A Patient Survey System to Measure Quality Improvement

Carey, Raymond G.; Seibert, Jerry H.

doi:10.1097/00005650-199309000-00008

Cited by 246 publications

(136 citation statements)

References 6 publications

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“…The questionnaire comprised three validated tools: the adapted Client Service Receipt Inventory 65 to estimate individual health service use; the Quality of Care Monitor (QCM 66 ) to measure patient satisfaction; and the SF-12 67 to measure patient-reported outcomes. We aimed to recruit samples of 800 patients stratified by risk level to complete the questionnaires at each time point (Table 12).…”

Section: Postal Questionnairesmentioning

confidence: 99%

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

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Background With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim To evaluate the introduction of predictive risk stratification in primary care. Objectives To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design Randomised stepped-wedge trial with economic and qualitative components. Setting Abertawe Bro Morgannwg University Health Board, south Wales. Participants Patients registered with 32 participating general practices. Intervention PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding The National Institute for Health Research Health Services Delivery and Research programme.

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Section: Postal Questionnairesmentioning

confidence: 99%

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

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“…Measures of satisfaction are, therefore, important tools for research, administration and planning. Patient satisfaction can also be used to evaluate the process of health care (Carey & Seibert, 1993;Etter, Perneger, & Rougemont, 1996;Fitzpatrick, 1991a, b;Guadagnoli & Cleary, 1995;Kane, Maciejewski, & Finch, 1997;Lasek, Barkley, Harper, & Rosenthal, 1997;Linn et al, 1995;Ross, Steward, & Sinacore, 1995;Williams, 1994), as greater satisfaction may be related with superior compliance, improved attendance at return visits and better outcomes (Cleary, Keroy, Karapanos, & McMullen, 1989).…”

Section: Hospital Services and Patient Satisfactionmentioning

confidence: 99%

Factors Determining Inpatient Satisfaction with Hospital Care in Bangladesh

Ashrafun¹,

Uddin²

2011

ASS

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The objective of this study is to identify factors associated with satisfaction among inpatients receiving medical and surgical care for urinary, cardiovascular, respiratory, and ophthalmology diseases at Dhaka Government Medical College Hospital, Bangladesh. The data of this study is collected from 190 inpatients by using a patient judgments questionnaire covering 10 dimensions of satisfaction (appointment waiting time for doctor after admission, doctor's treatment and behavior, behavior and services of nurses, boys and ayas (-care givers-), toilet and bath room condition, quality of food, number of days in the hospital, cost for treatment, and gift/tips culture in the hospital). Patient overall level of satisfaction is treated as dependent variable, while dimensions of satisfaction are each treated as independent variables. Additionally, inpatients' socio-economic characteristics such as education, occupation and monthly family income are used as independent variables. OLS regression models are used to identify key factors connected with inpatients satisfaction. The level of significance for variables retain in the regression models is set at 0.05. The final regression model is significant with F-value of 73.673 (p<0.001) and can explain 80.8% of the variation in the dependent variable as it is indicated by the R-Square. The nested model F-test suggests that inpatients' monthly family income and levels of education have significant effect on the dependent variable.

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“…With this end in view, a questionnaire survey was conducted twice in May 2008, in the same group of randomly selected parents of fifty girls and fifty boys, pupils of a primary school. The interval between the test and retest, during which the same questions were asked, was 7 days and it corresponded to arbitrarily-adopted criteria applied in evaluation of credibility of the research tool [2,[5][6][7]. In accordance with the rules of the validation procedure, it encompassed those variables which were expressed in a dichotomous form, e.g.…”

Section: Methodsmentioning

confidence: 99%

Validation of questionnaire used in epidemiological blood pressure studies

Stawińska-Witoszyńska

Kowalska

Krzyżaniak

et al. 2012

Open Medicine

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Background: Standardized questionnaires add validity to the results of research as well as allow comparison with other authors' results.The aim of the study was to present the procedure and the results of the validation of authors' own questionnaire that was used as a research tool in cross-sectional epidemiological studies of the arterial blood pressure of school children. Methods: The study was carried out twice in May 2008 on a drawn group of 100 respondents-parents of primary school children. The time gap between the first test and the retest was 7 days in accordance with arbitrary criteria used in the assessment of research tool validity. Only those variables that can be presented as a dichotomy, e.g. yes or no, were validated. Kappa statistics was utilized for the assessment of individual question's repeatability. Results: Out of the total of 35 validated questions, 100% repeatability was achieved for as many as 22 questions while more than 97% for 10. The repeatability assessed by Kappa statistics could be calculated for 25 questions. Conclusion:The results indicate the questions' repeatability was very good or good. The questionnaire applied in cross-sectional research of arterial pressure can be considered reliable.© Versita Sp. z o.o.

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A Patient Survey System to Measure Quality Improvement

Cited by 246 publications

References 6 publications

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Factors Determining Inpatient Satisfaction with Hospital Care in Bangladesh

Validation of questionnaire used in epidemiological blood pressure studies

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