Share this article on:

Characterizing Patient Preferences Surrounding Total Knee Arthroplasty

Reuter, John M., MS1; Hutyra, Carolyn A., BS2; Politzer, Cary S., BS2; Calixte, Christopher C., BS2; Scott, Daniel J., MD, MBA2; Attarian, David E., MD2; Mather, Richard C. III, MD, MBA2

doi: 10.2106/JBJS.OA.18.00017
Scientific Articles
Disclosures

Background: Episode-based bundled payments for total knee arthroplasty emphasize cost-effective patient-centered care. Understanding patients’ perceptions of components of the total knee arthroplasty care episode is critical to achieving this care. This study investigated patient preferences for components of the total knee arthroplasty care episode.

Methods: Best-worst scaling was used to analyze patient preferences for components of the total knee arthroplasty care episode. Participants were selected from patients presenting to 2 orthopaedic clinics with chronic knee pain. They were presented with descriptions of 17 attributes before completing a best-worst scaling exercise. Attribute importance was determined using hierarchical Bayesian estimation. Latent class analysis was used to evaluate varying preference profiles.

Results: One hundred and seventy-four patients completed the survey, and 117 patients (67%) were female. The mean age was 62.71 years. Participants placed the highest value on surgeon factors, including level of experience, satisfaction rating, and complication rates. Latent class analysis provided a 4-segment model of the population.

Conclusions: This study demonstrated differences in patient preferences for the components of a total knee arthroplasty care episode and characterized distinct preference profiles among patient subsets. Stakeholders can use this information to focus efforts and policy on high-value components and to potentially create customized bundles guided by preference profiles.

Clinical Relevance: This study is clinically relevant because the patient preferences identified here may help providers to design customized bundles for total knee arthroplasty care.

1University of Rochester School of Medicine and Dentistry, Rochester, New York

2Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina

E-mail address for J.M. Reuter: JohnM_Reuter@URMC.Rochester.edu

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Bundled payments are among several alternative payment models proposed in response to the increasing rates and economic burden of total knee arthroplasty1-7. Bundled payments incentivize value5,8-12, defined as outcomes that patients deem important divided by cost12,13.

Process standardization is cited as driving value in health care14, and reports of standardized joint replacement clinical pathways have noted improved outcomes at lower or maintained costs15-17. However, empirical economic theory finds that customization, particularly along defined patient profiles and preferences, may further increase value18-21, and patient-centeredness is acknowledged as a central tenet of health-care reform22,23. Understanding patient preferences for components of the total knee arthroplasty care episode may be an important step toward creating value-based experiences. Standardized pathways may be tailored on the basis of preferences, and multiple pathways could be developed after exhausting value gains from standardization.

Patient preferences can be measured using stated-preference methodology built on the utility theory24. Best-worst scaling is one such method, grounded in the premise that the value a person places on one object compared with another is proportional to how often the person chooses it over the other25. Originally deployed to measure consumer preferences as a shift from rating scales toward a choice-based methodology with valid theoretical foundations25,26, best-worst scaling use in health care has recently increased substantially27,28.

Best-worst scaling presents respondents with a series of attribute subsets from a master set27. Respondents identify their most and least preferred items in each subset27. From these selections, a person’s attribute preference ranking is constructed27.

The present study utilized best-worst scaling to assess patient preferences for aspects of total knee arthroplasty treatment. Prior studies evaluating preferences for perioperative total knee arthroplasty care29-32 used ranking and rating scales, which can be cognitively demanding to participants and are subject to known limitations26,27,33. Best-worst scaling accounts for many of these limitations, may be less cognitively demanding, and better simulates resource constraints by forcing participants to choose rather than deem all options important25-27,33.

For this current study, a best-worst scaling exercise elicited patient preferences with regard to components of the total knee arthroplasty care episode, and latent class analysis identified preference-based subgroups of patients. Some of these components could be customized within total knee arthroplasty bundles, and analysis of respondent subgroups may offer insight into the creation of patient-specific care bundles.

Back to Top | Article Outline

Materials and Methods

This prospective study was reviewed and was approved by the Duke University institutional review board.

Back to Top | Article Outline

Patient Population

Patients who were 50 to 80 years of age and had chronic knee pain and/or arthritis were recruited. Patients were included even if they had already undergone total knee arthroplasty or were not presently considering it. All patients were seeking care for knee pain at the time of participation. Patients with prior total knee arthroplasty may have been seeking care for the replaced or non-replaced knee. No other exclusion criteria were used. Recruitment took place between August 2016 and March 2017 in joint reconstruction practices at Duke University Health System and EmergeOrtho. Multiple sites were included to improve external validity for generalizability of preferences. Sample size was determined on the basis of prior best-worst scaling studies28,34. No personal health information was collected. Each participant completed the survey once either before or after seeing their physician.

Back to Top | Article Outline

Survey Design

The survey included 4 sections: demographic information collection, attribute education (information about and description of attributes), best-worst scaling exercise, and reflection questions. Demographic questions collected information such as age, sex, and knee pain history. The survey introduction is shown in Figure 1.

Fig. 1

Fig. 1

Back to Top | Article Outline

Best-Worst Scaling Design

An object case best-worst scaling exercise was used to determine the relative importance of the attributes35. Participants were randomly assigned to complete 1 of 300 potential questionnaire versions. All versions included the same content, but questions were ordered differently and contained different combinations of attributes. Each version contained 20 unique choice tasks with 5 attributes per task (Fig. 2). The instrument was programmed with Sawtooth Software version 8.2.0 (Sawtooth Software).

Fig. 2

Fig. 2

Back to Top | Article Outline

Attribute Identification

Stakeholder input and literature review were used to construct the initial attribute list. A panel of orthopaedic surgeons, health economists, and health services researchers contributed to refine the initial list. The survey was then tested using Amazon’s Mechanical Turk, a web-based participant pool well studied and used in social sciences and recently utilized in orthopaedics36-40. Preference results and qualitative comments were used to further refine the attribute list. The survey was then similarly tested and was refined for another round using Amazon’s Mechanical Turk.

Next, the survey was administered to 5 members of the research group who were unfamiliar with the instrument. Structured interviews were conducted to assess clarity of information, response times, and perception of the list of attributes. The survey instrument and attribute list were refined again on the basis of these interviews.

The survey was then administered to 4 patients who fit the study’s inclusion criteria. Formal, structured, qualitative interviews were conducted with the patients. Findings from these interviews were used to further refine the survey instrument and attribute list. Data from these participants were not included in the analysis.

The final list included 17 attributes describing various aspects of a total knee arthroplasty care episode (Table I). Defined attribute levels were not evaluated in the best-worst scaling design but were included in the patient education section to ensure information clarity. For example, to explain the attribute “wait time from clinic visit until surgery,” participants were told that they could theoretically wait less than 1 month, 2 to 3 months, or 3 to 6 months from scheduling the surgical procedure until their surgical procedure date. “Less than 1 month,” “2 to 3 months,” and “3 to 6 months” would be the levels for the attribute “wait time from clinic visit until surgery.” Levels were used in this survey to explain the attribute, but only attributes were presented to patients during the best-worst scaling exercise.

TABLE I - List of Attributes and Corresponding Levels
Attribute Levels
Surgeon factors
 Surgeon level of experience High volume, fellowship-trained surgeon; high volume, non-fellowship trained surgeon; moderate to low volume, non-fellowship trained surgeon
 Surgeon satisfaction rating 5 stars, 3 to 4 stars, <3 stars
 Surgeon complication rate Low: 0% to 1.6%; medium: 1.6% to 3.0%; and high: >3.0%
Hospital factors
 Hospital satisfaction rating 4 to 5 stars, 3 stars, 1 star
 Parking options Valet parking, regular parking
 Food quality Standard meal; gourmet, healthy meal; or personalized meal
 Driving distance to hospital 15 minutes, 1 hour, 3 hours
Implant factors
 Types of knee implant Upgraded bearing surface, regular implant
 Instruments used to place knee implants Patient-specific instruments, standard instruments
 Warranty for knee replacement surgery Lifetime warranty, 1-year warranty, no warranty
Preoperative and postoperative period
 Rehabilitation options after surgery Home health physical therapy, outpatient physical therapy, online or simulation physical therapy
 Preoperative and postoperative appointment options Appointments with a surgeon, appointments with a physician assistant and nurse, and appointments with a personal case manager after surgery only
 Length of stay in hospital after surgery Outpatient surgery or inpatient surgery
 Wait time from clinic visit until surgery <1 month, 2 to 3 months, or 3 to 6 months
Cost
 Out-of-pocket costs $200, $1,000, or $5,000
Referral
 Primary care physician’s referral Referral or no referral
 Family and friends’ experience with the institution Positive experience or negative experience

Back to Top | Article Outline

Statistical Analysis

Incomplete questionnaires were not analyzed. Descriptive statistics summarized demographic information. Hierarchical Bayesian estimations analyzed attribute importances, which were rescaled to permit relative comparisons (i.e., an attribute with an importance of 12 is said to be twice as important as one with an importance of 6).

A latent class analysis was conducted to evaluate heterogeneity within the sample. Latent class analysis identifies groups of participants with similar preferences and produces a probability estimate. This estimate is the mean membership probability for each participant, quantifying his or her probability of belonging to each segment in the latent class analysis model41.

Models containing 2 through 10 segments were examined. The Consistent Akaike Information Criterion (CAIC) was primarily used to determine the most appropriate number of segments42. The CAIC plateaued from 4 (13,619.41) to 7 segments (13,619.68), indicating a reasonable fit for models with 4 to 7 segments. A 4-segment model was chosen because of the low CAIC and more fluid patient classification for clinicians. Chi-square tests and post hoc tests43,44 were used to assess demographic differences between segments. All analyses were performed using Sawtooth Software version 8.2.0 and SPSS version 24 (IBM).

Back to Top | Article Outline

Results

Demographic Characteristics

Two hundred and sixteen patients participated; 174 (81%) completed the questionnaire and were included in the final analysis. The mean respondent age (and standard deviation) was 62.71 ± 7.50 years. The majority of participants were white (74%), female (67% [n = 117]), married (67%), and not currently employed (49%), and had completed at least some college education (81%).

Our study also gathered information relevant to the total knee arthroplasty attributes. Twenty-six percent of participants reported familiarity with Hospital Consumer Assessment of Healthcare Providers and Symptoms (HCAHPS) ratings. Eighty-four percent of respondents reported previously using a family member or friend’s recommendation to choose a physician. Fifty-six percent of patients selected outpatient physical therapy, rather than telerehabilitation or home-health physical therapy, as their preferred rehabilitation option. A demographic summary is presented in Table II.

TABLE II - Whole-Sample Demographic Characteristics
Demographic Characteristic Value
Age* (yr) 62.71 ± 7.50
Sex
 Male 33 (57)
 Female 67 (117)
Race
 White 74 (128)
 Hispanic or Latino 1 (2)
 Black or African American 24 (42)
 Native American or American Indian 0 (0)
 Asian/Pacific Islander 1 (2)
 Other 0 (0)
Marital status
 Single 14 (25)
 Married 67 (116)
 Divorced 9 (16)
 Widowed 7 (12)
 Other 1 (2)
 Did not answer 2 (3)
Education level
 Did not complete high school 4 (7)
 High school/GED (general equivalency development) 15 (26)
 Some college 32 (55)
 Bachelor’s degree 29 (50)
 Master’s degree 14 (25)
 Advanced graduate work or PhD 6 (11)
 Not sure 0 (0)
Income level
 <$20,000 7 (12)
 $20,000 to $39,999 12 (21)
 $40,000 to $59,999 11 (19)
 $60,000 to $79,999 15 (26)
 $80,000 to $99,999 13 (22)
 ≥$100,000 28 (49)
 Not sure, prefer not to answer 13 (22)
 Did not answer 2 (3)
Previous diagnosis of arthritis
 Yes 82 (143)
 No 18 (31)
Previous total knee replacement
 Yes 34 (60)
 No 66 (114)
How difficult was this survey to complete?
 Not difficult at all 64 (114)
 Somewhat difficult 22 (38)
 Moderately difficult 13 (22)
 Very difficult 0 (0)
 Extremely difficult 0 (0)
Your opinion on the length of this survey
 Too long 43 (75)
 Too short 1 (1)
 Just right 56 (98)
Time to complete survey* (min) 33.73 ± 0.47
*The values are given as the mean and the standard deviation.†The values are given as the percentage of respondents, with the number of respondents in parentheses.

Back to Top | Article Outline

Relative Importance of Attributes

Whole-sample attribute importances are presented in Figure 3. “Surgeon’s experience level” (15.90), “surgeon’s satisfaction rating” (14.32), and “surgeon’s complication rate” (13.05) were the most important attributes. A comparison of mean importance ratings between those who had already undergone a total knee arthroplasty (n = 60) and those who had not (n = 114) showed similar profiles.

Fig. 3

Fig. 3

Back to Top | Article Outline

Latent Class Segmentation

Demographic analysis showed significant differences in race (p = 0.003), education level (p = 0.001), previous diagnosis of arthritis (p = 0.014), history of total knee arthroplasty (p < 0.001), ability to pay $1,000 out-of-pocket (p = 0.021), and previous use of family or friends’ referral for health-care providers (p = 0.021) between segments. Segment demographic characteristics are presented in Table III with by-segment attribute importances in Table IV. “Surgeon’s experience level,” “surgeon’s satisfaction rating,” and “surgeon’s complication rate” were the 3 most important attributes in all segments except segment 3.

TABLE III - Latent Class Segment Demographic Characteristics
Demographic Characteristics Segment 1 Segment 2 Segment 3 Segment 4
Age* (yr) 62.04 ± 7.66 64.04 ± 7.05 60.68 ± 7.26 63.39 ± 7.11
Sex
 Male 27.27 (21) 41.18 (14) 31.82 (7) 36.59 (15)
 Female 72.73 (56) 58.82 (20) 68.18 (15) 63.41 (26)
Race
 White 68.83 (53) 73.53 (25) 45.45 (10) 97.56 (40)
 Hispanic or Latino 1.30 (1) 2.94 (1) 0.00 (0) 0.00 (0)
 Black or African American 28.57 (22) 23.53 (8) 50.00 (11) 2.44 (1)
 Native American or American Indian 0.00 (0) 0.00 (0) 0.00 (0) 0.00 (0)
 Asian/Pacific Islander 1.30 (1) 0.00 (0) 4.55 (1) 0.00 (0)
 Other 0.00 (0) 0.00 (0) 0.00 (0) 0.00 (0)
Marital status
 Single 15.58 (12) 20.59 (7) 4.55 (1) 12.20 (5)
 Married 63.64 (49) 64.71 (22) 77.27 (17) 68.29 (28)
 Divorced 9.09 (7) 5.88 (2) 9.09 (2) 12.20 (5)
 Widowed 7.79 (6) 8.82 (3) 4.55 (1) 4.88 (2)
 Other 1.30 (1) 0.00 (0) 4.55 (1) 2.44 (1)
 No answer 2.60 (2) 0.00 (0) 0.00 (0) 0.00 (0)
Education status
 Did not complete high school 2.60 (2) 2.94 (1) 13.64 (3) 2.44 (1)
 High school or GED (general equivalency development) 20.78 (16) 5.88 (2) 27.27 (6) 4.88 (2)
 Some college 40.26 (31) 29.41 (10) 27.27 (6) 19.51 (8)
 Bachelor’s degree 27.27 (21) 26.47 (9) 13.64 (3) 41.46 (17)
 Master’s degree 7.79 (6) 20.59 (7) 18.18 (4) 19.51 (8)
 Advanced graduate work or PhD 1.30 (1) 14.71 (5) 0.00 (0) 12.20 (5)
 Not sure 0.00 (0) 0.00 (0) 0.00 (0) 0.00 (0)
Income level
 <$20,000 9.09 (7) 5.88 (2) 9.09 (2) 2.44 (1)
 $20,000 to $39,999 14.29 (11) 11.76 (4) 18.18 (4) 4.88 (2)
 $40,000 to $59,999 15.58 (12) 5.88 (2) 13.64 (3) 4.88 (2)
 $60,000 to $79,999 9.09 (7) 26.47 (9) 18.18 (4) 14.63 (6)
 $80,000 to $99,999 14.29 (11) 8.82 (3) 9.09 (2) 14.63 (6)
 ≥$100,000 22.08 (17) 29.41 (10) 13.64 (3) 46.34 (19)
 Not sure, prefer not to answer 15.58 (12) 11.76 (4) 18.18 (4) 9.76 (4)
 No answer 0.00 (0) 0.00 (0) 0.00 (0) 2.44 (1)
Previous diagnosis of arthritis
 Yes 72.73 (56) 88.24 (30) 100.00 (22) 85.37 (35)
 No 27.27 (21) 11.76 (4) 0.00 (0) 14.63 (6)
Previous total knee replacement
 Yes 23.38 (18) 67.65 (23) 50.00 (11) 29.51 (8)
 No 76.62 (59) 32.35 (11) 50.00 (11) 80.49 (33)
How difficult to pay $1,000 out-of-pocket
 Not difficult at all 33.77 (26) 35.29 (12) 31.82 (7) 56.10 (23)
 Somewhat difficult 33.77 (26) 47.06 (16) 31.82 (7) 36.59 (15)
 Very difficult 15.58 (12) 17.65 (6) 9.09 (2) 4.88 (2)
 Extremely difficult, could not pay 14.29 (11) 0.00 (0) 22.73 (5) 2.44 (1)
 No answer 2.60 (2) 0.00 (0) 4.55 (1) 0.00 (0)
Use of family and friend referral
 Yes 80.52 (62) 94.12 (32) 68.18 (15) 90.24 (37)
 No 19.48 (15) 5.88 (2) 31.82 (7) 7.32 (3)
 No answer 0.00 (0) 0.00 (0) 0.00 (0) 2.44 (1)
Mean membership probability 96.92% 97.75% 99.07% 97.49%
*The values are given as the mean and the standard deviation.†The values are given as the percentage of patients in the segment, with the number of patients in parentheses.‡Significant difference between segments.

TABLE IV - Latent Class Segment Attribute Rankings
Importance
Attribute Rankings Segment 1 Segment 2 Segment 3 Segment 4
Surgeon’s experience level 17.14 17.75 11.83 17.51
Surgeon’s satisfaction rating 13.80 14.72 10.97 15.99
Surgeon’s complication rate 13.14 14.05 6.50 15.71
Type of knee implant 8.74 2.37 6.43 10.89
Out-of-pocket cost 8.17 3.99 5.24 1.49
Hospital’s satisfaction rating 7.65 4.92 8.23 9.41
Rehabilitation options after surgery 7.60 4.69 9.69 2.46
Warranty for knee replacement surgery 7.27 5.57 4.57 4.95
Instruments used to place knee implant 5.42 1.36 4.33 4.88
Primary care physician’s referral 2.80 13.40 3.44 1.65
Length of stay in hospital after surgery 2.75 2.01 6.07 2.09
Preoperative and postoperative appointment options 2.43 2.95 8.29 1.18
Family and friends’ experiences with the surgeon or institution 1.18 10.02 3.73 10.29
Wait time from clinic visit until surgery 1.16 1.31 3.93 1.05
Driving distance to hospital 0.53 0.55 3.38 0.23
Hospital parking options 0.15 0.19 2.16 0.14
Hospital food quality 0.07 0.16 1.24 0.10

Back to Top | Article Outline

Segment 1

This segment contained 77 patients, 44% of the whole sample. The mean membership probability was 96.92%. Segment 1 preferences and demographic statistics best reflected the whole-sample results (Fig. 4).

Fig. 4

Fig. 4

Back to Top | Article Outline

Segment 2

Significantly more patients in segment 2 than in any other segment reported having completed advanced graduate education (p = 0.03). This segment contained 34 participants, 20% of the sample. The mean membership probability was 97.75%. Following the surgeon factors, “primary care physician’s referral” (13.40) and “family and friends’ experiences with the surgeon or institution” (10.02) were the most important attributes.

Back to Top | Article Outline

Segment 3

Significantly more patients in segment 3 than in any other segment identified as black or African American (p < 0.001) and reported “did not complete high school” as their highest level of education (p = 0.01). This segment contained 22 patients, 13% of the sample. The mean membership probability was 99.07%.

“Surgeon’s experience level” was the most valuable attribute, but with an importance of only 11.83. “Surgeon’s satisfaction rating” was the second most valuable (10.97), followed by “rehabilitation options after surgery” (9.69) and “preoperative and postoperative appointment options” (8.29). “Surgeon’s complication rate” (6.50) appeared fifth.

Back to Top | Article Outline

Segment 4

Significantly more participants in segment 4 than in any other group identified as white (p < 0.001) and reported being able to pay $1,000 out-of-pocket without difficulty (p = 0.01). This segment contained 41 participants, 24% of the sample. The mean membership probability was 97.49%. “Type of knee implant” (10.89) was the next most important attribute after the surgeon factors.

Back to Top | Article Outline

Discussion

Standardization of total knee arthroplasty care with episode-based reimbursement underscores a need to avoid unnecessary services and customize high-impact facets of treatment11,45,46, and determining treatment aspects that patients value may be a pivotal step in this process. Previous studies examining total knee arthroplasty patient preferences have used rating and ranking methods, techniques with known biases and limitations26,27,33. This current study used best-worst scaling, a preference measurement tool designed to move away from rating scales and toward a choice-based methodology with valid theoretical foundations25,26.

Our whole-sample results generally agreed with the existing literature. Prior work demonstrated that surgeon characteristics, including surgeons’ experience and perceived skill, are the most important factors to patients29-32, which was reflected in our analysis. Earlier studies have described waiting time for the surgical procedure, distance to the hospital, reputation of the hospital, others’ experiences, and hospital quality as being valuable to patients29-32. In our whole-sample analysis, such factors were valued well below surgeon factors.

Of note, “warranty for knee replacement surgery” was valued similarly to “hospital’s satisfaction rating,” the most valuable non-surgeon attribute, and well above non-surgeon factors such as driving distance and waiting time for the surgical procedure. If we assume converging equivalence in surgeon performance, a warranty was among the most significant factors to our participants. Warranties can be considered a natural extension of episode bundles, essentially expanding the episode while narrowing risk relative to traditional bundles. Warranties can be offered by various stakeholders, including device manufacturers, provider organizations, and payers.

These findings can guide the formation of episode-based bundled payment plans. Our patients distinctly expressed the desire to see a high-performing surgeon. This suggests that optimizing surgeon performance is the primary value driver from the patient perspective. Organizations should first focus on recruiting high-volume fellowship-trained surgeons and working with their current surgeons to reduce complications. Organizations may then explore warranty packages for patients. Although not yet widely available, warranties have been used with cardiac procedures and have been evaluated for colon surgery47,48. One study identified a potential 6% nationwide reduction in expenditures from a warranty47, which, when combined with our patients’ interest, offers an opportunity to create value.

Organizations can build on the core components of surgeon performance and a warranty and can produce greater value with customized offerings. The importance of understanding differences between patients when utilizing bundled payment plans has been noted46. Our latent class analysis results confirmed our hypothesis of heterogeneity and begin to shape potential customized bundles. In particular, our latent class analysis suggested 2 segments for which customized bundles could create additional value: segment 3 with a “high-touch” bundle and segment 4 with a “high-tech” bundle.

In segment 3, “rehabilitation options after surgery” and “preoperative and postoperative appointment options” attained higher values than in any other attribute or in the whole sample. This segment had the highest percentage of African-American participants and reported a lower education level. We offer 2 explanations for these preferences in light of these demographic characteristics. First, prior studies have shown that less educated patients seek continuous care, preferring to work with the same providers throughout a clinical episode, and that African-American patients harbor greater mistrust for health systems than their white counterparts29,49-51. It is possible then that these patients valuing choice in postoperative rehabilitation and preoperative and postoperative appointments indicates a desire to work continuously with their surgeon and therapists from the health system in which the surgical procedure was performed. This would satisfy a desire for continuous care and would allow these patients to develop trusting relationships with their providers and health systems. Second, minority patients have traditionally lacked access to care compared with white patients52-54. This segment’s preference profile may be a response to this. These patients valuing choice in postoperative rehabilitation and preoperative and postoperative appointments may be an expression of their desire for improved access to care.

With regard to a customized bundle, this segment valued choice and communication during the postoperative period, a time highlighted by multiple authors as the largest source of variability in total knee replacement cost and an ideal venue in which to drive value5,9,11,46. To realize this opportunity when working with populations similar to this segment, providers could offer a high-touch bundle. Such a model would include, in addition to being seen by a high-performing surgeon, consistent contact from the surgeon and care team, the ability to see the surgeon at postoperative appointments, and, if possible, therapy appointments within the health system in which the surgical procedure was performed. This could satisfy a desire for continuous care, could build the patient-provider relationship, and could ensure access to care for patient populations previously shown to lack it, promoting a patient-centered experience.

Segment 4 placed more importance on the type of implant than any other segment or the whole sample, and this was their most valued attribute following the surgeon factors. A significantly greater number of patients in this segment than in any other segment reported having no difficulty paying $1,000 out-of-pocket (p = 0.01). This preference and demographic profile may describe a segment with the means and willingness to acquire the best perceived technology possible. This is consistent with previous reports suggesting that >80% of patients would be willing to pay out-of-pocket for more sophisticated technology compared with a standard implant55. This population may then be interested in a high-tech bundle that includes, in addition to being seen by a high-performing surgeon, a non-standard implant.

Segments 1 and 2 are less easily characterized. Segment 2 placed more value on referrals than other segments or the whole sample. One way to deliver value to a population interested in referrals would be through outreach strategies to establish referral networks between joint replacement specialists and primary care physicians. However, the most notable characteristic of this segment was a higher education level. Further analysis is needed to better identify patients to whom a referral-centered bundle might appeal.

This study had 3 primary limitations. First, in a best-worst scaling exercise, patients can only rate the attributes presented to them. Certain attributes identified in other research as important to patients facing knee replacement, largely related to patients’ relationship with and subjective opinion of their surgeon, were not included in this study29,32. These factors are not customizable aspects of a care episode and are beyond this current study’s scope. Second, we did not exclude patients who already had undergone a total knee arthroplasty, so our sample did not perfectly represent patients considering total knee arthroplasty. However, the preference profiles between these groups did not vary notably, indicating that having experienced a total knee arthroplasty care episode did not dramatically change patients’ preferences. Third, although latent class analysis produced distinct groups, we were limited in our ability to define them. For example, although the segment 2 preferences were distinct, we were unable to characterize the patients in that segment with the demographic and descriptive data that we collected. These preference differences may have stemmed from previous health-care experiences or factors not captured in our survey. Further analysis, including qualitative interviews, may be needed for further characterization.

In conclusion, this study used an established preference elicitation method to measure patient preferences for aspects of a total knee arthroplasty care episode. Latent class analysis characterized groups of patients according to their preferences. Providers may use this information when designing total knee arthroplasty bundles to deliver patient-centered care. Further research may explore preference differences that we were unable to characterize.

Investigation performed at the Duke University Health System, Durham, North Carolina, and EmergeOrtho, Durham, North Carolina

Disclosure: One author of this study (C.C.C.) received a grant from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under Award Number TL1TR001116. On the Disclosure of Potential Conflicts of Interest forms, which are provided with the online version of the article, one or more of the authors checked “yes” to indicate that the author had a relevant financial relationship in the biomedical arena outside the submitted work (http://links.lww.com/JBJSOA/A72).

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Back to Top | Article Outline

References

1. Stacey D, Hawker G, Dervin G, Tugwell P, Boland L, Pomey MP, O’Connor AM, Taljaard M. Decision aid for patients considering total knee arthroplasty with preference report for surgeons: a pilot randomized controlled trial. BMC Musculoskelet Disord. 2014 Feb 24;15(54):1-10.
2. Skou ST, Roos EM, Laursen MB, Rathleff MS, Arendt-Nielsen L, Simonsen O, Rasmussen S. A randomized, controlled trial of total knee replacement. N Engl J Med. 2015 Oct 22;373(17):1597-606.
3. Singh JA, Vessely MB, Harmsen WS, Schleck CD, Melton LJ 3rd, Kurland RL, Berry DJ. A population-based study of trends in the use of total hip and total knee arthroplasty, 1969-2008. Mayo Clin Proc. 2010 Oct;85(10):898-904. Epub 2010 Sep 7.
4. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, Bridgett L, Williams S, Guillemin F, Hill CL, Laslett LL, Jones G, Cicuttini F, Osborne R, Vos T, Buchbinder R, Woolf A, March L. The global burden of hip and knee osteoarthritis: estimates from the Global Burden of Disease 2010 Study. Ann Rheum Dis. 2014 Jul;73(7):1323-30. Epub 2014 Feb 19.
5. Hogan CA, Sandoval MF, Uhler LM. Centers for Medicare & Medicaid Services’ Comprehensive Care for Joint Replacement: the present and future for orthopedic surgeons. Orthopedics. 2017 Mar 1;40(2):77-80.
6. McLawhorn AS, Buller LT. Bundled payments in total joint replacement: keeping our care affordable and high in quality. Curr Rev Musculoskelet Med. 2017 Sep;10(3):370-7.
7. Bozic KJ, Ward L, Vail TP, Maze M. Bundled payments in total joint arthroplasty: targeting opportunities for quality improvement and cost reduction. Clin Orthop Relat Res. 2014 Jan;472(1):188-93.
8. Siddiqi A, White PB, Mistry JB, Gwam CU, Nace J, Mont MA, Delanois RE. Effect of bundled payments and health care reform as alternative payment models in total joint arthroplasty: a clinical review. J Arthroplasty. 2017 Aug;32(8):2590-7. Epub 2017 Mar 20.
9. Maniya OZ, Mather RC 3rd, Attarian DE, Mistry B, Chopra A, Strickland M, Schulman KA. Modeling the potential economic impact of the Medicare Comprehensive Care for Joint Replacement episode-based payment model. J Arthroplasty. 2017 Nov;32(11):3268-3273.e4. Epub 2017 Jun 8.
10. Navathe AS, Troxel AB, Liao JM, Nan N, Zhu J, Zhong W, Emanuel EJ. Cost of joint replacement using bundled payment models. JAMA Intern Med. 2017 Feb 1;177(2):214-22.
11. Jiranek W, Iorio R. Comprehensive Care for Joint Replacement (CJR), a mandatory program with winners and losers. Semin Arthroplasty. 2016 Sep;27(3):193-5.
12. Dundon JM, Bosco J, Slover J, Yu S, Sayeed Y, Iorio R. Improvement in total joint replacement quality metrics: year one versus year three of the Bundled Payments for Care Improvement Initiative. J Bone Joint Surg Am. 2016 Dec 7;98(23):1949-53.
13. Porter ME. What is value in health care? N Engl J Med. 2010 Dec 23;363(26):2477-81. Epub 2010 Dec 8.
14. Koenig KM, Bozic KJ. Orthopaedic healthcare worldwide: the role of standardization in improving outcomes. Clin Orthop Relat Res. 2015 Nov;473(11):3360-3. Epub 2015 Aug 7.
15. Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010 Nov 17;92(16):2643-52.
16. Froemke CC, Wang L, DeHart ML, Williamson RK, Ko LM, Duwelius PJ. Standardizing care and improving quality under a bundled payment initiative for total joint arthroplasty. J Arthroplasty. 2015 Oct;30(10):1676-82. Epub 2015 May 5.
17. Jubelt LE, Goldfeld KS, Blecker SB, Chung WY, Bendo JA, Bosco JA, Errico TJ, Frempong-Boadu AK, Iorio R, Slover JD, Horwitz LI. Early lessons on bundled payment at an academic medical center. J Am Acad Orthop Surg. 2017 Sep;25(9):654-63.
18. Kimberly JR, Minvielle E. Can health care be “built to order?” — making the shift toward customized care. NEJM Catalyst. 2017 Jul 10.
19. Minvielle E, Waelli M, Sicotte C, Kimberly JR. Managing customization in health care: a framework derived from the services sector literature. Health Policy. 2014 Aug;117(2):216-27. Epub 2014 Apr 24.
20. Balakrishnan M, Raghavan A, Suresh GK. Eliminating undesirable variation in neonatal practice: balancing standardization and customization. Clin Perinatol. 2017 Sep;44(3):529-40. Epub 2017 Jul 5.
21. Varian HR. Intermediate microeconomics: a modern approach. 8th ed. New York: W. W. Norton; 2009. p 806.
22. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press; 2001.
23. Van Citters AD, Fahlman C, Goldmann DA, Lieberman JR, Koenig KM, DiGioia AM 3rd, O’Donnell B, Martin J, Federico FA, Bankowitz RA, Nelson EC, Bozic KJ. Developing a pathway for high-value, patient-centered total joint arthroplasty. Clin Orthop Relat Res. 2014 May;472(5):1619-35. Epub 2013 Dec 3.
24. Mühlbacher A, Johnson FR. Choice experiments to quantify preferences for health and healthcare: state of the practice. Appl Health Econ Health Policy. 2016 Jun;14(3):253-66.
25. Flynn TN. Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling. Expert Rev Pharmacoecon Outcomes Res. 2010 Jun;10(3):259-67.
26. Finn A, Louviere JJ. Determining the appropriate response to evidence of public concern: the case of food safety. J Pub Pol Market. 1992 Fall;11(2):12-25.
27. Mühlbacher AC, Kaczynski A, Zweifel P, Johnson FR. Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev. 2016 Dec;6(1):2. Epub 2016 Jan 8.
28. Cheung KL, Wijnen BF, Hollin IL, Janssen EM, Bridges JF, Evers SM, Hiligsmann M. Using best-worst scaling to investigate preferences in health care. Pharmacoeconomics. 2016 Dec;34(12):1195-209.
29. Zwijnenberg NC, Damman OC, Spreeuwenberg P, Hendriks M, Rademakers JJ. Different patient subgroup, different ranking? Which quality indicators do patients find important when choosing a hospital for hip- or knee arthroplasty? BMC Health Serv Res. 2011 Nov 3;11(299):299.
30. Moser A, Korstjens I, van der Weijden T, Tange H. Patient’s decision making in selecting a hospital for elective orthopaedic surgery. J Eval Clin Pract. 2010 Dec;16(6):1262-8. Epub 2010 Aug 19.
31. Damman OC, Spreeuwenberg P, Rademakers J, Hendriks M. Creating compact comparative health care information: what are the key quality attributes to present for cataract and total hip or knee replacement surgery? Med Decis Making. 2012 Mar-Apr;32(2):287-300. Epub 2011 Aug 15.
32. Bozic KJ, Kaufman D, Chan VC, Caminiti S, Lewis C. Factors that influence provider selection for elective total joint arthroplasty. Clin Orthop Relat Res. 2013 Jun;471(6):1865-72.
33. Mühlbacher AC, Zweifel P, Kaczynski A, Johnson FR. Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. Health Econ Rev. 2016 Dec;6(1):5. Epub 2016 Jan 29.
34. de Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient. 2015 Oct;8(5):373-84.
35. Louviere JJ, Flynn TN. Using best-worst scaling choice experiments to measure public perceptions and preferences for healthcare reform in australia. Patient. 2010 Dec 1;3(4):275-83.
36. Streufert B, Reed SD, Orlando LA, Taylor DC, Huber JC, Mather RC 3rd. Understanding preferences for treatment after hypothetical first-time anterior shoulder dislocation: surveying an online panel utilizing a novel shared decision-making tool. Orthop J Sports Med. 2017 Mar 20;5(3):2325967117695788.
37. Michl GL, Katz JN, Losina E. Risk and risk perception of knee osteoarthritis in the US: a population-based study. Osteoarthritis Cartilage. 2016 Apr;24(4):593-6. Epub 2015 Nov 7.
38. Losina E, Michl GL, Smith KC, Katz JN. Randomized controlled trial of an educational intervention using an online risk calculator for knee osteoarthritis: effect on risk perception. Arthritis Care Res (Hoboken). 2017 Aug;69(8):1164-70. Epub 2017 Jul 10.
39. Paolacci G, Chandler J. Inside the Turk: understanding Mechanical Turk as a participant pool. Curr Dir Psychol Sci. 2014;23(3):184-8.
40. Shammas RL, Mela N, Wallace S, Tong BC, Huber J, Mithani SK. Conjoint analysis of treatment preferences for nondisplaced scaphoid fractures. J Hand Surg Am. 2018 Feb 15;(17):30703-7. Epub 2018 Feb 15.
41. Sawtooth Software, Inc. Latent class v4.5: software for latent class estimation for CBC data. 2012. https://www.sawtoothsoftware.com/download/techpap/lclass_manual.pdf. Accessed 2018 Jun 7.
42. Sawtooth Software, Inc. The CBC latent class technical paper (version 3). 2004. https://www.sawtoothsoftware.com/download/techpap/lctech.pdf. Accessed 2018 Jun 7.
43. García-pérez MA, Núñez-antón V. Cellwise residual analysis in two-way contingency tables. Educ Psychol Meas. 2003;63(5).
44. Beasley TM, Schumacker RE. Multiple regression approach to analyzing contingency tables: post hoc and planned comparison procedures. J Experim Educat. 1995;64(1):79-93.
45. Kim K, Iorio R. The 5 clinical pillars of value for total joint arthroplasty in a bundled payment paradigm. J Arthroplasty. 2017 Jun;32(6):1712-6. Epub 2017 Feb 14.
46. Froimson MI, Rana A, White RE Jr, Marshall A, Schutzer SF, Healy WL, Naas P, Daubert G, Iorio R, Parsley B. Bundled payments for care improvement initiative: the next evolution of payment formulations: AAHKS Bundled Payment Task Force. J Arthroplasty. 2013 Sep;28(8)(Suppl):157-65.
47. Fry DE, Pine M, Jones BL, Meimban RJ. Surgical warranties to improve quality and efficiency in elective colon surgery. Arch Surg. 2010 Jul;145(7):647-52.
48. Millenson ML. Geisinger CABG warranty. A worthwhile experiment. Manag Care. 2008 Jan;17(1):6.
49. Jung HP, Baerveldt C, Olesen F, Grol R, Wensing M. Patient characteristics as predictors of primary health care preferences: a systematic literature analysis. Health Expect. 2003 Jun;6(2):160-81.
50. Armstrong K, McMurphy S, Dean LT, Micco E, Putt M, Halbert CH, Schwartz JS, Sankar P, Pyeritz RE, Bernhardt B, Shea JA. Differences in the patterns of health care system distrust between blacks and whites. J Gen Intern Med. 2008 Jun;23(6):827-33. Epub 2008 Feb 26.
51. Armstrong K, Ravenell KL, McMurphy S, Putt M. Racial/ethnic differences in physician distrust in the United States. Am J Public Health. 2007 Jul;97(7):1283-9. Epub 2007 May 30.
52. Abdus S, Mistry KB, Selden TM. Racial and ethnic disparities in services and the Patient Protection and Affordable Care Act. Am J Public Health. 2015 Nov;105(Suppl 5):S668-75. Epub 2015 Oct 8.
53. Sommers BD, McMURTRY CL, Blendon RJ, Benson JM, Sayde JM. Beyond health insurance: remaining disparities in US health care in the post-ACA era. Milbank Q. 2017 Mar;95(1):43-69.
54. Zuvekas SH, Taliaferro GS. Pathways to access: health insurance, the health care delivery system, and racial/ethnic disparities, 1996-1999. Health Aff (Millwood). 2003 Mar-Apr;22(2):139-53.
55. Schwarzkopf R, Sagebin FM, Karia R, Koenig KM, Bosco JA, Slover JD. Factors influencing patients’ willingness to pay for new technologies in hip and knee implants. J Arthroplasty. 2013 Mar;28(3):390-4. Epub 2012 Nov 8.

Supplemental Digital Content

Back to Top | Article Outline
Copyright © 2018 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.