Online assessment of patients' views on hospital performances using Rasch model's KIDMAP diagram

Critiques of traditional Likert-type patient satisfaction surveys

Inappropriate individual item-by-item box plots of disclosure

Items in Picker's questionnaire are often analyzed and presented individually, one item at a time (e.g., item-by-item box plots of disclosure as shown in Figure 1). In so doing, global interrelationships between items are invisible [20]. Besides, measurement error in a single item is often very substantial but it is ignored in such an item-by-item analysis. We are therefore concerned with the interrelationship between items when they are, in effect, measuring a single construct. Advanced analysis is required.

Item response theory (IRT) or Rasch measurement [21] provides such an advanced analysis to take into account the interrelationship among items. A newly designed diagram can be provided to accurately report patients' experiences for each hospital.

Objective measurement requirements

There are three major problems in conventional analyses of Picker's questionnaires: (a) interval scores: Raw scores of Picker's questionnaires are ordinal but have been mistakenly treated as interval and analyzed with linear factor analysis; (b) graphic diagrams with correlated items: more efforts should be made to provide meaningful and simple diagrams (e.g., Google Image Labeler labels digital photographs according to their content) by making the task a simple game in which contestants (i.e., hospitals in this study) must both collaborate and compete with each other; and (c) aggregate scores: the benchmark reports that present item-by-item results for patients to compare with national results not only fail to help patients understand the overall performance of each hospital with an aggregate score, but also lack an objective metric to quantify the difference between hospitals. Details are described as follows:

(1) Interval and additive scores for comparison

How to measure individual differences on an interval scale [22–24] is an important issue in health care systems. The United Kingdom [25] has introduced a pay-for-performance contract for family practitioners according to performance with respect to 146 quality indicators covering clinical care for 10 chronic diseases, organization of care, and patient experience. In this contract, interval scores should be ensured beforehand such that they can be meaningfully compared with each practitioner. Stout [26] quoted Lord and Novick [27] as saying, "A major problem of mental test theory is to determine a good interval scaling to impose when the supporting theory implies only ordinal properties".

(2) KIDMAP improvement of communication on the Internet

KIDMAP (also called Diagnostic Map), derived from Rasch measurement, provides an output for each individual and summarizes expected and unexpected response patterns. The first large-scale implementation of KIDMAP took place in the Los Angeles Independent County School District in the early 1980s. Hundreds of thousands of KIDMAP reports have been distributed to parents [28, 29]. In this study, we implement KIDMAP to summarize patients' perception about each individual hospital's performances, distribute KIDMAP reports to hospital staff through the Internet, so that they can easily recognize key indicators for improvement. The reports also allow patients to compare hospitals' performance in quality of healthcare.

Quest [30] is the first software that produces KIDMAP reports. Chien et al. [31] developed a web-based KIDMAP (shortly Web-KIDMAP) on hospital indicator management, and they argued that Web-KIDMAP empowers diagnostic information by placing responses in four quadrants, not only for hospitals to improve their performances, but also for patients to better choose hospitals.

(3) Aggregate scores produce useful results for questionnaires

The EPIE questionnaires require patients to report what happened to them rather than how satisfied they are. EPIE believes that questionnaires about satisfaction do not produce very useful results. A better approach is to require patients to report their experiences in hospitals so as to identify hospital strength and weakness, which allows hospitals to target problems and improve services [32]. Accordingly, an aggregate score for satisfaction level of each hospital is essential and a quality control fit statistic indicator [33] is needed so that hospitals' performance can be quantified and compared.

Aims of the study

In this study, we apply Rasch analysis to (a) examine whether items in the EPIE questionnaire fit the model's expectations so that the underlying latent trait (henceforth referred to as satisfaction level) can be quantified and an interval scale can be obtained; (b) display the KIDMAP diagram [28, 29] on the web to help patients understand the overall performance of individual hospitals; and (c) quantify the performances among hospitals along a continuum single construct.

Instrument and data collections

With permission, we downloaded datasheets from a total of 169 hospitals in the 2003 English inpatient questionnaire from EPIE [17]. The original 45-indicator questions (Table 1) have been scored using a scale of 0 to 100. The scores indicate the extent to which the patient's experience could have been improved. A score of 0 is assigned to all responses that reflect considerable need for improvement, whereas an answer option that has been assigned a score of 100 refers to a positive patient experience.

Data transformation and Rasch analysis

Assessment of differential item functioning (DIF)

To make comparison across different groups of hospitals, the test construct must remain invariant across groups. DIF analysis is a means to verify construct equivalence over groups [49]. If construct equivalence does not hold over groups, meaning that different groups respond to individual questions differently after holding their latent trait levels constant, then the estimated measures could not be compared directly over groups. In this study hospital type (small – large acute, teaching, etc) were examined for DIF. A p-value less than .05 indicates a DIF.

KIDMAP shown on the web

In order to improve the traditional KIDMAP profile, we developed an innovative web-based KIDMAP, called Web-KIDMAP, which reveals valuable information for hospital staff and patients in real time. Together with infit and outfit MNSQ, Web-KIDMAP reveals the strength and weakness of each hospital.

Examining a single underlying construct

Overall, these 18 items exhibited a good model-data fit. Hence, they measured a single construct for patient satisfaction and an interval scale of logits was achieved for further comparison and analysis [38]. The hospital measures ranged from -1.59 to 9.63 with mean 2.64 and standard deviation 2.09, indicating items were easier for these hospitals and a wide range of hospitals dispersed on the interval scale.

The hospital sample separation reliability was 0.94 (Cronbach's α [50] = 0.96), indicating that these 18 items could differentiate the hospitals very well. The separation index for the items (a measure of the spread of the estimates relative to their precision) was as high as 4.01, allowing us to differentiate between five statistically distinct strata of item difficulties with the formula of strata = (4 × 4.01 + 1)/3 [51].

Analysis of variance on the hospital measures reveals a significant difference (F = 8.318; p < .001) among types of hospitals: General practices (M = 4.72 logits) performed the best, followed by Large hospitals (M = 3.47 logits), Teaching hospitals (M = 2.62 logits), Small hospitals (M = 2.25 logits), and Medium hospitals (M = .82 logits).

Item properties

The three most difficult items to be satisfied by patients were: item 39 (Staff told you about any medication side effects to watch when going home), item 41 (Doctors or nurses gave your family information needed to help you) and item 27 (Hospital staff talks about your worries and fears). The easiest one was item 34 (Hospital staff did everything they could to help you control your pain). The mean and standard deviation of items was 0.00 and 1.52, respectively. All of items were in a range of absolute 4.9 logits. The item difficulties were well spread out across the hospitals, indicating that these items could differentiate hospitals fairly well so as to reach a hospital separation reliability of 0.94.

Item invariance refers to the fact that the estimated item location parameters should not depend on the sample used to calibrate the estimates [50]. Table 3 shows that no DIF items were found across different types of hospitals, suggesting these items measure the same construct across types of hospitals such that their performances can be directly compared.

KIDMAP used for diagnosing hospitals

Figure 2 shows a Web-KIDMAP for a particular hospital. In the right-hand bottom corner (the 4th quadrant), there are six 'easier not achieved' items that the hospital was expected to have achieved given the performance estimate of 6.14 logits and the percentile rank of 10 (see to the right of the percentile column in Figure 2).

The most unexpected errors among the three items, one noted with an asterisk (*) and two with a caret (^), are indicated as statistically significantly different (p < 0.05 and p < 0.01, respectively). The label 6.5.4 in the 4th quadrant means that the 5^th category of item 6 was endorsed as 4 by the hospital. Actually, the hospital (6.41 logits) had a very good chance to achieve a score higher than 4 but failed to achieve (e.g., see the left-hand side in Figure 2). These unexpected responses are informative and worth noting, because the hospital's weakness did not match the patients' perception. Note that the unexpected response was identified by inspecting the hospital's own performance level (i.e., self comparison), rather than the averaged scores in Picker's item-by-item diagram, Figure 1.

Three steps to read the Web-KIDMAP

We analyzed the data from the 2003 EPIE inpatient questionnaire [17] and developed a Web-KIDMAP diagram that could be visualized on the Internet for (a) inter-hospital comparison (by inspecting performance levels along the logit scale), (b) intra-hospital comparison (by inspecting response patterns and residual Z-scores), and (c) model-data fit checking (by inspecting MNSQ statistics).

Findings

We use Rasch measurement to construct an interval logic scale for patient satisfaction on hospital performance, utilize fit statistics to detect aberrant response pattern, and develop a visual representation on website, Web-KIDMAP, to overcome the drawback in traditional individual item-by-item box plots of disclosure for patient views on hospital service quality.

The item-by-item analysis of the Picker's questionnaire ignores measurement error that is embedded in an item. In reality, a self-report item (like those in Picker's questionnaire) is very imprecise and contains a large measurement error, which invalidates item-by-item analysis. Besides measurement error, item-by-item analysis is useful only when each provides feedback information about an individual practice (or program, treatment, etc.). It is hard to believe that there are 45 kinds of practice that have been routinely carried out in a hospital, such that we need 45 items to provide feedback information, one item for one kind of practice.

Rasch analysis considers measurement error in an item and yields global and local feedback information for hospital performance (e.g., item and person fit analysis, DIF analysis, item difficulty hierarchy, the Wright map, the KIDMAP, etc.).

Strengths of the study

Limitations of the study

High quality healthcare feedback is important. More work is needed to further enable the administration of an effective feedback system in healthcare settings. In this study, we demonstrate how a visualized Web-KIDMAP can be used to compare patient perception on healthcare service on the Internet. Users may need some background knowledge to interpret Web-KIDMAP properly.

Due to DIF and misfit to the model, 27 items are removed. This is because these items do not work with the other items harmoniously to measure the same construct, not because they are useless or unimportant to hospital performance. Future studies can investigate importance of these 27 items. If they are very important to hospital performance, then a stand-along scale should be developed. Future study can further macro-examine the test properties and to micro-investigate the item response with/without errors or abnormalities [52].

Applications

KIDMAP is not designed to replace traditional item-by-item box plots as shown in Figure 1, but to complement them. Furthermore, Web-KIDMAP is highly dynamic and easily interpretable with graphic tools as well as the exemplary comparisons in quality of healthcare, especially on the Internet referring to Figure 3.

Fit MNSQ statistics describe measurement quality. As for the illustration in Figure 2, an outfit MNSQ of 3.15 indicates that the patients' responses to the hospital does not provide useful information and there is too much unexplained noise in the observation (such as those three red colored items in Figure 2)[52].

The indicators in the top-left corner (the 2nd quadrant) are the hospital's strength associated with the category 'harder ones but achieved'. The probability of endorsing such an item by the hospital is shown in the ultimate left column. "4/5", located in the middle of the Web-KIDMAP, indicating the performance estimate on location of 6.14 logits classified as the 4th best stratum (note: the separation index for the persons is as high as 4.01, allowing us to differentiate between five statistically distinct strata of person abilities with the formula of strata (= (4 × 4.01 + 1)/3). Percentile ranks, frequencies and the distribution of the norm-reference from 169 hospitals are innovated and shown on the right-hand side, differing from the traditional KIDMAP provided by the Quest software [30].

Further studies and suggestions

Web-KIDMAP provides a response profile of an individual hospital. Hospital staff can use Web-KIDMAP to inspect patients' perception about hospital performance. Aberrant responses in those items of "easier not achieved" deserve more attention. The Z-scores in Figure 2 provide an opportunity for a hospital to examine its strength and weakness by self-comparison rather than compared with other hospitals, so as to upgrade the overall performance level.

The transformation of ordinal raw scores into interval logits using Rasch measurement [21.38] within patient-centered research is worth further research. We believe that Web-KIDMAP [31] is indicative, efficient and effective and can easily facilitate patient-centered clinical environment.

We herein propose that healthcare informatics be regarded as a whole newly integrated academic discipline, one that should be devoted to the exploration of the new possibilities that informatics is creating for both hospital staff and patients in relation to health and healthcare issues [53, 54].

These 18 items can be added to a static questionnaire as a daily routine for examining healthcare quality of a hospital or as item banks created for a touch screen version of computer adaptive testing (CAT) to reducing patient burdens in responding questions [55], which studies are worth carrying out in the future.