Fear in Healthcare Settings, Myth or Reality: A Case Study of the Private Healthcare Sector in Mauritius

2020 ◽  
Author(s):  
Catriona Planel-Ratna ◽  
Thanika Devi Juwaheer
Keyword(s):  
Healthcare Sector ◽  
Private Healthcare ◽  
Healthcare Settings

Better healthcare setting for better healthcare service quality

2019 ◽  
Vol 36 (10) ◽  
pp. 1665-1682
Author(s):  
Rama Koteswara Rao Kondasani ◽  
Rajeev Kumar Panda ◽  
R. Basu
Keyword(s):  
Service Quality ◽  
Business Performance ◽  
Rank Order ◽  
Healthcare Setting ◽  
Healthcare Sector ◽  
Comparative Performance ◽  
Content Type ◽  
Perceived Service Quality ◽  
Private Healthcare ◽  
Healthcare Settings

Purpose The purpose of this paper is to assess and compare different private healthcare settings based on perceived service quality in Indian context using analytical hierarchy process (AHP). The Indian private healthcare sector has been controlled by three categories of healthcare settings, namely, nursing clinics (NCs), non-corporate hospitals (NCHs) and corporate hospitals (CHs). Design/methodology/approach AHP was used to rank order of healthcare setting regarding the service quality dimensions and relative standings of every service provider with respect to its competitors. The authors collected random data from nine private hospitals situated in eastern and southern India. The authors proposed a feasible appraising approach based on AHP to decide the sustainability priority of dimensions like reliability, physical environment, empathy, efficiency, timeliness, transparency, affordability, communication, appropriateness and consistency in the Indian private healthcare industry. Findings The result demonstrates that Indian NCHs are performing better on customers’ perceived service quality, followed by CHs and NCs. The comparative performance of healthcare settings is an attempt to establish a performance ranking. Originality/value This research assesses dimensions of the service quality that need urgent attention. The results may provide insight to healthcare managers as to how they can improve their service quality in order to match customer expectation and to improve business performance.

scholarly journals How healthcare structures and communication delivery influence trust: a parallel-group randomized controlled trial

2021 ◽  
Author(s):  
Stephen Cantarutti ◽  
Emmanuel M. Pothos
Keyword(s):  
Controlled Trial ◽  
Random Order ◽  
Healthcare Sector ◽  
Public Healthcare ◽  
The Public ◽  
Private Healthcare ◽  
Public Healthcare Sector ◽  
Parallel Group ◽  
Therapy Compliance ◽  
Study Offer

Abstract Background According to recent polling, public trust in the healthcare sector remains low relative to other industries globally. The implications of low healthcare trust permeate throughout the industry in a number of ways, most visibly by discouraging therapy compliance. Methods This study investigated four putative determinants of trust in healthcare-related scenarios: individuals vs. collective groups as communicators of healthcare advice; expert vs. laypeople as providers of healthcare communication; public vs. private healthcare sector; and positive vs. negative information. Two hundred seventy-four participants were recruited via Prolific Academic and were presented with four statements in random order, related to a positive reflection of the public healthcare sector, a negative reflection of the public healthcare sector, a positive reflection of the private healthcare sector and a negative reflection of the private healthcare sector. According to these reflection, participants were repeatedly asked to rate the system on its trustworthiness. Trust outcomes were constructed using a four-dimension framework, consisting of benevolence, reliability, competence and predictability. Results Claims relating to the public sector had a significantly stronger impact on benevolence and reliability than claims relating to the private sector; claims from individuals had a significantly stronger impact on all trust variables than claims from collectives; and claims from laypeople had a significantly greater impact on reliability and competence ratings than claims from experts. Conclusions The findings in this study offer insight into the patterns with which trust decisions are made in healthcare contexts. More importantly, this research offers a novel perspective of how different factors interact to affect the various facets of trust. These results provide a foundation for future study in this evolving area, and offer insights into designing effective communication strategies that cultivate greater levels of individual trust in the healthcare sector.

scholarly journals Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1615
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
K Nearest Neighbors ◽  
Iot Applications ◽  
Processing Resources ◽  
Multiple Services ◽  
Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

scholarly journals Market concentration trends in South Africa’s private healthcare sector

2016 ◽  
Vol 19 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Marine Erasmus ◽  
Nicola Theron
Keyword(s):  
Market Structure ◽  
Private Hospital ◽  
Market Concentration ◽  
Competitive Dynamics ◽  
Healthcare Sector ◽  
Private Hospitals ◽  
Hospital Market ◽  
Private Healthcare ◽  
Structure Changes ◽  
Relative Bargaining Power

The Competition Commission (CC) commenced with an enquiry into South Africa’s private healthcare sector at the beginning of 2014, the outcome of which could have far-reaching consequences for the medical industry in South Africa. The panel appointed to consider competition in the private healthcare sector has indicated that they are interested in understanding increased consolidation in the private hospital market and the effect this may have on competitive dynamics. This article considers historical concentration trends in the private hospital market from 2000 to 2012. In addition it also deals with changes in market structure in the medical scheme and administrator markets. These trends provide a complete picture of market structure changes and the implications for relative bargaining power of the various parties. It finds that whereas the market concentration of private hospitals has remained relatively stable since 2004, the market concentration of medical schemes and administrators has increased over this period.

Adopting a knowledge translation approach in healthcare co-production. A case study

2020 ◽  
Vol 58 (9) ◽  
pp. 1841-1862 ◽  
Author(s):  
Francesca Dal Mas ◽  
Helena Biancuzzi ◽  
Maurizio Massaro ◽  
Luca Miceli
Keyword(s):  
Breast Cancer ◽  
Knowledge Translation ◽  
Cancer Patients ◽  
Healthcare Sector ◽  
Emotional Engagement ◽  
Breast Cancer Patients ◽  
Case Study Methodology ◽  
Content Type ◽  
Production Program

PurposeThe paper aims to contribute to the debate concerning the use of knowledge translation for implementing co-production processes in the healthcare sector. The study investigates a case study, in which design was used to trigger knowledge translation and foster co-production.Design/methodology/approachThe paper employs a case study methodology by analysing the experience of “Oncology in Motion”, a co-production program devoted to the recovery of breast cancer patients carried on by the IRCCS C.R.O. of Aviano, Italy.FindingsResults show how design could help to translate knowledge from various stakeholders with different skills (e.g. scientists, physicians, nurses) and emotional engagement (e.g. patients and patients' associations) during all the phases of a co-production project to support breast cancer patients in a recovery path. Stewardship theory is used to show that oncology represents a specific research context.Practical implicationsThe paper highlights the vast practical contribution that design can have in empowering knowledge translation at different levels and in a variety of co-production phases, among different stakeholders, facilitating their engagement and the achievement of the desired outcomes.Originality/valueThe paper contributes to the literature on knowledge translation in co-production projects in the healthcare sector showing how design can be effectively implemented.

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