Measuring patient satisfaction with health care treatment using the Short Assessment of Patient Satisfaction measure delivered superior and robust satisfaction estimates.

Journal of Clinical Epidemiology 67 (2014) 527e537

Measuring patient satisfaction with health care treatment using the Short Assessment of Patient Satisfaction measure delivered superior and robust satisfaction estimates Graeme Hawthornea,b,*, Jan Sansonic,d, Laura Hayesb,e, Nick Marosszekyf, Emily Sansonic,d a Department of Medicine, Northern Clinical Research Centre, The University of Melbourne, 185 Cooper Street, Epping, Victoria 3076, Australia Mental Health Evaluation Unit, Department of Psychiatry, The University of Melbourne, Level 1 North, Royal Melbourne Hospital, Grattan Street, Parkville, Victoria 3050, Australia c Centre for Health Service Development, University of Wollongong, Northfields Avenue, New South Wales 2500, Australia d Australian Health Services Research Institute, University of Wollongong, Northfields Avenue, Wollongong, New South Wales 2500, Australia e Psychosocial Research Centre, 130 Bell St, Coburg, Victoria 3058, Australia f Department of Psychology, Macquarie University, Building C3A, Faculty of Human Sciences, New South Wales 2109, Australia

b

Accepted 18 December 2013

Abstract Objectives: Reviews of patient satisfaction suggest seven dimensions, each of which should be assessed. This study reports development of a short generic patient satisfaction measure for use in routine clinical practice. Study Design and Settings: Participants were randomly recruited from two Australian incontinence clinics. Participants completed a follow-up questionnaire including patient satisfaction items. Iterative Mokken and Rasch analyses derived the Short Assessment of Patient Satisfaction (SAPS) scale from the item bank. Results: The SAPS psychometric properties illustrated the following features, namely its descriptive system covers all seven patient satisfaction dimensions, there were no misfitting items, and the scale exceeded the Loevinger H criteria for a strong unidimensional scale. The reliability of the SAPS was Cronbach a 5 0.86. When discriminatory function was examined, the SAPS scale was more sensitive than two other generic patient satisfaction instruments. Conclusion: The SAPS scale is based on a firm theoretical model of patient satisfaction and its descriptive system covers the known dimensions contributing to patient satisfaction. Its internal psychometric properties exceeded standard psychometric standards, and it discriminated at least as well as other longer patient satisfaction measures. Although it needs further validation, the study results suggest that it may be useful for assessing patient satisfaction with health care. Copyright. Ó 2011. Copyright in the SAPS is held and will continue to be held in perpetuity by the authors with a license to the Commonwealth of Australia. Researchers are welcome to use the SAPS subject to acknowledgement/citation of the authors’ rights in the usual way. Keywords: Assessing health care; Instrument development; Mokken analysis; Outcome measurement; Patient satisfaction; Rasch analysis

1. Introduction During the past 30 years, the measurement of patient satisfaction has increased in popularity mainly owing to three changes in health care. First, the role of clinicians has changed from one of helping patients through their illness to one where the clinician is expected to either cure the patient or alleviate chronic symptoms. Second, the patient-centered care movementdwhich presents patients as consumers of health caredhas changed the priority in * Corresponding author. Tel.: þ61-3-8405-2317. E-mail address: [email protected] (G. Hawthorne).

health care from a belief in beneficence to autonomy and has led to patient views being taken into account during medical decision making. Third, patient perspectives are increasingly sought for inclusion in the monitoring of health care and the legitimizing of health policy [1,2]. Despite this rise in the popularity of patient satisfaction assessment, there are conflicting definitions of it. The major patient satisfaction theories were all published during the 1980s; almost all research since then is based on these. Ware et al. [3] argued that patient satisfaction was a function of patients’ subjective responses to experienced care mediated by personal preferences and expectations. Linder-Pelz [4] postulated that it was mediated by personal

0895-4356/$ - see front matter Copyright. Ó 2011. Copyright in the SAPS is held and will continue to be held in perpetuity by the authors with a license to the Commonwealth of Australia. Researchers are welcome to use the SAPS subject to acknowledgement/citation of the authors’ rights in the usual way. http://dx.doi.org/10.1016/j.jclinepi.2013.12.010

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G. Hawthorne et al. / Journal of Clinical Epidemiology 67 (2014) 527e537

What is new? Key findings There are few valid and reliable outcome measures assessing patient satisfaction with health care. The Short Assessment of Patient Satisfaction (SAPS) scale is a new instrument with initially demonstrated validity and reliability. What this adds to what was known? Based on a sound conceptual model of patient satisfaction, the SAPS scale meets several calls over the past 20 years for a modern, short generic measure of patient satisfaction. Initial validation tests suggested that it outperformed leading existing measures of patient satisfaction. What is the implication and what should change now? The SAPS scale needs to be tested in a variety of clinical settings. If validation studies support its psychometric properties, it will meet the need for a modern, psychometrically valid measure of patient satisfaction. It can be used in a wide variety of clinical settings in place of older instruments, which have known measurement problems.

beliefs and values about care as well as prior expectations of the care. Fox and Storms [5] advocated that a person’s orientation determined satisfaction; dissatisfaction occurred where there was transgression of the relationship between expectation and experience. Fitzpatrick and Hopkins [6] argued that expectations were socially mediated, reflecting the health goals of the patient and the extent to which illness and health care violated the patient’s personal sense of self. Finally, Donabedian [7,8] postulated that it was based on personal relationships within health care systems and health care outcomes from treatment, where these were mediated by the values of the patient. Consistent with this, subsequent research has shown that the dominant predictor appears to be the patientepractitioner relationship, mediated by expectations of this relationship, prior experiences, and health outcomes [9,10]. The implication is that the construct of patient satisfaction covers all aspects of care quality, particularly the interpersonal processes. A review of the literature arising from these theories [1] revealed that although these theories have been operationalized in various ways, an overall inclusive model of patient satisfaction should cover the following key dimensions:

1. Appropriate access to health services, including the environment within which treatment takes place and the level of care coordination [3,5,11,12]; 2. The provision of health information [5,8,9,11e14]; 3. The relationship between the patient and health care staff, specifically empathy with the patient [3,7,9,11,12, 15e18]; 4. Participation in making choices regarding health treatment, including the associated fears and sense of loss of control as well as the appropriate use of treatment therapies and medications [11,19e21]; 5. Satisfaction with the treatment provided, that is, the technical quality of the care provided [3,5,11,14, 18,22]; 6. The effectiveness of treatment, including the extent to which treatment meets patient expectations of care and helps the patient in their daily life [3,7,9,11,12,14]; and 7. General satisfaction [23,24]. Regarding assessing patient satisfaction, as this operationalized model implies, there is an obvious tension between condition-specific instruments, which cover just one or two of these dimensions and generic instruments, which cover all. Although condition-specific instruments are attractive, the limitations are that they may not be valid when used in other settings, with other conditions or provide estimates that are comparable. Generic instruments overcome these restrictions. Where their descriptive systems cover all the dimensions described previously, construct validity and generalizability may be claimed, although there may be a loss of discriminatory power in particular diseases or situations. However, few generic instruments have been published to date for which adequate psychometric profiles are available, and there seems to be a general dissatisfaction with published instruments (especially in light of the fact that there are, literally, thousands of patient satisfaction measures available on the Internet). These judgments rest on the findings of review articles. Sitzia [25] reviewed the literature and found that just half of all reviewed articles reported any psychometric data; yet, 81% reported using a new patient satisfaction instrument and a further 10% reported modifying a previously existing instrument. Most of the instruments used had little evidence of reliability or validity; and of articles reporting a new instrument, 60% reported no psychometric data whatsoever. More recently, Hawthorne [1] reported similar findings; viz., that many studies reported patient satisfaction in a single sentence where it was offered as complimentary evidence of treatment success. Few articles reported the instruments used, their psychometric properties, or the actual results. It is possible that this long-standing unsatisfactory situation is, in part, a function of available instruments. Available instruments may be culturally specific [26e28], they may be too long to be used in busy clinical or research settings [3,29], they may be condition specific [30,31], biased (eg, where there is over measurement of some dimensions and


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The methods used in the study are fully described in Hawthorne et al. [35]. Because this report may not be widely available to those without Internet access, the methods are briefly described.

pretreatment incontinence severity, and as current state assessments after questions probing treatment. Patient satisfaction was assessed through administration of three generic leading patient satisfaction instruments and one incontinence-specific measure. These instruments were chosen after detailed review of patient satisfaction theories and instruments [1]. The three generic measures were the Client Satisfaction Questionnaire (CSQ-18), which consists of 18 items assessing satisfaction with health care services [24]; the Consultation Satisfaction Questionnaire (ConsultSQ), which has 18 items assessing clinician consultations [23]; and the Patient Satisfaction Index (PSI), which has 23 items covering satisfaction with medical care [53]. The incontinence measure was the revised version of the Genito-Urinary Treatment Satisfaction Scale (GUTSS), which comprises eight items assessing satisfaction with treatment outcomes for urinary incontinence [14]. Items from these instruments were checked against the model of patient satisfaction reviewed previously to ensure adequate coverage of the dimensions. Instruments were scored as per the developers’ instructions; and for the development of the SAPS, all the items were pooled.

2.1. Participants

2.3. Statistical analyses

The participants were a random sample of females undergoing posturinary incontinence treatment (physiotherapy or surgery), recruited from two incontinence clinics in Melbourne and Sydney, Australia in 2005/2006. Study eligibility criteria were having received treatment 3e12 months previously and having sufficient English knowledge to complete a self-report questionnaire. A stratified random sample of eligible cases was drawn from participating clinics’ patient lists. Stratification was by primary treatment (physiotherapy/physiotherapy with surgery/surgery). The sample size was based on detecting small differences in effect sizes by treatment cohort (Cohen’s d 5 0.25 [36]), the sample size requirements for stable factor [37] and item response theory (IRT) analyses [38] (N 5 250 cases). A participation rate of 60% was expected [39e42]. The sample size was therefore increased to over-sample (250/0.6 5 420).

After data entry, the raw data were assessed for missing data, outliers, and other irregularities. Missing data were imputed using horizontal imputation for scale items [54]. No attempt was made to impute categorical missing data (eg, gender). Scale reliabilities are reported using Cronbach alpha (a). All the measures used, however, contained non-normally distributed item-level data. Although the use of a under these circumstances violates its axioms, studies that have examined it using skewed data have reported that it is a robust measure [55e58]. In addition, Mokken’s r (rho) is reported because under nonparametric IRT scale models, a underestimates reliability [59]. Categorical data are reported as frequencies with percentages. Effect sizes were calculated with Cohen’s d with scores of d 5 0.20 indicating a small effect, d 5 0.50 a moderate effect, and d 5 0.80 a large effect [36]. Owing to data skew, Spearman correlations were performed between scales. Analysis of variance was used to examine differences between known groups. For direct comparison between patient satisfaction scales, raw scores were computed as percentage scores and T-scores (mean 5 50, standard deviation 5 10) [60]. Dependent t-tests were used to examine differences between groups. Skew was assessed and non-normal data transformed using reciprocal and log transformations before analysis. For comparing the relative responsiveness of scales, the relative efficiency (RE) statistic was used [61,62]. Given the non-normality of the data, scale homogeneity was examined with Mokken analysis where the criterion for a unidimensional scale is Loevinger H greater than 0.40 [63]. Although Mokken analysis is appropriate for

under measurement of others) [32,33], or they may lack psychometric robustness in the sense that there is very little evidence supporting instrument validity or reliability [4,28,34]. Both the Sitzia and Hawthorne reports also suggested that there are no modern, generic patient satisfaction instruments that have been developed using contemporary psychometric practices, which are short and easy to use and for which robust psychometric data are available. In light of this situation, Hawthorne recommended the development of a short, valid, and reliable generic patient satisfaction instrument. Consistent with this call, this study reports the development of a new generic, short, valid, and reliable measure of patient satisfaction, the Short Assessment of Patient Satisfaction (SAPS) scale.

2. Method

2.2. Measures Basic demographic data were collected, including age, country of birth, relationship status, education attainment, and work status. Screening for depression likelihood was assessed with the WHO5 [43,44], social isolation with the Friendship Scale [45,46], and quality of life with the Assessment of Quality of Life (AQoL) measure [47,48]. Details of incontinence type and treatment were also collected. Incontinence severity was assessed by the Incontinence Severity Index (ISI) [49,50] and the Urogenital Distress Inventory-6 (UDI-6) [51,52]. The ISI and UDI-6 were administered twice at the start of the study questionnaire as retrospective assessments (then-tests) probing

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examining the properties of a scale (and in particular the relationship between an item and a scale), it does not enable examination of individual items and their contribution to fundamental measurement [64], for which purpose partial credit Rasch IRT analysis was used [65,66]. The criteria for item removal were examination of the item stems for double-barreled or verbose statements; poor item data distributions (after recoding); and statistical comparison of item fit, assessed by Crit values (ie, the number of violations of the underlying probability Guttman pattern). Where two items were compared, the item with the higher Crit value (greater number of violations) was considered for removal under both P-matrix and rest-of-test score models (the sample was randomly split into three groups for this latter test); and comparison of Loevinger Hi-values, where the item with the lowest Hi-value was considered for removal (ie, the least well-fitting item). For the Rasch analysis, item misfit was considered if the c2 or F-statistic probability value was less than 0.05 or the fit residuals were greater than j2.50j, which is equivalent to setting the test value to P-value lower than 0.01. The reason for setting this stringent criteria was to avoid chance capitalization owing to item parameter estimation errors, which may occur where P-value is lower than 0.05 or j2.00j [67]. Where two items performed similarly on these criteria, each was removed in turn and the model tested. The model with the higher scale Loevinger H-value and better Rasch fitting was accepted. The Mokken and Rasch iterations were performed such that the number of items measuring each patient satisfaction dimension was maintained at one or more items. This ensured equal and consistent representation of the dimensions within the ever-diminishing item pool. This procedure was an attempt to achieve a balance between fidelity to the model of patient satisfaction, the statistical requirements for a strong homogenous scale, and the measurement properties of individual items. Differential item functioning (DIF) was examined by treatment group (physiotherapy/ physiotherapy with surgery/surgery). Percentages are reported to the nearest integer. Statistical analyses were performed using SPSS Version 14.0 [68], MSP5 Version 5.0 [64], Instat Version 3.06 [69], and RUMM2020 Version 4.0 [70]. This study was approved by the Ethics Committees at participating hospitals and the University of Wollongong.

3. Results A total of 178 completed questionnaires were returned, giving a simple response rate of 42%. Because questionnaires were distributed through the clinics without the researchers’ participation, the number of patients out of scope is unknown. Participants’ demographic details were that 80% were Australian born; age cohorts were 31e45 years (17%), 46e60 (41%), 61e75 (32%), and 75þ (10%). Education attainment was primary school only (9%), high school

Table 1. Urinary incontinence treatment and severity, pre- and posttreatment Parameters Incontinence treatment Physiotherapy Physiotherapy and surgery Surgery Other (not stated) Expected outcomes Incontinence cured Partly cured Some improvement That treatment would not help at all Pretreatment information Excellent Very good Good Fair Poor Very poor Perceived treatment success Incontinence cured Partly cured Some improvement Treatment did not help at all Did treatment meet expectations?a Outcomes better than expected Outcomes as expected Outcomes worse than expected ISIb Pre (retrospective; mean, SD) Post (current; mean, SD) Then-test mean change score (mean, SD) Percentage of cases reporting improvement Percentage of cases with no change Percentage of cases with worse outcomes UDI-6c Pre (retrospective; mean, SD) Post (current; mean, SD) Then-test mean change score (mean, SD) Percentage of cases reporting improvement Percentage of cases with no change Percentage of cases with worse outcomes

N (%) 47 57 69 3

(27) (33) (40) (2)

91 52 27 2

(53) (30) (16) (1)

57 63 36 16 2 1

(33) (36) (21) (9) (1) (1)

62 57 42 13

(36) (33) (24) (8)

28 (17) 76 (45) 66 (39) 13.49 6.05 7.47 135 26 13

(5.75) (4.97) (6.57) (78) (15) (7)

9.20 3.72 5.25 147 12 9

(3.72) (3.17) (4.28) (88) (7) (5)

Abbreviations: ISI, Incontinence Severity Index; SD, standard deviation; UDI, Urogenital Distress Inventory; df, degrees of freedom. a Composite measure derived from expected outcomeseperceived outcomes. b Dependent t-test, preepost, tISI 5 14.95, df 5 172, P ! 0.01. c Dependent t-test, preepost, tUDI 5 15.88, df 5 167, P ! 0.01.

(43%), a trade certificate (21%), and college/university graduate (27%). Most participants were partnered (78%); widowed (12%); and had never married, were divorced, or separated (10%). Regarding labourforce status, most were those who were working, students or unemployed (48%), homemaker (25%), and retired or in receipt of sickness benefits (28%). Regarding participants’ current mental health status, the WHO5 standard classification scores indicated that 24% (N 5 42) were probably suffering depression. For social isolation, 84% were socially connected, 8% reported some social isolation, and 8% were socially isolated based on their Friendship Scale scores. In terms of health-related


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Table 2. Final model of a unidimensional short assessment of patient satisfaction scale (SAPS) Dimension

N

Item stem (abbreviated and reworded)

Item source

Effectiveness Information Technical skill Participation Relationship Access and facilities Satisfaction general/other

1 2 3 4 5 6 7

Satisfied with the effect of your treatment Satisfaction with explanations of treatment results The clinician was careful to check everything Satisfaction with health care choices How much were you respected The time with the clinician was too short Satisfied with the care received

GUTSS GUTSS ConsultSQ PSI PSI ConsultSQ GUTSS

Abbreviations: GUTSS, Genito-Urinary Treatment Satisfaction Scale; ConsultSQ, Consultation Satisfaction Questionnaire; PSI, Patient Satisfaction Index.

quality of life, the mean score on the AQoL was 0.78 (standard deviation [SD] 5 0.19, N 5 173), which was only just below the published population norm (0.83, SD 5 0.20) [71]. Table 1 shows treatment type, expectations, and incontinence severity pre- and post-treatment. Participants were fairly well split between those receiving physiotherapy, physiotherapy with surgery, and surgery only; about half of the participants expected that their incontinence would be cured, and most participants considered that the pretreatment information provided by the clinician was very good or good. Regarding perceived treatment success, 92% reported at least some improvement in their condition with a one-third reporting cure and a further one-third reporting part cure. Generally, these outcomes were as expected. On both the ISI and UDI, the mean scores indicated statistically significant improvement in incontinence severity between pre- and post-treatment. The items from each of the four patient satisfaction instruments were reviewed for language clarity, missing data, and response distribution. Double-barreled or verbose items were deleted as were nondiscriminatory items (ie, those that failed to discriminate between known groups). The surviving 49 items were examined for data distribution. This revealed that few cases used the extreme negative options. To overcome leverage owing to this sparse data, all response scales were collapsed into three points before analysis. Items were then mapped by item content against the model of patient satisfaction described in the

Introduction section. The results showed that none of the four instruments’ descriptive systems covered all seven dimensions of patient satisfaction, and that particular instruments over-assessed some dimensions (eg, consider the PSI where nine items assessed participation). Importantly, across all items, all dimensions of the patient satisfaction model were covered. As described in the Methods section, iterative Mokken and partial credit IRT analyses were conducted with the removal of an item at each iteration until the most parsimonious solution was obtained consistent with measuring the seven dimensions of patient satisfaction. The final modeld hereafter the SAPSdconsisted of seven items with one item representing each of the dimensions of patient satisfaction. Three items were drawn from the GUTTS, and two from each of the ConsultSQ and PSI. The final SAPS model, reflecting these changes, is shown in Table 2. The internal psychometric properties of the SAPS are presented in Table 3. The Loevinger Hi fit of each item exceeded the conventional cut point for inclusion in a homogenous scale (0.40), and there were almost no violations of Guttman monotonicity as assessed by the Crit values. The Rasch analysis results showed a very consistent relationship between the items (the point biserial correlations). The fit residuals, c2, and the P-values indicated that no item was misfitted to the model. There was no evidence of DIF by treatment cohort. Given this evidence of unidimensionality (Mokken analysis) and fundamental measurement (Rasch

Table 3. Psychometric properties of SAPS items and the SAPS scale Mokken analysis Item 1 2 3 4 5 6 7

Partial credit IRT analysis

Hia

Crit valueb

Point biserialc

Locationd

SE

Fit residual

c2

P-value

0.52 0.55 0.56 0.55 0.58 0.51 0.56

0 0 0 1 0 3 0

0.74 0.75 0.74 0.78 0.81 0.61 0.74

0.48 0.15 0.50 0.77 0.57 1.67 0.46

0.14 0.15 0.17 0.16 0.16 0.16 0.16

0.14 0.20 0.55 0.03 0.05 0.34 1.66

1.39 2.80 1.59 0.77 0.41 0.69 4.93

0.50 0.25 0.45 0.68 0.81 0.71 0.85

Abbreviations: SAPS, Short Assessment of Patient Satisfaction; IRT, item response theory; SE, standard error. Scale statistics: Loevinger H 5 0.55, r 5 0.86, Cronbach a 5 0.86. Rasch model statistics for item-trait interaction c2 5 12.59, df 5 14, P 5 0.56, Person separation index 5 0.83. a Hi 5 Item coefficient of scalability. b Crit value under P-matrix analysis. c Point biserial correlation. d In logits.

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G. Hawthorne et al. / Journal of Clinical Epidemiology 67 (2014) 527e537 Table 4. Tests of convergent and divergent validity for the SAPS, Tscores SAPS Parameters

Fig. 1. Distribution of SAPS scores. SAPS, Short Assessment of Patient Satisfaction.

analysis), it is reasonable to assume that each SAPS item is weighted equally. Regarding the targeting of item thresholds to persons, this was reasonable and covered the range from 2 to þ3 logitsdalthough it should be noted that the logits suggested there were some gaps in measurement. The easiest item for respondents to endorse was #6 (The time with the clinician); perhaps, this should not be unexpected! The distribution of SAPS’ scores is presented in Fig. 1. This suggests that the SAPS spreads cases out reasonably evenly across the scale range. Most cases were generally satisfied (those with high scores), there were a smaller number who were somewhat dissatisfied (with modest scores) and some who were dissatisfied (with low scores). The mean score on the SAPS was 21.91 (SD 5 5.29), which was at the 78th percentile of the possible score range. Twelve cases (7%) obtained the ceiling score. The SAPS scores correlated with the ConsultSQ scores rs 5 0.73, the CSQ-18 rs 5 0.78, the GUTSS rs 5 0.83, and the PSI rs 5 0.83 (P ! 0.01 for all). Table 4 shows four tests of convergent and divergent validity. The SAPS statistically discriminated by treatment, self-reported treatment outcomes, and clinician information, but did not discriminate by age. When SAPS scores were examined by selfreported treatment outcomes expressed as Cohen’s d effect sizes, the results showed that between those partly cured/ cured d 5 0.91, improvedenot helped (combined owing to small numbers)/partly cured d 5 0.48. An estimate of the construct of patient satisfaction was derived through computing the mean percentage score across all the four patient satisfaction measures (Consult, CSQ-18, GUTSS, and PSI) on the assumption that between the four instruments, the construct was adequately represented as described previously. The mean pooled percentage scores were then quartiled and were used as the criterion for assessing the sensitivity of the instruments, including the SAPS. The results showed that (as expected) all instruments were highly sensitive, but that the SAPS was more sensitive than any of the other measures. The ConsultSQ was the least efficient instrument and was therefore used as the denominator

N

Treatment Physiotherapy 45 Physiotherapy and surgery 55 Surgery 69 F-valueb How successful was the treatment (self-report) Cured 62 Partly cured 55 Improved 41 Not helped 12 F-value Information given Excellent 57 Very good 61 Good 36 Fair 17 F-value Age group (yr) 31e45 31 46e60 71 61e75 56 75þ 16 F-value

Meana (SD) 47.54 (10.11) 49.23 (10.77) 52.25 (9.14) 3.12* 55.83 (5.10) 49.30 (8.99) 46.95 (9.75) 35.11 (14.12) 22.47*** 55.83 (7.86) 50.04 (8.81) 45.82 (8.62) 39.11 (11.51) 26.18*** 48.58 (10.88) 50.39 (11.15) 50.33 (8.34) 50.34 (9.32) 0.35

Abbreviations: SAPS, Short Assessment of Patient Satisfaction; SD, standard deviation; ANOVA, analysis of variance. *P ! 0.05, ***P ! 0.001. a Nontransformed T-scores. b ANOVA, all ANOVAs carried out on transformed data.

in calculating the RE of the other instruments. As shown in Table 5, the SAPS clearly outperformed all the other instruments, although it was the shortest instrument. On the more objective measures of preepost change on ISI and UDI, the ConsultSQ was the least discriminatory measure and the SAPS the second most sensitive measure behind the GUTSS. The results are given in Table 5. Following international review after presentation at the International Society for Quality of Life Research conferences (Budapest [2007] and Montevideo [2008]), slight changes were made to the wording of item stems to remove local cultural colloquialisms (eg, replacing the word ‘‘happy’’ on items #1 and #7 with the word ‘‘satisfaction’’), reversing four items to achieve a balanced scale, and standardizing all item response scales. The full descriptive system of the SAPS, including scoring instructions is given in the Appendix.

4. Discussion Although patient satisfaction is a notoriously slippery concept as evidenced by the number of patient satisfaction theories and measures, its popularity has increased quite dramatically over the past decade. In November 2012, typing the term into the Internet produced more than 19 million hits. What is surprising about this situation is the


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Table 5. Test of responsiveness to patient satisfaction, five measures, T-scores Parameters

N

ConsultSQ

CSQ-18

GUTSS

PSI

SAPS

Meana (SD)

Mean (SD)

Mean (SD)

Mean (SD)

Mean (SD)

Pooled percentage quartiles 1 (least satisfied) 42 38.63 (8.83) 2 41 47.92 (7.10) 3 45 52.78 (5.08) 4 (most satisfied) 44 59.13 (5.22) F-valuesb 70.18*** RE 1.00 Change scores on the UDI-6 (preepost) Worse/No changec 21 49.18 (11.35) Improvement 147 49.52 (9.92) F-valuesb !0.01 RE 1.00 Change scores on the ISI (preepost) Worse/No changec 37 46.15 (12.82) Improvement 137 50.81 (8.87) F-valuesb 4.68 RE 1.00

37.62 (9.23) 48.58 (5.45) 53.67 (5.18) 59.55 (2.19) 117.65*** 1.68

38.36 (8.29) 47.68 (6.97) 55.32 (5.05) 59.22 (2.18) 112.96*** 1.61

38.57 (9.81) 49.15 (7.56) 53.52 (5.36) 58.43 (2.16) 80.89*** 1.15

37.14 (9.02) 48.52 (5.73) 54.72 (3.74) 59.24 (1.95) 152.88*** 2.18

45.54 (10.06) 50.33 (9.98) 4.04* 404.00

41.11 (8.57) 51.46 (9.64) 19.34*** 1,934.00

48.65 (9.52) 49.88 (10.12) 0.39 39.00

44.95 (10.45) 50.61 (9.96) 5.60* 560.00

43.71 (12.78) 51.80 (8.19) 17.86 3.82

41.27 (10.13) 52.77 (8.43) 43.06 9.20

45.71 (13.02) 51.15 (8.80) 6.66 1.42

42.82 (13.01) 51.99 (7.97) 22.02 4.71

Abbreviations: ConsultSQ, Consultation Satisfaction Questionnaire; CSQ, Client Satisfaction Questionnaire; GUTSS, Genito-Urinary Treatment Satisfaction Scale; PSI, Patient Satisfaction Index; SAPS, Short Assessment of Patient Satisfaction; SD, standard deviation; RE, relative efficiency; ISI, Incontinence Severity Index; UDI, Urogenital Distress Inventory; ANOVA, analysis of variance. *P ! 0.05, ***P ! 0.001. a Nontransformed T-scores. b ANOVA, all ANOVAs carried out on transformed data. c Groups combined owing to small numbers.

basic contradiction between this popularity; the availability of thousands of different measures of patient satisfaction; and the paucity of high quality, short, valid, and reliable generic patient satisfaction instrumentsda situation that has been noticed for at least the last 30 years [1,25,72]. After review of the leading patient satisfaction measures, Hawthorne recommended the development of a short, valid, and reliable generic patient satisfaction instrument [1]. The first steps toward this are reported in this study through an analysis of all the pooled items from four leading patient satisfaction instruments. After the review of the leading theories of patient satisfaction, the theoretical model used in this study was based on Donabedian’s argument that patient satisfaction was an outcome of care, particularly interpersonal processes [7]. This was then operationalized, and seven dimensions of patient satisfaction were identified [1]. When the descriptive systems of four leading instruments were examined against this model, all fell short in that either there was overmeasurement in some dimensions or undermeasurement in others. After pooling of items, an iterative procedure was followed using Mokken and Rasch analyses to identify and remove the worst-fitting items, subject to maintaining fidelity to the patient satisfaction model. The final measurement model consisted of seven representative items, one for each of the identified dimensions. The strength of the descriptive system of the SAPS, then, is its broad coverage of the theoretical components of patient satisfaction. A further strength is its length. With seven items, it is the shortest of the measures used in this study. Because of its

construct breadth and that it takes just over a minute to complete, it has wide applicability. It can be used in instrument batteries without adding substantially to overall questionnaire length, it can be quickly completed in busy clinical settings and makes minimal demands on study participants. The test of comparative discrimination shows that its length does not appear to have compromised its discriminative function; indeed, it appears more sensitive than any of the other generic measures used in the study. The only instrument that on these three tests outperformed the SAPS was the GUTSS; this was expected because the GUTSS is an incontinence-specific instrument. The psychometric properties of the SAPS exceeded standard criteria. The scale Loevinger H exceeded the conventional requirement (0.50) for evidence of a strong unidimensional scale, and the very low Crit values implied that Guttman violations may be owing to sampling errors [63]. The spread of logits on the Rasch analysis suggested that a wide range of respondents may find the items acceptable. That there was no DIF by treatment cohort is evidence it may be applicable in different settings. The point biserial correlations and fit residuals from the Rasch analysis provided strong evidence of good measurement properties. The correlations with other previously developed measures of patient satisfaction, suggesting common shared variance in the range of 0.53e0.69, is indicative that the SAPS not only assesses patient satisfaction but also offers some unique insights (almost certainly owing to its broad breadth of coverage of the patient satisfaction construct). That the Mokken r and Cronbach a were identical at 0.86 suggests

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that the SAPS is a reliable measure of patient satisfaction as these values exceed the conventionally accepted minimum values for group assessment [73]. These findings relate to the original wording of the items. The item changes in item wordings described previously are being currently assessed in a prospective clinical validation study where both item versions were administered. Preliminary analyses suggest that there is no difference in the psychometric properties between the original and modified wording of the items. The mean score on the SAPS (rounded up to the 22 point mark, SD 5 5 points) was at the 78th percentile point of the scale range. This mean is typical of patient satisfaction measures, which since Cartwright [74] have routinely report that 70e90% of all cases are satisfied with their health care. Regarding the interpretation of SAPS’ scores, the discriminatory function of the SAPS expressed as Cohen’s d by self-reported treatment outcomes between the partly cured and cured cohorts was probably distorted by ceiling effects; therefore, the effect size between those for whom treatment was not particularly helpful and those for whom treatment was helpful was preferred. The result suggested that an effect size of 0.48 on the SAPS would differentiate between those for whom treatment was not particularly helpful and those for whom treatment was helpful. Translated into SAPS raw scores, this suggests that participants scoring five or more points below the mean SAPS score would be actively dissatisfied with their health care, that is, scores of less than 17 on the raw SAPS score. The study findings are subject to several important caveats. Although the initial psychometric properties exceeded standard criteria, it should be borne in mind that the SAPS represents a compromise between fidelity to the model of patient satisfaction, the need for a homogenous scale, and good item measurement properties. It is not a de novo purpose-designed measure of patient satisfaction, but is rather a composite measure from previously published items. Although the response rate was lower than expected at 42%, the response rate was typical for initial response rates for postal surveys in health studies [75e78]. Followup telephone calls could not be made because the researchers had no knowledge of the patient’s names, addresses, or other contact details. The low response rate has implications for the generalizability of the SAPs; this would occur if nonresponding patients were systematically different to participants. The demographic profile of participants revealed that they were highly educated, partnered, and that a quarter met the criteria for depression. Given these limitations, it is important that validation studies of the SAPS are carried out in other populations, and we are currently conducting a validation study in a clinical sample.

5. Conclusion The SAPS scale was developed in response to calls for a short, valid, and reliable measure of patient satisfaction.

The SAPS scale is based on a firm theoretical model of patient satisfaction, and its descriptive system covers the known dimensions that contribute to patient satisfaction. Its internal properties appear to exceed standard psychometric criteria, and it discriminates as least as well as other longer patient satisfaction measures. Given that the SAPS is a short measure at just seven items, it can be administered quickly and without making heavy demands on participant cognition. Although the SAPS scale needs further validation, the results of this study suggest that it may be a useful instrument in a wide range of settings for assessing patient satisfaction with health care.

Acknowledgments This study was funded by the Australian Commonwealth Government through the Department of Health Care and Aging. In addition to the authors’ thanks for this support, they would like to thank all the study participants who gave their time to complete the questionnaire, and to the clinicians and staff of the participating clinics.

Appendix Descriptive system of the SAPS, including scoring instructions SAPS1 Instructions: After reading each question, circle the answer that best describes your situation. The order of the answers varies between the questions, so take a moment to read each question carefully. We know that sometimes answers may not describe you exactly, so please pick the answer that most closely describes you. When you have finished, please check that you have answered all questions. Q1. How satisfied are you with the effect of your {treatment/care}? Very satisfied............0 Satisfied..............1 Neither satisfied nor dissatisfied.....2 Dissatisfied.............3 Very dissatisfied...........4

1 Copyright. Ó2011. Copyright in the SAPS is held and will continue to be held in perpetuity by the authors with a license to the Commonwealth of Australia. Researchers are welcome to use the SAPS subject to acknowledgment/citation of the authors’ rights in the usual way.


Q2. How satisfied are you with the explanations the {doctor/other health professional} has given you about the results of your {treatment/care}?

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Dissatisfied..............3 Very dissatisfied............4

Very dissatisfied...........0 Dissatisfied.............1

Scoring:

Neither satisfied nor dissatisfied....2 Satisfied..............3

1. Score each item as marked (0 5 0), (1 5 1), (2 5 2), (3 5 3), (4 5 4). 2. Reverse the scores for items #1, #3, #5, #7 (0 5 4), (1 5 3), (2 5 2), (3 5 1), (4 5 0). 3. Add up the item scores. The score range is 0e28, where higher scores represent higher levels of patient satisfaction.

Very satisfied............4 Q3. The {doctor/other health professional} was very careful to check everything when examining you. Strongly agree............0 Agree...............1 Not sure..............2 Disagree..............3 Strongly disagree...........4 Q4. How satisfied were you with the choices you had in decisions affecting your health care? Very dissatisfied...........0 Dissatisfied.............1 Neither satisfied nor dissatisfied.....2 Satisfied..............3 Very satisfied............4 Q5. How much of the time did you feel respected by the {doctor/other health professional}? All of the time............0 Most of the time...........1 About half the time..........2 Some of the time...........3 None of the time...........4 Q6. The time you had with the {doctor/other health professional} was too short. Strongly agree............0 Agree...............1 Not sure..............2 Disagree..............3 Strongly disagree...........4 Q7. Are you satisfied with the care you received in the {hospital/clinic}? Very satisfied.............0 Satisfied...............1 Neither satisfied nor dissatisfied......2

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