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Developing an efficient statistical model to select out-patients department patient satisfaction improvement strategy

In the Proceedings of the International Medical Conference on Progress in Medical Practice and Education:  Innovations for the Future and Beyond, London, 21 – 25 August 2015, London, UK

 

 

Developing an efficient statistical model to select out-patients department patient satisfaction improvement strategy by Dr Ahmed Al-Kuwaiti

 

Dr Ahmed Al-Kuwaiti, from the University of Dammam, Saudi Arabia, reported to conference that the background of the study was that although measuring of patients' satisfaction is one of the vital roles of healthcare management, most studies put an emphasis on finding the significant factors, but do not focus on building a model to increase overall patient satisfaction.

 

He stated that the aim of the study was to find the most influential attributes and how much these attributes need to be improved to achieve the target level of patient satisfaction.

 

Dr Al-Kuwaiti informed conference that the subjects were 689 outpatients, recruited from 52 outpatients' clinics of a University Teaching Hospital in Saudi Arabia. Measurements included 5 outpatients' service domains (ie: Reception, Facility, Treatment, Tests, personal issues) and patient satisfaction. He went on to state that the Ordinal Logistic regression model was used to build a model because there are five ordered categorical answers for the dependent variable.

 

He went on to show conference the results with the following table:

 

Independent variable

Estimate

P-value

Odds ratio

Contribution level

Reception

-1.646

<0.001

0.214

High

Facilities

-0.143

<0.001

0.825

Low

Treatment

-0.296

<0.001

0.716

Low

Tests

-0.645

<0.001

0.427

Medium

Personal Issues

-0.183

<0.001

0.894

Low

Age

0.034

0.246

1.241

no contribution

Gender, Female

0.046

0.746

1.924

no contribution

 

The model for the data is:

 

logit(P (Y≤j)= αj1(Reception) + β2(Facilities) +β3(Treatment) + β4(Tests) +β5 (Personal Issues) + β6 (Age) + β7(Gender)

 

 

Where (P (Y≤j)….Patients overall satisfaction, αj is Intercept and β1, β2…… β7 are the common regression coefficients.

Dr Al-Kuwaiti concluded his presentation by stating that the Ordinal logistic regression model was fitted to study the relationship between the dependent (overall satisfaction) and independent variables. The adjusted generalised coefficient of determination R2 = 0.58, indicated that at least 58% of the variation existing in the data could be explained by the ordinal logistic regression model, which was reasonable. From the table he showed to conference, it could be seen that the negative value of the estimate indicates that if the corresponding attribute was improved, the probability of getting excellent rating would also increase.

 

He went on to state that out of the independent variables that related to overall satisfaction, Reception (Waiting time, registration process…..) was the most influential variable because its coefficient (β1 = -1.646) was the largest in absolute value. He also informed conference that point estimate of odds ratios were also provided. For example the odds ratio for Reception was 0.214; indicating that there was a high level significance of influence on the dependent variable (more significantly affecting the dependent variable). According to the model, reception service would need to be improved to increase overall satisfaction and, as a result, this model would significantly enhance health care managers to attain a certain target goal of overall satisfaction.

 

Address for correspondence: Dr Ahmed Al Kuwaiti, King Fahd Hospital of the University, University of Dammam, Kingdom of Saudi Arabia

 

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