Predictors of satisfaction of patients at the regional hospitals of Mauritius

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1 Predictors of satisfaction of patients at the regional hospitals of Mauritius Hemant Kassean, Lecturer Department of Health & Management, University of Mauritius Abstract Keywords: Quality, patient-care, international standards Objectives: This paper examines the predictors and level of patients satisfaction across the 5 regional hospitals of Mauritius. Methodology: A random sample of 300 patients who had attended an outpatient department, (excluding maternity and psychiatric clinics), who had been an inpatient within the past fourteen months were selected. Among the various dimensions that were used to measure satisfaction were: admission to hospital, attitude to medical staff, attitude to nursing staff, ward/hospital environment, patients' amenities, discharge planning and coordination. A questionnaire survey was administered using face-to-face interview with the respondents between 12th April th May Analytical techniques: Correlation co-efficient tests were used to determine the direction and strength of relationship which exists between two variables and regression analysis were used to determine the interrelationships among the various dependent and independent dimensions that influenced satisfaction. Main results: The highest predictor was the Perceptions of ward/hospital environment with a standardised co-efficient of when compared with the Perception of medical staff 0.273, and Perception of nursing staff with Conclusion: Patients perceptions of ward/hospital environment, and Patients perceptions of medical and nursing staff were found to be good predictors of patients satisfaction in the survey. Introduction The future of the publicly-funded health care system of Mauritius faces many challenges in order to remain affordable and sustainable in the future. Among the greatest challenges are for the health sector to become more patient centred, provide care that is of high quality, effective and safe and use the available resources more efficiently. It also has to modernise and provide a service that meets the expectations of patients which reflect the highest international standards. The public health care sector of Mauritius Health care is provided free to all Mauritians. There are five large regional hospitals (Table 1), which offer a full range of medical, surgical and specialist services. Smaller district hospitals provide general medical care. Primary care services are provided by the community health centres, area health centres and the medi-clinics. Mauritius has an AIDS prevention and control program since Family planning services and maternal and child health clinics are also available. Private clinics serve those who can afford to pay for medical treatment. The government is encouraging the private health care system and offers incentives to the operators of private facilities. 1

2 Name of Regional Hospitals Number of beds Jawaharlal Nehru Hospital 399 Victoria Hospital (Candos) 535 Sir Seewoosagur Ramgoolam National Hospital (SSRN) 479 Dr. A.G. Jeetoo Hospital 388 Flacq Hospital 237 Total 2030 Table 1: Source: Health Statistics Annual, (2002) In recent years, the pattern of health problems has dramatically changed. The infectious diseases of the past such as malaria, polio, diphtheria, typhoid and cholera, which still seriously affect many countries in the African region have been largely eliminated. (White paper on Health, 2002). But these have been replaced by a new and growing problem of non-communicable diseases including heart disease, diabetes, stroke and mental illness. Mauritius ranks 2 nd in the world for the incidence of diabetes (Diabetes Atlas, 2000). These changes call for an urgent reassessment of the services required to meet the changing health needs and the way in which those services are managed in order to create a modern and sustainable health service fit for the 21 st century. Given the limited amount of financial resources, it is crucial to explore ways in which the local health services can be improved within the existing resources. In the private sector, customer satisfaction and loyalty secured through high quality products and services, providing value for money to the customer are seen as essential for long-term survival let alone long-term success. (Wisniewski and Donnelly, 1996). The positive elements from the private sector and best practices from other health sectors can be introduced to make our health care system more accountable to the patients who are well placed to judge the quality of the health care as they are expert witnesses to the health care process. Fitzpatrick (1991) suggests that patients views have become an important element in the evaluation of health care. However, despite the increasing focus on customer satisfaction, research into patients' perceptions of the dimensions of service quality is scarce. This paper is an attempt to address some of these concerns. Methodology: In order to explore patients needs and concerns as patients view them themselves and to promote models of care that make the experience of illness and hospitalization more humane, the author conducted focus group interviews with patients and their families, reviewed literature and consulted with health care professionals. On the basis of what the author read and heard, a multiple item scale (HEALTHQUAL) consisting of 6 broad dimensions of patient centred care was defined for measuring satisfaction including: 1) Admission process 2) Attitudes of Medical Staff, 3) Attitudes of nursing officers 4) Attitudes towards ward /hospital environment 5) Attitudes towards patients amenities 6) Discharge planning and Co-ordination. A questionnaire was administered using a face-to-face interview with 300 respondents. 2

3 Findings Analysis - correlation & regression Correlation co-efficient analysis was done to determine the direction and strength of the relationship that exists between two variables and regression analysis were used to determine the interrelationships among the various dimensions dependent and independent dimensions that influenced satisfaction. Independent variables are variables which stand on their own. Dependent variables are variables which are influenced by other factors (independent variables). The dependent and independent variables used in the analysis are shown in Table 2 below. Dependent variables Satisfaction with medical staff Satisfaction with nursing staff Satisfaction with ward/hospital environment Satisfaction with patients amenities/facilities Satisfaction with equipment used. Satisfaction with care received Likeliness to recommend to friends Independent variables Patients perceptions of admission process Patients perceptions of medical staff Patients perceptions of nursing staff Patients perceptions of ward/hospital environment Patients perceptions of patients amenities/facilities Patients perceptions of discharge planning Likeliness to return to same hospital Table 2: Dependent and independent variables. Correlation Correlation among the independent variables have been undertaken to judge whether high correlations exist among them. Too much high correlation may adversely affect the regressions results due to the effect of high collinearity. Table 3 depicts the Pearson correlation co-efficients among the independent variables as well as among the dependent variables. Independent variables P - Admission P - Doctor P- Nurse P- Ward P - Amenities P - Discharge P Admission Process - P Doctor 0.500* - 3

4 P Nurse 0.449* 0.652* - P Ward Environment 0.400* 0.579* 0.574* - P Amenities/Facilities 0.354* 0.540* 0.528* 0.697* - P Discharge 0.392* 0.425* 0.487* 0.440* 0.493* - Dependent variables: S - Doctors * 0.589* 0.403* 0.459* 0.376* 0.319* S - Nurses * 0.478* 0.720* 0.512* 0.471* 0.488* S - Ward/Hospital * 0.356* 0.400* 0.614* 0.529* 0.347* S - Patient Amenities/facilities * 0.450* 0.468* 0.662* 0.554* 0.376* S - Equipment * 0.456* 0.419* 0.564* 0.422* 0.302* S - Care Received * 0.617* 0.603* 0.621* 0.569* 0.443* L - Recommend to Friends 0.395* 0.501* 0.513* 0.495* 0.435* 0.386* L - Return to same hospital 0.311* 0.430* 0.503* 0.469* 0.416* 0.437* *All items have p<0.01 (2 tailed) P = Patient s Perceptions S = Patients Satisfaction with L = Patients Likeliness to Table 3: Correlation table of independent and dependent variables. Correlation co-efficient shows the direction and strength of a relationship which exists between two variables. It lies between -1 and +1, with 0 representing absolutely no association between the two variables, and ±1 representing perfect link between the two variables. The sign of the coefficient indicates the direction of the relationship. A positive sign means that as one variable increases, the other one also increases. A negative sign implies that as one variable increases the other one decreases. The value of the coefficient indicates the strength of the relationship. Hair et al. (2000) propose the following rule of thumb: Table 4 Range of Coefficient Description of Strength ± 0.81 to ± 1.00 Very Strong ± 0.61 to ± 0.80 Strong ± 0.41 to ± 0.60 Moderate ± 0.21 to ± 0.40 Weak ± 0.00 to ± 0.20 None Table 4: Range of coefficient with description of strength Among the independent variables Table 3, only 2 out of the 6 independent variable items had correlation co-efficients greater than 0.6. These were P-Nurse & P-Doctors and P- Amenities/Facilities & P-Ward. Other independent variables did correlate among themselves but the relationship was not that strong, thus making the data suitable for regression analysis. 4

5 Also, the relatively high correlation co-efficient between the dependent and independent variables suggested that the independent variables may be good predictors of the dependent variables. These were is investigated using regression analysis. Regression analysis Regression analyses were conducted to determine the extent to which the independent variables (dimensions) as identified in Table 3 explained the scores of the dependent variables. For all the following regressions, the stepwise regression method was used, and the resulting standardised beta co-efficients gave an indication of the explanatory importance of each independent variable towards the dependent variable under investigation. It is to be noted that the figures presented are the significant ones only. The F value represents the ANOVA F-test value and the larger the figure, the better is the explanatory power of the model. The greater the T value, the better is the respective dimension in explaining the variance of the dependant variable. (For more details, see Appendix 1) Table 5: Satisfaction with medical s 0.368/ Perception of Medical Staff Satisfaction with Doctors Perception of Ward / Hospital Environment Dependent Variable: Satisfaction with Medical Staff (Doctors) Constant: Total explained Variance (R 2 ): (36.8 %) F: (Sig = 0.000). Table 5: Regression Patients satisfaction with Medical Staff - General. Two dimensions namely Perceptions of medical staff, and Perception of ward/hospital environment (Table 5) were indicated by the model as being good predictors of the dependent variable Satisfaction with Medical Staff. This regression model explained 36.8 % (the R 2 value) of the dependent variable variation. Out of the two independent variables, the Perception of medical staff had a greater impact on the dependent variable with a standardised co-efficient of Therefore, the patients were more likely to be satisfied with the medical staff if they had the following attributes: i.e. medical staff were competent, polite and used a simple language that patients could easily understand, diagnosed correctly, listened to their problems, respected their privacy, and thoroughly checked them before writing prescriptions. The perception of ward/hospital environment also accounted to a lesser extent to the satisfaction of patients towards the medical staff. Among the attributes of this dimension were: cleanliness and hygienic conditions of ward, toilets and bathrooms. The regression analysis was used to generate a formula whereby the Satisfaction with medical staff score may be determined using the independent variables. The derived formula in this respect has been calculated by using the unstandardised coefficients and is as follows: 5

6 Satisfaction with medical staff s score = *(Patient perception of Medical Staff) *(Patient perception of ward/hospital environment). Satisfaction with nursing officers: Table 6 Three dimensions namely Perception of nursing staff, Perception of discharge, and Perception of admission (Table 6) were indicated by the model as being good predictors of the dependent variable Satisfaction with nursing staff. This regression model explained 55.4 % (the R 2 value) of the dependent variable variation. Perception of Nurses had the most significant impact on the dependent variable with a standardised co-efficient of Therefore patients were more likely to be satisfied with the nursing officers if they showed the following attributes are: They were competent, polite, caring, listened to their problems and involved them in their care. They explained any specific procedure/treatment and could alleviate their anxieties and fears. The Perception of discharge accounted to a relatively lesser extent to the satisfaction of patients towards the nursing officers with a score of Among the attributes in this dimension were: Staff should inform on danger signals, precautions to take, inform family members on how to help patients to recover, discuss need for special support and a contact phone number/address in case of queries they may have from home. The third dimension was Perceptions of admission with a beta value of The attributes in this dimension were: Speed of a required hospital admission, well-organised admission process, and admission to room/ward or bed within 30 minutes. Satisfaction with nursing staff score = *(Patient perception of nursing staff) *(Patient perception of discharge process) *(Patient perception of admission process). Satisfaction with ward/hospital environment: Table 7 Two dimensions namely Perception of ward/hospital environment and Perception of patients amenities/facilities were chosen. (Table 7) % (the R 2 value) of the dependent variable variation could be explained by this regression model. Out of the two independent variables, the perception of ward/hospital environment had a higher impact on the dependent variable with a standardised co-efficient of compared with for the second predictor Perceptions. Among the attributes of Patient amenities/facilities were: are: Patients were free from pain and discomfort, encouraged to make suggestions, available of a proper grievance handling system made available, a choice of menu with tasty food, and provision of a phone in emergency situations. Satisfaction with Ward/Hospital Environment score = *( Patient perceptions of ward/hospital environment) *(Patient perceptions of amenities/facilities). Satisfaction with patients amenities/facilities: Table 8 With respect to Satisfaction with Patients amenities/facilities, three predictors identified were: Perception of ward/hospital environment, Perception of admission process, and Perception of patients amenities/facilities. (Table 8) These three variables accounted for 47.8 % of the variation in the dependent variable Satisfaction with patients amenities/facilities. 6

7 The perception of ward/hospital environment had the most significant impact on the dependent variable with a standardised co-efficient of 0.488, followed by Perception of admission with a beta value of and Perception of patients amenities/facilities with a Beta value of This result is a bit surprising as this dimension was expected to be the first predictor. A possible explanation could be a blending of the amenities/facilities dimension and ward/hospital environment dimension by respondents. Satisfaction with patients Amenities/Facilities score = *(Patient perceptions of ward/hospital environment) *(Patient perceptions of the Admission) *(Patient perceptions of Amenities/Facilities). Satisfaction with Equipment Used: Table 9 Two dimensions namely and Perception of ward/hospital environment and Perception of medical staff were indicated as good predictors of the dependent variable Satisfaction with equipment used (Table 9). This regression model explained 34.3 % (the R 2 value) of the dependent variable variation. The perception of ward/hospital environment had a higher impact on the dependent variable as with a standardised co-efficient of 0.451, followed by Perceptions of medical staff with a standardised co-efficient of Satisfaction with Equipment used score = *(Patient perception of ward/hospital environment) *(Patient perceptions of medical staff). Satisfaction with overall care received: Table / Satisfaction with overall care received Perception of Ward Perception of medical staff Perception of Nursing staff Dependent Variable: Satisfaction with Overall Care received. Constant: Total explained Variance (R 2 ): (51.5 %) F: (Sig = 0.000). Table 10: Regression Patients satisfaction with Overall care - General. Satisfaction with the overall care received was predicted by three dimensions namely: Perception of ward/hospital environment, Perception of medical staff, and Perception of nursing staff. (Table 10) These three predictors accounted for 51.5 % (the R 2 value) of the total variance of the dependent variable Satisfaction with the overall care received. The first predictor was the perception of ward/hospital environment with a standardised coefficient of The second dimension was Perception of medical staff with and the third, Perception of nursing staff with

8 Satisfaction with overall care received score = * (Patient perception of ward/hospital environment) *(Patient perception of Medical Staff) *(Patient perception of nursing officers). Likeliness to recommend hospital to friends: Table 11 Three dimensions namely: Perception of nursing staff, Perception of ward/hospital environment, and Perception of medical staff (Table 11were indicated by the model as being good predictors of the dependent variable Likeliness that patient recommends hospital to friends. These three dimensions altogether explained 34.5 % (the R 2 value) of the dependent variable variation. The first predictor was Perception of nursing staff with a standardised co-efficient of 0.244, followed by the perception of ward/hospital environment with and Perception of medical staff with Likeliness to recommend the hospital to friends score = *(Patient perception of nursing Officers) *(Patient perception of ward/hospital environment) *(Patient perception of Medical staff (Doctors)). Patients likeliness to return to same hospitals: Table 12 With respect to Likeliness to return to same hospital 3 predictors were identified. These were: Perception of nursing officers, Perception of ward/hospital environment, and Perception of discharge planning. These three dimensions together were able to explain 33.2 % (the R 2 value) of the dependent variable variation. The first predictor was Perception of nursing staff with a standardised co-efficient of followed by perception of ward/hospital environment with 0.220, and Perception of discharge planning with The regression formula for in this case is as follows: Likeliness to return to the same hospital score = *(Patient perception of nursing Officers) *(Patient perception of ward/hospital environment) *(Patient perception of discharge planning). Table 13 Summary of findings/discussion This Section is a summary of the predictors found for each dependent variable. Patients Perceptions of: Admission Doctors Nurses Ward/hos pital Amenities Discharge S- Doctors S- Nurses S-Ward/Hospital S- Amenities S-Equipment S-Care Received L-Recommend L-Return TOTAL

9 S = Satisfaction with L = Likeliness to Table 13: Summary of Regression analysis General. Table 13 above shows a summary of the dependent variables affecting patients satisfaction at the 5 regional hospitals in Mauritius. It shows that Patients perceptions of ward/hospital environment as most significant predictor of satisfaction, occurring seven times out of eight. This, therefore suggests that those responsible for the health care should pay particular attention to the attributes related to this dimension: The ward, toilets and bathrooms should always be kept clean and hygienic. The ward should have all the appropriate medical equipment, with basic furniture, i.e. a bed, side-table, a locker and a chair as a minimum, and basic facilities: e.g. running water, electricity, appropriate bed linen that are changed regularly. The noise level should be acceptable to them and they should feel safe and secure. The hospital should be well ventilated and odor free. In second position come Patients perceptions of medical staff, and Patients perceptions of nursing staff together with a score of four each. As these persons are in regular contact with the patients, they are well placed to influence their level of satisfaction. The attributes related to these dimensions were: Medical and nursing staff should be competent, spend time to explain any specific procedure/treatment, respond to their queries satisfactorily and involve them in their care and treatment. They should always be polite, professional, and listen to their problems. They should talk in a simple language, discuss their anxiety and fears. They should maintain confidentiality and respect their privacy. Limitations and future directions There may be some limitations of the data and the analysis presented in this report. The data are based on self-reported information for both service utilization and difficulties experienced by the patients over a 14 - month period. The information may be subject to recall bias and has not been clinically validated. To reduce reporting error due to recall bias, questions repeatedly referred to services used by the patients in the last 14 months. Some respondents may have felt wary of giving an accurate report of their hospital stay, particularly if they were bad. Future studies may consider using larger sample sizes, and interviewing the patients outside the hospital environment. Conclusion In order to improve the quality of care in our public hospitals, the care given should not only focus on local health care needs and priorities but also reflect international high standards. The positive element of the private sector where the emphasis is to become closer to the customers in order to improve quality, in this case the patients, can be harnessed by involving patients in their care and measuring their level of satisfaction. The public health care system in Mauritius has some challenging tasks ahead and has to set specific targets in order to meet the increasing demands from patients who are becoming increasingly aware of their rights and are insisting on the very high standards of care. An analysis of this survey s results on patients satisfaction has shown that Patients perceptions of ward/hospital environment, and Patients perceptions of medical and nursing staff as good predictors of patients satisfaction. Therefore, this implies that patients level of satisfaction can be improved if the attributes of these dimensions are met. 9

10 References Diabetes Atlas (2000) Country profile of diabetes accessed on 24 th Sept Fitzpatrick, R. (1991), 'Surveys of patient satisfaction: I - important general considerations', BMJ 302, Hair, J.F., Bush, R.P., and Ortinau, D.J. (2000), Marketing Research A practical Approach for the New Millennium, Singapore: McGraw-Hill Higher Education. Health Statistics Annual (2002).Ministry of Health and Quality of Life publication White paper on Health sector development and reform (2002). Ministry of Health and Quality of Life. Accessed on 24 th Sept Winsniewski, M. and Donnelly, M. (1996), 'Measuring Service Quality in the Public Sector: The potential for SERVQUAL', Total Quality Management, Vol. 7 (4). 10

11 Appendix 1 Table 4 below is an illustration of how to interpret the figures under this section / Perception of Admission (B adm ) HOSPITAL. Perception of Discharge (B dis ) Dependent Variable: Satisfaction with Admission Process. Constant: Total explained Variance (R 2 ): (67.0 %) F: (Sig = 0.000). Table 4: Illustration for regression analysis. The R 2 value represents the percentage of the variances in the dependent variable which can be explained by the regression model. In this case, the model is able to explain 87 % (0.870) of the variation in the patients satisfaction score towards the Admission process. The R 2 value is also coupled with a significance value which indicates to what extent the R 2 is significant. In this illustration, the significance value is Usually, investigators consider a value to be significant if it is less than 0.05 (p<0.05) or 0.01 (p<0.01). Note that the significance value may vary between 0.00 and 1.00, and the closer the value is to 0, the more significant is the R 2. The Dimensions column represents those independent variables which are explaining the dependent variable. In this case, the independent variables are patients perceptions of admission process and patients perceptions of discharge process. In other words, the better patients perceive the admission and discharge processes, the more satisfied they will be with the Admission process. The B value is the unstandardised beta value for each respective independent variable. In this case, it is for the Patients perceptions of admission process and for Patients perceptions for Discharge process. The B value indicates the strength of each independent variable in the latter s own measurement unit. The Beta value is the standardised beta value for each respective independent variable and ranges from 0.00 to As it is reported in the same unit, it allows comparisons to be made among the different independent variable. In this case, the Beta value is for the Patients perceptions of admission process and for Patients perceptions for discharge process. Hence, it is concluded that Patients perceptions of Admission process has a higher weight in explaining patients satisfaction with the admission process than the Patients perceptions of Discharge process dimension as it exhibits a higher Beta value. The T value along with its significance value indicates the strength of each respective independent variable. The T statistics also can help to determine the relative importance of each independent variable in the model. The greater the T value, the greater is its importance. In this case, the T value is (significant at 0.000) for the Patients perceptions of Admission process and (significant at 0.006) for Patients perceptions for Discharge process. 11

12 Again, this illustrates that the Patients perceptions of Admission process has a relatively greater importance. The F value represents the ANOVA F-test value and the larger the figure, the better is the explanatory power of the model. In this case, it is and also significant (Sig = 0.000). The Constant refers to the constant value of the model irrespective of the independent variables. In this case it is indicating that at the start itself without perception of admission and discharge processes, the patient already exhibit a rating of with respect to satisfaction with the admission process. All the above can be used to come up with a regression model to determine the satisfaction score of a patient with respect to the Admission process. The derived formula in this respect is calculated by using the unstandardised coefficients and is as follows: Satisfaction with Admission process score = Constant value + (B adm )*(Patient perception score of Admission process) + (B dis )*(Patient perception score of Discharge process). In this case, the values will be : Satisfaction with Admission process score = *(Patient perception score of Admission process) *(Patient perception score of Discharge process). Table 6: Regression Patients satisfaction with nursing officers - general 0.554/ Perception of Nursing Officers Satisfaction with Nursing Officers. Perception of Discharge Perception of Admission Dependent Variable: Satisfaction with Nursing Officers. Constant: Total explained Variance (R 2 ): (55.4 %) F: (Sig = 0.000). Table 7: Regression Patients satisfaction with ward environment - general. Satisfaction with Ward / Perception of Ward Perception of Amenities Dependent Variable: Satisfaction with Ward/Hospital Environment. Constant: Total explained Variance (R 2 ): (39.7 %) F: (Sig = 0.000). 12

13 Table 8: Regression Patients satisfaction with amenities - general Satisfaction with Patient Amenities 0.478/ Perception of Ward Perception of Admission Perception of Amenities Dependent Variable: Satisfaction with Patient Amenities/Facilities. Constant: Total explained Variance (R 2 ): (47.8 %) F: (Sig = 0.000). Table 9: Regression Patients satisfaction with equipment used - general Satisfaction with Equipment 0.343/ Perception of Ward Perception of Medical Staff (Doctors) Dependent Variable: Satisfaction with Equipment used. Constant: Total explained Variance (R 2 ): (34.3 %) F: (Sig = 0.000) Table 11: Regression Patients likeliness to recommend hospitals - general. Likeliness to Recommend Hospital 0.345/ Perception of Nursing Officers Perception of Ward Perception of Medical Staff (Doctors) Dependent Variable: Likeliness to recommend hospital to friends Constant: Total explained Variance (R 2 ): (34.5 %) F: (Sig = 0.000)

14 Table 12: Regression Patients likeliness to return to same hospitals - general / Perception of Nursing Officers. Likeliness to Return to same Hospital in future Perception of Ward Perception of Discharge Dependent Variable: Likeliness to Return to same hospital. Constant: Total explained Variance (R 2 ): (33.2 %) F: (Sig = 0.000). 14