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      How do frontline staff use patient experience data for service improvement? Findings from an ethnographic case study evaluation

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          Abstract

          Objectives

          Improving patient experience is widely regarded as a key component of health care quality. However, while a considerable amount of data are collected about patient experience, there are concerns this information is not always used to improve care. This study explored whether and how frontline staff use patient experience data for service improvement.

          Methods

          We conducted a year-long ethnographic case study evaluation, including 299 hours of observations and 95 interviews, of how frontline staff in six medical wards at different hospital sites in the United Kingdom used patient experience data for improvement.

          Results

          In every site, staff undertook quality improvement projects using a range of data sources. Teams of health care practitioners and ancillary staff engaged collectively in a process of sense-making using formal and informal sources of patient experience data. While survey data were popular, ‘soft’ intelligence – such as patients’ stories, informal comments and observations – also informed staff’s improvement plans, without always being recognized as data. Teams with staff from different professional backgrounds and grades tended to make more progress than less diverse teams, being able to draw on a wider net of practical, organizational and social resources, support and skills, which we describe as team-based capital.

          Conclusions

          Organizational recognition, or rejection, of specific forms of patient experience intelligence as ‘data’ affects whether staff feel the data are actionable. Teams combining a diverse range of staff generated higher levels of ‘team-based capital’ for quality improvement than those adopting a single disciplinary approach. This may be a key mechanism for achieving person-centred improvement in health care.

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          Most cited references22

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          Official statistics and claims data records indicate non-response and recall bias within survey-based estimates of health care utilization in the older population

          Background The validity of survey-based health care utilization estimates in the older population has been poorly researched. Owing to data protection legislation and a great number of different health care insurance providers, the assessment of recall and non-response bias is challenging to impossible in many countries. The objective of our study was to compare estimates from a population-based study in older German adults with external secondary data. Methods We used data from the German KORA-Age study, which included 4,127 people aged 65–94 years. Self-report questions covered the utilization of long-term care services, inpatient services, outpatient services, and pharmaceuticals. We calculated age- and sex-standardized mean utilization rates in each domain and compared them with the corresponding estimates derived from official statistics and independent statutory health insurance data. Results The KORA-Age study underestimated the use of long-term care services (−52%), in-hospital days (−21%) and physician visits (−70%). In contrast, the assessment of drug consumption by postal self-report questionnaires yielded similar estimates to the analysis of insurance claims data (−9%). Conclusion Survey estimates based on self-report tend to underestimate true health care utilization in the older population. Direct validation studies are needed to disentangle the impact of recall and non-response bias.
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            Systematic review of approaches to using patient experience data for quality improvement in healthcare settings

            Objectives Explore how patient-reported experience measures (PREMs) are collected, communicated and used to inform quality improvement (QI) across healthcare settings. Design Systematic review. Setting Various primary and secondary care settings, including general practice, and acute and chronic care hospitals. Participants A full range of patient populations from (children through to the elderly) and staff (from healthcare practitioners to senior managers). Methods Scientific databases were searched (CINAHL, PsycINFO, MEDLINE and Cochrane Libraries) as was grey literature. Qualitative and quantitative studies describing collection of PREM data and subsequent QI actions in any healthcare setting were included. Risk of bias was assessed using established criteria. Of 5312 initial hits, 32 full texts were screened, and 11 were included. Results Patient experience data were most commonly collected through surveys and used to identify small areas of incremental change to services that do not require a change to clinician behaviour (eg, changes to admission processes and producing educational materials). While staff in most studies reported having made effective improvements, authors struggled to identify what those changes were or the impact they had. Conclusions Findings suggest there is no single best way to collect or use PREM data for QI, but they do suggest some key points to consider when planning such an approach. For instance, formal training is recommended, as a lack of expertise in QI and confidence in interpreting patient experience data effectively may continue to be a barrier to a successful shift towards a more patient-centred healthcare service. In the context of QI, more attention is required on how patient experience data will be used to inform changes to practice and, in turn, measure any impact these changes may have on patient experience.
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              Quick and dirty? A systematic review of the use of rapid ethnographies in healthcare organisation and delivery

              The ability to capture the complexities of healthcare practices and the quick turnaround of findings make rapid ethnographies appealing to the healthcare sector, where changing organisational climates and priorities require actionable findings at strategic time points. Despite methodological advancement, there continue to be challenges in the implementation of rapid ethnographies concerning sampling, the interpretation of findings and management of field research. The purpose of this review was to explore the benefits and challenges of using rapid ethnographies to inform healthcare organisation and delivery and identify areas that require improvement. This was a systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We used the Mixed Methods Appraisal Tool to assess the quality of the articles. We developed the search strategy using the Population, Intervention, Comparison, Outcomes, Settingframework and searched for peer-reviewed articles in MEDLINE, CINAHL PLUS, Web of Science and ProQuest Central. We included articles that reported findings from rapid ethnographies in healthcare contexts or addressing issues related to health service use. 26 articles were included in the review. We found an increase in the use of rapid ethnographies in the last 2‰years. We found variability in terminology and developed a typology to clarify conceptual differences. The studies generated findings that could be used to inform policy and practice. The main limitations of the studies were: the poor quality of reporting of study designs, mainly data analysis methods, and lack of reflexivity. Rapid ethnographies have the potential to generate findings that can inform changes in healthcare practices in a timely manner, but greater attention needs to be paid to the reflexive interpretation of findings and the description of research methods. CRD42017065874.
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                Author and article information

                Journal
                J Health Serv Res Policy
                J Health Serv Res Policy
                HSR
                sphsr
                Journal of Health Services Research & Policy
                SAGE Publications (Sage UK: London, England )
                1355-8196
                1758-1060
                14 February 2020
                July 2020
                : 25
                : 3
                : 151-161
                Affiliations
                [1 ]Professor of Health Services Research, Health Services Research Unit, University of Aberdeen, UK
                [2 ]Senior Researcher, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                [3 ]Research Fellow, National Addiction Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK
                [4 ]Qualitative Researcher, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                [5 ]Lay Research Advisor, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                [6 ]Rhodes Trust Professor of Organisational Behaviour, Saïd Business School, University of Oxford, UK
                [7 ]Follow-up Sister in Critical Care, Royal Berkshire NHS Foundation Trust, UK
                [8 ]Senior Research Scientist, Nuffield Department of Population Health, University of Oxford, UK
                [9 ]Chief Executive, Picker Institute Europe, UK
                [10 ]Chief Research Officer, Picker Institute Europe, UK
                [11 ]Programme Coordinator, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                [12 ]Associate Professor, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                [13 ]Professor of Medical Sociology, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
                Author notes
                [*]Catherine Montgomery, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK. Email: Catherine.montgomery@ 123456phc.ox.ac.uk
                Author information
                https://orcid.org/0000-0002-5829-6137
                https://orcid.org/0000-0002-6496-4859
                Article
                10.1177_1355819619888675
                10.1177/1355819619888675
                7307415
                32056464
                173577d8-ee77-40f8-b353-97a746367e1c
                © The Author(s) 2020

                Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Health Services and Delivery Research Programme, FundRef https://doi.org/10.13039/501100002001;
                Award ID: 14/156/06
                Categories
                Original Research
                Custom metadata
                ts2

                Social policy & Welfare
                learning community,patient experience data,team-based capital
                Social policy & Welfare
                learning community, patient experience data, team-based capital

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