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Quantifying Magnitude of Group-Level Differences in Patient Experiences with Health Care.

ABSTRACT: OBJECTIVE:Review approaches assessing magnitude of differences in patient experience scores between different providers. DATA SOURCES:1990-2016 literature. STUDY DESIGN:Systematic literature review. DATA EXTRACTION METHODS:Of 812 articles mentioning "CAHPS," "patient experience," "patient satisfaction," "important(ce)," "difference," or "significance," we identified 79 possible articles, yielding 35 for data abstraction. We included 22 articles measuring magnitude of differences in patient experiences. PRINCIPAL FINDINGS:We identified three main ways of estimating magnitude of differences in patient experience scores: (1) by distribution/range of patient experience variable, (2) against external anchor, and (3) comparing a difference in patient experience on one covariate to differences in patient experience on other covariates. CONCLUSIONS:We suggest routine estimation of magnitude in patient experience research. More work is needed documenting magnitude of differences between providers to make patient experience data more interpretable and usable.

SUBMITTER: Quigley DD

PROVIDER: S-EPMC6056572 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Quantifying Magnitude of Group-Level Differences in Patient Experiences with Health Care.

Quigley Denise D DD Elliott Marc N MN Setodji Claude Messan CM Hays Ron D RD

Health services research 20180212

<h4>Objective</h4>Review approaches assessing magnitude of differences in patient experience scores between different providers.<h4>Data sources</h4>1990-2016 literature.<h4>Study design</h4>Systematic literature review.<h4>Data extraction methods</h4>Of 812 articles mentioning "CAHPS," "patient experience," "patient satisfaction," "important(ce)," "difference," or "significance," we identified 79 possible articles, yielding 35 for data abstraction. We included 22 articles measuring magnitude of ...[more]

PMID: 29435975

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Project description:BackgroundPatient experience with primary health care services can vary markedly between different types of health care facilities, even within the same country setting. Given known benefits of high quality primary health care, the performance of these facilities may significantly impact population health. The aim of this study was to compare the quality of primary care in different types of health facilities as experienced by Vietnamese consumers.Methods1662 people who utilized primary health care services at least once over the past two years in various types of facilities in central Vietnam were surveyed in a cross-sectional study using the Vietnamese version of the Primary Care Assessment Tool (VN PCAT-AE) to assess overall primary care quality as well as several different domains of high quality primary care services.ResultsCommune health centers were associated with the highest overall primary care quality (PCAT expanded score 21.07, p < 0.001) as well as high scores in nearly all individual domains of primary care quality experienced by consumers compared with other types of facilities. Conversely, private facilities such as private clinics and pharmacies were rated lowest overall (PCAT expanded score 18.45, p < 0.05 and 16.90, p < 0.001 respectively). District hospitals and other government hospitals (PCAT expanded score 20.10 and 19.72 respectively) were reported as the best quality in comprehensiveness of available services (p < 0.001). Polyclinics performed quite well in comprehensiveness of services available (3.11) and first contact-access (2.79) but less so in other domains, especially in cultural competency (1.87).ConclusionsThe high quality of primary care services experienced by consumers in commune health centers compared with other facilities gives Vietnam ample reason to promote greater use of these community-based primary care facilities. Populations may benefit most from building and strengthening grassroots networks of such community-based health centers as an effective solution for overcrowding at hospitals while simultaneously providing better overall health outcomes.

Project description:BackgroundWith the increased demand for health care services and with simultaneous staff shortages, new work models are needed in primary health care. In November 2015, a Swedish primary health care centre introduced a work model consisting of a structured patient sorting system with triage and Nurse on Call. The aim of this study was to describe the staff's experiences of introducing the triage and Nurse on Call model at the primary health care centre.MethodsFive focus group discussions with staff (n = 39) were conducted 4 years after the introduction of the work model. Groups were divided by profession: medical secretaries, nursing assistants, physicians, primary health care nurses, and registered nurses. The transcribed text from the discussions was analysed using qualitative inductive content analysis.ResultsThe analysis generated one overarching theme: The introduction of triage and Nurse on Call addresses changed preconditions in primary health care, but the work culture, organization, and acquisition of new knowledge are lagging behind. The overarching theme had five categories: (1) Changed preconditions in primary health care motivate new work models; (2) The triage and Nurse on Call model improves teamwork and may increase the quality of care; (3) Unclear purpose and vague leadership make introducing the work model difficult; (4) Difficulties to adopt the work model as it challenges professional autonomy; and (5) The triage and Nurse on Call model requires more knowledge and competence from nurses in primary health care.ConclusionsThis study contributes with knowledge about implications of a new work model in primary health care from the perspective of health care staff. The work model using triage and Nurse on Call in primary health care was perceived by participants to increase availability and optimize the use of resources. However, before introduction of new work models, it is important to identify barriers to and facilitators for successful improvements in the local health care context. Additional education for the health care staff is important if the transition is to be successful. Complementary skills and teamwork, supported by a facilitator seems important to ensure a well-prepared workforce.

Project description:Genetic selection programs have driven development of most lactation models, to estimate the magnitude of animals' productive capacity from sampled milk production data. There has been less attention to management and research applications, where it may also be important to quantify the shape of lactation curves, and predict future daily milk production for incomplete lactations since residuals between predicted and actual daily production can be used to quantify the response to an intervention. A model may decrease the confounding effects of lactation stage, parity, breed, and possibly other factors depending on how the model is constructed and used, thus increasing the power of statistical analyses. Models with a mechanistic derivation may allow direct inference about biology from fitted production data. The MilkBot(®) lactation model is derived from abstract suppositions about growth of udder capacity. This permits inference about shape of the lactation curve directly from parameter values, but not direct conclusions about physiology. Individual parameters relate to the overall scale of the lactation, the ramp , or rate of growth around parturition, decay describing the senescence of productive capacity (inversely related to persistence ), and the relatively insignificant time offset between calving and the physiological start of milk secretion. A proprietary algorithm was used to fit monthly test data from two parity groups in 21 randomly selected herds, and results displayed in box-and-whisker charts and Z-test tables. Fitted curves are constrained by the MilkBot(®) equation to a single peak that blends into an exponential decline in late lactation. This is seen as an abstraction of productive capacity, with actual daily production higher or lower due to random error plus short-term environmental effects. The four MilkBot(®) parameters, and metrics calculated directly from them including fitting error, peak milk and cumulative production, can be used to describe and compare individual lactations or groups of lactations. There is considerable intra-herd and inter-herd variability in scale, ramp, decay, RMSE, peak milk, and cumulative production, suggesting that management and environment have significant influence on both shape and magnitude of normal lactation curves.

Dataset Information

Quantifying Magnitude of Group-Level Differences in Patient Experiences with Health Care.

Publications

Quantifying Magnitude of Group-Level Differences in Patient Experiences with Health Care.

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