Patients' experiences with quality of hospital care: the Consumer Quality Index Cataract Questionnaire
© Stubbe et al; licensee BioMed Central Ltd. 2007
Received: 29 January 2007
Accepted: 19 September 2007
Published: 19 September 2007
Patients' feedback is of great importance in health care policy decisions. The Consumer Quality Index Cataract Questionnaire (CQI Cataract) was used to measure patients' experiences with quality of care after a cataract operation. This study aims to evaluate the reliability and the dimensional structure of this questionnaire and assesses its ability to measure differences between hospitals in patients' experiences with quality of care.
Survey data of 4,635 respondents were available. An exploratory factor analysis was performed to evaluate the construct validity of the questionnaire and item-correlations and inter-factor correlations were calculated. Secondly, Cronbach's alpha coefficients were calculated to assess the internal consistency of the scales. Thirdly, to evaluate the ability of the questionnaire to discriminate between hospitals, multilevel analyses were performed with patients hierarchically nested within hospitals.
Exploratory factor analysis resulted in 14 quality of care items subdivided over three factors (i.e. communication with ophthalmologist, communication with nurses, and communication about medication). Cronbach's alpha coefficients of 0.89, 0.76 and 0.79 indicated good internal consistency. Multilevel analyses showed that the questionnaire was able to measure differences in patients' experiences with hospital care regarding communication with ophthalmologist and communication about medication. In addition, there was variation between hospitals regarding ophthalmologist ratings, hospital ratings and one dichotomous information item.
These findings suggest that the CQI Cataract is a reliable and valid instrument. This instrument can be used to measure patients' experiences with three domains of hospital care after a cataract operation and is able to assess differences in evaluated care between hospitals.
Managed competition between health insurance organizations has been introduced in several industrialized countries . In the Netherlands, consumers have a free choice of health insurers. Competition between insurers is possible with respect to premiums and quality of contracted providers. The basic benefits package is set by law  and is therefore no element in competition. Competition is expected to take place on the basis of prices (premiums), the service quality of insurers, and the quality of the care providers that they contract . Therefore, transparent information about the performance of health care providers plays a key role in the Dutch system.
In Dutch hospitals, remuneration takes place on the basis of 'diagnosis treatment combinations' (DBCs). Prices for reimbursement are either based on fixed tariffs ('list A') or are subject of negotiations between health insurers and hospitals ('list B') . Cataract surgery is a procedure on 'list B' for which individual consumers and insurance companies can "shop" among several providers. Comparative information about the performance and costs of these providers is therefore particularly valuable for individual consumers and for insurance companies.
To map the quality of care from a patient's perspective, specific instruments are needed. A wide variety of questionnaires is available, of which two 'families' of surveys stand out. One of these families of surveys is the Dutch QUOTE family with the acronym QUOTE standing for QUality Of care Through the patients' Eyes [5–11]. The QUOTE surveys distinguish themselves in two different ways. First of all, they not only measure experiences of health consumers, but also focus on the importance consumers attach to the different quality aspects of care. While experiences of patients will change when situations in health care services change, importance scores are less subject to situational changes as they are linked to the attitudes and opinions of patients . Secondly, apart from the generic items that each questionnaire comprises, group-specific (e.g. focused on the elderly), care-specific (e.g. focused on physiotherapy), or disease-specific (e.g. focused on cataract patients) items are included in the questionnaire. The QUOTE Cataract [5, 6] was developed to assess patient's experiences with quality of care after a cataract operation, however, one of the disadvantages of this questionnaire is that it uses answering categories that are internationally not widely used: no, not really, on the whole yes, and yes.
The second family of surveys is the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) [12–18]. In contrast to the QUOTE methodology, these CAHPS questionnaires do not take the importance of quality aspects into account, and do not include group-, care- or disease-specific items. However, these questionnaires are widely used and translated into different languages [12, 19–21]. Furthermore, the four-point Likert scale answering structure of the CAHPS questionnaires (never, sometimes, often, always), and the three-point scale (not a problem, a small problem, a big problem) affiliates well to international research.
To be able to measure both performance and importance of quality aspects on widely used scales, this paper proposes a new instrument for measuring patients' experiences with quality of care after a cataract operation: the Consumer Quality Index Cataract (CQI Cataract). The CQI Cataract is the first instrument that includes generic, and group-, care- or disease-specific items, measure the patients' experiences with and the importance patients attach to quality aspects, and uses the internationally accepted four-point Likert-scale answering structure. The development of the Consumer Quality Index Cataract Questionnaire (CQI Cataract) has been described in detail elsewhere .
The aim of this article is two-folded. First of all, we evaluate the psychometric properties of the CQI Cataract assessing patients' experiences with quality of care after a cataract surgery. The second aim of the paper is to assess the questionnaire's ability to measure differences in quality of care between hospitals. The two research questions derived from these two aims are: "Is the CQI Cataract reliable and what is the dimensional structure of the instrument?", and "Does the CQI Cataract measure differences between hospitals in experiences with quality of care of patients who underwent cataract surgery?".
Case finding was done by contacting Dutch hospitals and through the administration of four Dutch insurance companies. Four Dutch health insurance companies recruited 5,323 patients for whom costs of cataract surgery were claimed within the last 12 months and 1,145 patients were directly recruited via hospitals after they had their cataract surgery, resulting in a total of 6,468 cataract patients who received a survey sent by mail. At the end of the data collection, 5,436 patients had returned the questionnaire (gross response rate = 84%). Of these patients, 447 respondents were not willing or able to participate. Patients were not included into the analyses if they responded negatively to the question whether or not they underwent a cataract operation (n = 32) or if this information was missing (n = 87). Furthermore, patients who stated that they did not answer the questions themselves (n = 203) or who filled in less than half of the core items (n = 32) were also excluded from the analyses. Therefore, a total of 801 subjects were excluded from the analyses, resulting in a sample of 4,635 patients (net response rate = 72%).
Consumer Quality Index Cataract Questionnaire (CQI Cataract)
The development of the Dutch CQI Cataract was based on two different families of questionnaires . Firstly, items from the QUOTE-Cataract [5, 6] were used. The QUOTE-Cataract is a reliable, valid, and feasible instrument for assessing the quality of care from the perspective of cataract patients and is part of the QUOTE family of surveys [5–11, 22, 23]. These questionnaires conceptualize patients' experiences with quality of care in two dimensions: performance and importance . Performance refers to the actual experience of patients with the quality aspects, and importance relates to the fact that people see some quality aspects as more significant than others. It reflects what people see as desired qualities in health care. The answering formats of importance items were: not important, fairly important, important, and extremely important. The original QUOTE answering formats for the performance items were: no, not really, on the whole yes, and yes. However, response options of the performance categories were adjusted to fit in with the internationally accepted four-point Likert-scale answering structure ranging from 'never' to 'always'. Some original QUOTE items could not be adjusted to fit the four-point scale, and were therefore transformed into a dichotomous variable (no/yes).
Besides the QUOTE-Cataract, the Dutch H-CAHPS measuring patients' experiences with quality of hospital care was used to generate items . This questionnaire is part of the CAHPS family [12–14, 16–18], and was shown to be a reliable, valid and feasible instrument for assessing the quality of hospital care from Dutch patients' perspectives . Answering categories are based on a four-point Likert scale ranging from 'never' to 'always', or based on the three-point scale: 'not a problem', 'a small problem', and 'a big problem'. Items measuring quality of hospital care from the patient's perspective were selected.
Selecting items from the H-CAHPS and QUOTE-Cataract questionnaire resulted in the CQI Cataract, which consists of two questionnaires, i.e. the CQI Cataract Experience and the CQI Cataract Importance. The CQI Cataract Experience contains general items (e.g. age, education, ethnicity, and patient's health), three global ratings (of ophthalmologist, nurses and hospital), and 41 performance items referring to the actual experience of patients with the quality aspects (e.g. How often did the ophthalmologist treat you with respect). The global ratings range from 0 to 10, with a score of 10 indicating the best possible score.
The CQI Cataract Importance also comprises demographical items, and in addition consists of importance items asking how important cataract patients value the 41 quality aspects of the CQI Cataract Experience with answering categories ranging from not important to very important (e.g. My ophthalmologist treats me with respect). The outcome of the CQI Cataract is valuable, because it shows which quality aspects patients find important and how they evaluate these aspects.
In this paper we focus on patients' experiences with quality of hospital care and therefore, we only used the CQI Cataract Experience and selected the 41 items measuring quality aspects of hospital care. To evaluate the validity of this questionnaire, an exploratory factor analysis was conducted and item-total correlations correcting for item overlap were calculated. When variables are measured on a dichotomous (yes/no) scale, linear factor analysis (e.g. common factor analysis) may yield biased estimates of the factor structure [24, 25]. Therefore, we did not include 21 dichotomous items measuring quality aspects.
The 20-item exploratory factor analysis was performed with a direct oblimin rotation. This oblique rotation was preferred to an orthogonal rotation (i.e. varimax), because it takes correlations between factors into account. An oblique rotation could also result in independent factors if that provides a better fit. The number of factors was determined by Kaiser's criterion . In general, factor loadings are considered meaningful when they exceed 0.30 or 0.40 . Therefore, items were only assigned to a factor if the magnitude of their factor loading exceeded 0.40. Item-total correlations (ITC) correcting for item overlap were calculated to evaluate the construct validity . Correlations greater than 0.40 indicate good construct validity . To get insight into the multidimensionality of the instrument, inter-factor correlations were computed. Correlations of less than 0.70 indicate that the constructed factors can be seen as separate scales .
Secondly, Cronbach's alpha coefficients of the different domains were calculated to evaluate the internal consistency of the questionnaire . An alpha exceeding the value of 0.70 indicates that the scale is reliable . After assigning the items to the different scales, mean sum scores were calculated by summing the responses to the items and dividing these sum scores by the number of items filled in. The higher the score on the domain, the more positive the patient's experience with quality of care.
Thirdly, to evaluate whether part of the variation in patients' evaluations of care is related to the hospital in which they were operated, multilevel analyses were performed.
Individual characteristics of patients were taken into account as case-mix adjusters to estimate the contribution of each characteristic. One of the goals of the questionnaire is to understand individual variations within hospitals. Therefore, it is important to investigate whether survey results might be influenced by factors that are not distributed randomly across hospitals, and if so, to adjust for differences in patient mix when making comparisons between hospitals. The case-mix adjusters consisted each of multiple answering categories and were recoded into dichotomous variables. The variable age consisted of eight answering categories ranging from "18–24 years" to "80 years and older", and was recoded into a variable consisting of two age groups "18–74 years" and "75 years and older". The variable education consisted of 11 categories ranging from no education to post academic education. The answering categories "no education" and "primary education" were recoded into the category "low education". All other educational levels were categorized as "high education". Self-reported health consisted of five answering categories, ranging from "excellent" to "poor". The three categories "excellent", "very good" and "good" were recoded into the category "good health" and "moderate" and "poor" were recoded into "bad health". We excluded respondents with missing values on age (N = 97), gender (N = 18), education (N = 183), and self-reported health status (N = 125).
Finally, only hospitals with a minimum of 10 patients in our dataset were included in the analyses, and therefore we had to exclude 176 respondents from 57 hospitals. In total, 599 respondents were excluded, resulting in 4,036 respondents from 57 different hospitals. The mean number of patients per hospital in our dataset was 45, with a minimum of 15 and a maximum of 141 patients. Three separate multilevel analyses were carried out on the following three domains of the CQI Cataract Experience: communication with ophthalmologist, communication with nurses, and communication about medication. Furthermore, we performed multilevel analyses on the three global ratings of ophthalmologist, nurses and hospital, because we hypothesised that these ratings may vary between hospitals.
The MLwiN software package was used , which deals with data that are hierarchically structured . This means that the data are nested, i.e. ordered in such a way that one dependent variable is measured at the lowest level and exploratory variables at the same and higher levels. In our data, individual patients (level 1) are nested within hospitals (level 2). Our hypothesis is that experiences of patients measured at the first level depend partly on the hospital in which they were operated (second level). This should result in the fact that patients within the same hospital should agree more on experiences with quality of care than patients from different hospitals. The intra-class correlation (ICC) is an index of the ratio of the within-hospital variation and the between-hospital variation . Values of the ICC range between 0 and 1. An ICC of zero indicates that the variance in patients' experiences of quality of care cannot be explained by the hospital in which they were operated.
The multilevel models used in the analyses can be viewed as hierarchical systems of regression coefficients. Regression coefficients and variance components are estimated based on the observed data. We fitted two different, nested models to the data. The first model is a random-intercept model in which no explanatory variables are included (Model 1). In this model, the variance of the dependent variable is partitioned into variance that can be attributed to the individual level, and to the hospital level.
Just like in regression analysis, explanatory variables can be used in the random-intercept model to try to explain part of the variability of the dependent variable . These variables can be entered in the model as level-one (patients' characteristics) and level-two explanatory variables (hospital characteristics) and have the same interpretation as unstandardized regression coefficient in multiple regression models . In the second model, individual characteristics (age, gender, education, and self-reported health), and one hospital characteristic (type of anaesthesia) were entered into the equation (Model 2).
Three different types of anaesthesia were used in the 57 Dutch hospitals, i.e. injection, topical preoperative drops and general anaesthesia. Currently, there is no consensus as to the optimal approach to anaesthesia . However, since 2000, the use of topical preoperative drops has increased immensely , because topical anaesthesia bear no risk of injection-related complications and allow for a more rapid recovery after surgery , which may influence the experience of patients with the quality of care. Entering the percentage of preoperative drops used as anaesthesia in the hospital enables us to investigate whether patients are more positively about hospitals that use preoperative drops as anaesthetics compared to hospitals using other anaesthetics. Model 2 estimates how much of the variance is explained at the patient and hospital level after correcting for these individual and hospital variables and investigates whether survey results might be influenced by factors that are not distributed randomly across hospitals. Regression coefficients were estimated to get insight into the contribution of each characteristic.
Although we did not take the dichotomous variables into account in the factor analyses, they may be able to measure differences between hospitals in patients' experiences with quality of care. Of the dichotomous quality aspects, we selected one dichotomous item which was rated by patients as most important according to the CQI Cataract Importance and performed a logistic multilevel analysis. This information item asked patients if someone informed them about what to do in case of an emergency after the cataract operation. As with the previous multilevel analyses, first the random-intercept model was fitted to the data, followed by the model in which the individual and hospital characteristics were taken into account. Logistic multilevel analysis does not estimate regression coefficients, but calculates the odds ratios (OR). Furthermore, ρ is calculated, which can be interpreted as the ICC in linear multilevel analyses.
Individual characteristics of the 4,036 respondents
No or less than secondary education
Secondary or higher education
Type of anaesthesia
Factor loadings of quality aspects items according to the exploratory factor analysis with oblimin rotation
Factor 1: Communication with ophthalmologist
Ophthalmologist treated me with respect
Ophthalmologist listened carefully
Ophthalmologist explained things clearly
Ophthalmologist spent enough time
Ophthalmologist went seriously into merits of my questions
Ophthalmologist shared decision making
Ophthalmologist took specific wishes into account
Ophthalmologist talked about things that went wrong
Factor 2: Communication with nurses
Nurses treat me with respect
Nurses listened carefully
Nurses explained things clearly
Information was adequately coordinated*
Factor 3: Communication about medication
Questions asked about allergic iodine
Questions asked about allergic medication
Clear information about side-effects medication
Factor 4: Rest items
Ophthalmologist talked about things that went wrong
Information was adequately coordinated
Care was adequately coordinated
How often did insurers compensate for costs medication
Assigning items to a factor if the loading exceeded 0.40 resulted in the exclusion of Q14 ("How often did you enter the observation room of the ophthalmologist within 15 minutes of your appointment?"), Q15 ("How often did you have contact with the same ophthalmologist?"), and Q49 ("How often did nurses or other hospital employees clearly inform you about eye drops and/or eye crème that were prescribed?") from further scale construction. Furthermore, two items (Q16, and Q38) had loadings exceeding 0.40 on two different factors. Q16 loaded on factor 1 (0.40) and on factor 4 (0.41). Q38 loaded on factor 2 (0.41) and factor 4 (0.53).
Cronbach's alphas are displayed in the fourth column (α1) of Table 2 and their values range from 0.52 to 0.89. Factor 4 is the only scale that had a poor reliability coefficient (α = 0.52). Removing any of the items from this scale did not increase the coefficient to the threshold of 0.70 (see column 5 of Table 2). Therefore, it was decided to assign Q38 to factor 2 and Q16 to factor 1. However, low corrected item total correlation (ITC) and an increase of the Cronbach's alpha of factor 2 if Q38 was deleted (α = 0.79) led to the conclusion of excluding Q38 from factor 2.
The inter-factor correlation between communication with ophthalmologist and communication with nurses was 0.45. This correlation was 0.31 between communication with ophthalmologist and communication about medication and was 0.18 between communication with nurses and communication about medication. Correlations did not exceed the threshold of 0.70, which indicates that the scales could be read as separate scales.
Model fitting results of the multilevel analyses for the domains communication with ophthalmologist, communication with nurses, and communication about medication and for the global rating of ophthalmologist, nurses and hospitals (standard errors added in parentheses)
Domain ophthalmologist (N = 3942)
Domain nurses (N = 3925)
Domain medication (N = 3887)
Rating ophthalmologist (N = 3940)
Rating nurses (N = 3910)
Rating hospital (N = 3970)
Model fitting results of the logistic multilevel analyses for the dichotomous variable "Did someone inform you on what to do in case of an emergency after the cataract operation?"
Outcome variable (N = 3,871)
0.79 (0.76, 0.81)
0.84 (0.80, 0.88)
OR age (18–74 = reference)1
0.64 (0.54, 0.75)*
OR sex (males = reference)1
1.09 (0.92, 1.28)
OR education (low education = reference)1
0.91 (0.76, 1.08)
OR health (high = reference)1
0.61 (0.72, 0.85)*
OR anaesthesia (injection = reference)1
0.98 (0.81, 1.19)
In this paper, we investigated the construct validity and internal consistency of the CQI Cataract and evaluated its ability to discriminate between hospitals in quality of care. Exploratory factor analysis showed that 14 items could be subdivided into the following three scales: communication with ophthalmologist, communication with nurses, and communication about medication. Cronbach's alpha coefficients ranged from 0.76 to 0.89, indicating good internal consistency. Item-total correlations corrected for overlap all exceeded the threshold of 0.40, suggesting good construct validity. Multidimensionality of the scales was supported by the inter-factor correlations, which were all smaller than the commonly used rule of thumb of 0.70. However, these correlations were not equal to zero and therefore, the use of an oblique rotation (i.e. oblimin rotation) was justified. Three items did not meet the psychometric standards (Q14, Q15, and Q49). However, these items might give important information on the quality of care. Therefore, we are reluctant to eliminate these items from the questionnaire, even though they cannot be assigned to a certain domain.
Multilevel analyses showed that hospitals accounted for part of the variance in patients' experiences with quality of care on the following five outcome variables: communication with ophthalmologist, communication about medication, global rating of ophthalmologist, and global hospital rating, and the dichotomous variable about emergency information. Experiences of patients with communication with nurses and global ratings of nurses did not differ significantly between hospitals. This is not in agreement with result of a study on patients' experiences with quality of care after a total hip or knee arthroplasty, which showed that communication with nurses differed significantly between hospitals . This discrepancy might be explained by the fact that a total hip or knee arthroplasty needs more hospital follow-up than a cataract operation, and therefore, nurses play a less important role in the recovery process of cataract patients.
Multilevel analyses showed that self-reported health and age were significantly associated with patients' health care evaluations on most of the outcome variables. Patients with low levels of self-reported health gave lower ratings and scored lower on the quality domains, except for the domain communication about medication. A review by Pascoe and colleagues  showed similar results and found that patients with better health tend to be more satisfied with evaluations of health centres. Gender did not explain any variation in patients' experiences with quality of hospital care.
Furthermore, we found that the individual characteristic age gave mixed results. Older patients scored higher on global ratings of care and lower on communication about medication. Contrary to our results, several studies showed that age was the variable having the most consistent effect, being associated with higher satisfaction with care [19, 39]. The reason for not being able to replicate this finding in our sample might be the fact that our respondents were part of a homogenous age group. More than 50% of the cataract patients were older than 74 years and less than 5% was younger than 55 years. In both studies that found an age effect, age of the sample was more heterogeneously distributed than in our sample.
Cataract surgery is performed using general or local anaesthetics. Local anaesthetic may be administered through either injection (peribular, retrobullar, subconjunctival, etc.) or the use of topical anaesthetic drops (with or without intracameral application) . Retrobulbar and peribulbar blocks provide better pain control during surgery than topical anaesthesia. However, the application of anaesthesia by injection is more painful than by topical means for patients not receiving sedation . Furthermore, topical anaesthesia does not cause injection-related complications and there is a more rapid recovery after surgery . Despite these differences, there was no variation in patients' experiences with quality of care between hospitals using preoperative drops on a small scale compared to hospitals that used preoperative drops to a large degree. Although not described in the results section, instead of using type of anaesthesia as a hospital characteristic, we also entered the type of anaesthesia as a patient's characteristic in the model. Topical anaesthesia did not explain any variation between patients and therefore, it seemed not to influence patients' experiences with care. This might be explained by the fact that due to small numbers we had to combine injection and general anaesthetics into one group and we were not able to make distinctions between the different types of injection (peribular, retrobullar, subconjunctival) and topical anaesthetic drops (with or without intracameral application).
For the multilevel analysis an arbitrary cut off point of 10 patients per hospital was used, resulting in a dataset that consisted of 57 hospitals. Several papers investigated the minimal group size and the minimal number of groups needed in multilevel analysis. Kreft and De Leeuw  pointed out that the size of the highest and lowest number of groups is based on literature: 30 groups are mentioned as minimum, while 100 groups are seen as sufficient. In practice, 50 groups is a frequently occurring number. The latter is in agreement with our number of 57 hospitals. Similarly, the size of the group is also chosen on the basis of the literature. A group size of 30 is normal in educational research, and a group size of five is normal in family research and in longitudinal research. Our arbitrary cut-off point of 10 patients per hospital fits well in this range.
In general, Dutch cataract patients are very satisfied with the quality of care given by hospitals. As a result the mean global ratings for hospitals, nurses and ophthalmologist by cataract patients are high. A study by Stubbe and colleagues showed that Dutch patients who underwent a total hip or knee operation also evaluated the quality of care very positively . As in our sample, respondents were part of a homogenous sample of older patients, which might explain the high ratings of quality of care. Two studies confirm this hypothesis [41, 42]. Both studies found that older patients are more satisfied and suggest that this can be explained by the fact that older people are generally mellow and accepting, feel more reluctant than younger patients to pass negative judgement on their care, have lower expectations and therefore are more satisfied. Two other studies provided another reason for the higher satisfaction ratings of older patients. [43, 44]. These two studies showed that older patients happened to be treated in a more thorough or responsive manner than younger patients.
There is little variation between patients in experiences with quality of care and therefore variation in evaluations of care is small between hospitals, which is reflected in the small intra-class correlation (ICC) ranging from 0.02 to 0.03. However, mostly ICCs are lower, for example, the median ICC calculated for more than 1000 primary care variables was 0.01 .
Similar multilevel results were found for the dichotomous information variable. Age and self-rated health explained part of the probability of receiving information about what patients should do in case of an emergency after their cataract operation. Younger and healthier patients were more likely to report having received information than older and unhealthier patients. However, after entering these variables in the model still part of the probability of receiving information was explained by the hospital in which patients were operated. Therefore, we can conclude that on an item level the CQI Cataract is able to measure differences between hospitals in patients' evaluation of quality of care.
The CQI Cataract questionnaire is a reliable and valid instrument for measuring patients' experience with quality of care after a cataract operation. Use of this instrument allows comparisons between hospitals on two domains (i.e. communication with ophthalmologist, and communication about medication), two global ratings (i.e. of hospital and ophthalmologist rating), and on at least one item.
This study was supported by ZonMw (the Netherlands Organization for Health Research and Development).
- Dixon A, Pfaff M, Hermesse J: Solidarity and competition in social health insurance countries. Social health insurance systems in Western Europe: European observatory on health systems and policies series. Edited by: Saltman RB, Busse R, Figueras J. 2004, Berkshire: Open University Press, 170-186.Google Scholar
- Stolk EA, Rutten FF: The "health benefit basket" in the Netherlands. Eur J Health Econ. 2005, 53-57. 10.1007/s10198-005-0319-9. Suppl
- Kerssens JJ, Groenewegen PP: Consumer choice of social health insurance in managed competition. Health Expect. 2003, 6: 312-322. 10.1046/j.1369-7625.2003.00229.x.View ArticlePubMedGoogle Scholar
- Zuurbier J, van Gerven E: DBC's als middel om e diffusie van verbeterde werkwijzen in de zorg te versnellen. Weten wat we doen: Verspreiding en innovaties in de zorg. Edited by: Schrijvers AJP, Jeurissen PPT, Ottes L, Blank JLT, van Hulst BL, Zuurbier J, et al. 2006, Zoetermeer: Raad voor de Volksgezondheid en Zorg, 163-191.Google Scholar
- Nijkamp MD, Sixma HJ, Afman H, Hiddema F, Koopmans SA, van den BB, Hendrikse F, Nuijts RM: Quality of care from the perspective of the cataract patient: the reliability and validity of the QUOTE-cataract. Br J Ophthalmol. 2002, 86: 840-842. 10.1136/bjo.86.8.840.View ArticlePubMedPubMed CentralGoogle Scholar
- Nijkamp MD, Sixma HJ, Afman H, Hiddema F, Koopmans SA, van den BB, Hendrikse F, Nuijts RM: Quality of care from the perspective of the cataract patient. QUOTE cataract questionnaire. J Cataract Refract Surg. 2002, 28: 1924-1931. 10.1016/S0886-3350(02)01373-1.View ArticlePubMedGoogle Scholar
- Sixma HJ, van Campen C, Kerssens JJ, Peters L: Quality of Care from the patients' perspective: from theoretical concept to a new measuring instrument. Health Expectations. 1998, 1: 82-95. 10.1046/j.1369-6513.1998.00004.x.View ArticlePubMedGoogle Scholar
- Sixma HJ, van Campen C, Kerssens JJ, Peters L: Quality of care from the perspective of elderly people: the QUOTE-elderly instrument. Age Ageing. 2000, 29: 173-178. 10.1093/ageing/29.2.173.View ArticlePubMedGoogle Scholar
- van Campen C, Sixma HJ, Kerssens JJ, Peters L: Assessing noninstitutionalized asthma and COPD patients' priorities and perceptions of quality of health care: the development of the QUOTE-CNSLD instrument. J Asthma. 1997, 34: 531-538.View ArticlePubMedGoogle Scholar
- van Campen C, Sixma HJ, Kerssens JJ, Peters L, Rasker JJ: Assessing patients' priorities and perceptions of the quality of health care: the development of the QUOTE-Rheumatic-Patients instrument. Br J Rheumatol. 1998, 37: 362-368. 10.1093/rheumatology/37.4.362.View ArticlePubMedGoogle Scholar
- van der Eijk I, Sixma H, Smeets T, Veloso FT, Odes S, Montague S, Fornaciari G, Moum B, Stockbrugger R, Russel M: Quality of health care in inflammatory bowel disease: development of a reliable questionnaire (QUOTE-IBD) and first results. Am J Gastroenterol. 2001, 96: 3329-3336.View ArticlePubMedGoogle Scholar
- Farley DO, Short PF, Elliott MN, Kanouse DE, Brown JA, Hays RD: Effects of CAHPS health plan performance information on plan choices by New Jersey Medicaid beneficiaries. Health Serv Res. 2002, 37: 985-1007. 10.1034/j.1600-0560.2002.62.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Goldstein E, Fyock J: Reporting of CAHPS quality information to medicare beneficiaries. Health Serv Res. 2001, 36: 477-488.PubMedPubMed CentralGoogle Scholar
- Hargraves JL, Hays RD, Cleary PD: Psychometric properties of the Consumer Assessment of Health Plans Study (CAHPS) 2.0 adult core survey. Health Serv Res. 2003, 38: 1509-1527. 10.1111/j.1475-6773.2003.00190.x.View ArticlePubMedGoogle Scholar
- Hays RD, Shaul JA, Williams VS, Lubalin JS, Harris-Kojetin LD, Sweeny SF, Cleary PD: Psychometric properties of the CAHPS 1.0 survey measures. Consumer Assessment of Health Plans Study. Med Care. 1999, 37: MS22-MS31. 10.1097/00005650-199903001-00003.View ArticlePubMedGoogle Scholar
- Morales LS, Elliott MN, Weech-Maldonado R, Spritzer KL, Hays RD: Differences in CAHPS adult survey reports and ratings by race and ethnicity: an analysis of the National CAHPS benchmarking data 1.0. Health Serv Res. 2001, 36: 595-617.PubMedPubMed CentralGoogle Scholar
- Solomon LS, Hays RD, Zaslavsky AM, Ding L, Cleary PD: Psychometric properties of a group-level Consumer Assessment of Health Plans Study (CAHPS) instrument. Med Care. 2005, 43: 53-60.PubMedGoogle Scholar
- Spranca M, Kanouse DE, Elliott M, Short PF, Farley DO, Hays RD: Do consumer reports of health plan quality affect health plan selection?. Health Serv Res. 2000, 35: 933-947.PubMedPubMed CentralGoogle Scholar
- Arah OA, ten Asbroek AH, Delnoij DM, de Koning JS, Stam PJ, Poll AH, Vriens B, Schmidt PF, Klazinga NS: Psychometric properties of the Dutch version of the Hospital-level Consumer Assessment of Health Plans Survey instrument. Health Serv Res. 2006, 41: 284-301. 10.1111/j.1475-6773.2005.00462.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Hurtado MP, Angeles J, Blahut SA, Hays RD: Assessment of the equivalence of the Spanish and English versions of the CAHPS Hospital Survey on the quality of inpatient care. Health Serv Res. 2005, 40: 2140-2161. 10.1111/j.1475-6773.2005.00469.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Weidmer B, Brown J, Garcia L: Translating the CAHPS 1.0 Survey Instruments into Spanish. Consumer Assessment of Health Plans Study. Med Care. 1999, 37: MS89-MS96. 10.1097/00005650-199903001-00010.View ArticlePubMedGoogle Scholar
- Brouwer W, Sixma HJ, Triemstra M, Delnoij DM: Kwaliteit van zorg rondom een staaroperatie vanuit het perspectief van patienten: meetinstrumentontwikkeling. 2006, Utrecht: NIVELGoogle Scholar
- Hekkink CF, Sixma HJ, Wigersma L, Yzermans CJ, Van Der Meer JT, Bindels PJ, Brinkman K, Danner SA: QUOTE-HIV: an instrument for assessing quality of HIV care from the patients' perspective. Qual Saf Health Care. 2003, 12: 188-193. 10.1136/qhc.12.3.188.View ArticlePubMedPubMed CentralGoogle Scholar
- Muthen BO: Dichotomous factor analysis of symptom data. Soc Methods Res. 1989, 18: 19-65. 10.1177/0049124189018001002.View ArticleGoogle Scholar
- Waller NG, Tellegen A, McDonals RP, Lykken DT: Exploring non-linear models in personality assessments: development and preliminary validation of a negative emotionality scale. J Pers. 1996, 64: 545-576. 10.1111/j.1467-6494.1996.tb00521.x.View ArticleGoogle Scholar
- Kaiser HF: The application of electronic computers to factor analysis. Educational and Psychological Measurement. 1960, 20: 141-151. 10.1177/001316446002000116.View ArticleGoogle Scholar
- Floyd FJ, Widaman KF: Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess. 1995, 7: 286-299. 10.1037/1040-3522.214.171.1246.View ArticleGoogle Scholar
- Howard KI, Forehand GC: A method for correcting item-total correlations for the effect of relevant item inclusion. Education and Psychological Measurement. 1962, 22: 731-735. 10.1177/001316446202200407.View ArticleGoogle Scholar
- Nunnally JC, Bernstein IH: Psychometric Theory. 1978, New York: McGraw-HillGoogle Scholar
- Carey RG, Seibert JH: A patient survey system to measure quality improvement: questionnaire reliability and validity. Med Care. 1993, 31: 834-845. 10.1097/00005650-199309000-00008.View ArticlePubMedGoogle Scholar
- Rasbash J, Steele F, Browne W, Prosser B: A user's guide to MLwiN version 2.0. 2004, London: Institute of Education, University of LondonGoogle Scholar
- Hox JJ: Applied Mulitlevel Analysis. 1995, AmsterdamGoogle Scholar
- Merlo J, Chaix B, Yang M, Lynch J, Rastam L: A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon. J Epidemiol Community Health. 2005, 59: 443-449. 10.1136/jech.2004.023473.View ArticlePubMedPubMed CentralGoogle Scholar
- Snijders TAB, Bosker RJ: Multilevel analysis: An introduction to basic and advanced multilevel modeling. 1999, London: Sage PublicationsGoogle Scholar
- Friedman DS, Bass EB, Lubomski LH, Fleisher LA, Kempen JH, Magaziner J, Sprintz M, Robinson K, Schein OD: Synthesis of the literature on the effectiveness of regional anesthesia for cataract surgery. Ophthalmology. 2001, 108: 519-529. 10.1016/S0161-6420(00)00597-2.View ArticlePubMedGoogle Scholar
- Friedman DS, Reeves SW, Bass EB, Lubomski LH, Fleisher LA, Schein OD: Patient preferences for anaesthesia management during cataract surgery. Br J Ophthalmol. 2004, 88: 333-335. 10.1136/bjo.2003.028258.View ArticlePubMedPubMed CentralGoogle Scholar
- Monestam E, Kuusik M, Wachtmeister L: Topical anesthesia for cataract surgery: a population-based perspective. J Cataract Refract Surg. 2001, 27: 445-451. 10.1016/S0886-3350(00)00637-4.View ArticlePubMedGoogle Scholar
- Stubbe JH, Gelsema T, Delnoij DM: Psychometric properties of a survey measuring patients' experience with quality of care after a total hip or knee arthroplasty. BMC Health Serv Res. 2007, 26: 60-10.1186/1472-6963-7-60.View ArticleGoogle Scholar
- Pascoe GC, Attkisson CC: The evaluation ranking scale: a new methodology for assessing satisfaction. Eval Program Plann. 1983, 6: 335-347. 10.1016/0149-7189(83)90013-7.View ArticlePubMedGoogle Scholar
- Kreft I, De Leeuw J: Instroducing multilevel modeling. 1998, London: SageView ArticleGoogle Scholar
- Hall JA, Dornan MC: Patient sociodemographic characteristics as predictors of satisfaction with medical care: a meta-analysis. Soc Sci Med. 1990, 30: 811-818. 10.1016/0277-9536(90)90205-7.View ArticlePubMedGoogle Scholar
- Williams SJ, Calnan M: Convergence and divergence: assessing criteria of consumer satisfaction across general practice, dental and hospital care settings. Soc Sci Med. 1991, 33: 707-716. 10.1016/0277-9536(91)90025-8.View ArticlePubMedGoogle Scholar
- Harris IB, Rich EC, Crowson TW: Attitudes of internal medicine residents and staff physicians toward various patient characteristics. J Med Educ. 1985, 60: 192-195.PubMedGoogle Scholar
- Street RL, Buller DB: Patients' characteristics affecting physician-patient nonverbal communication. Human Communication Research. 1988, 15: 60-90. 10.1111/j.1468-2958.1988.tb00171.x.View ArticleGoogle Scholar
- Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ: Patterns of intra-cluster correlation from primary care research to inform study design and analysis. J Clin Epidemiol. 2004, 57: 785-794. 10.1016/j.jclinepi.2003.12.013.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2415/7/14/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.