Real-world research and its importance in respiratory medicine

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Integrating Kano’s Model and SERVQUAL to Improve Healthcare Service Quality

The purpose of this research is focus on customer relationship management (CRM) strategies and relationship between service attributes and customer satisfaction through Kano’s model especially on healthcare service at the private hospital. The paper specifically investigates the applicability of the model and the key factors in the hospital service business. The hospital service quality much depends on the performance of the attributes that define a service. The aim of this paper is first to investigate the attribute of service quality using Servqual perspective, thus the management is able to adjust the relationship between performance of service attributes and customer satisfaction, and second, through a case study in the private hospital to prove that the importance of a service attribute is a function of the performance of that attribute. An empirical study using questionnaires with a focus on service enquiring about the performance of service key attributes and overall customer satisfaction was conducted using Servqual perspective including 5 parameters i.e. Tangibles, Reliability, Responsiveness, Assurance, Empathy. The data were fed into the Kano customer satisfaction model which used Five-level Kano questionnaire for analysis and comparison between one attribute to the others. This research found that there are three of the total 26 service quality attributes have been categorized as “attractive”. Four service quality attributes have been categorized as “must be”, and sixteen of them as “one-dimensional”. However, there is no service quality attribute can be categorized as “reverse” and “questionable”. It can be predicted that offering customers “must be” or expected quality attributes will not be enough for customer satisfaction in few next days cause of the contemporary world and the environment changing. Hence, companies should focus on “attractive” quality attributes instead of “must be” or “one-dimensional” attributes in order to satisfy customers and to achieve competitive advantage. The research limitations is the Kano model of customer satisfaction needs to be extended to other customer behavior variables and also management strategic response to increase customer loyalty; which not include in this paper. The implication is the methodology employed here can be easily applied by hospital management to evaluate customer behaviors and service quality performance

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

A conceptual treadmill: the need for ‘middle ground’ in clinical decision making theory in nursing

This paper explores the two predominant theoretical approaches to the process of nurse decision making prevalent within the nursing research literature: systematic-positivistic approaches as exemplifed by information processing theory, and the intuitive-humanistic approach of Patricia Benner. The two approaches' strengths and weaknesses are explored and as a result a third theoretical stance is proffered: the idea of a cognitive continuum. According to this approach the systematic and intuitive theoretical camps occupy polar positions at either end of a continuum as opposed to separate theoretical planes. The methodological and professional benefits of adopting such a stance are also briefly outlined