Article | April 21, 2020
While artificial intelligence (AI) offers numerous advantages across a wide range of businesses and applications, an ongoing report spreads out some convincing focuses on the different difficulties and perils of using AI in the social insurance segment. As of late, AI has been progressively consolidated all through the medicinal services space. Machines would now be able to give emotional wellness help by means of a chatbot, screen tolerant wellbeing, and even anticipate heart failure, seizures, or sepsis.
Article | April 21, 2020
As medical science has improved rapidly, life expectancy around the world also has risen. Still, as longevity increases, healthcare systems are facing a growing demand for advanced services, increased costs, and a struggling workforce to meet various requirements of patients. Demand is driven by many unstoppable forces: a shift in lifestyle choices, shifting patient expectations, population aging, and the never-ending cycle of innovation are a few among others.
Challenges Faced by the Healthcare Industry
According to Mckinsey, one in four people in North America and Europe will be over the age of 65 by 2050. This shows that, soon, the healthcare industry will have to deal with a larger number of patients with more complex requirements. Catering to these patients is expensive and requires health systems for providing long-term focused and proactive care. To remain sustainable, healthcare systems need major transformational and structural changes.
The industry also needs a larger workforce because according to the World Health Organization (WHO), globally there is a shortfall of approximately 9.9 million nurses, physicians, and midwives. Apart from attracting, training, and retaining these healthcare professionals, you also have to ensure that their time and effort add value to patient care. Utilizing the solutions powered by modern technologies, such as Artificial Intelligence (AI) in the healthcare industry, will add perfection and more value to human efforts.
AI in the healthcare industry has the potential required to transform and revolutionize healthcare by addressing the challenges in the industry mentioned earlier. AI can better the outcomes, improve efficiency, and augment productivity in healthcare delivery. This article takes an in-depth look at the impact of AI in healthcare.
Impact of AI in the Healthcare Industry
In the coming years, AI in the healthcare industry will improve the day-to-day life of healthcare practitioners, augment the patient experience, improve care delivery, and can even facilitate life-saving treatments and revolutionize the industry. Additionally, AI will improve population-health management, operations, and strengthen innovations.
According to Statista, the global AI healthcare market will increase to more than US$28 billion by 2025. Here is a detailed look into the areas where and how AI in the healthcare industry will be impactful.
Chronic Care Management
Chronic diseases, such as cancer, diabetes, kidney diseases, are the leading cause of disability and death in the US and the main drivers of the country’s annual health cost. Effectively managing various chronic diseases is an overarching and long-term process. But with the help of the right tool, healthcare providers can meet the needs of these patients without delay.
Artificial intelligence tools in the healthcare industry can help healthcare providers overcome the complexities of chronic disease management and make it more effective and provide quality treatment. AI in the healthcare industry is increasingly being leveraged by organizations to improve chronic disease management, enhance patient health, and drive down costs, which will also eventually result in data-driven and personalized care. AI in the healthcare industry is expected to move the industry toward proactive care delivery from a reactive one and lead the industry to provide more individualized treatments. This is just one of the ways AI in the medical industry is going to revolutionize chronic care management in hospitals.
Artificial intelligence in the healthcare industry is changing the way care is delivered; it is expected to make healthcare more efficient, accurate, and accessible. Reducing costs and improving health outcomes are the values health systems and hospitals are trying to deliver to patients every day. Hospitals are increasingly incorporating technologies, which are powered by the use of AI in healthcare to meet the challenge.
According to the American Hospital Association (AHA), AI in the healthcare industry has unlimited potential to solve most of the vexing challenges in the industry. They identify AI use cases in the healthcare industry in four broad areas, which are administrative, operational, financial, and clinical areas.
Administrative Use Cases for AI in the Healthcare Industry
• Admission procedures
• Appointment scheduling
• Customer service responses
• Discharge instructions
• Hiring and orientation protocols
• Licensure verification
• Patient check-in procedure
• Prior authorizations
• Quality measure reporting
Operational Use Cases of AI in the Healthcare Industry
• Inventory management
• Materials management
• Supply chain management
• Facilities management
Financial Use Cases for AI in the Healthcare Industry
• Billing and collections
• Claims management
• Insurance eligibility verification
• Revenue cycle management
Clinical Use Cases of AI in the Healthcare Industry
• Predictive technologies
• Interventional technologies
By incorporating and utilizing these scopes with AI in the healthcare industry, the industry can be transformed into a next-gen level in no time. It also allows healthcare practitioners to focus more on patients, which would eventually help in raising staff morale and improving retention.
Clinical Decision Support
Recent advancements in AI in the health industry are capable of enhancing the currently used clinical decision support (CDS) tools to have value-based imaging and to improve patient safety. According to the National Institute of Health (NIH), the synergy between CDS systems and AI in the healthcare industry will be able to:
• Reduce friction in radiology workflows
• Identify relevant imaging features easily
• Generate structured data to develop machine learning algorithms
• Enable an evolution toward decision support for a holistic patient perspective
• Suggest imaging examinations in complex clinical scenarios
• Assist in identifying appropriate imaging opportunities
• Suggest appropriate individualized screening
• Aid health practitioners to ensure continuity of care
AI in the healthcare industry is competent in making CDS a next-gen one, enhancing the experiences of radiologists and providers, and improving patient care.
Slowly but surely, AI is improving almost every aspect of human life with innovations and advancements. The latest is that AI in the healthcare industry is impending a revolution in medical diagnostics by providing accurate risk assessments, accelerating disease detection, and boosting hospital productivity. By automatically prioritizing urgent cases and accelerating reading time, image recognition AI enhances the workflow of radiologists. It even helps in the prevention of diseases by the early detection of diseases.
In medical images such as x-rays, MRIs, and CT scans, AI-driven software can efficiently be used to accurately spot signs of many diseases, especially in detecting many chronic diseases such as cancer. According to the NIH, AI will be widely applied in the healthcare industry especially for various tasks such as patient engagement and adherence, diagnosis, and treatment recommendations. So, there is no doubt that AI in healthcare will revolutionize the diagnostic process in the approaching years by detecting diseases, classifying diseases, and improving the decision-making process. The application of AI in the healthcare industry will make people live longer.
Triage and Diagnosis
AI can be effectively used to automatically triage cases. AI algorithms will analyze the cases and forward cases to pathologists after determining the priority based on the probability of cases according to the criteria set by labs. This makes the workflow of pathologists easier and efficient. Through the process the algorithm will be able to:
• Verify the digital images attached to the case belong to that case
• Validate the tests ordered and match the specimen type
• Identify cases marked as stat
• Determine the cases, which can be positive or are most likely to be negative
Moreover, AI technologies in the healthcare industry also can be effectively used to provide more accurate and faster diagnoses. This speeds up the entire process of triage and diagnosis and is expected to revolutionize the healthcare industry soon.
The Future Outlook for AI in the Healthcare Industry
Over the next few years, AI in the healthcare industry has the best opportunities in hybrid models to support clinicians in diagnosis, identifying risk factors, and in treatment planning. This scope will result in faster adoption of AI technology in healthcare, which will show measurable improvements in operational efficiency and patient outcomes.
With a plethora of issues to overcome, which are driven by documented factors such as growing rates of chronic diseases and the aging population, it is obvious that the healthcare industry needs new innovative solutions. AI-powered solutions in the healthcare industry will achieve a clear impact on the global healthcare industry in a short time.
Frequently Asked Questions
Which is the best application of AI in the healthcare sector?
Cognitive surgical robotics is the best application of AI in the healthcare sector as it helps practitioners collect data from real surgical processes, which would help in improving existing surgical approaches.
Why is artificial intelligence important in healthcare?
Artificial intelligence in healthcare is vital as it can help make decisions, analyze and manage data, and have conversations. So, AI will drastically change the everyday practices and roles of clinicians.
When was AI-first used in healthcare?
The term, Artificial intelligence (AI) was first described in 1950, but the limitations of the term prevented its acceptance. In the 2000s, these limitations were overcome and people started to accept the term.
Article | April 21, 2020
With medical system consolidation and increasing numbers of medical records created, the need for digital access and storage is gaining steam. Digitizing records allows clinicians to improve accuracy and decrease redundant testing and studies, as well as reduce treatment delays. Greater availability of digitized records has other perks too. With vast amounts of accessible medical data, researchers can move public health studies forward, also potentially improving care and treatment of individual patients. As a result, cloud storage is taking off, though healthcare organizations are adopting it more slowly than other industries. According to a 2019 Nutanix report, 71% of healthcare organizations using cloud were considered the least mature – relative beginners – in that they were using fewer cloud services. Compare that figure to finance or retail, where 13% and 15% respectively were beginners. However, that is changing.
Article | April 21, 2020
The key concerns of healthcare management today are data processing and data security. Patients don’t have full ownership of their medical records, and are unable to control how their information is updated. There isn’t enough transparency to the process. There are also significant concerns related to data security, especially in the areas of personalized medicine and the rise of wearables. Patients and medical staff need secure and straightforward ways to record data, send it over networks, and receive advice without security concerns. Blockchain technology can help solve these problems. Introduced in 2008 as a means of recording cryptocurrency transactions, blockchain is a distributed cloud-based ledger that offers the ability to verify the origin of data and prevent breaches. When a user wants to make a transaction, they issue a request signed with their private key. The network verifies its authenticity by using a public key. If the verification is successful, the transaction is broadcast to the network and included in a block. The network of “miners” solves the block to get a reward, and once the block is revealed, it is added to the blockchain, making it permanent. It’s impossible to introduce new information in a block unnoticed, because that would change the structure of the entire chain. This feature makes the system safe and transparent.