The relation between usage of an eHealth intervention for stress urinary incontinence and treatment outcomes: an observational study.

BACKGROUND: Stress urinary incontinence (SUI), though a prevalent condition among women, is undertreated in primary care. EHealth with pelvic floor muscle training is an evidence-based alternative to care-as-usual. It is unknown, however, how eHealth usage is related to treatment outcome, and this knowledge is required for general practitioners to implement eHealth in their practice. This study examines the relation between usage of eHealth for SUI and treatment outcomes by examining log data. Baseline factors were also explored for associations with treatment success. METHOD: In this pre-post study, women with SUI participated in "Baasoverjeblaas.nl", a web-based intervention translated from the Swedish internet intervention "Tät®-treatment of stress urinary incontinence". Usage was based on log data and divided into three user groups (low, intermediate and high). Online questionnaires were sent before, after treatment and at six-months follow-up. The relation between usage and the primary outcome - treatment success (PGI-) - was studied with a binomial logistic regression analysis. Changes in the secondary outcomes - symptom severity (ICIQ-UI SF) and quality of life (ICIQ-LUTSqol) - were studied per user group with linear mixed model analysis. RESULTS: Included were 515 users with a mean age of 50.5 years (12.0 SD). The majority were low users (n = 295, 57.3%). Treatment success (PGI-I) was reached by one in four women and was more likely in high and intermediate users than in low users (OR 13.2, 95% CI 6.1-28.5, p < 0.001 and OR 2.92, 95% CI 1.35-6.34, p = 0.007, respectively). Symptom severity decreased and quality of life improved significantly over time, especially among high users. The women's expected ability to train their pelvic floor muscles and the frequency of pelvic floor muscle exercises at baseline were associated with treatment success. CONCLUSION: This study shows that usage of eHealth for SUI is related to all treatment outcomes. High users are more likely to have treatment success. Treatment success is more likely in women with higher expectations and pelvic floor muscle training at baseline. These findings indicate that general practitioners can select patients that would be more likely to benefit from eHealth treatment, and they can enhance treatment effect by stimulating eHealth usage. TRIAL REGISTRATION: Landelijk Trial Register NL6570;  https://onderzoekmetmensen.nl/nl/trial/25463 .

Non-indicated vitamin B12- and D-testing among Dutch hospital clinicians: a cross-sectional analysis in data registries.

OBJECTIVES: To assess the extent of non-indicated vitamin B12- and D-testing among Dutch clinicians and its variation among hospitals. DESIGN: Cross-sectional study using registration data from 2015 to 2019. PARTICIPANTS: Patients aged between 18 and 70 years who received a vitamin B12- or D-test. PRIMARY AND SECONDARY OUTCOME MEASURES: The proportion of non-indicated vitamin B12- and D-testing among Dutch clinicians and its variation between hospitals (n=68) over 2015-2019. RESULTS: Between 2015 and 2019, at least 79.0% of all vitamin B12-tests and 82.0% of vitamin D-tests lacked a clear indication. The number of vitamin B12-tests increased by 2.0% over the examined period, while the number of D-tests increased by 12.2%. The proportion of the unexplained variation in non-indicated vitamin B12- and D-tests that can be ascribed to differences between hospitals remained low. Intraclass correlation coefficients ranged between 0.072 and 0.085 and 0.081 and 0.096 for non-indicated vitamin B12- and D-tests, respectively. The included casemix variables patient age, gender, socioeconomic status and hospital size only accounted for a small part of the unexplained variation in non-indicated testing. Additionally, a significant correlation was observed in non-indicated vitamin B12- and D-testing among the included hospitals. CONCLUSION: Hospital clinicians order vitamin B12- and D-tests without a clear indication on a large scale. Only a small proportion of the unexplained variation could be attributed to differences between hospitals.

Importance of Patient Involvement in Creating Content for eHealth Interventions: Qualitative Case Report in Orthopedics.

BACKGROUND: In many industries, collaboration with end users is a standard practice when developing or improving a product or service. This process aims for a much better understanding of who the end user is and how the product or service could be of added value to them. Although patient (end user) involvement in the development of eHealth apps is increasing, this involvement has mainly focused on the design, functionalities, usability, and readability of its content thus far. Although this is very important, it does not ensure that the content provided aligns with patients' priorities. OBJECTIVE: In this study, we aimed to explore the added value of patient involvement in developing the content for an eHealth app. By comparing the findings from this study with the existing app, we aimed to identify the additional informational needs of patients. In addition, we aimed to help improve the content of apps that are already available for patients with knee replacements, including the app our group studied in 2019. METHODS: Patients from a large Dutch orthopedic clinic participated in semistructured one-on-one interviews and a focus group session. All the patients had undergone knee replacement surgery in the months before the interviews, had used the app, and were therefore capable of discussing what information they missed or wished for before and after the surgery. The output was inductively organized into larger themes and an overview of suggestions for improvement. RESULTS: The interviews and focus group session with 11 patients identified 6 major themes and 30 suggestions for improvement, ranging from information for better management of expectations to various practical needs during each stage of the treatment. The outcomes were discussed with the medical staff for learning purposes and properly translated into an improved version of the app's content. CONCLUSIONS: In this study, patients identified many suggestions for improvement, demonstrating the added value of involving patients when creating the content of eHealth interventions. In addition, our study demonstrates that a relatively small group of patients can contribute to improving an app's content from the patient's perspective. Given the growing emphasis on patients' self-management, it is crucial that the information they receive is not only relevant from a health care provider's perspective but also aligns with what really matters to patients. TRIAL REGISTRATION: Netherlands Trial Register NL8295; https://trialsearch.who.int/Trial2.aspx?TrialID=NL8295.

Usage of a Web-Based eHealth Intervention for Women With Stress Urinary Incontinence: Mixed Methods Study.

BACKGROUND: Stress urinary incontinence (SUI) is highly prevalent among women and has an impact on physical and mental well-being. eHealth with pelvic floor muscle training (PFMT) has shown to be effective in reducing complaints. The usage and nonusage attrition of eHealth for SUI is unknown, but knowledge about users and their usage patterns is crucial for implementation purposes. OBJECTIVE: This study aimed to evaluate how an eHealth intervention for SUI was used and by whom, explore reasons for nonusage attrition, and determine what factors are associated with usage. METHODS: In this observational, mixed methods study, women with SUI independently registered to a web-based eHealth intervention, Baas over je blaas, a translation of the Swedish internet program Tät-treatment of stress urinary incontinence. Log-in data were collected during 3-month access to the website, and surveys were sent at baseline. Participants were divided into three user groups (low, intermediate, and high) and were compared based on sociodemographic and incontinence-related characteristics. Nominal logistic regression analysis was used to study factors associated with eHealth usage. Qualitative content analysis was used for open-ended questions about nonusage attrition and about facilitators of and barriers to eHealth usage. RESULTS: Participants (n=561) had a mean age of 50.3 (SD 12.1) years, and most of them (340/553, 61.5%) had never visited a health care professional for SUI before. Most users were low users (295/515, 57.3%), followed by intermediate users (133/515, 25.8%) and high users (87/515, 16.9%). User groups differed significantly in age (48.3, SD 12 years; 52.1, SD 11.6 years; and 55.3, SD 10.9 years; P<.001) and in their expected ability to train the pelvic floor muscles (7.5, SD 1.4; 7.7, SD 1.4; and 8.1, SD 1.5 for low, intermediate, and high users, respectively; P=.006). Nonusage attrition was mainly caused by problems in integrating PFMT into everyday life. High age (>50 years), previous PFMT, and high expected ability to train the pelvic floor muscles are associated with high usage. Facilitators for eHealth usage were the clear explanation of exercises and the possibility of self-management. Barriers were its noncommittal character and the absence of personal contact. CONCLUSIONS: eHealth fulfills a need for women with SUI who have never received treatment. Those who discontinued prematurely did so mainly because it was difficult to integrate the training schedule into their everyday lives. High eHealth usage was more likely for women aged >50 years, with previous PFMT, and with high expectations about their ability to train the pelvic floor muscles. Knowledge of these user characteristics can guide clinicians and correct their misunderstandings about the suitable target population for this intervention. Furthermore, strategies for reinforcing expectations and self-efficacy are important to upscale eHealth usage, together with paying attention to people's need for personal contact. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13164.

Overuse of diagnostic testing in healthcare: a systematic review.

BACKGROUND: Overuse of diagnostic testing substantially contributes to healthcare expenses and potentially exposes patients to unnecessary harm. Our objective was to systematically identify and examine studies that assessed the prevalence of diagnostic testing overuse across healthcare settings to estimate the overall prevalence of low-value diagnostic overtesting. METHODS: PubMed, Web of Science and Embase were searched from inception until 18 February 2020 to identify articles published in the English language that examined the prevalence of diagnostic testing overuse using database data. Each of the assessments was categorised as using a patient-indication lens, a patient-population lens or a service lens. RESULTS: 118 assessments of diagnostic testing overuse, extracted from 35 studies, were included in this study. Most included assessments used a patient-indication lens (n=67, 57%), followed by the service lens (n=27, 23%) and patient-population lens (n=24, 20%). Prevalence estimates of diagnostic testing overuse ranged from 0.09% to 97.5% (median prevalence of assessments using a patient-indication lens: 11.0%, patient-population lens: 2.0% and service lens: 30.7%). The majority of assessments (n=85) reported overuse of diagnostic testing to be below 25%. Overuse of diagnostic imaging tests was most often assessed (n=96). Among the 33 assessments reporting high levels of overuse (≥25%), preoperative testing (n=7) and imaging for uncomplicated low back pain (n=6) were most frequently examined. For assessments of similar diagnostic tests, major variation in the prevalence of overuse was observed. Differences in the definitions of low-value tests used, their operationalisation and assessment methods likely contributed to this observed variation. CONCLUSION: Our findings suggest that substantial overuse of diagnostic testing is present with wide variation in overuse. Preoperative testing and imaging for non-specific low back pain are the most frequently identified low-value diagnostic tests. Uniform definitions and assessments are required in order to obtain a more comprehensive understanding of the magnitude of diagnostic testing overuse.

Can the Use of Health Insurance Claim Data Benefit the Risk-Based Supervision of General Practitioner Practices? An Exploratory Study in the Netherlands.

BACKGROUND: The Dutch Health and Youth Care Inspectorate has organized a study investigating whether there are benefits to using claim data in the risk-based supervision of general practitioner (GP) practices. METHODS: We identified and selected signals of risks based on interviews with experts. Next, we selected 3 indicators that could be measured in the claim database. These were: the expected and actual costs of the GP practice; the percentage of reserve antibiotics prescribed; and the percentage of patients undergoing an emergency admission during the weekend. We corrected the scores of the GP practices based on their casemix and identified practices with the most unfavorable scores, 'red flags,' in 2015, or the trend between 2013-2015. Finally, we analysed the data of GP practices already identified as delivering substandard care by the Health and Youth Care Inspectorate and calculated the sensitivity and specificity of using the indicators to identify poor performing GP practices. RESULTS: By combining the 3 indicators, we identified 1 GP practice with 3 red flags and 24 GP practices with 2 red flags. The a priori chance of identifying a GP practice that shows substandard care is 0.3%. Using the indicators, this improved to 1.0%. The sensitivity was 26.7%, the specificity was 92.8%. CONCLUSION: The Dutch Health and Youth Care Inspectorate might use claim data to calculate indicators on costs, the prescribing of reserve antibiotics and emergency admissions during the weekend, when setting priorities for its visits to GP practices. Visiting more GP practices by the Health and Youth Care Inspectorate, and identifying substandard care, is necessary to validate the use of these indicators.

Women's adoption of a web-based intervention for stress urinary incontinence: a qualitative study.

BACKGROUND: Stress urinary incontinence (SUI) is common among women and affects their quality of life. Pelvic floor muscle training is an effective conservative therapy, but only a minority of women seek help. E-health with pelvic floor muscle training is effective and increases access to care. To implement an e-Health intervention in a sustainable way, however, we need to understand what determines adoption. The aim is to investigate the barriers and facilitators to adopting an e-Health intervention among Dutch women with stress urinary incontinence. METHODS: Semi-structured telephonic interviews were carried out among participants of the Dutch e-Health intervention for women with stress urinary incontinence. Women were purposively sampled. The 'Fit between Individuals, Task and Technology' (FITT) framework was used for both the data collection and data analysis, to gain a more in-depth insight into the adoption of the intervention. RESULTS: Twenty women were interviewed, mean age 51 years and mostly highly educated. The adoption of e-Health for women with SUI mainly depends on the interaction between users and e-Health, and users and pelvic floor muscle training exercises. Facilitators for the adoption were the preference for an accessible self-management intervention, having a strong sense of self-discipline and having the ability to schedule the exercises routinely. Women needed to possess self-efficacy to do this intervention independently. Barriers to the adoption of e-Health were personal circumstances restricting time for scheduling pelvic floor muscle training and lacking skills to perform the exercises correctly. Despite guidance by technical features several women remained uncertain about their performance of the exercises and, therefore, wanted additional contact with a professional. CONCLUSIONS: For stress urinary incontinence e-Health is an appropriate option for a target audience. Use of the FITT framework clearly demonstrates the conditions for optimal adoption. For a subgroup it was a suitable alternative for medical care in person. For others it identified the need for further support by a health care professional. This support could be provided by improvements of technical features and incorporating modes for digital communication. The additional value of integration of the e-Health intervention in primary care might be a logical next step. TRIAL REGISTRATION: The study was prospectively registered in the Netherlands Trial Registry (NTR) NTR6956 .

Barriers and facilitators to reduce low-value care: a qualitative evidence synthesis.

OBJECTIVE: To assess barriers and facilitators to de-implementation. DESIGN: A qualitative evidence synthesis with a framework analysis. DATA SOURCES: Medline, Embase, Cochrane Library and Rx for Change databases until September 2018 were searched. ELIGIBILITY CRITERIA: We included studies that primarily focused on identifying factors influencing de-implementation or the continuation of low-value care, and studies describing influencing factors related to the effect of a de-implementation strategy. DATA EXTRACTION AND SYNTHESIS: The factors were classified on five levels: individual provider, individual patient, social context, organisational context, economic/political context. RESULTS: We identified 333 factors in 81 articles. Factors related to the individual provider (n=131; 74% barriers, 17% facilitators, 9% both barrier/facilitator) were associated with their attitude (n=72; 55%), knowledge/skills (n=43; 33%), behaviour (n=11; 8%) and provider characteristics (n=5; 4%). Individual patient factors (n=58; 72% barriers, 9% facilitators, 19% both barrier/facilitator) were mainly related to knowledge (n=33; 56%) and attitude (n=13; 22%). Factors related to the social context (n=46; 41% barriers, 48% facilitators, 11% both barrier/facilitator) included mainly professional teams (n=23; 50%) and professional development (n=12; 26%). Frequent factors in the organisational context (n=67; 67% barriers, 25% facilitators, 8% both barrier/facilitator) were available resources (n=28; 41%) and organisational structures and work routines (n=24; 36%). Under the category of economic and political context (n=31; 71% barriers, 13% facilitators, 16% both barrier/facilitator), financial incentives were most common (n=27; 87%). CONCLUSIONS: This study provides in-depth insight into the factors within the different (sub)categories that are important in reducing low-value care. This can be used to identify barriers and facilitators in low-value care practices or to stimulate development of strategies that need further refinement. We conclude that multifaceted de-implementation strategies are often necessary for effective reduction of low-value care. Situation-specific knowledge of impeding or facilitating factors across all levels is important for designing tailored de-implementation strategies.

Identifying and de-implementing low-value care in primary care: the GP's perspective-a cross-sectional survey.

OBJECTIVE: General practitioners have an important role in reducing low-value care as gatekeepers of the health system. The aim of this study was to assess the experiences of Dutch general practitioners regarding low-value care and to identify their needs to decrease low-value primary care. DESIGN: We performed a cross-sectional study. PARTICIPANTS: We sent a survey to 500 general practitioners. SETTING: Primary care in the Netherlands. PRIMARY AND SECONDARY OUTCOMES: The survey contained questions about the provision of low-value care and on clinical cases about lumbosacral spine X-rays in patients with low back pain and vitamin B12 laboratory tests without an evidence-based indication. We also asked general practitioners what they needed to reduce low-value care. RESULTS: A total of 182 general practitioners (37%) responded. 67% indicated that low-value care practices are regularly provided in general practice. 57% of the general practitioners have seen negative consequences of low-value care, in particular side effects of medication. The most provided low-value care practices are medication prescriptions such as antibiotics and laboratory tests such as vitamin B12 tests. The most reported drivers are patient-related. General practitioners want to maintain a good relationship with their patients by offering their patients an intervention instead of watchful waiting. Lack of time also plays a major role. In order to reduce low-value care, general practitioners suggested that educating patients on the value of tests and treatments might help. Supporting general practitioners and other healthcare professionals with clear guidelines as well as having more time for consultation were also mentioned by general practitioners. CONCLUSION: General practitioners are aware of providing unnecessary care despite their role as gatekeepers and have reasons for this. They need support in order to change their practice. This support might consist of better education of healthcare professionals and providing more time for consultation. Local and national media, such as websites and television, could be used to educate patients while guidelines could support professionals in reducing low-value care.

Assessing volume and variation of low-value care practices in the Netherlands.

BACKGROUND: There have been contributions to quantify the volume of low-value care practices in the USA, Canada and Australia but we have no knowledge about the volume in Europe. The purpose of this study was to assess the volume and variation of Dutch low-value care practices. METHODS: We conducted a cross-sectional study with data of a Dutch healthcare insurance company from general practioners (GP's) and hospitals in the Netherlands from 2016. We used all billing claims made by healthcare providers of 3.5 million Dutch inhabitants. We studied Choosing Wisely recommendations in order to select low-value care practices. We used the percentage low-value care practices per hospital and number of low-value care practices per GP as outcomes. RESULTS: We assessed the volume of low-back imaging by GPs, screening of patients over 75 years for colorectal cancer and diagnosing varices with Doppler or Plethysmography. We found that 0.4% (range 0-7%) of the eligible patients received low-value screening for colorectal cancer and 8.0% (range 0-88%) of eligible patients received low-value diagnosing of varices. About 52.4% of the GPs ordered X-rays and 11.2% ordered magnetic resonance imagings of the lumbosacral spine. Most healthcare providers did not provide the measured low-value care practices. However, 1 in 12 GPs ordered at least one low-back X-ray a week. CONCLUSIONS: The three Choosing Wisely recommendations showed a lot of practice variation; many healthcare providers did not order these low-value diagnostic tests; a minor part did order a substantial amount, low-back spine radiology in particular. These healthcare providers should start reducing these activities.

An Electronic Health Intervention for Dutch Women With Stress Urinary Incontinence: Protocol for a Mixed Methods Study.

BACKGROUND: Stress urinary incontinence (SUI) is a common problem with a great potential influence on quality of life. Although SUI can be treated effectively with pelvic floor muscle training (PFMT), only a minority of women with this complaint seek help. An internet-based electronic health (eHealth) intervention could make care more accessible. The Swedish eHealth intervention Tät-treatment of Stress Urinary Incontinence offers PFMT and has shown to be effective in reducing symptoms in women with SUI. This intervention might be helpful for Dutch women too, but its adoption needs to be studied as the Netherlands differs from Sweden in terms of geographical characteristics and health care organization. OBJECTIVE: The objective of this protocol is to investigate the barriers and facilitators to the adoption of an eHealth intervention for Dutch women with SUI and the effects of this intervention. METHODS: We are conducting an explanatory sequential mixed methods study among 800 Dutch women with SUI who participate in the translated version of Tät-treatment of Stress Urinary Incontinence. This eHealth intervention takes 3 months. A pre-post study is conducted using surveys, which are sent at baseline (T0), 3 weeks after baseline (T1), posttreatment (T2), and 3 months posttreatment (T3). After the intervention, semistructured interviews will be held with 15 to 20 participants. The primary outcomes are barriers and facilitators to using the Tät-treatment of Stress Urinary Incontinence. This will also be analyzed among groups that differ in age and severity of incontinence. A thematic content analysis of the qualitative data will be performed. The secondary outcomes are: (1) effect on symptoms of urinary incontinence, (2) effect on quality of life, and (3) factors that are potentially associated with success. Effects will be analyzed by a mixed model analysis. Logistic regression analysis will be used to study what patient-related factors are associated with success. RESULTS: Enrollment started in July 2018 and will be finished by December 2019. Data analysis will start in March 2020. CONCLUSIONS: An eHealth intervention for Dutch women with SUI is promising because it can make treatment more accessible. The strength of this study is that it explores the possibilities for an internet-based-only treatment for women with SUI by using both quantitative and qualitative research methodologies. The study elaborates on existing results by using a previously tested and effective eHealth program. Insight into the barriers and facilitators to using this program can enhance the implementation of the intervention in the Dutch health care system. TRIAL REGISTRATION: Netherlands Trial Registry (NTR) NTR6956; https://www.trialregister.nl/trial/6570. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13164.

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

OBJECTIVES: The indicator unexpectedly long length of stay (UL-LOS) is used to gain insight into quality and safety of care in hospitals. The calculation of UL-LOS takes patients' age, main diagnosis and main procedure into account. University hospitals have relatively more patients with a UL-LOS than other hospitals. Our main research question is whether the high number of patients with a UL-LOS in university hospitals is caused by differences in additional patient characteristics between university hospitals and other hospitals. DESIGN: We performed a cross-sectional study and used administrative data from 1 510 627 clinical admissions in 87 Dutch hospitals. Patients who died in hospital, stayed in hospital for 100 days or longer or whose country of residence was not the Netherlands were excluded from the UL-LOS indicator. We identified which patient groups were treated only in university hospitals or only in other hospitals and which were treated in both hospital types. For these last patient groups, we added supplementary patient characteristics to the current model to determine the effect on the UL-LOS model. RESULTS: Patient groups treated in both hospital types differed in terms of detailed primary diagnosis, socioeconomic status, source of admission, type of admission and amount of Charlson comorbidities. Nevertheless, when adding these characteristics to the current model, university hospitals still have a significantly higher mean UL-LOS score compared with other hospitals (p<0.001). CONCLUSIONS: The difference in UL-LOS scores between both hospital types remains after adding patient characteristics in which both hospital types differ. We conclude that the high UL-LOS scores in university hospitals are not caused by the investigated additional patient characteristics that differ between university and other hospitals. Patients might stay relatively longer in university hospitals due to differences in work processes because of their education and research tasks or financing differences of both hospital types.

Influence of Intensified Supervision by Health Care Inspectorates on Online Patient Ratings of Hospitals: A Multilevel Study of More Than 43,000 Online Ratings.

BACKGROUND: In the Netherlands, hospitals with quality or safety issues are put under intensified supervision by the Dutch Health Care Inspectorate, which involves frequent announced and unannounced site visits and other measures. Patient rating sites are an upcoming phenomenon in health care. Patient reviews might be influenced by perceived quality including the media coverage of health care providers when the health care inspectorate imposes intensified supervision, but no data are available to show how these are related. OBJECTIVE: The aim of this study was to investigate whether and how being under intensified supervision of the health care inspectorate influences online patient ratings of hospitals. METHODS: We performed a longitudinal study using data from the patient rating site Zorgkaart Nederland, from January 1, 2010 to December 31, 2015. We compared data of 7 hospitals under intensified supervision with a control group of 28 hospitals. The dataset contained 43,856 ratings. We performed a multilevel logistic regression analysis to account for clustering of ratings within hospitals. Fixed effects in our analysis were hospital type, time, and the period of intensified supervision. Random effect was the hospital. The outcome variable was the dichotomized rating score. RESULTS: The period of intensified supervision was associated with a low rating score for the hospitals compared with control group hospitals; both 1 year before intensified supervision (odds ratio, OR, 1.67, 95% CI 1.06-2.63) and 1 year after (OR 1.79, 95% CI 1.14-2.81) the differences are significant. For all periods, the odds on a low rating score for hospitals under intensified supervision are higher than for the control group hospitals, corrected for time. Time is also associated with low rating scores, with decreasing ORs over time since 2010. CONCLUSIONS: Hospitals that are confronted with intensified supervision by the health care inspectorate have lower ratings on patient rating sites. The scores are independent of the period: before, during, or just after the intervention by the health care inspectorate. Health care inspectorates might learn from these results because they indicate that the inspectorate identifies the same hospitals as "at risk" as the patients rate as underperformers.

Patient satisfaction revisited: a multilevel approach.

Patient satisfaction surveys are increasingly used for benchmarking purposes. In the Netherlands, the results of these surveys are reported at the univariate level without taking case mix factors into account. The first objective of the present study was to determine whether differences in patient satisfaction are attributed to the hospital, department or patient characteristics. Our second aim was to investigate which case mix variables could be taken into account when satisfaction surveys are carried out for benchmarking purposes. Patients who either were discharged from eight academic and fourteen general Dutch hospitals or visited the outpatient departments of the same hospitals in 2005 participated in cross-sectional satisfaction surveys. Satisfaction was measured on six dimensions of care and one general dimension. We used multilevel analysis to estimate the proportion of variance in satisfaction scores determined by the hospital and department levels by calculating intra-class correlation coefficients (ICCs). Hospital size, hospital type, population density and response rate are four case mix variables we investigated at the hospital level. We also measured the effects of patient characteristics (gender, age, education, health status, and mother language) on satisfaction. We found ICCs on hospital and department levels ranging from 0% to 4% for all dimensions. This means that only a minor part of the variance in patient satisfaction scores is attributed to the hospital and department levels. Although all patient characteristics had some statistically significant influence on patient satisfaction, age, health status and education appeared to be the most important determinants of patient satisfaction and could be considered for case mix correction. Gender, mother language, hospital type, hospital size, population density and response rate seemed to be less important determinants. The explained variance of the patient and hospital characteristics ranged from 3% to 5% for the different dimensions. Our conclusions are, first, that a substantial part of the variance is on the patient level, while only a minor part of the variance is at the hospital and department levels. Second, patient satisfaction outcomes in the Netherlands can be corrected by the case mix variables age, health status and education.