An IoT-Based Smart Environment for Sustainable Healthcare Management Systems

2021 ◽  
pp. 51-74
Author(s):  
M.N. Mohammed ◽  
Brainvendra Widi Dionova ◽  
Salah Al-Zubaidi ◽  
Siti Humairah Kamarul Bahrain ◽  
Eddy Yusuf
Keyword(s):  
Healthcare Management ◽  
Management Systems ◽  
Smart Environment

Perception of privacy issues and awareness in health-care knowledge management systems: empirical study in Indian health-care context

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prathamesh Churi ◽  
Ambika Vishal Pawar ◽  
Amir A. Abdulmuhsin
Keyword(s):  
Health Care ◽  
Knowledge Management ◽  
Large Population ◽  
Empirical Work ◽  
Healthcare Management ◽  
Management Systems ◽  
Easy Access ◽  
Content Type ◽  
Health Care Data ◽  
Privacy Issues

Purpose Focusing on the Indian context, with the increase in the amount of data and its analysis in health-care knowledge management (KM), the privacy concerns rise which results in loss of trust of an individual in e-health-care systems. Privacy issues in health care, specific to India, are caused by prevalent complacency, culture, politics, budget limitations, large population and infrastructures. Because of these factors, data security requires a backseat that allows easy access to confidential information. Furthermore, the prevalent culture affects health-care disclosure in India. In many cultures, disclosing sensitive personal health-care data is considered ill mannered. This leads to discrepancies in the recorded health-care data and a decrease in the level of treatment meted out. The results and statistics of treatments given do not match the records because of inaccurate data reporting. With the significant rise in the analysis and use of technology in health-care KM systems, it is important to understand the perception of KM in terms of its use and awareness about data sharing in the KM system. The purpose of the paper is to measure the perception of privacy issues in the context of Indian healthcare management systems. Design/methodology/approach To measure the perception of the use of the KM system, a set of 20 questions was circulated with a sample size of 337 which includes health-care researchers, doctors, practitioners and patients. The questions focused upon the use, share the sensitive health data in the KM platform. All the demographic information such as age, sex, religion, occupation is recorded. The privacy of the individual is maintained while circulating the questionnaire. The usage of health KM system and its privacy is measured through means and t-test. Findings The results of the t-test were found positive. This research study finds that the privacy factor is important among the Indians to share the information with the KM repository. It is also found that medical practitioners or data custodians are not much serious about sensitive data is being stored for analysis. From the statistical perception of usage of KM and its privacy, new architecture and privacy guidelines were suggested which can be considered in future research. Research limitations/implications From the literature review, the questionnaire has developed which can help policymakers and hospital administrators collect information about KM processes in health-care organizations, and this can result in higher performance of health organizations. The privacy factor can also be included in typical health KM architecture ensure that while knowledge acquisition process, privacy of individual or organization can be maintained. Social implications KM enhances the value of corporations and business industries through knowledge production, distribution and provides reliable access to the knowledge resources. KM in health care can comprise a confluence of formal methodologies and techniques to facilitate the creation, identification, acquisition, development, preservation, dissemination and finally the utilization of the various facets of a health-care enterprise’s knowledge assets. According to IBM Global executive report in the year 2012, the entire health-care system has changed from diseases-centric to patient-centric. India is emerging in terms of revenue and employment in the health-care field. The advances of information and communication technology help the health-care sector streamline for data structure and access and health analytics. Originality/value In India, the KM is frequently used in health-care industries majorly by health-care practitioners and professionals. As health-care data and knowledge are considered to be sensitive, the privacy of an individual while using the data cannot be compromised. The proposed empirical work will provide a solution in determining the main barriers of implementing privacy policies that need to be solved first and to ensure effective implementation of KM in the health care of India.

Sustaining Healthcare Through Waste Elimination

2011 ◽  
Vol 6 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Sharie L. Falan ◽  
Bernard Han ◽  
Linda H. Zoeller ◽  
J. Michael Tarn ◽  
Donna M. Roach
Keyword(s):  
Business Processes ◽  
Health Expenditures ◽  
Healthcare Management ◽  
Management Systems ◽  
Healthcare Waste ◽  
Key Factor ◽  
Research Findings ◽  
Efficient Information ◽  
Effective Use ◽  
Domestic Products

The growth in U.S. national health expenditures (NHE) has continuously outpaced its Gross Domestic Products (GDP) growth since 1997 and this trend will continue with a 2.1% annual gap for the next decade (RAND, 2010). This nonstop healthcare cost increase make healthcare one of the most urgent issues in USA. Concurred by this study, the key factor that drives up the healthcare costs is waste. In this paper, a taxonomy on the root causes of healthcare waste is developed with a corroboration on why healthcare waste could be eliminated through effective use of health information technology (HIT). Furthermore, real world cases are used to highlight the research findings that waste can be avoided by: (a) recognizing the precursor of each potential waste, (b) examining business processes using defined detection criteria, and (c) implementing HIT systems that support efficient information sharing among all healthcare stakeholders. Finally, recommendations for implementing IT enabled healthcare management systems are presented.

A Forecasting Model for Patient Arrivals of an Emergency Department in Healthcare Management Systems

2019 ◽  
pp. 266-284 ◽  
Author(s):  
Melih Yucesan ◽  
Muhammet Gul ◽  
Suleyman Mete ◽  
Erkan Celik
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Medical Devices ◽  
Moving Average ◽  
Healthcare Management ◽  
Management Systems ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Artificial Neural Network Ann

Emergency departments (EDs) are one of the most valuable departments of healthcare management systems. Patient arrivals at the EDs are crucial for planning of the future. Accurate forecasting of patient arrivals contributes to better organized human resources and medical devices in the EDs. Therefore, in this chapter, the authors aim to develop a hybrid model including the methods of autoregressive integrated moving average with external variables (ARIMAX) and artificial neural network (ANN) in a hospital ED. The arrival data was collected from the hospital information system of a public hospital in eastern Turkey. The model incorporates factors related to ED arrivals such as climatic and calendar variables. By the aid of the proposed model, an insight to arrangement and planning of ED resources can be provided in a better way.

Need for Flexibility and Innovation in Healthcare Management Systems

2007 ◽  
Vol 8 (1-2) ◽  
pp. 45-54 ◽  
Author(s):  
Subhash Wadhwa ◽  
Jitendra Madaan ◽  
Avneet Saxena
Keyword(s):  
Healthcare Management ◽  
Management Systems

scholarly journals Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

2021 ◽  
pp. 39-48
Author(s):  
Abedallah Zaid Abualkishik ◽  
◽  
◽  
Ali A. Alwan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Simulation Analysis ◽  
Fitness Function ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Healthcare Management ◽  
Management Systems ◽  
Multi Objective ◽  
Healthcare Data

Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.

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