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The impact of big data analytics and artificial intelligence on green supply
chain process integration and hospital environmental performance
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Abstract In chapter four, it was highlighted how big data analytics and healthcare organization
management have a favorable relationship. Studies were picked out of the reviews and grouped
into Different study areas, such as the potential of big data analytics, to identify similar themes.
Resource administration. Administration of health surveillance systems and big data analytics.
Technology and big data analytics for the healthcare industry
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Table of Contents
Chapter 4: Results and Findings
......................................................................................................
4
Introduction
..................................................................................................................................
4
Qualitative analysis
......................................................................................................................
4
Discussion
..................................................................................................................................
13
Summary
....................................................................................................................................
15
References
......................................................................................................................................
16
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Chapter 4: Results and Findings Introduction One of the most significant chapters in this research project is this one. The information gathered
here is used to examine the study's anticipated vision. According to the chapter, researchers have
made every effort to use secondary data to rule out derivatives. The result was presented in
thematic format. Also, this analytical section will help frame the study's research objectives. The
researcher connected the qualitative data analysis to the literature review component as well. It
improves both the data's validity and authenticity. Qualitative analysis Green Supply Chain Process Integration
Green supply chain integration is becoming more crucial as businesses work to reduce their
environmental effect and meet sustainability targets. Several advantages come from
incorporating green practices into supply chains, such as lower costs, enhanced brand
recognition, and a healthier planet (Benzidia et al
., 2021). Big data analytics and artificial
intelligence (AI) can significantly influence the integration of green concepts into supply chains
by enabling businesses to gather, process, and analyze massive volumes of data and raise
awareness of their operations and improvement prospects.
Figure: Green Supply Chain Process Integration
Source: (Bag et al
., 2021)
The optimization of transportation routes is one-way big data analytics and AI can have an
impact on the integration of green supply chain activities. The most cost-effective routes for
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transportation can be found by using AI algorithms to examine transportation data such as traffic
patterns and fuel consumption rates. Companies can optimize transportation routes to use less
fuel and emit less CO2 (Nahr et al
., 2021), resulting in a more sustainable supply chain. By
monitoring and improving resource utilization, big data analytics and AI can also have an impact
on the integration of green supply chain activities. by gathering information on how resources
are used, such as water and energy consumption. Resources that are being misused or wasted can
be found by organizations. This data can be evaluated and optimization opportunities found with
the aid of big data analytics. For instance, businesses can utilize AI algorithms to find places
where energy use can be cut, such as when to turn off the lights or change the thermostat in an
empty room (Mageto, 2021).
Big data analytics and AI can also be used to improve inventory management. This is yet another
essential component of the supply chain's integration of green processes. Businesses may
manage inventory levels, cut waste, and lessen the environmental effect of surplus inventory by
examining sales data and demand patterns. This may result in cost reductions as well as a more
sustainable supply chain (Raut et al
., 2021). Using big data analytics and AI can assist firms in
adhering to rules and fulfilling reporting requirements for sustainability in addition to the
advantages mentioned above. Organizations can report on progress toward attaining
sustainability goals and complying with regulatory obligations by gathering and evaluating data
on environmental impacts and sustainability metrics (Dhamija and Bag, 2020).
However, there are also difficulties in incorporating big data analytics and AI into the procedures
of a green supply chain. Data accuracy and quality are among the major obstacles. If the data
obtained is erroneous or lacking, big data analytics and AI will not produce meaningful insights.
To provide reliable insights, organizations must gather high-quality data and appropriately
process and evaluate it. Implementing new technology and systems presents another difficulty.
Organizations must make sure they have the infrastructure and personnel to support big data
analytics and AI initiatives because these programs need considerable resources and skills (Bag
et al
., 2021).
For businesses wanting to lessen their environmental effect and accomplish sustainability
objectives, integrating green concepts into the supply chain is essential. By enabling businesses
to gather, process, and analyze massive volumes of data and gain insights into their operations
and chances for improvement, big data analytics and AI can significantly impact the integration
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of green supply chain processes (Zhang et al
., 2021). Businesses can create a more sustainable
supply chain by streamlining inventory management, streamlining resource tracking, and
optimizing transportation routes. Yet, issues like data quality and implementation must be
addressed to fully realize the potential of big data analytics and AI in green supply chain process
integration.
Hospital Environmental Performance
The necessity for hospitals to lessen their environmental effect and enhance their environmental
performance has come to light in recent years. Hospital activities can have a substantial negative
influence on the environment, including trash generation, energy use, and greenhouse gas
emissions. Hospitals are significant resource consumers. By enabling hospitals to gather, process,
and analyze massive volumes of data, big data analytics, and AI can have a substantial impact on
how well hospitals operate in terms of their environmental conditions (Inamdar et al
., 2021).
This offers a perception of hospital operations and areas for development. Patient flow
optimization is one method by that big data analytics and AI can have an impact on a hospital's
environmental performance. AI algorithms can identify regions that are bottlenecks and
recommend changes to optimize patient flow by assessing patient data such as wait times,
treatment times, and discharge rates (Naz et al
., 2021). Hospitals can decrease wait times,
consumption of non-renewable resources like paper and plastic, and patient outcomes by
maximizing patient flow. Hospitals can more efficiently distribute resources and cut down on
waste, for instance, by utilizing AI algorithms to estimate patient demand.
Tracking and maximizing energy use is another way that big data analytics and AI are
influencing a hospital's environmental performance. Hospitals are significant energy consumers,
and energy use has a significant influence on the environment, including greenhouse gas
emissions. Big data analytics can be used to monitor energy use across departments and pinpoint
inefficiencies (Ebinger and Omondi, 2020). Hospitals may cut money and lessen their impact on
the environment by managing their energy use. AI algorithms can be used, for instance, to
optimize HVAC systems, lower energy use, and enhance air quality. In addition to the advantages
listed above, using big data analytics and AI can assist hospitals in adhering to rules and
completing sustainability reporting obligations. Hospitals can report on progress toward attaining
sustainability goals and satisfying regulatory obligations by gathering and evaluating data on
environmental impact and sustainability metrics (Manavalan and Jayakrishna, 2019).
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Integrating big data analytics and AI into hospital environmental performance is not without its
difficulties, though. Data accuracy and quality are among the major obstacles. If the data
obtained is erroneous or lacking, big data analytics and AI will not produce meaningful insights.
To produce reliable insights, hospitals must gather high-quality data and appropriately process
and evaluate it (Pournader et al
., 2021). Implementing new technology and systems presents
another difficulty. Big data analytics and AI implementation demand a significant investment in
time and talent. The infrastructure and personnel needed to support these activities must be
present in hospitals. By enabling hospitals to gather, process, and analyze massive volumes of
data, big data analytics and AI can have a substantial impact on how well hospitals operate in
terms of their environmental conditions. This offers a perception of hospital operations and areas
for development. Hospitals may enhance their environmental performance and lessen their
environmental effect by tracking and optimizing energy use and patient flow. Yet, obstacles like
poor data quality and implementation must be solved to fully exploit the potential of big data
analytics and AI for hospital environmental performance (Riahi et al
., 2021). To enhance their
environmental performance and guarantee a healthier environment for future generations,
hospitals must continue to invest in innovative technology and systems.
Predictive analytics
Using big data analytics and AI to integrate sustainable supply chain practices and hospital
environmental performance, predictive analytics is a vital component. It also entails creating
predictive models that analyze data to find patterns and trends that may be used to forecast future
events. These models can be applied to reduce waste production, maximize resource efficiency,
and lessen carbon impact. Predictive analytics can be employed as part of the integration of
green supply chain processes to optimize transportation routes, lower fuel costs, and lower CO2
emissions (Riahi et al
., 2021). Predictive models can find the most effective routes for moving
goods by examining data on transportation routes, weather patterns, and other variables. This
decreases fuel usage, lowers CO2 emissions, and enhances the organization's environmental
performance.
Optimizing hospital energy use can also be done via predictive analytics. The high energy use of
hospitals may increase their carbon impact. Predictive models can find possibilities to cut energy
use by examining data on energy consumption patterns. Predictive models, for instance, can
detect spaces with high energy use, like operating rooms, and recommend activities to lower
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energy use in those spaces. Predictive analytics can also be used to optimize waste management
in addition to transportation and energy utilization (Gray-Hawkins and Lăzăroiu, 2020.). A lot of
the garbage produced by hospitals can be recycled or utilized again. Predictive models can find
opportunities to decrease trash generation and boost recycling rates by examining data on waste
generation and disposal. As a result, less waste is dumped in landfills, lowering the organization's
carbon impact.
The hospital and clinic at the University of Iowa are examples of how predictive analytics are
used in healthcare. A hospital put in place a predictive analytics system to make the most of its
operating room. The technology was able to decrease wait times and find areas where patient
flow might be improved by analyzing data on surgery plans and patient flow. The hospital has
thereby drastically decreased energy use and trash output while also reducing surgical times by
12%. The Emory Healthcare system in Atlanta, Georgia, is a further example of the application
of predictive analytics in the healthcare industry. An optimization of bed occupancy was
achieved by this system's implementation of predictive analytics (Ramirez-Peña et al
., 2020).
The system found chances to increase bed occupancy and decrease patient wait times by
evaluating patient flow and bed availability data. By doing so, energy use and waste production
were both noticeably decreased, and patient outcomes were also improved.
To integrate green supply chain practices and hospital environmental performance, predictive
analytics is a crucial part of big data analytics and AI. Businesses may maximize resource
consumption, cut waste production, and lessen their carbon footprint by leveraging data to
construct predictive models that spot patterns and trends (Ramirez-Peña et al
., 2020). Predictive
analytics can be utilized to improve patient outcomes and environmental performance by
optimizing waste management, transportation routes, and energy use, among other crucial
healthcare sectors.
Potentialities of big data analysis BDA capabilities are described as "collecting, storing, processing, and analyzing large amounts
of health data in diverse formats and enabling organizations to quickly identify important
information" by Wang and Hajli (2017) in the context of healthcare which is described as the
capacity to offer users. The connection between BDA and advantages for healthcare
organizations is best described by the "road to the value chain" approach. This path represents a
significant contribution to the study of business value because it empirically demonstrates how
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capabilities can be developed and what benefits can be realized in healthcare organizations, in
addition to drawing the general and well-established connection between big data capabilities
and benefits (
Benzidia et al.,
2021). Another study in this area looks at the critical role that BDA
capabilities play in creating healthcare supply chain linkages and how that affects hospitals'
flexibility. Particularly, the BDA plays a crucial part in creating operational flexibility and a
supply chain for healthcare integration. Given the health and financial difficulties brought on by
Covid-19, managers have embraced this BDA dimension as a particularly effective tool to
increase the operational flexibility of healthcare companies.
The BDA has a strong potential for assisting managers and healthcare professionals in their
decision-making processes through its capacity to give predictive models and real-time insights.
To facilitate the gathering, administration, and integration of data in healthcare organizations, the
literature offers several big data in healthcare applications. Moreover, BDA makes it possible to
integrate sizable datasets, supporting manager decisions and keeping an eye on the managerial
facets of healthcare organizations (
Rashid, and Rasheed, 2022). Discovering the big data keys
that can apply ad-hoc methods to improve efficiency along the healthcare value chain is the first
step in developing a decision-making process based on BDA. In order to do this, the research
conducted by Sousa et al., (2019), highlights the benefits that BDA may provide to the decision-
making process through predictive models and real-time analytics, assisting in the collection,
administration, and integration of data in healthcare organizations.
It is currently able to offer individualized healthcare services, gathers a vast amount of clinical
and biometric data, and implement BDA equipment because of an integrated and networked
ecosystem. However, to truly benefit from these tools and make them into usable decision
support systems (DSS), R&D must concentrate on data filtering procedures to collect high-
quality, trustworthy data (
Doss et al.,
2022). The introduction of new healthcare programs and
BDA-based healthcare models allows for the support of administrative and medical decision-
making in the delivery of healthcare services. Future interactions between and among users of
the healthcare ecosystem will generate a variety of complicated data, therefore information
processing and analytics will be the primary issues.
In light of the aforementioned, the RA1 contains studies that demonstrate how high-performance
filtering mechanisms and data quality are crucial to the success of BDA-based management
systems in healthcare organizations (
Aldaas et al.,
2022). For instance, the research by
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Maglaveras et al., (2016), which is covered in this field, examines novel R&D pathways in
biological information processing and management as well as the creation of new intelligent
decision support systems.
Resource management The literature evaluation revealed another significant study direction that addresses the beneficial
effects of the BDA on resource management. The management of medical waste, energy
consumption, and the environmental burden is inadequate, which limits the utilization of scarce
resources. The BDA is very helpful in this regard, and it can make a significant contribution in
the future to the implementation of circular economy processes and the support of sustainable
development initiatives in healthcare organizations (
Benzidia et al.,
2023). To this purpose, the
study produced by Kazançolu et al. (2021) emphasizes the significance of circularity and
sustainability ideas to reduce the damaging environmental effects of the sector. The report also
identifies the circular economy constraints in the healthcare organization and offers remedies by
putting BDA-based management systems in place. Finally, to assist healthcare managers in
launching sustainable initiatives within the setting of healthcare organizations, the authors have
created a managerial, policy, and theoretical framework. Studies that have connected the advantages of BDA and artificial intelligence with the process of
integrating a green supply chain have also looked at the influence on performance. Digital
learning has a significant positive impact on the environmental performance of healthcare
organizations and is increasingly acting as a "moderator" of the green supply chain processes.
BDA-AI technologies will increase the collaboration within the green supply chain and the
integration of environmental activities, which will support the managerial decisions involved in
the supply processes (
Zhu et al.,
2022). This research also offers a crucial foundation for
reference for supply chain and logistics managers who want to apply BDA-AI technology to
assist green supply chains and improve the environmental performance of healthcare
organizations. Many academics are now concentrating on BDA-driven decision support systems
to help hospital managers. These BDA-based analytical tools will offer administrators of
healthcare companies helpful quantitative support. The design and technical specifications of the
system implementations have been described by the authors utilizing case studies (
Al-Khatib,
2022). They have created a toolbox that serves as a framework for resource management,
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enabling the creation of strategic models and the acquisition of analytical data for fact-based
judgments and managerial assessments. It is crucial to optimize supply chain operations to keep healthcare costs low. Medical device and
equipment data can be effectively used for forecasting, decision-making, and improving the
efficiency of healthcare supply chain management. Hence, the research conducted by Alotaibi et
al. (2020) provides an overview of the use of big data in healthcare organizations, highlighting
the potential and difficulties associated with the implementation of BDA-based management
systems inside the companies (
Abu Afifa, and Nguyen, 2022). As previously stated, successful
implementation of BDA in the healthcare organization will be essential to enhancing clinical
outcomes management, providing useful information for managers and decision-makers to help
prevent illnesses, lower healthcare costs, and enhance the efficiency of the healthcare
organization. The research will need to find a way to rationalize heterogeneous data from various
sources to make it simple to use and affordable to attain these ambitious goals (
Liu et al.,
2022).
The study conducted by Kundella and Gobinath (2019) makes a significant contribution to the
study of the major issues, solutions, privacy concerns, security algorithms, and future directions
of BDA applications in the healthcare industry. BDA and management of health surveillance system
The emergence of BDA holds great promise for addressing numerous healthcare issues in poor
nations. The BDA used in healthcare organizations aids management in rationalizing resource
allocation and improving the quality of patient care. In this sense, the Zambian government is
considering implementing BDA solutions to deliver healthcare services that are both more
effective and efficient. In developing nations, where severe resource shortages impede economic
growth, a well-managed health surveillance system is a key factor in raising living standards and
reducing medical waste (
Jain et al.,
2022). In order to produce new knowledge, enhance clinical
treatment, and increase the effectiveness of the management of the public health surveillance
system, Europe is investing in BDA efforts in the public health and cancer sectors. The ability of
BDA to recognize certain population patterns, manage large amounts of data, and transform them
into real-time insights helps to establish it as a potent tool to assist managers in their decision-
making. Despite this, adopting a BDA-based management system within a healthcare
organization necessitates a financial investment in human resources, strong stakeholder
participation, and data connectivity within and across the hospital units (
Bentahar et al.,
2023).
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In support of this, Gunapal et al. (2016) emphasized Singapore's establishment of a Regional
Health System (RHS) database to promote BDA for proactive population health management
(PHM) and health services research. The Healthcare Database Structure (AH) was created for
three RHSs: National Healthcare Group (NHG), Tan Tock Seng Hospital (TTSH), National
University Hospital (NUH), and Alexandra Hospital. A database with information on patient
demographics, chronic illnesses, and healthcare utilization is the ultimate product. Healthcare
administrators need to use this database. These traits make it easier to link prior healthcare usage
and information on chronic diseases to the paths taken by particular patients (
TORUN, 2022).
Understanding the cross-utilization of healthcare services among the three RHSs is made easier
with the consolidation of data into a single database. Such a strategy enables the establishment of
the RHS's structure for proactive population health management (PHM) and enhances the
functionality of healthcare organizations. BDA technology for the healthcare organization
The foundation of customized medicine will be formed by wearable technology and various
types of sensors that can gather clinical data in conjunction with Big Data Analytics. These
technologies will also be essential instruments for enhancing the efficiency of healthcare
organizations. The significant difficulty for scientific research is modifying data collection,
storage, transmission, and analytics to meet healthcare needs (
AL-Khatib, and Shuhaiber, 2022).
Nonetheless, the organization's needs should be taken into account while categorizing,
homogenizing, and implementing the healthcare data into specific models using machine
learning. Diagnostic imaging is a lucrative area of interest for the application of BDA in healthcare
organizations. Digital platforms and applications must be used to reap the greatest benefits from
them and to be helpful to the management of healthcare companies. Simply producing a lot of
data does not guarantee that the performance of the healthcare industry will improve. To
facilitate the proper and advantageous administration of diagnostic images, specific apps are
needed (
AL-Khatib, 2023). Another area of research that was looked into by the articles included
in this RA was the relationship between BDA and IoT technologies as a tool to incorporate the
usability, adaptability, and accessibility of clinical data. These tools enable healthcare
organizations to reduce costs, people to self-regulate their treatments, and practitioners to make
judgments as rapidly as possible while working remotely and in continual communication with
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their patients. These findings allow us to conclude that the Internet of Things (IoT), big data, and
artificial intelligence (AI) in the form of machine-learning algorithms are three of the most
significant developments in the healthcare sector (
Cozzoli et al.,
2022). These companies are
putting intelligent BDA systems based on machine learning into place, as well as home-centric
data collection networks. For instance, these systems have been successfully implemented at a
high level for hypertensive patients in Cartagena, Colombia, using an e-Health sensor and
Amazon Web Services components. The authors emphasize the significance of using IoT, big
data, and artificial intelligence as tools to improve community health outcomes and healthcare
organization performance. The efficacy of public health interventions can be increased by using
the standardized data sets produced by these sources by the new generation of machine learning
algorithms (Naz et al.,
2022). Due to the importance of fully standardized dataset protocols, as
demonstrated by multiple studies in the field of BDA applied to healthcare organizations, it is
imperative that the next research focus its R&D efforts in this direction. Discussion
Hospital environmental performance and the integration of green supply chain activities can both
be significantly impacted by the application of big data analytics and AI. With the help of these
technologies, healthcare organizations can gather, handle, and analyze massive volumes of data,
giving them information about their operations and potential areas for development (Naz et al
.,
2021). They can enhance patient outcomes, cut costs, and improve the health of the environment
by incorporating green practices into the supply chain and enhancing the environmental
performance of hospitals. To lower their carbon footprint and lessen the effects of climate
change, healthcare institutions must integrate green concepts into their supply chains. AI
algorithms can be used to streamline travel plans, save on gasoline, and cut back on CO2
emissions (Manavalan and Jayakrishna, 2019). Healthcare businesses may create the most
efficient routes to cut down on fuel use and carbon footprint by examining delivery frequency,
distance, and weight data. For additional route optimization, these algorithms can take into
account additional variables like traffic and weather. Big data analytics can be employed to
monitor and improve resource consumption, lower waste production, and lessen the
environmental effect (Ramirez-Peña et al
., 2020). Healthcare organizations can find areas for
improvement and conduct focused interventions by evaluating resource consumption statistics.
For instance, analysis of water consumption data can reveal regions of excessive usage, allowing
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for targeted interventions like the replacement of inefficient equipment or the introduction of
water-saving techniques. Similarly to this, energy consumption data can be examined to find
areas that can be improved, such as optimizing the HV AC system or changing out inefficient
lighting fixtures (Naz et al
., 2021).
Healthcare businesses must also consider a hospital's environmental performance to lessen their
carbon footprint and enhance patient outcomes. This is in addition to the supply chain. AI
algorithms can be applied to hospitals to improve patient flow, shorten wait times, and utilize
less non-renewable resources like paper and plastic. Healthcare institutions can spot bottlenecks
and take targeted action to enhance patient flow and cut down on wait times by evaluating
patient flow data (Ebinger and Omondi, 2020). Through process simplification and a decrease in
the requirement for paper-based forms and documentation, these interventions can help lower the
use of non-renewable resources like paper and plastic. Big data analytics is crucial for
monitoring and optimizing energy use, cutting expenses, and enhancing environmental
performance. Healthcare businesses can find areas for improvement and take specific action by
examining data on energy consumption. For instance, analysis of energy consumption data can
reveal regions of excessive usage and result in targeted actions like HVAC system optimization
or the replacement of old lighting fixtures with more energy-efficient ones. Healthcare firms may
enhance their environmental performance, cut costs, and boost their bottom line by consuming
less energy. Another important element of integrating green processes into hospital supply chains
and environmental performance is predictive analytics. Using AI algorithms, predictive analytics
creates models that help businesses reduce waste production, increase resource efficiency, and
lower their carbon impact. Predictive analytics, for instance, can be used to improve travel
routes, save fuel costs, and cut back on CO2 emissions (Ramirez-Peña et al
., 2020). Predictive
models can create the most efficient routes with the lowest fuel consumption and lowest CO2
emissions by accessing data on transport frequency, distance, and weight. To further improve
transportation routes, these models can also consider additional variables like traffic and weather.
Moreover, predictive analytics can be used to enhance cost savings, environmental performance,
and energy consumption (Bag et al
., 2021). Predictive models can develop optimized energy
usage plans with the lowest costs and the least negative environmental impact by analyzing
energy consumption data and identifying areas for improvement. Predictive models, for instance,
can pinpoint periods of high energy consumption and design optimal energy usage plans that
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save money and have a smaller negative impact on the environment. Healthcare firms can
enhance their environmental performance, cut costs, and boost their bottom line by reducing
peak energy usage (Ramirez-Peña et al
., 2020).
Summary Future research should offer practical knowledge and established practices for educating
healthcare management and practitioners to meet these goals. In healthcare organizations,
managerial techniques, AI, and machine learning will all contribute as knowledge producers.
Healthcare data privacy concerns, as well as the requirement to homogenize sensor data, are now
pressing research issues that must be addressed. Furthermore, given the diversity of information
sources, future research should focus on integrating protocol standardization into data analysis as
well as managerial sector strategies for implementing more BDA-based management systems in
future healthcare organizations. 15 | P a g e
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