Introduction: Artificial intelligence in healthcare business process improvement
Artificial intelligence in healthcare business process improvement, has a huge role to play in global healthcare. Almost 50% of the global population doesn’t have access to key health services. Mainly driven by poverty, those who can’t cover the out-of-pocket costs, end up avoiding treatment for as long as possible.
UNCTAD statistics show that to keep up with sustainable development goals, more than $140 billion should be spent yearly between 2015 to 2030. Investing in artificial intelligence is expected to bridge that unaffordable health service gap to reach underserved patients. That leads to one question. What is the role of artificial intelligence in healthcare business process improvement? Here is my take on emerging markets.
Current Healthcare State and AI Impact
In 2017, the number of uninsured Americans skyrocketed for three whole years. It went from 26.7 million (2016) to 28.9 million (2019), increasing by 2.2 million. That is over a 10% growth. In many countries, healthcare is very expensive.
Improving access to healthcare is crucial to help everyone in our society become productive members of our community. Technology has always been here to provide better delivery of healthcare.
The first case of AI in this field was recorded in the 90s when machine learning (ML) became a key component in medical imaging and identifying cancers. The better it became, the more improvements it provided, enabling proliferation, analyzing, and tracking of massive amounts of data. So, what is it that artificial intelligence can offer in terms of business process improvement?
Also Read: Artificial Intelligence in Healthcare.
The Role of Artificial Intelligence in Healthcare Business Process Improvement
The confluence of AI and other digital tech has massive potential. The role of artificial intelligence in healthcare business process improvement can be seen in multiple segments. For example, assessing and aggregating patient data.
This can be used to boost risk analytics, imaging solutions, and ML platforms. But, most importantly, it can decrease unit costs of administration. All of that can be achieved with the help of automated scheduling. When physicians don’t have to pay that much attention to making notes or doing administrative tasks, they can free up some valuable time to care for patients.
Marketing experts believe that taking care of mundane medical records is costing $90-140 billion in doctor time annually. That’s a lot of money saved over something that’s done on a regular basis. But, there is more.
ML technology can hold a significant share of AI in the healthcare market. The demand to decrease healthcare costs and provide better data is driving the growth of this market.
Because of COVID-19, some AI applications were pushed ahead to accommodate the emerging markets. Take vaccine and drug research, for example. Then, we also have a surveillance and patient tracking system. These might all come in handy for predicting serious COVID cases.
Also Read: How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?
Three phases of scaling AI in healthcare
In the first phase of scaling AI in healthcare, the focus is primarily on laying the groundwork for integrating AI technologies. This includes conducting thorough research and development to understand the specific needs and challenges within the healthcare sector that AI can address. During this initial phase, healthcare organizations need to invest in collecting high-quality data, which forms the backbone of AI applications. There must be an emphasis on building partnerships with technology providers and ensuring compliance with regulatory requirements. It’s essential to identify the right AI tools and algorithms that can provide value in areas such as diagnostics, patient care, and administrative tasks. Importantly, healthcare professionals need to be educated and trained on the potential applications of AI, and the infrastructure should be updated to support AI integration.
As healthcare organizations move into the second phase, the emphasis shifts to implementation and deployment. In this phase, AI solutions that have been researched and developed are integrated into real-world healthcare settings. The implementation process often involves deploying pilot programs to test the efficacy and accuracy of AI systems. By starting with smaller-scale implementations, healthcare providers can gather critical feedback and data which can be used to optimize AI systems. During this phase, it’s crucial to monitor the performance of AI technologies closely and ensure that they are contributing positively to patient outcomes and operational efficiency. Communication and collaboration among multidisciplinary teams, including medical professionals, data scientists, and regulatory experts, are crucial during this phase to ensure that AI systems are tuned to the needs of both patients and healthcare providers.
The third phase involves scaling and optimization. Once AI systems have been successfully implemented and shown to be effective, healthcare organizations can begin to scale these solutions across various departments or locations. In this phase, continuous monitoring and optimization are key. Healthcare organizations need to continually collect data and feedback to refine AI algorithms and tools, ensuring they remain effective as they are scaled. There is also an increasing need for interoperability, as AI systems should work seamlessly with other digital health systems. As AI becomes more embedded in healthcare processes, there must be ongoing evaluations regarding ethical considerations, ensuring that AI systems are used in ways that are transparent, fair, and respect patient privacy. This phase should also include ongoing education and training programs for healthcare professionals to keep up with the evolving AI landscape.
How Companies Are Adopting AI Technology
Artificial intelligence adoption is prominent in sectors that are already profound digital adopters. Financial services, automotive, and high-tech industries are highly relying on AI technology. Unfortunately, health care has one of the lowest AI use.
The problem is, not many healthcare providers are truly ready to embrace an AI-driven workplace.
I think it has a lot to do with digital maturity. We can see that with the tourism and education sector. Media, retail and CPG, have already made some efforts to adopt some level of AI technology.
But, for an industry to thrive, particularly the healthcare sector, they should be working to boost their interoperability and access to data. Early AI usage can provide real value and could become a key force for distribution. For many, it is a crucial investment.
Here, I will delve a little deeper into how to make that happen.
Growing number of use cases
In recent years, there has been a surge in the utilization of artificial intelligence (AI) for improving business processes in healthcare. One such growing use case is in revenue cycle management (RCM). RCM involves the financial processes that healthcare facilities use to manage the administrative and clinical functions associated with claims processing, payment, and revenue generation. AI can streamline the RCM process by automatically verifying patient eligibility, optimizing claim submissions, and reducing billing errors. Predictive analytics, a subset of AI, can also be used to forecast payment delays or denials, allowing healthcare facilities to take proactive steps in mitigating such issues. By enhancing the efficiency and accuracy of RCM, AI aids in optimizing revenue flows and decreasing administrative costs for healthcare organizations.
Another rapidly expanding application of AI in healthcare business process improvement is patient scheduling and resource allocation. Optimizing patient scheduling is critical for maximizing the utilization of healthcare resources and improving patient satisfaction. AI algorithms can analyze historical data, such as patient no-shows and cancellations, to make predictions and optimize scheduling. AI can facilitate more efficient allocation of resources such as hospital beds, operating rooms, and medical staff by analyzing patterns in patient flow and resource demands. By ensuring that resources are allocated efficiently, AI can significantly reduce patient waiting times and improve the overall quality of care.
AI is revolutionizing supply chain management within the healthcare sector. Ensuring an efficient supply chain is essential for healthcare organizations to deliver timely and effective care. AI can be utilized for predictive analytics to anticipate the demand for medical supplies, and optimize inventory levels, thereby reducing costs associated with overstocking or stock-outs. AI-powered chatbots and automated communication systems can streamline the procurement process, by facilitating seamless communication with suppliers, and automating order placements and tracking. In essence, AI contributes to creating a more responsive and efficient supply chain, which is crucial, especially in times of increased demand, such as during a pandemic or natural disaster.
How will AI change the healthcare workforce?
The integration of AI into healthcare holds the potential to dramatically improve various processes within the sector, resulting in a more streamlined and efficient workflow for healthcare professionals. For example, AI systems can greatly enhance the process of patient triage by accurately assessing patient conditions and prioritizing cases based on severity. This ensures that healthcare professionals can focus their attention on critical cases without being bogged down by administrative decision-making. The automation of mundane tasks, such as appointment scheduling, follow-up reminders, and record-keeping, also allows healthcare workers to concentrate on patient care, leading to increased efficiency and potentially reducing the chances of burnout.
AI can play a crucial role in optimizing clinical decision-making processes. AI-driven tools and algorithms can support healthcare professionals in diagnosing illnesses, recommending treatments, and predicting patient outcomes. By arming the workforce with comprehensive, data-driven insights, healthcare professionals can make more informed and accurate decisions. This not only improves patient outcomes but also enhances resource allocation within healthcare facilities. With AI taking over repetitive and data-intensive tasks, healthcare professionals can participate in continuous learning and development programs to sharpen their analytical and decision-making skills. Consequently, the workforce becomes more adept at utilizing AI tools for process improvement, while also contributing to a cycle of innovation and efficiency within healthcare systems.
What this could mean for healthcare organizations
Business process improvements in healthcare organizations have the potential to yield a wide range of benefits, from operational efficiency to enhanced patient care and financial savings. By refining workflows, eliminating bottlenecks, and using resources effectively, the main objective is to streamline processes.
One of the notable impacts of process improvement in healthcare is the enhancement of patient experiences. Streamlined appointment scheduling, reduced waiting times, and improved communication channels contribute to a more pleasant interaction for patients with healthcare providers. This can lead to increased patient satisfaction and loyalty, essential components in the competitive healthcare industry.
Optimizing administrative processes like billing, coding, and claims processing reduces errors and speeds up these tasks. Timely and accurate billing directly affects the revenue cycle and minimizes financial losses attributable to billing mistakes or delays. Automating mundane administrative tasks also liberates staff time, enabling healthcare professionals to concentrate on activities that add more value.
Clinical process improvements have a profound effect on patient outcomes. Implementing evidence-based care protocols, employing clinical decision support systems, and organizing care coordination are some avenues for improvement. These measures facilitate more informed clinical decisions and can curtail unnecessary or duplicate testing and treatment.
Efficient supply chain processes ensure the availability of vital medical supplies and medications, managed in a cost-effective manner. In times of crisis such as a pandemic, an efficient supply chain is instrumental in a healthcare organization’s ability to deliver care.
Business process improvements in healthcare contribute to a more nimble and adaptive organization. Not only does this lead to improved patient care, but it also allows healthcare organizations to be more amenable to changes or challenges in the healthcare environment. Through continuous evaluation and enhancement of processes, healthcare organizations can cultivate a culture of excellence and innovation.
Healthcare business process management
Let’s delve into certain critical business processes within the healthcare industry to understand the sheer volume of information that organizations need to manage:
Emergency room operations
As a patient arrives in the ER, registration and admission processes kick off. In emergency cases, obtaining a comprehensive patient history may not always be possible, leaving gaps to be completed later. Subsequently, tracking ER doctors, involved specialists, and medications administered becomes essential. Each stage calls for diligent documentation, ensuring data is available for immediate and accurate use.
Drug supply chain
The drug supply chain in a hospital, which is an intricate task, engulfed with substantial paperwork. Records have to be meticulously maintained, encompassing stock levels, daily consumption, and future necessities. It’s imperative that such records are easily accessible to the relevant staff members.
Infection control
Infection control is a process that demands unwavering attention, especially in light of recent events such as the coronavirus outbreak. The margin for error is nonexistent here as mistakes can cause infected individuals to come in contact with those who are not infected, exacerbating the situation.
AI and modern technology has enabled the digitization and automation of many such processes. However, there is significant room for further improvement in data utilization, and artificial intelligence is a key catalyst in this transformation.
How to Make the Most of AI for Healthcare Business?
To achieve true scale in the use of artificial intelligence, hospitals might have to establish trust with their patients. By first establishing a framework that takes care of patient data, algorithms that help with monitoring, governing, and more.
Many companies are already investing in AI for their healthcare business. The number of health-related technology deals and funding went up in the last decade.
It reached 800 deals and $15 billion in funding. However, in 2019, that interest slightly fell, only to grow yet again in 2021. By 2025, AI health in China is estimated to experience an annual growth rate of 52.8%.
The same forecast period is projected to provide growth in the diagnostics and medical imaging segment. AI healthcare can contribute to numerous factors by providing a volume of imaging data for various uses. Due to the shortfall of medical staff from the corona outbreak, AI tech can surely prove useful.
Once you take a look at these statistics, it is easy to see where those benefits are coming from. Overall, corporations and major players in the industry believe that AI has potential. In terms of business process and improvement, there is little that this technology can’t offer.
For a company that is constantly engaged with the use of Artificial intelligence in their industry, they can fortify its capabilities and provide top-quality care. Most importantly, they can aim to extend and improve their services. Something like this is always considered valuable within the healthcare field.
Also Read: Covid-19 – India’s Second Wave – When will it peak? A Predictive analysis.
Conclusion
Countless people can’t afford proper healthcare. All those expensive out-of-pocket costs are making it difficult to cover the expenses. But, with AI technology, it is possible to revamp these services and provide more affordable treatment.
At the moment, many companies are already using artificial intelligence and machine learning. Because of its significant potential, it is easy to see why. When you think about the way AI can provide better business improvement for medical uses, it too has its positive sides. By taking some of the workloads of the physician’s shoulders, it is easier to focus on boosting patient care. All in all, healthcare facilities can use AI to build trust, productivity, efficiency, and convenience.
References
Bohr, Adam, and Kaveh Memarzadeh. Artificial Intelligence in Healthcare. Academic Press, 2020.
Panesar, Arjun. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Apress, 2019.
Dey, Nilanjan, et al. Big Data Analytics for Intelligent Healthcare Management. Academic Press, 2019.
Introduction: Artificial intelligence in healthcare business process improvement
Artificial intelligence in healthcare business process improvement, has a huge role to play in global healthcare. Almost 50% of the global population doesn’t have access to key health services. Mainly driven by poverty, those who can’t cover the out-of-pocket costs, end up avoiding treatment for as long as possible.
UNCTAD statistics show that to keep up with sustainable development goals, more than $140 billion should be spent yearly between 2015 to 2030. Investing in artificial intelligence is expected to bridge that unaffordable health service gap to reach underserved patients. That leads to one question. What is the role of artificial intelligence in healthcare business process improvement? Here is my take on emerging markets.
Table of contents
Current Healthcare State and AI Impact
In 2017, the number of uninsured Americans skyrocketed for three whole years. It went from 26.7 million (2016) to 28.9 million (2019), increasing by 2.2 million. That is over a 10% growth. In many countries, healthcare is very expensive.
Improving access to healthcare is crucial to help everyone in our society become productive members of our community. Technology has always been here to provide better delivery of healthcare.
The first case of AI in this field was recorded in the 90s when machine learning (ML) became a key component in medical imaging and identifying cancers. The better it became, the more improvements it provided, enabling proliferation, analyzing, and tracking of massive amounts of data. So, what is it that artificial intelligence can offer in terms of business process improvement?
Also Read: Artificial Intelligence in Healthcare.
The Role of Artificial Intelligence in Healthcare Business Process Improvement
The confluence of AI and other digital tech has massive potential. The role of artificial intelligence in healthcare business process improvement can be seen in multiple segments. For example, assessing and aggregating patient data.
This can be used to boost risk analytics, imaging solutions, and ML platforms. But, most importantly, it can decrease unit costs of administration. All of that can be achieved with the help of automated scheduling. When physicians don’t have to pay that much attention to making notes or doing administrative tasks, they can free up some valuable time to care for patients.
Marketing experts believe that taking care of mundane medical records is costing $90-140 billion in doctor time annually. That’s a lot of money saved over something that’s done on a regular basis. But, there is more.
ML technology can hold a significant share of AI in the healthcare market. The demand to decrease healthcare costs and provide better data is driving the growth of this market.
Because of COVID-19, some AI applications were pushed ahead to accommodate the emerging markets. Take vaccine and drug research, for example. Then, we also have a surveillance and patient tracking system. These might all come in handy for predicting serious COVID cases.
Also Read: How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?
Three phases of scaling AI in healthcare
In the first phase of scaling AI in healthcare, the focus is primarily on laying the groundwork for integrating AI technologies. This includes conducting thorough research and development to understand the specific needs and challenges within the healthcare sector that AI can address. During this initial phase, healthcare organizations need to invest in collecting high-quality data, which forms the backbone of AI applications. There must be an emphasis on building partnerships with technology providers and ensuring compliance with regulatory requirements. It’s essential to identify the right AI tools and algorithms that can provide value in areas such as diagnostics, patient care, and administrative tasks. Importantly, healthcare professionals need to be educated and trained on the potential applications of AI, and the infrastructure should be updated to support AI integration.
As healthcare organizations move into the second phase, the emphasis shifts to implementation and deployment. In this phase, AI solutions that have been researched and developed are integrated into real-world healthcare settings. The implementation process often involves deploying pilot programs to test the efficacy and accuracy of AI systems. By starting with smaller-scale implementations, healthcare providers can gather critical feedback and data which can be used to optimize AI systems. During this phase, it’s crucial to monitor the performance of AI technologies closely and ensure that they are contributing positively to patient outcomes and operational efficiency. Communication and collaboration among multidisciplinary teams, including medical professionals, data scientists, and regulatory experts, are crucial during this phase to ensure that AI systems are tuned to the needs of both patients and healthcare providers.
The third phase involves scaling and optimization. Once AI systems have been successfully implemented and shown to be effective, healthcare organizations can begin to scale these solutions across various departments or locations. In this phase, continuous monitoring and optimization are key. Healthcare organizations need to continually collect data and feedback to refine AI algorithms and tools, ensuring they remain effective as they are scaled. There is also an increasing need for interoperability, as AI systems should work seamlessly with other digital health systems. As AI becomes more embedded in healthcare processes, there must be ongoing evaluations regarding ethical considerations, ensuring that AI systems are used in ways that are transparent, fair, and respect patient privacy. This phase should also include ongoing education and training programs for healthcare professionals to keep up with the evolving AI landscape.
How Companies Are Adopting AI Technology
Artificial intelligence adoption is prominent in sectors that are already profound digital adopters. Financial services, automotive, and high-tech industries are highly relying on AI technology. Unfortunately, health care has one of the lowest AI use.
The problem is, not many healthcare providers are truly ready to embrace an AI-driven workplace.
I think it has a lot to do with digital maturity. We can see that with the tourism and education sector. Media, retail and CPG, have already made some efforts to adopt some level of AI technology.
But, for an industry to thrive, particularly the healthcare sector, they should be working to boost their interoperability and access to data. Early AI usage can provide real value and could become a key force for distribution. For many, it is a crucial investment.
Here, I will delve a little deeper into how to make that happen.
Growing number of use cases
In recent years, there has been a surge in the utilization of artificial intelligence (AI) for improving business processes in healthcare. One such growing use case is in revenue cycle management (RCM). RCM involves the financial processes that healthcare facilities use to manage the administrative and clinical functions associated with claims processing, payment, and revenue generation. AI can streamline the RCM process by automatically verifying patient eligibility, optimizing claim submissions, and reducing billing errors. Predictive analytics, a subset of AI, can also be used to forecast payment delays or denials, allowing healthcare facilities to take proactive steps in mitigating such issues. By enhancing the efficiency and accuracy of RCM, AI aids in optimizing revenue flows and decreasing administrative costs for healthcare organizations.
Another rapidly expanding application of AI in healthcare business process improvement is patient scheduling and resource allocation. Optimizing patient scheduling is critical for maximizing the utilization of healthcare resources and improving patient satisfaction. AI algorithms can analyze historical data, such as patient no-shows and cancellations, to make predictions and optimize scheduling. AI can facilitate more efficient allocation of resources such as hospital beds, operating rooms, and medical staff by analyzing patterns in patient flow and resource demands. By ensuring that resources are allocated efficiently, AI can significantly reduce patient waiting times and improve the overall quality of care.
AI is revolutionizing supply chain management within the healthcare sector. Ensuring an efficient supply chain is essential for healthcare organizations to deliver timely and effective care. AI can be utilized for predictive analytics to anticipate the demand for medical supplies, and optimize inventory levels, thereby reducing costs associated with overstocking or stock-outs. AI-powered chatbots and automated communication systems can streamline the procurement process, by facilitating seamless communication with suppliers, and automating order placements and tracking. In essence, AI contributes to creating a more responsive and efficient supply chain, which is crucial, especially in times of increased demand, such as during a pandemic or natural disaster.
How will AI change the healthcare workforce?
The integration of AI into healthcare holds the potential to dramatically improve various processes within the sector, resulting in a more streamlined and efficient workflow for healthcare professionals. For example, AI systems can greatly enhance the process of patient triage by accurately assessing patient conditions and prioritizing cases based on severity. This ensures that healthcare professionals can focus their attention on critical cases without being bogged down by administrative decision-making. The automation of mundane tasks, such as appointment scheduling, follow-up reminders, and record-keeping, also allows healthcare workers to concentrate on patient care, leading to increased efficiency and potentially reducing the chances of burnout.
AI can play a crucial role in optimizing clinical decision-making processes. AI-driven tools and algorithms can support healthcare professionals in diagnosing illnesses, recommending treatments, and predicting patient outcomes. By arming the workforce with comprehensive, data-driven insights, healthcare professionals can make more informed and accurate decisions. This not only improves patient outcomes but also enhances resource allocation within healthcare facilities. With AI taking over repetitive and data-intensive tasks, healthcare professionals can participate in continuous learning and development programs to sharpen their analytical and decision-making skills. Consequently, the workforce becomes more adept at utilizing AI tools for process improvement, while also contributing to a cycle of innovation and efficiency within healthcare systems.
What this could mean for healthcare organizations
Business process improvements in healthcare organizations have the potential to yield a wide range of benefits, from operational efficiency to enhanced patient care and financial savings. By refining workflows, eliminating bottlenecks, and using resources effectively, the main objective is to streamline processes.
One of the notable impacts of process improvement in healthcare is the enhancement of patient experiences. Streamlined appointment scheduling, reduced waiting times, and improved communication channels contribute to a more pleasant interaction for patients with healthcare providers. This can lead to increased patient satisfaction and loyalty, essential components in the competitive healthcare industry.
Optimizing administrative processes like billing, coding, and claims processing reduces errors and speeds up these tasks. Timely and accurate billing directly affects the revenue cycle and minimizes financial losses attributable to billing mistakes or delays. Automating mundane administrative tasks also liberates staff time, enabling healthcare professionals to concentrate on activities that add more value.
Clinical process improvements have a profound effect on patient outcomes. Implementing evidence-based care protocols, employing clinical decision support systems, and organizing care coordination are some avenues for improvement. These measures facilitate more informed clinical decisions and can curtail unnecessary or duplicate testing and treatment.
Efficient supply chain processes ensure the availability of vital medical supplies and medications, managed in a cost-effective manner. In times of crisis such as a pandemic, an efficient supply chain is instrumental in a healthcare organization’s ability to deliver care.
Business process improvements in healthcare contribute to a more nimble and adaptive organization. Not only does this lead to improved patient care, but it also allows healthcare organizations to be more amenable to changes or challenges in the healthcare environment. Through continuous evaluation and enhancement of processes, healthcare organizations can cultivate a culture of excellence and innovation.
Healthcare business process management
Let’s delve into certain critical business processes within the healthcare industry to understand the sheer volume of information that organizations need to manage:
Emergency room operations
As a patient arrives in the ER, registration and admission processes kick off. In emergency cases, obtaining a comprehensive patient history may not always be possible, leaving gaps to be completed later. Subsequently, tracking ER doctors, involved specialists, and medications administered becomes essential. Each stage calls for diligent documentation, ensuring data is available for immediate and accurate use.
Drug supply chain
The drug supply chain in a hospital, which is an intricate task, engulfed with substantial paperwork. Records have to be meticulously maintained, encompassing stock levels, daily consumption, and future necessities. It’s imperative that such records are easily accessible to the relevant staff members.
Infection control
Infection control is a process that demands unwavering attention, especially in light of recent events such as the coronavirus outbreak. The margin for error is nonexistent here as mistakes can cause infected individuals to come in contact with those who are not infected, exacerbating the situation.
AI and modern technology has enabled the digitization and automation of many such processes. However, there is significant room for further improvement in data utilization, and artificial intelligence is a key catalyst in this transformation.
How to Make the Most of AI for Healthcare Business?
To achieve true scale in the use of artificial intelligence, hospitals might have to establish trust with their patients. By first establishing a framework that takes care of patient data, algorithms that help with monitoring, governing, and more.
Many companies are already investing in AI for their healthcare business. The number of health-related technology deals and funding went up in the last decade.
It reached 800 deals and $15 billion in funding. However, in 2019, that interest slightly fell, only to grow yet again in 2021. By 2025, AI health in China is estimated to experience an annual growth rate of 52.8%.
The same forecast period is projected to provide growth in the diagnostics and medical imaging segment. AI healthcare can contribute to numerous factors by providing a volume of imaging data for various uses. Due to the shortfall of medical staff from the corona outbreak, AI tech can surely prove useful.
Once you take a look at these statistics, it is easy to see where those benefits are coming from. Overall, corporations and major players in the industry believe that AI has potential. In terms of business process and improvement, there is little that this technology can’t offer.
For a company that is constantly engaged with the use of Artificial intelligence in their industry, they can fortify its capabilities and provide top-quality care. Most importantly, they can aim to extend and improve their services. Something like this is always considered valuable within the healthcare field.
Also Read: Covid-19 – India’s Second Wave – When will it peak? A Predictive analysis.
Conclusion
Countless people can’t afford proper healthcare. All those expensive out-of-pocket costs are making it difficult to cover the expenses. But, with AI technology, it is possible to revamp these services and provide more affordable treatment.
At the moment, many companies are already using artificial intelligence and machine learning. Because of its significant potential, it is easy to see why. When you think about the way AI can provide better business improvement for medical uses, it too has its positive sides. By taking some of the workloads of the physician’s shoulders, it is easier to focus on boosting patient care. All in all, healthcare facilities can use AI to build trust, productivity, efficiency, and convenience.
References
Bohr, Adam, and Kaveh Memarzadeh. Artificial Intelligence in Healthcare. Academic Press, 2020.
Panesar, Arjun. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Apress, 2019.
Dey, Nilanjan, et al. Big Data Analytics for Intelligent Healthcare Management. Academic Press, 2019.
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