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      Closing the patient experience chasm: A two‐level validation of the Consumer Quality Index Inpatient Hospital Care

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          Evaluation of patients’ health care experiences is central to measuring patient‐centred care. However, different instruments tend to be used at the hospital or departmental level but rarely both, leading to a lack of standardization of patient experience measures.


          To validate the Consumer Quality Index (CQI) Inpatient Hospital Care for use on both department and hospital levels.


          Using cross‐sectional observational data, we investigated the internal validity of the questionnaire using confirmatory factor analyses ( CFA), and the generalizability of the questionnaire for use at the department and hospital levels using generalizability theory.

          Setting and participants

          22924 adults hospitalized for ≥24 hours between 1 January 2013 and 31 December 2014 in 23 Dutch hospitals (515 department evaluations).

          Main variable

          CQI Inpatient Hospital Care questionnaire.


          CFA results showed a good fit on individual level ( CFI=0.96, TLI=0.95, RMSEA=0.04), which was comparable between specialties. When scores were aggregated to the department level, the fit was less desirable ( CFI=0.83, TLI=0.81, RMSEA=0.06), and there was a significant overlap between communication with doctors and explanation of treatment subscales. Departments and hospitals explained ≤5% of total variance in subscale scores. In total, 4‐8 departments and 50 respondents per department are needed to reliably evaluate subscales rated on a 4‐point scale, and 10 departments with 100‐150 respondents per department for binary subscales.

          Discussion and conclusions

          The CQI Inpatient Hospital Care is a valid and reliable questionnaire to evaluate inpatient experiences in Dutch hospitals provided sufficient sampling is done. Results can facilitate meaningful comparisons and guide quality improvement activities in individual departments and hospitals.

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          Most cited references 25

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          Confirmary Factor Analysis for Applied Research

           TA Brown,  T. BROWN,  T Brown (2006)
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            Collecting data on patient experience is not enough: they must be used to improve care.

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              Multiple imputation with large data sets: a case study of the Children's Mental Health Initiative.

              Multiple imputation is an effective method for dealing with missing data, and it is becoming increasingly common in many fields. However, the method is still relatively rarely used in epidemiology, perhaps in part because relatively few studies have looked at practical questions about how to implement multiple imputation in large data sets used for diverse purposes. This paper addresses this gap by focusing on the practicalities and diagnostics for multiple imputation in large data sets. It primarily discusses the method of multiple imputation by chained equations, which iterates through the data, imputing one variable at a time conditional on the others. Illustrative data were derived from 9,186 youths participating in the national evaluation of the Community Mental Health Services for Children and Their Families Program, a US federally funded program designed to develop and enhance community-based systems of care to meet the needs of children with serious emotional disturbances and their families. Multiple imputation was used to ensure that data analysis samples reflect the full population of youth participating in this program. This case study provides an illustration to assist researchers in implementing multiple imputation in their own data.

                Author and article information

                Health Expect
                Health Expect
                Health Expectations : An International Journal of Public Participation in Health Care and Health Policy
                John Wiley and Sons Inc. (Hoboken )
                20 February 2017
                October 2017
                : 20
                : 5 ( doiID: 10.1111/hex.2017.20.issue-5 )
                : 1041-1048
                [ 1 ] Department of Educational Development and Research Maastricht University Maastricht The Netherlands
                [ 2 ] Center for Evidence‐Based Education Academic Medical Center Amsterdam The Netherlands
                [ 3 ] Department of Epidemiology UCLA Fielding School of Public Health Los Angeles USA
                [ 4 ] Center for Health Policy Research UCLA Fielding School of Public Health Los Angeles USA
                Author notes
                [* ] Correspondence

                Alina Smirnova, Professional Performance Research Group, Center for Evidence‐Based Education, Academic Medical Center, Amsterdam, The Netherlands.

                Email: a.smirnova@

                © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 0, Tables: 5, Pages: 9, Words: 7125
                Original Research Paper
                Original Research Papers
                Custom metadata
                October 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.9 mode:remove_FC converted:15.09.2017


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