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Development of a model to integrate patient, staff & doctor satisfaction attributes and predictors into senior level healthcare management decisionmaking & policy development

EI-Sharkawi, H.K. (2000). Development of a model to integrate patient, staff & doctor satisfaction attributes and predictors into senior level healthcare management decisionmaking & policy development. (Unpublished Doctoral thesis, City University London)


This thesis addressesth e question of the significance or addedv alue derived from directly integrating client (defined as patients, doctors, and staff) satisfaction level predictors and attributes into senior level healthcare decision-making and policy development processes. It poses the questions: 1. Is satisfaction level measurement for patients, doctors and staff an important requirement for improved managerial efficiency and effectiveness? If so, then why? 2. Are the satisfaction attributes for each of these groups associated? What are the implications of such an association on senior managerial decision outcomes? 3. How to best integrate satisfaction predictors and attributes to improve decisionmaking and policy development? To address the above, the thesis proposes a unified model to allow for the utilisation of satisfaction study findings to inform both policy and decision-making processes. Through client satisfaction impact assessment (CSIA) methods, the model may permit healthcare managers to achieve higher levels of client loyalty, by better understanding, predicting and possibly influencing client needs, expectations and satisfaction. Modelling is a means that enables senior managers to simulate realistic scenarios while avoiding costly and/or unethical trial and error strategies. Therefore, modelling acts as a decision-aiding methodology. The model links health management decision-making process and frameworks with key attributes and predictors of user/patient, doctor and staff satisfaction, to show implications on the development of sound policy and decision outcomes, while avoiding pitfalls. It goes beyond simple measurements of satisfaction, by examining its multi-dimensional nature, decomposing it into constituent attributes, and investigating its predictors. Satisfaction attributes are viewed as an extension of people's needs and expectations. The data corroborates the work of other researchers as to the complexity of the concept of satisfaction and its expression. Data were collected through focus groups, household surveys, and exit questionnaires in the West Bank (Palestine) as a case study; the thesis outlines the need and practical methods to harmonise healthcare organisation policy setting and evolution with patient, staff and doctor expectations and beliefs, to the extent possible. The resulting synergy from this harmonisation would work to reduce some of the inherit uncertainty associated with decision outcomes by lowering the risk of dissonance between management and its main client groups (patients, staff, medical doctors). Dissonance, or position discrepancy, is viewed as a key contributing factor to reduced client satisfaction and increased decision uncertainty. From the organisational policy development perspective, the model reveals the significance satisfaction attributes and predictors of all three client groups (patients, staff, medical doctors) and subsequent decisions they make (observed behaviour) through the institutionalisation of systematic methods to incorporate vital information at policy levels. The determinants of these decisions are further analysed including beliefs, perceptions, attitudes, and intentions to enhance the understanding of how these factors fit into decision-making and policy development processes. It further points to the consequences healthcare managers may encounter when the opposing needs and expectations (multi-attributes of satisfaction) on these groups are not closely examined.

Publication Type: Thesis (Doctoral)
Subjects: H Social Sciences
Departments: School of Health & Psychological Sciences
Doctoral Theses
School of Health & Psychological Sciences > School of Health & Psychological Sciences Doctoral Theses
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