Healthcare Service Industry Research Articles

The disparate impact of Hurricane Maria: the relationship between recovery of public services, traumatic stress and household income of healthcare and social service industry workers

ABSTRACT The relationship between social vulnerability and disaster recovery has been well examined; however, less attention paid to how differences in vulnerability affect recovery for healthcare and social service industry employees. The widespread impact of Hurricane Maria on Puerto Rico, including failure of the entire electrical grid, presents a unique case study for examination of the relationship between income and disaster recovery of frontline workers. Using data collected from healthcare and social service industry employees 10 months post-Maria, we found a significant relationship between household income and recovery of public services. Households with lower incomes experienced nearly a full month longer wait for electricity restoration as compared to higher-income households, after controlling for home damage and demographic characteristics. Additionally, lower income households were more likely to report higher levels of PTSS. Our findings suggest a need for targeted workplace interventions during disaster response and recovery that take into account economic inequities.

Disruptive Innovation in China’s Primary Healthcare Market: A Case Study of a Startup to Challenge Incumbent and Business Growth Sustainability

This article examines primary healthcare reform in mainland China through a private-public partnership approach from a novel angle using disruptive innovation theory. The research is the first of its kind to apply Christensen’s disruptive innovation theory to the primary healthcare industry in China. A qualitative single case study was designed to focus on the GBA Healthcare Group Limited (“GBAH”). The disruptive innovation theory was framed under the 3H framework, hence offering managers of healthcare organizations a new holistic management framework for applying disruptive innovation theory in their business initiatives. It is also suggested that due to the highly regulated nature of the healthcare services industry, private organizations should actively engage with the relevant government organizations as core stakeholders in their strategic planning.

AN EFFICIENT DATA SECURITY IN MEDICAL REPORT USING BLOCKCHAIN TECHNOLOGY

The health care services industry is always showing signs of change and supporting new advancements and advances. One of the predominant requirements in today’s health care systems is to protect the patient's medical report against potential attackers. Hence, it is basic to have secure information that can just approve people can get to the patient's medical report. So, it has proposed Block chain technology as a disbursed approach to grant security in accessing the medical report of a patient. It’s composed of three phases: Authentication, Encryption and Data Retrieval using Block Chain Technology. For authentication –Quantum Cryptography. Key Words: Block chain, Medical Report, Data Accessibility, Security, Encryption, Data Retrieval.

Feature-Based Suitability Study of EIGRP and OSPF Routing Protocols for Healthcare, Banking and ISPs Enterprises Networks’ Optimization

Due to the broadening of services from industrial enterprises to customers, the industrial network has gained more significance than what is really thought. As a part of this revolution, this paper tried to introduce the best suitable routing option for the healthcare, banking and internet services industries to optimize the routing department of their enterprises. RIP is not associated with this study, being out of favor in the industries due to hop count limitation. The idea behind doing such a thing is to avoid improper implantation of routing inside the enterprises which causes lack of optimization in routing department. The method which is selected for this paper is qualitative case study of banking, healthcare and internet services industries with unstructured interviews of IT experts of the respected industries in an open-ended questions fashion. The results were illustrated based on the stories and the discussion of the stories is the main contributor to the result of the study. The evaluation results of the stories have illustrated, that EIGRP is a suitable routing option for health care and banking, while OSPF can be suitable for the internet services industry. The implication of the study can be optimized network of industries such as healthcare, banking and internet services and it introduces the best suitable routing option for the demanded industries with the case study method and narrative analysis of the stories, which start from load balancing type and ends to demanded routing protocol, while reaching to demanded routing protocol, it introduces the best suitable routing option of that industry.

Going the extra mile for patients: Service-oriented high-performance work systems drive nurses' job crafting and extra-role service behaviour.

This study intends to investigate whether, how and when service-oriented high-performance work systems (SHPWSs) drive nurses' extra-role service behaviour. This was a quantitative cross-sectional study conducted with matched nurse-patient participants. We tested hypotheses using data from 284 nurses and their matched 566 patients. The data were collected in 2019. We conducted a set of hierarchical regression analyses to test our hypotheses. The results showed that SHPWSs have a positive impact on job crafting, which, in turn, mediates the link between SHPWSs and extra-role service behaviours. Additionally, the influence of professional identification moderates these relationships. Specifically, SHPWSs are significantly and positively associated with job crafting among highly professionally identified nurses. The indirect effect is significantly positive when nurses strongly identify with their profession but not significant when their professional identification is low. The results indicated that SHPWSs can elicit job crafting among higher professional identifiers, which further increases extra-role service behaviours towards patients. Our research emphasizes the significance of HRM themes in the healthcare service industry and their direct impact on healthcare personnel. Shifting from a management-centric to an individual-centric perspective, we focus on the proactive role of nurses. Furthermore, this study enhances the understanding of the boundary conditions for the effectiveness of SHPWSs. Nurses and their mated patients from a Chinese hospital contributed to this study by completing the survey.

Big Data Analysis in Consumer Behavior: Evidence from the Retail, Healthcare, and Financial Services Industries

As a matter of fact, big data analytics has quickly become one of the cornerstones of various industries such as retail, healthcare, and financial services. This study delves into the applications of big data analytics within various sectors, exploring how models such as AISAS, PDCA, and customer segmentation can assist organizations in crafting successful data-driven strategies. Based on case studies, it is found that retailers utilizing the AISAS model can significantly increase customer engagement and optimize marketing strategies, while healthcare organizations applying PDCA through big data analytics improve patient outcomes and operational efficiencies. Financial services using customer segmentation models experience improved customer satisfaction and profitability. Big data analytics enable more customized, efficient decision-making processes. This extensive review explores how big data analytics can provide tailored solutions to complex industry problems. The findings from this investigation can assist businesses across multiple sectors understand both its practical benefits and possible downsides when implemented into operations.

Digital formats of the global healthcare service industry

Nowadays, both diversified channels of medical care for patients in remote mode, as well as extensive telemedical networks and systems of exchange of specialized medical information between all participants in the process of public reproduction of medical services at the national and supranational levels are actively being formed. As a result, we are all witnessing the development in global coordinates of a multi-level integrated system of a digital ecosystem of health care, which is based on digital technologies, forms qualitatively new channels of value creation for consumers of medical services and ensures deep convergent interaction of all key subjects of the medical care system citizens - from doctors, patients, health care institutions and states to health insurance companies, intergovernmental and non-governmental international organizations, business communities, etc. In view of this, there is every reason to claim that the qualitatively new business models of the digital health care system implemented in a fragmented manner in the conditions of digitalization will already in the next decade accumulate a powerful institutional and technological resource for "undermining" traditional business models of medical care with their departure from the narrowed format of the primary link of medical services to deep penetration into all links of the value chain formed in health care, and therefore - the formation of sustainable cash flows for producers of medical services. In the systemic processes of the global digital transformation of economic activity, a special place has been occupied by the service industry in recent years, which, as international experience shows, in the post-industrial paradigm of world economic development largely determines the structural dynamics of the national economies of most countries of the world. The purpose of the article is to reveal conceptual approaches to the systematic implementation in global coordinates of the key priority of digital health — the formation of values for patients in an ethical, safe, reliable, fair and sustainable way, while fully complying with such principles of providing medical services as: its transparency, safety and accessibility to broad segments of the population, scalability of medical care and its regularity, digital compatibility of structural components of the digital ecosystem, and strict confidentiality of medical information.

Social contagion of online physician choice: the infection and immunity mechanism

PurposeIn the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.Design/methodology/approachData collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.FindingsThe obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.Originality/valueThis finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.

Seismic risk assessment and hotspots prioritization: a developing country perspective.

Natural disasters pose a negative impact not only on human lives but also on infrastructures such as healthcare systems, supply chains, logistics, manufacturing, and service industries. The frequency of such calamities has grown over time, which not only poses a threat to human survival and the living environment but is also detrimental to the economic growth and sustainable development of society. Earthquakes cause the most destruction compared to other natural disasters, especially in developing countries where the conventional reactive approach to dealing with disasters gives less chance for the appropriate utilization of already limited resources. Additionally, mismanagement of the resources and the lack of a unified action plan hinder the purpose of helping the grieving population. Considering the foregoing, this study presents a methodology for identifying hotspots and helping prioritize pre- and post-disaster management action by conducting a thorough seismic risk assessment while taking into consideration the case of a developing country as its focus. This methodology allows for rapid risk assessment against any given scenario by providing quantitative estimates of the repercussions such as physical damage to the buildings, casualties including injuries, economic losses, displaced households, debris, shelter requirements, and hospital functionality. In short, it could help prioritize actions with greater impacts and serve as a foundation for the formulation of policies and plans intended to increase the resilience of a resource-constrained community. Thus, the findings can be utilized by government agencies, emergency management organizations, non-government organizations, and aiding countries as a decision support tool.

What is coming next in health technology startups? Some insights and practice guidelines.

Health technology startups are experiencing a significant surge in growth, particularly since the COVID-19 pandemic, as they address gaps in the sector. However, despite their increasing prevalence, there is still relatively limited knowledge about this sector's evolution. This opinion article explores emerging trends in health startups, including their market size, growth, significant challenges, and guidelines for key stakeholders from a global healthcare service industry perspective. By gaining a better understanding of these trends, new research opportunities and evidence-based practices can be identified.

28 Addressing Workplace Violence: Health Care Staff Safety, a Culture of Caring

28 Addressing Workplace Violence: Health Care Staff Safety, a Culture of Caring

Current status and development potential of scalp healthcare in the COVID-19 blue era: A critical review focused on Republic of Korea.

AimsThe purpose of this paper was to review the literature focusing on the scalp health of the Korean people in the COVID‐19 Blue era and the possibility of significant development as an academicMethodsThis review paper is a literature review, and the method is a narrative review.ResultsIt was found that the higher the awareness of hair loss, the better the scalp and hair management behavior. South Korea needs to develop systematic customized management methods, scalp programs and products due to the development of the COVID‐19 era and the development of the 4th industry.ConclusionSouth Korea still needs dto improve the expertise of tricolologists and national social security insurance and research along with the growth of the beauty and healthcare service industry in the COVID‐19 Blue era.

Application of the artificial bee colony algorithm to power contract capacity optimization

In recent years, the artificial intelligence of imitation human neuron operation has become an aid to human thinking. Therefore, we applied this technique to forecast medium-term load systems. Power loads represent an indispensable energy in our life, so effectively forecasting power load usage may not only decrease energy consumption but also reduce the cost of contract capacity. Currently, people have used end-use models, load density, trend analysis, and statistical methods to predict Medium-Term Load Forecasts (MTLF) and have employed artificial neural networks to predict Short-Term Load Forecasts (STLF). In this paper we propose an optimized structure to predict the power load by combining an artificial bee colony (ABC) and an artificial neural network (ANN) to forecast MTLF. After considerable trial-and-error testing, the influence of different structures, max iteration, and datasets leading to different errors all became points in our study. These structures were also put into both a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to test the experiment. The experimental results verified that ABC demonstrated better performance in max iteration 30 and structure 7-n-1. An appropriate contract capacity prediction can be obtained by applying this structure. To show the effectiveness of the proposed structure, we verified it with six years’ statistical data of the health care service industry from Taiwan Power Company and then discuss the results.

Long-Term General Index Prediction Based on Feature Selection and Search Methods: Amman Stock Exchange Market

Stock markets are an essential backbone for the economy worldwide; their indices provide all interested parties with indicators regarding the performance of firms listed in the financial market due to tracking the daily transactions. This study aims to investigate factors that affect the stock exchange directly so that it simplifies building a prediction model for the exchange index in Jordan’s financial market. The study hypothesis assumes that some sub-sectors are most influential in creating the stock market prediction model. Therefore, this study applies four feature selection methods on 23 sub-sectors and Amman Stock Exchange Index (ASEI100) for the period 2008–2018. The top 10 attributes from each selection method are combined, and the frequency table is used to find the highly trusted attributes. Moreover, linear regression with ordinary least square regression is used to test the validity of the top factors that frequently occurred in the four methods and their effect on ASEI. The results found that there are six main sub-sectors directly affecting the general index in Jordan: Health Care Services, Mining and Extraction Industries, Textiles, Leather and Clothing, Real Estate, Financial Services and Transportation. These sectors can be utilised to predict the movements of the Amman Stock Exchange Index in Jordan. Also, the linear regression model output showed a statistically significant relationship between the six sub-sectors (independent variables) and ASEI (dependent variable). Investors can use this paper’s findings to signal the most important sectors in Jordan. Thus, it helps in taking investment decisions.

Impact of Covid-19 on sectoral stock prices: An event study based on US stock market

The impact of COVID-19 on stock prices of different United States economic sectors is examined using event study methodology. Results revealed that the first confirmed case in US on January 21, 2020 was underestimated by investors and did not disturb stock prices significantly. However, the first local transmission case on February 26, 2020 hit the stock market in the post event window. Due to this event, the stock prices of industries in energy, financials, industrials, real estate and utilities sectors declined but observed price increases in health care, information technology and telecom services industries. First “stay at home” order issued by California State in USA on March 19, 2020, a form of lockdown which restricted public gathering, reduced the market value of consumer staples, health care, industrials, real estate and utilities sectors. This order brought positive changes in stock prices of energy, consumer discretionary, financials, information technology and telecom services sectors. Results of the study can help portfolio managers and investors in the process of asset selection and risk minimization during these types of pandemics. On the other hand, these findings can assist policy makers in designing strategies in advance for financial stability as well as minimizing the negative consequences resulting from COVID-19 related potential health crises in future.

Chinese Stock Market’s Reaction to COVID‐19 in the Short and Long Run

We study the impact of COVID‐19 on Chinese stock market which can be seen as a complex system. We use the event study method to evaluate its performance change in terms of the return rate, turnover rate, etc. We show that the abnormal return of stock market was significantly negative after the outbreak of COVID‐19 and did not turn positive until May 2020. Moreover, the five‐factor model is used to estimate the ordinary returns of different industries and show that abnormal returns for medical and food industries were significantly positive, while energy and public utility industries had significantly negative abnormal returns which persisted for a long time. COVID‐19 had lag effects on clothes industry, finance industry, transportation industry, and IT industry. We also find that energy and finance industries had negative abnormal turnover rates during the sample period, while other industries, such as healthcare and telecommunications service industries, had positive abnormal turnover rates.

The Internet of Things in Geriatric Healthcare.

There is a significant increase in the geriatric population across the globe. With the increase in the number of geriatric people and their associated health issues, the need for larger healthcare resources is inevitable. Because of this, healthcare service-providing industries are facing a severe challenge. However, technological advancement in recent years has enabled researchers to develop intelligent devices to deal with the scarcity of healthcare resources. In this regard, the Internet of things (IoT) technology has been a boon for healthcare services industries. It not only allows the monitoring of the health parameters of geriatric patients from a remote location but also lets them live an independent life in a cost-efficient way. The current paper provides up-to-date comprehensive knowledge of IoT-based technologies for geriatric healthcare applications. The study also discusses the current trends, issues, challenges, and future scope of research in the area of geriatric healthcare using IoT technology. Information provided in this paper will be helpful to develop futuristic solutions and provide efficient cost-effective healthcare services to the needy.

Application of Radio frequency identification technique in Developing Countries

Radio frequency identification (RFID) technique is a practice for managing the supply Chain of healthcare. Technique of Radio Frequency Identification (RFID) not just offers following ability of different resources continuously, yet in addition gives productive and exact access to healthcare information for specialists and other experts of healthcare sector. The primary challenge faced by RFID is its acceptance and utilization by doctors and medical attendants only. This paper utilizes semi-organized questionnaires on RFID advisors, scientists, and medical hospitals directors/doctors. Information was gathered from 150 doctors and attendants/nurses. The development of RFID adoption in the developing countries is still in the initial phase. The significant barriers, advantages, application and critical success factors of RFID have been recognized in this paper. This study adds to the developing interest for understanding the RFID adoption in the healthcare services industry. The motivation behind this research is to understand the current opportunities, potential advantages of RFID adoption in Indian healthcare industry.

The Early Impact of Covid-19 on Job Losses among Black Women in the United States

ABSTRACT Given that a high proportion of workers in “essential” sectors of the US economy are Black women, this paper seeks to answer the following: in which occupations did Black women in the US experience the greatest job losses during the early phase of the pandemic? Drawing on feminist economic and stratification economic theories, this quantitative analysis suggests that the greatest losses were cashier jobs in the hotel and restaurant industry, and childcare worker positions in the healthcare and social services industry. These two occupations are low wage, dominated by women, and considered essential. This study posits that Black women disproportionately lost these jobs for three reasons: (1) Black women’s strong attachment to the US workforce; (2) Black women’s overrepresentation in the hotel/restaurant and healthcare/social services industries; and (3) women’s overrepresentation in low-wage occupations. The study offers policy solutions that could help sustain the Black community during the pandemic-inspired economic downturn. HIGHLIGHTS Black women face occupational segregation that is specific to both their gender and their race. Black women’s employment is more narrowly concentrated by industry than any other demographic group. Job losses due to COVID-19 especially hit industries in which Black women are concentrated. Black women lost the most jobs in the cashier occupation. Any pandemic-recovery policy agenda must include full employment for Black women.

An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

The healthcare services industry has seen a huge transformation since the prominent rise of the Internet of Things (IoT). IoT in healthcare services includes a large number of unified and interconnected sensors, and medical devices that generate and exchange sensitive information. Thus, an enormous amount of data is transmitted through the network which raises an alarming concern for the privacy of patient information. Therefore, privacy preserving data collection (PPDC) is on-demand to ensure the privacy of patient data. Several pieces of research on PPDC have been proposed recently. However, the research literatures have fallen short in privacy requirements and are prone to various privacy attacks. In this paper, we propose a novel privacy-preserving data collection scheme for IoT based healthcare services systems. A clustering-based anonymity model is utilized to develop an efficient privacy-preserving scheme to meet privacy requirements and to prevent healthcare IoT from various privacy attacks. We formulated the threat model as client-server-to-user to ensure privacy on both ends. On the client-side, a modified clustering-based k-anonymity model with α-deassociation is used to anonymize the data generated from the IoT nodes. The base-level privacy is then ensured through a bottom-up clustering method which generates clusters of records as per the privacy requirements. On the server-side, the cluster-combination method-UPGMA is utilized to reduce communication costs and to achieve a better level of privacy. The proposed scheme is efficient in tackling privacy attacks such as attribute disclosure, identity disclosure, membership disclosure, sensitivity attacks, similarity attacks, and skewness attacks. The effectiveness and efficiency of the proposed scheme are proven through theoretical and experimental analyses.