, able to work | 4 | 4.9 | |
| Unemployed, unable to work | 10 | 12 | |
| Physician | |||
| Surgeon 01 | 10 | 12 | |
| Surgeon 02 | 21 | 25 | |
| Surgeon 03 | 53 | 63 |
After logging into the website, each observer entered their demographic and professional information: 1) sex, 2) country or region of practice, 3) years in independent practice, 4) supervision of trainees and 5) surgical subspecialty and completed the Decisional Conflict Scale. After the medical encounter with the hand specialist, patients completed the Decisional Conflict Scale, the Pain Self-Efficacy Questionnaire (PSEQ), and the Patient Doctor Relationship Questionnaire – 9 (PDRQ-9) (5, 6).
Measurement tools
The Decisional Conflict Scale (DCS) is a reliable and valid measure of personal perceptions of: a) uncertainty in the face of options, b) modifiable factors contributing to uncertainty such as feeling uninformed, unclear about personal values, or unsupported in decision making; and c) effective decision making such as feeling the choice is informed, values-based, likely to be implemented, and expressing satisfaction with the choice (2). It consists of 16 questions, with a total score ranging from 0 (no decision conflict) to 100 (highest level of decision conflict) (2).
The Pain Self Efficacy Questionnaire (PSEQ) is a 10-item patient-reported outcome inventory. The PSEQ assesses a patient’s confidence in their ability to reach their goals in spite of pain (7, 8). The questions are scored on a 7-point Likert scale ranging from 0 (“not at all confident”) to 6 (“completely confident”). The outcome score is calculated by adding up the items on a scale ranging from 0 to 70, with a higher score indicating greater self-efficacy. We used mean imputation for three missing values on the PSEQ score (8).
The Patient Doctor Relationship Questionnaire – 9 (PDRQ-9) is a validated 9-item questionnaire used to assess the patient’s perception of their physician as effective and helpful (6).
Statistical analysis
An a priori power analysis for our primary study question determined that 84 patients in the physician cohort and 84 patients in the patients cohort would provide 80% power to detect a 0.30 standard deviation (minimal) difference in average DCS score, with alpha level 0.05 (probability of a type I error), using a two-tailed Student T-test.
Continuous data were presented as the mean when normally distributed. We used an ANOVA to test for differences in continuous response variables by categorical explanatory variables. In bivariate analysis Pearson’s correlation was used for continuous variables, Student T-test for dichotomous variables and one-way ANOVA for categorical variables. Variables with P<0.10, were inserted in a backward, stepwise, multivariable linear regression analysis to find predictors of the DCS score. When categorical variables were inserted in multivariable analysis dummy codes were generated when there were more than two categories.
Results
On average, patients had significantly greater decision conflict than hand surgeons on the total score of decisional conflict scale (19 vs. 6.5; P< 0.001). On average, patients had significantly more decisional conflict than hand surgeons on most subscales [Table 3]. In bivariable analysis factors associated with greater decisional conflict were specific hand surgeon, lower PSEQ (r=-0.29, P<0.01), and higher PDRQ (r=0.36, P<0.01) [Table 4]. The best multivariable model of factors associated with a higher DCS score included lower PSEQ and Doctor-2 and explained 32% of the variance in the DCS [Table 5].
Table 3.
Comparison of Decisional Conflict Scale between patients and physicians
| Parameter | Patients | Physicians | |||
|---|---|---|---|---|---|
| Mean | (±SD) | Mean | (±SD) | P-value | |
| Decisional conflict scale (total) | |||||
| Total score | 19 | 22 | 6.5 | 12 | <0.001 |
| Subscale | |||||
| Uncertainty | 20 | 27 | 8.5 | 14 | <0.007 |
| Informed | 23 | 31 | 6.7 | 1.5 | <0.001 |
| Values clarity | 11 | 21 | 7.2 | 13 | 0.63 |
| Support | 28 | 35 | 5.5 | 15 | <0.001 |
| Effective decision | 15 | 22.0 | 4.9 | 11 | <0.001 |
| PSEQ | 45 | 12 | |||
| PDRQ | 4.20 | 0.87 | |||
Uncertainty: Score range from 0 (feels extremely certain about best choice) to 100 (feels extremely uncertain about best choice).
Informed: Scores range from 0 (feels extremely certain about best choice) to 100 (feels extremely uncertain about best choice)
Values clarity: Scores range 0 (feels extremely clear about personal values for benefits & risks/side effect to 100 (feels extremely unclear about personal values)
Support: Scores ragne from 0 (feels extremely supported in decision making) to 100 (feels extremely unsupported in decision making)
Effective: Score range from 0 (good decision) to 100 (bad decision)
Table 4.
Bivariable analysis, comparing Decisional Conflict Scale
| Parameter | Patients | - | |
|---|---|---|---|
| Sex | Mean | (±SD) | 0.68 |
| Man | 17 | 18 | |
| Woman | 21 | 25 | |
| Marital status | 0.28 | ||
| Single | 20 | 22 | |
| Living with partner | 31 | 28 | |
| Married | 17 | 22 | |
| Separated/Divorced | 17 | 20 | |
| Widowed | 36 | 24 | |
| Work status (n=81) | 0.24 | ||
| Working full time | 16 | 20 | |
| Working part time | 12 | 11 | |
| Homemaker | 20 | 34 | |
| Retired | 26 | 25 | |
| Unemployed, able to work | 13 | 19 | |
| Unemployed, unable to work | 32 | 30 | |
| Physician | 0.00 | ||
| Surgeon 01 | 19 | 22 | |
| Surgeon 02 | 32 | 18 | |
| Surgeon 03 | 15 | 22 | |
| Health outcomes | Correlation | P-value | |
| Age (y) | 0.14 | 0.22 | |
| Education (y of School, n=84) | -0.13 | 0.25 | |
| PSEQ | -0.29 | <0.01 | |
| PDRQ-9 | -0.36 | <0.01 | |
| Physicians | |||
| Mean | (±SD) | P-value | |
| Sex | 0.90 | ||
| Man | 6.7 | 12 | |
| Woman | 4.4 | 6.9 | |
| Location of practice | 0.01 | ||
| Asia | 16 | 11 | |
| Canada | 0.0 | 0.0 | |
| Europe | 26 | 27 | |
| United Kingdom | 5.5 | 7.7 | |
| United States of America | 4.8 | 9.7 | |
| Other | 8.8 | 7.0 | |
| Years In practice | 0.95 | ||
| 0-5 | 6.6 | 12 | |
| 6-10 | 7.6 | 16 | |
| 11-20 | 4.5 | 6.0 | |
| 21-30 | 7.4 | 13 | |
| Supervise | 0.44 | ||
| Yes | 6.8 | 13 | |
| No | 5.3 | 9.0 | |
| Specialization | 0.16 | ||
| General orthopaedics | 6.3 | 12 | |
| Other | 20.3 | N/A | |
Table 5.
Multivariable analyse predicting Decisional Conflict Scale
| Coefficient | Std. Err | P | Adj R-squared | 95% CI | ||
|---|---|---|---|---|---|---|
| pseq | -0.54 | 0.17 | 0.005 | 0.17 | -0.91 | -0.17 |
| Doctor-2 | 15 | 5.16 | 0.004 | 6.7 | 27 | |
*In model PSEQ, PDRQ, Doctor-1, Doctor-2, Doctor-3
In bivariable analyses of surgeon, the only significant finding was that surgeons in Europe have—on average--significantly more decisional conflict than surgeons in the United States of America (26 vs. 5.5; P<0.01) [Table 4]. There were no significant differences between factors cited by both, patients and surgeons as making their decisions difficult [Table 6].
Table 6.
What factors made deciding about the treatment difficult, comparing patients and physicians
| Patiënt (n=33) | Physician (n=80) | P-value* | |
|---|---|---|---|
| Factors making deciding about the treatment difficult | n | n | |
| Not enough time | 4 | 11 | |
| Lack of information about options, benefits, risks | 5 | 3 | |
| Lack of information on the incidence/chance of each benefit and side effect | 8 | 10 | |
| Lack of tools to support the explanation of diagnosis and treatment | 1 | 10 | |
| Overwhelmed from information overload | 3 | 7 | 0.08 |
| Unclear about what is important | 6 | 8 | |
| Feeling unsupported in the decision-making process | 0 | 3 | |
| Feeling pressure from others | 0 | 9 | |
| Lack of motivation or not feeling ready to make a final decision | 4 | 13 | |
| Lack of the ability or skill to make a final decision | 2 | 6 |
P-value: describing the difference between patients and physicians regarding factors making deciding about the treatment difficult
Discussion
Patients with CTS have more decisional conflict than hand surgeons. The finding that coping strategies (low self-efficacy) and specific surgeon are related to decision conflict suggests that improvements in patient coping strategies and surgeon communication style or information delivery might help reduce decisional conflict. A decision aid could help with both by addressing surgeon and patient biases, empowering patients with information about their options, and by helping patients understand their values and preferences. A systematic review of 86 studies demonstrated that patients managed with decision aids were more actively involved in the decision making process and had lower decisional conflict (9).
This study should be considered in light of its shortcomings. Only half of the patients indicated factors that contributed to decisional conflict. The surveyed physicians are not directly related to the enrolled patients. We have no data regarding the specific surgeon communication style.
The decisional conflict found in this study is lower than reported in prior studies, perhaps because those studies dealt with life-threatening conditions (such as breast cancer and cystic fibrosis) and involved more treatment options (10-13).
Our finding that time constraints and inadequate instructional material were important contributors to decisional conflict are consistent with prior studies(9), emphasize the limitations of a single office visit, and support the potential value of support tools such as decision aids (14). Decision aids have been found to improve satisfaction with care and physical function and decrease anxiety and use of health care resources.(9) In one orthopedic surgery study, decision aids were associated with 26% fewer hip replacements, 38% fewer knee replacements, and 12-21% lower costs over six months (14-16).
The correlation between self-efficacy and decisional conflict emphasizes the central role of effective coping strategies in human health. Self-efficacy (or it’s counterpart catastrophic thinking) is often the factor most responsible for the level of symptoms and disability for a given pathophysiology, and it also correlates with patient activation and preferences for shared decision making (17). These findings reinforce the rationale that coaching and training patients in effective coping strategies (self-efficacy) and reducing feelings of helplessness, can reduce decisional conflict (17, 18).
Future studies should address the ability of decision aids to reduce decision conflict in hand surgery. It would also be interesting to determine if reducing decision conflict reduces symptom intensity, magnitude of disability, and resource utilization.
No benefits in any form have been received or will be received related directly or indirectly to the subject of this article.
Acknowledgement
MG Hageman is supported by Dutch research grants from Marti-Keunig Eckhart Stichting and Anna Foundation.
Footnotes
*The Science of Variation Group: Abzug M. Joshua, Julie Adams, Gallo Fabio Arbelaez, T. Aspard, George W. Balfour, Brent H. Bamberger, Jose Camilo Barreto Romero, Michael Baskies, Arnnold W. Batson, Taizoon Baxamusa, Ramón De Bedout, Steven Beldner, Prosper Benhaim, Leon Benson, Jorge G. Boretto, Martin Boyer, Gregory Byrd Dee, Ryan P. Calfee, Cecilia Gladys Caro Zambrano, Charles Cassidy, Louis III Catalano, Karel Chivers, Ralph M. Costanzo, Phani Dantuluri, Gregory DeSilva, Seth Dodds, John P. Evans, Luis Felipe Náquira Escobar, C.H. Fernandes, Thomas J. Fischer, Jochen Fischer, Renato M. Fricker, Gary K. Frykman, Aida E. Garcia, Glenn R. Gaston, Fernando José Giovanni Di, Charles A. Goldfarb, Michael W. Grafe, H.W. Grunwald, Warren C. Hammert, Randy Hauck, Ricardo German Hernandez, Eric Hofmeister, Richard L. Hutchison, Asif Ilyas, Jonathan Isaacs, Sidney M. Jacoby, Peter Jebson, Christopher M. Jones, Michael Jones, Sanjeev Kakar, David M. Kalainov, Kaplan D. Thomas, Saul Kaplan, Leonid Katolik, Stephen A. Kennedy, Michael W. Kessler, Hervey L. Kimball, G.A. Kraan, Paul A. Martineau, John McAuliffe, Steven J. McCabe, Desirae M. McKee, Greg Merrell, Charles Metzger, Michael Nancollas, David L. Nelson, Ralf Nyszkiewicz, Jose A. Ortiz, Patrick W. Owens, Jason M. Palmer, Lior Paz, Gary M. Pess, Daniel Polatsch, J. Frank Raia, Marc J. Richard, Marco Rizzo, Rozental, David Ruchelsman, Oleg M. Semenkin, Javier Francisco Sierra Aguilar, Todd Siff, Samir Sodha, Catherine Spath, Sander Spruijt, Thomas G. Stackhouse, Carrie Swigart, Robert M. Szabo, John Taras, Jason D. Tavakolian, Andrew L. Terrono, Thomas F. Varecka, Abhijeet L. Wahegaonkar, Christopher J. Walsh, Frank L. Walter, Lawrence Weiss, Brian P.D Wills, Chris Wilson, Christopher J. Wilson, Jennifer Wolf Moriatis, Megan M. Wood, Colby Young.
References
- 1.Davis RE, Dolan G, Thomas S, Atwell C, Mead D, Nehammer S, et al. Exploring doctor and patient views about risk communication and shared decision-making in the consultation. Health Expect. 2003;6(3):198–207. doi: 10.1046/j.1369-6513.2003.00235.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.O’Connor AM. Validation of a decisional conflict scale. Med Decis Making. 1995;15(1):25–30. doi: 10.1177/0272989X9501500105. [DOI] [PubMed] [Google Scholar]
- 3.Wennberg JE, Mulley AG, Jr, Hanley D, Timothy RP, Fowler FJ, Jr, Roos NP, et al. An assessment of prostatectomy for benign urinary tract obstruction. Geographic variations and the evaluation of medical care outcomes. JAMA. 1988;259(20):3027–30. [PubMed] [Google Scholar]
- 4.Shaw D, Elger B. Evidence-based persuasion: an ethical imperative. JAMA. 2013;309(16):1689–90. doi: 10.1001/jama.2013.2179. [DOI] [PubMed] [Google Scholar]
- 5.O’Connor A. Ottawa decision support framework to address dicisional conflict. 2006 Available at: URL: http://decisionaidohrica/docs/develop/ODSFpdf. ;
- 6.Van der Feltz-Cornelis CM, Van Oppen P, Van Marwijk HW, De Beurs E, Van Dyck R. A patient-doctor relationship questionnaire (PDRQ-9) in primary care: development and psychometric evaluation. Gen Hosp Psychiatry. 2004;26(2):115–20. doi: 10.1016/j.genhosppsych.2003.08.010. [DOI] [PubMed] [Google Scholar]
- 7.Asghari A, Nicholas MK. Pain self-efficacy beliefs and pain behaviour. A prospective study. Pain. 2001;94(1):85–100. doi: 10.1016/S0304-3959(01)00344-X. [DOI] [PubMed] [Google Scholar]
- 8.Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur J Pain. 2007;11(2):153–63. doi: 10.1016/j.ejpain.2005.12.008. [DOI] [PubMed] [Google Scholar]
- 9.Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;10(10):1–217. doi: 10.1002/14651858.CD001431.pub3. [DOI] [PubMed] [Google Scholar]
- 10.Goel V, Sawka CA, Thiel EC, Gort EH, O’Connor AM. Randomized trial of a patient decision aid for choice of surgical treatment for breast cancer. Med Decis Making. 2001;21(1):1–6. doi: 10.1177/0272989X0102100101. [DOI] [PubMed] [Google Scholar]
- 11.Vandemheen KL, O’Connor A, Bell SC, Freitag A, Bye P, Jeanneret A, et al. Randomized trial of a decision aid for patients with cystic fibrosis considering lung transplantation. Am J Respir Crit Care Med. 2009;180(8):761–8. doi: 10.1164/rccm.200903-0421OC. [DOI] [PubMed] [Google Scholar]
- 12.Vodermaier A, Caspari C, Koehm J, Kahlert S, Ditsch N, Untch M. Contextual factors in shared decision making: a randomised controlled trial in women with a strong suspicion of breast cancer. Br J Cancer. 2009;100(4):590–7. doi: 10.1038/sj.bjc.6604916. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Whelan T, Levine M, Willan A, Gafni A, Sanders K, Mirsky D, et al. Effect of a decision aid on knowledge and treatment decision making for breast cancer surgery: a randomized trial. JAMA. 2004;292(4):435–41. doi: 10.1001/jama.292.4.435. [DOI] [PubMed] [Google Scholar]
- 14.Adam JA, Khaw FM, Thomson RG, Gregg PJ, Llewellyn-Thomas HA. Patient decision aids in joint replacement surgery: a literature review and an opinion survey of consultant orthopaedic surgeons. Ann R Coll Surg Engl. 2008;90(3):198–207. doi: 10.1308/003588408X285748. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Phelan EA, Deyo RA, Cherkin DC, Weinstein JN, Ciol MA, Kreuter W, et al. Helping patients decide about back surgery: a randomized trial of an interactive video program. Spine. 2001;26(2):206–11. doi: 10.1097/00007632-200101150-00016. [DOI] [PubMed] [Google Scholar]
- 16.Slover J, Shue J, Koenig K. Shared decision-making in orthopaedic surgery. Clin Orthop Relat Res. 2011;470(4):1046–53. doi: 10.1007/s11999-011-2156-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Vranceanu AM, Cooper C, Ring D. Integrating patient values into evidence-based practice: effective communication for shared decision-making. Hand Clin. 2009;25(1):83–96. doi: 10.1016/j.hcl.2008.09.003. [DOI] [PubMed] [Google Scholar]
- 18.van Randenborgh A, de Jong-Meyer R, Huffmeier J. Decision making in depression: differences in decisional conflict between healthy and depressed individuals. Clin Psychol Psychother. 2010;17(4):285–98. doi: 10.1002/cpp.651. [DOI] [PubMed] [Google Scholar]