Attempts to Limit Censoring in Measures of Patient Satisfaction.

BACKGROUND: Measures of patient satisfaction are increasingly used to measure patient experience. Most satisfaction measures have notable ceiling effects, which limits our ability to learn from variation among relatively satisfied patients. This study tested a variety of single-question satisfaction measures for their mean overall score, ceiling and floor effect, and data distribution. In addition, we assessed the correlation between satisfaction and psychological factors and assessed how the various methods for measuring satisfaction affected net promoter scores (NPSs).
METHODOLOGY: A total of 212 patients visiting orthopedic offices were enrolled in this randomized controlled trial. Patients were randomized to 1 of 5 newly designed, single-question satisfaction scales: (a) a helpfulness 11-point ordinal scale from 0 to 10, (b) a helpfulness ordinal 11-point scale from 0 to 5 (ie, with 1.5, 2.5, etc), (c) a helpfulness 100-point slider, (d) a satisfaction 11-point ordinal scale from 0 to 10, and (e) a willingness to recommend 11-point ordinal scale from 0 to 10. Additionally, patients completed the 2-item Pain Self-Efficacy Questionnaire (PSEQ-2), 5-item Short Health Anxiety Inventory (SHAI-5) Scale, and Patient-Reported Outcomes Measurement Information System (PROMIS) Depression. We assessed mean and median score, ceiling and floor effect, and skewness and kurtosis for each scale. Spearman's correlation tests were used to test correlations between satisfaction and psychological status. Finally, we assessed the NPS for the various scales.
RESULTS: Ceiling effects ranged from 29% to 68%. The 11-point ordinal helpfulness scale from 0 to 10 had the least ceiling effect (29%). All of the scales were asymmetrically distributed, with the 11-point ordinal scale from 0 to 5 having the most Gaussian distribution (skew = 0.64 and kurtosis = 2.3). Satisfaction scores did not correlate with psychological factors: PSEQ-2 (r = 0.04; P = .57), SHAI-5 (r = 0.01; P = .93), and PROMIS Depression (r = -0.04; P = .61). Net promoter scores varied substantially by scale design, with higher scores corresponding with greater ceiling effects.
CONCLUSIONS: Variations in scale types, text anchors, and lead-in statements do not eliminate the ceiling effect of single-question measures of satisfaction with a visit to an orthopedic specialist. Further studies might test other scale designs and labels. LEVEL OF EVIDENCE: Diagnostic; Level II.

Introduction

Patient satisfaction is an increasingly used measure of patient experience (1 –7). Patients have high expectations regarding when they present for medical care. They expect a high level of customer experience with their medical care and rate their satisfaction on many aspects (ie, medical staff, clinic location, parking). A high level of patient satisfaction largely depends on the interaction and communication with the physician (8). Access to physician and medical center patient satisfaction data might influence a person’s decision on where to seek care. Waters et al identified 7 themes influencing satisfaction in orthopedic outpatient clinics, including trust, relatedness—extent to which a patient feels connected to, respected, or understood by the clinician—, expectations, wait time, visit duration, communication, and empathy (7). Satisfaction is associated with adherence to clinician recommendations (8).

There are several measures of patient satisfaction. An 11-point ordinal measure of satisfaction is the most commonly used. Measures of patient experience tend to have strong ceiling effects (more than half give one of the top 2 scores) that hinder attempts to learn from patient experience and evolve and improve care (9 –14). There have been many attempts to limit ceiling effects (9,11,12,14,15). Using a 5-point very positively worded scale significantly lowered mean scores in comparison to a moderately positively worded scale, but there was no difference in skewness and ceiling effects between the scales (9). A 4-point labeled scale and an 11-point numeric scale showed both strong floor and ceiling effects, but ceiling effects were more pronounced in the 4-point labeled scale (11). Comparing a 5-point scale with descriptors with a 10-point scale with descriptors, favored the 5-point scale when assessing means, floor, and ceiling effects (12). A visual analog scale avoided the ceiling effect better than a Likert scale (14). A 10-item visual analog format showed more variability than a 5-item Likert format, 5-item satisfaction format, 5-item valuation format, or 4-item Chernoff faces (15). These previous studies were able to reduce ceiling effects somewhat, but were not able to eliminate them.

The purpose of this study is to compare various scales to measure patient satisfaction in musculoskeletal specialty care. The primary null hypothesis was that there is no difference in mean and median satisfaction, ceiling or floor effect, and data distribution (by looking at skewness and kurtosis) of various satisfaction scales. The secondary hypothesis we assessed was that there is no correlation between scaled satisfaction and psychological status. Finally, we assessed how the satisfaction scores compared to the net promoter scores (NPSs).

Methodology

Study Design

After obtaining approval by our institutional review board (The University of Texas at Austin; protocol number 2018-04-0039), a total of 212 patients from multiple orthopedic practices were asked to participate in this randomized controlled trial. The patients included both new and return visits. Enrollment took place over a 2-month period in 4 orthopedic offices in a large urban area. All English-speaking patients, aged 18 to 89 years, visiting an orthopedic surgeon were eligible for this study. Patients were randomized by research assistants, using an unblocked Excel random number generator, to 1 of 5 satisfaction scales. We were granted a waiver of written informed consent. Completing questionnaires indicated informed consent. All questionnaires were completed using an encrypted tablet via a secure, US Health Insurance Portability and Accountability Act-compliant electronic platform: REDCap (Research Electronic Data Capture: a secure web-based application for building and managing online surveys and databases) (16).

After the patients’ clinic visit, the surgeon provided the clinical diagnosis. From there, patients completed a demographic survey, including sex, age, race/ethnicity, marital status, education, work status, insurance status, and comorbidities. Patients were then provided the randomly assigned satisfaction scale, the 2-item short form of the Pain Self-efficacy Questionnaire (PSEQ-2), the 5-item Short Health Anxiety Inventory (SHAI-5), and Patient-Reported Outcomes Measurement Information System (PROMIS) Depression.

Outcome Measures

The primary outcome was patient satisfaction directly after visiting the orthopedic surgeon. We used 5 different satisfaction scales: (a) a helpfulness 11-point ordinal Likert scale from 0 to 10 and 5 anchor points, (b) a helpfulness ordinal 11-point Likert scale from 0 to 5 (ie, with 1.5, 2.5, etc) and 5 anchor points, (c) a helpfulness 100-point slider with 5 anchor points, (d) a satisfaction 11-point ordinal Likert scale from 0 to 10 and 5 anchor points, and (e) a willingness to recommend 11-point ordinal Likert scale from 0 to 10 and 5 anchor points (Figure 1). For all scales, higher scores indicated more satisfaction.

Figure 1.

Score Distributions for All Scales.

The PSEQ-2 is a measure to assess the confidence in performing activities while in pain (17 –19). Each item is scored from 0 to 6, with higher scores suggesting more self-efficacy (17).

The SHAI-5 is a measure to assess health anxiety (20). Each item can be scored 0 to 3, with higher total scores representing more health anxiety.

The PROMIS Depression was used to measure symptoms of depression (21).

Patient Characteristics

Two hundred twelve patients were enrolled in the study, including 90 (42%) men and patient had a median age of 50 (interquartile range [IQR]: 38-62). One hundred and forty-three patients (67%) were self-reported of white ethnicity (Table 1). Median PSEQ-2 score was 11 (9-12), 4 (3-6) for SHAI-5, and 48 (43-53) for PROMIS Depression. Patients had a variety of diagnoses (Supplemental Appendix 1), while the most common diagnoses were trigger finger, carpal tunnel syndrome, ganglion cyst, and knee arthritis.

Table 1.

Patient and Clinical Characteristics.a

Variables	N = 212
Age in years	50 (38-62)
Male	90 (42)
Race
White	143 (67)
Other	69 (33)
Marital status
Married	123 (58)
Single	49 (23)
Divorced/separated/widowed	40 (19)
Level of education
High school or less	45 (21)
2-year college	42 (20)
4-year college	71 (33)
Postcollege graduate degree	54 (25)
Work status
Employed	132 (62)
Retired	45 (21)
Homemaker/student/other	35 (17)
Insurance status
Private/military	155 (73)
Other	57 (27)
Diabetes	21 (9.9)
Other comorbidities	43 (20)
PSEQ-2	11 (9 –12)
SHAI-5	4 (3 –6)
PROMIS Depression	48 (43-53)

Abbreviations: PROMIS: Patient-Reported Outcomes Measurement Information System; PSEQ-2, 2-item Pain Self-Efficacy Questionnaire; SHAI-5, Short Health Anxiety Inventory.

a Continuous variables as median (interquartile range); discrete variables as number (percentage).

Statistical Analysis

Continuous data (both psychological measures and satisfaction scales) showed both normal and non-normal distributions. We reported continuous variables using mean, SD, and median (IQR). Categorical data are presented as frequencies and percentages. We calculated floor and ceiling effect and the skewness and kurtosis of every scale. We scaled every scale to 10 and also standardized every scale. Difference in satisfaction scores were analyzed using Kruskal-Wallis tests and differences between floor and ceiling effects were calculated using Fisher’s exact test. When a ceiling effect occurs, there could be a normal distribution, but we are not able to detect this because of a threshold to the measurement. We want to limit loss of data above the threshold of our measurement, which is known as censoring (22). Skewness and kurtosis are rough indicators for a normal distribution of values. Skewness (γ1) is an index of the symmetry of a distribution. Symmetric distributions have a skewness of 0. If skewness has a positive value, it suggests relatively many low values, having a long right tail. Negative skewness suggests relatively many high values, having a long left tail (23,24). Kurtosis (γ2) is a measure to describe tailedness of a distribution. Kurtosis of a normal distribution is 3. Negative kurtosis represents little peaked distribution, and positive kurtosis represents more heavy peaked distribution (23,24). If skewness is 0 and kurtosis is 3, there is a normal or Gaussian distribution. Correlation between scaled satisfaction and psychological status is tested by Spearman’s correlation tests for satisfaction scores with PSEQ-2, SHAI-5, and PROMIS Depression. We calculated the NPS using our scaled scores (25). The NPS is widely used in the service industry to assess customer satisfaction (26). Respondents are grouped as promotors if they score 9 or 10, as passives if they score 7 or 8, or as detractors if they score from 0 to 6. The NPS is calculated by subtracting the percentage detractors from the percentage promoters. The NPS ranges between −100 and 100.

An a priori power analysis indicated that 200 patients would be needed to assess a difference in satisfaction of 0.5 on a 0 to 10 scale with an effect size of 90%, and α set at .05. In order to account for incomplete responses, we aimed for a sample size of 210 patients.

Results

Difference in Satisfaction Scores

Mean scaled satisfaction scores (range 0-10) were 8.6 ± 1.4 for the helpfulness 11-point ordinal scale from 0 to 10, 9.0 ± 1.0 for the helpfulness ordinal 11-point scale from 0 to 5, 8.8 ± 1.5 for the helpfulness 100-point slider, 8.7 ± 1.4 for the satisfaction 11-point ordinal scale from 0 to 10, and 9.4 ± 1.0 for the willingness to recommend 11-point ordinal scale from 0 to 10 (Table 2)

Table 2.

Score Distributions of the Scales.a

Scale	Number completed	Visible anchors (N)	Visible numbers	Mean score	Median score	Range	Possible range	Mean scaled score	Median scaled score	Mean scaled range	Possible scaled range	P value scaled score	P value standardized score	Net promoter score
1	45 (21)	Yes (5)	Yes	8.6 ± 1.4	9.0 (8.0-10)	2.0-10	0-10	8.6 ± 1.4	9.0 (8.0-10)	2.0-10	0-10	.026	<.001	44
2	42 (20)	Yes (5)	Yes	4.5 ± 0.51	4.5 (4.0-5.0)	3.5-5.0	0-5.0	9.0 ± 1.0	9.0 (8.0-10)	7.0-10	0-10			60
3	39 (18)	Yes (5)	Yes	88 ± 15	97 (74-100)	54-100	0-100	8.8 ± 1.5	9.7 (7.4-10)	5.4-10	0-10			44
4	45 (21)	Yes (5)	Yes	8.7 ± 1.4	9.0 (8.0-10)	3.0-10	0-10	8.7 ± 1.4	9.0 (8.0-10)	3.0-10	0-10			53
5	41 (19)	Yes (5)	Yes	9.4 ± 1.0	10 (9.0-10)	6.0-10	0-10	9.4 ± 1.0	10 (9.0-10)	6.0-10	0-10			76

a Bold indicates statistically significant difference; continuous variables as mean ± standard deviation (range): or median (interquartile range); discrete variables as number (percentage).

Because of non-normal distributions, we tested for a difference using median scores. We found a difference in median scaled scores (range 0-10) for the 5 different satisfaction scales: helpfulness 11-point ordinal scale from 0 to 10: 9.0 (8.0-10), helpfulness ordinal 11-point scale from 0 to 5: 9.0 (8.0-10), helpfulness 100-point slider: 9.7 (7.4-10), satisfaction 11-point ordinal scale from 0 to 10: 9.0 (8.0-10), and willingness to recommend 11-point ordinal scale from 0 to 10 (10 [9.0-10]; P = .026; Table 2).

Difference in Ceiling and Floor Effects

We found a difference in ceiling effect for the satisfaction scales (P = .003), with the willingness to recommend 11-point ordinal scale from 0 to 10 showing the highest ceiling effect (68%) and the helpfulness 11-point ordinal scale from 0 to 10 showing the lowest (29%; Table 3). None of the scales showed a floor effect. No patient scored the lowest rating on any scale.

Table 3.

Floor and Ceiling Effect and Skewness and Kurtosis of the Scales.a

Scale	Floor effect	P value	Ceiling effect	P value	Skewness	Kurtosis
1	0 (0)	–	13 (29)	.003	−2.2	11
2	0 (0)		16 (38)		−0.64	2.3
3	0 (0)		18 (46)		−0.83	2.3
4	0 (0)		16 (36)		−1.6	6.9
5	0 (0)		28 (68)		−1.6	4.6

a Bold indicates statistically significant difference; discrete variables as number (percentage).

Skewness and Kurtosis

We found negative skewness for every satisfaction scale (the helpfulness 11-point ordinal scale from 0 to 10: γ1 −2.2; the helpfulness ordinal 11-point scale from 0 to 5: γ1 −.64; the helpfulness 100-point slider: γ1 −.83; the satisfaction 11-point ordinal scale from 0 to 10: γ1 −1.6; the willingness to recommend 11-point ordinal scale from 0 to 10: γ1 −1.6; Table 3). We found positive kurtosis for all satisfaction scales (the helpfulness 11-point ordinal scale from 0 to 10: γ2 11; the helpfulness ordinal 11-point scale from 0 to 5: γ2 2.3; the helpfulness 100-point slider: γ2 2.3; the satisfaction 11-point ordinal scale from 0 to 10: γ2 6.9; the willingness to recommend 11-point ordinal scale from 0 to 10: γ2 4.6). The helpfulness ordinal 11-point scale from 0 to 5 had the most Gaussian distribution (γ1 −.64 and γ2 2.3).

Correlation Satisfaction and Psychological Status

Scaled satisfaction scores were not significantly correlated with PSEQ-2 (r = 0.04; P = .57), SHAI-5 (r = 0.01; P = .93), or PROMIS Depression (r = −0.04; P = .61; not in table).

Net Promoter Scores

Net promoter scores were 44 for the helpfulness 11-point ordinal scale from 0 to 10, 60 for the helpfulness ordinal 11-point scale from 0 to 5, 44 for the helpfulness 100-point slider, 53 for the satisfaction 11-point ordinal scale from 0 to 10, and 76 for the willingness to recommend 11-point ordinal scale from 0 to 10.

Discussion

Patient satisfaction is increasingly emphasized, beginning to be tied to reimbursement, and even board certification. Patient satisfaction is a component of health care quality and reflects the ability of health care professionals to meet the needs and expectations of their patients (27). It is difficult to distinguish satisfied patients from less-satisfied patients because of ceiling effects of current satisfaction scales (10 –12). The purpose of this study was to assess ceiling (and floor) effects of various satisfaction scales. Secondarily, to assess skewness and kurtosis and means of various scaled satisfaction scales.

As with all survey measures, an inherent bias is present that induces limitations to our study. Despite a sample size of 212 patients, we utilized 5 satisfaction scales, which resulted in 39 to 45 patients for each satisfaction scale. A larger sample size may give more variety in scores on every scale and result in a more normal distribution. Secondly, this study was conducted in one large urban area in 4 separate orthopedic offices. Results may not be generalizable to other subspecialty offices or countries, nor to nonspecialty care. Third, most patients of this sample size were self-reported as white, did not have self-reported comorbidities, and were fluent in English. Results may vary in a more mixed study population. For example, Menendez et al found that Spanish-speaking patients are less satisfied than English-speaking patients (28). Fourth, both new and follow-up patients were eligible for this study. While new patients only have a single encounter from which to develop an opinion on satisfaction, follow-up patients may have a previous relationship with the physician and score accordingly for the entire relationship. Dissatisfied patients are less likely to return for follow-up, resulting in less response variability on the scales. On the contrary, this mix of new and follow-up patients could reflect the usual mix in a specialist’s practice contributing to the heterogeneity of our cohort. Fifth, in our study, we did not ask specifically to rate satisfaction with either the process of care or treatment outcome. Asking to rate satisfaction of the process of care or treatment outcome may give different scores (29). Sixth, we did not perform separate analysis for different diagnoses because none of the diagnoses had sufficient numbers and—based on prior work—we did not feel diagnosis was likely to be important (30,31). Differentiating between preoperative and postoperative patients and between acute and chronic disorders may lead to different results. The results of this type of study might be different if restricted to people with a specific diagnosis. Patients with more pain, for example, have the tendency to rate their overall hospital experience lower, giving different satisfaction scores (32). Finally, we did not formally validate any of these measures. That step can be completed once we have successfully reduced or eliminated ceiling effects.

The satisfaction scales showed differences in mean and median satisfaction. Our mean scaled scores range from 8.6 to 9.4 and our median scaled scores range from 9.0 to 10, which highlights the issue we sought to address in evaluating the ceiling effect (5). We hypothesize that patients are likely being respectful, deferential, and appreciative upon completion of their office visit despite our earnest attempts to obtain nonbiased feedback to identify areas of improvement in the clinical setting.

The satisfaction scales showed differences in ceiling effects for our various satisfaction scales. We have not been able to eliminate the ceiling effect for measures of satisfaction with a visit to an orthopedic specialist. Consistent with a study of Dell-Kuster et al, a numeric 11-point Likert scale provides the least ceiling effect (11). However, this scale still has a substantial (29%) ceiling effect, implying loss of data beyond the threshold of this scale. In our study, we found that an 11-point Likert scale with 5 visible anchors seems to have less ceiling effect when inquiring about helpfulness compared to asking about satisfaction (36%) or willingness to recommend (68%). Floor effects are uncommon with satisfaction scores, except for the occasional very dissatisfied patient who chooses the lowest score, but otherwise scores below 5 are uncommon.

Differences in data distribution were identified by analyzing skewness and kurtosis for our various satisfaction scores. The scales were asymmetrically distributed. Every scale had a negative skewness reflecting censoring at the top values. The helpfulness ordinal 11-point scale from 0 to 5 had the most normal skewness (−0.64). None of the scales had a normal kurtosis of 3. The helpfulness ordinal 11-point scale from 0 to 5 and helpfulness 100-point slider were closest to a normal kurtosis, with a kurtosis of 2.3. We were able to determine that the type of scale affects censoring, skewness, and kurtosis and therefore the distribution.

There was no significant correlation between scaled satisfaction and psychological status. Intrinsic factors that specifically drive patient satisfaction remain unclear (33). Smith et al studied extrinsic factors that influence patient satisfaction scores and found that patients are more satisfied if they feel that their physician provides them with compassionate, coordinated care (34). The characteristics of the individual provider were the largest factor influencing patient satisfaction.

Net promoter scores varied substantially by scale design, with higher scores corresponding with greater ceiling effects. It may be that organizations will need to choose between higher scores and learning more from people by having less censoring. Our 11-point ordinal “willingness to recommend the doctor” scale, which is similar to the Friends and Family Test (FFT; the FFT asks how likely a patient is to recommend the same service to a friend or family member with the same condition) (27) had the largest ceiling effect and the best NPS.

Conclusion

Future studies might consider using other scale methods to try to limit ceiling effects and censoring. One option would be developing a computer adaptive test to measure patient satisfaction. A computer adaptive test uses answers to previous questions, customizing the subsequent question, resulting in a higher level of precision using fewer questions, and better able to limit ceiling effects (35). Another option would be using open-ended questions where answers would be scored to assess satisfaction, rather than using numeric scales. We hope others will join us in attempts to reduce ceiling effects in patient experience measures. When effective techniques are identified, the resulting new scales can go through a more thorough validation.

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Supplemental Material, JPX_Suppmaterial for Attempts to Limit Censoring in Measures of Patient Satisfaction by Cindy Nguyen, Joost T P Kortlever, Amanda I Gonzalez, David Ring, Laura E Brown and Jason R Somogyi in Journal of Patient Experience