By Rashi Srivastava, Executive Vice President (Engineering) at Qentelli
March 10 2022: AI in healthcare defines the application of machine learning algorithms, cognitive technologies etc for the betterment of Healthcare in general. It is the use of ‘machines’ for the analysis of medical data that leads to subsequent actions for predicting specific outcomes.
Artificial intelligence has multiple use cases, ranging from revolutionizing the delivery and science of healthcare to impacting healthcare workforces in today’s context. We will be discussing these aspects in detail in this article.
AI in healthcare can potentially impact workforces in the context of three aspects – the jobs, the nature of work itself, and the introduction of new professionals.
When we talk about jobs in healthcare, AI automation has the ability to impact each job profile. However, the degree of impact may vary from one role to another. Research by McKinsey says that AI automation can have the highest impact on medical equipment manufacturers by freeing up to 48% of their working hours, followed by medical technicians (32%) and occupational health and safety technicians (30%).
When we talk about work in healthcare, AI has the ability to transform and simplify the nature of work by:
Minimizing healthcare professionals’ time spent on routine and administrative tasks
Augmenting a variety of clinical activities
Assisting professionals to access information quickly, consequentially leading to better patient outcomes and high-quality care
Speeding up diagnostics with more accuracy
Enabling remote monitoring
Empowering patients with self-care
When we talk about introducing new professionals, multiple roles can emerge with the implementation of AI in healthcare. For example, professionals who specialize in human-machine interactions pertaining to clinical decision making; hybrid roles like clinical bioinformaticians; medical leaders who shape clinically meaningful AI with insights to support decisions.
Artificial Intelligence is revolutionizing healthcare
Artificial intelligence has endless opportunities to revolutionize the delivery and science of healthcare. A few such opportunities are:
-Next-generation AI-enabled imaging and radiology tools
-Allowing clinicians to rapidly access, extract and electronically export EHR (Electronic Health Records)
-AI-based computational and high precision analytics for pathology
-Advanced immunotherapy for life-threatening medical ailments
-Remote patient monitoring with the use of personal devices and wearables
-Strengthened and more accurate clinical decision making
-Brain-computer interfaces to restore fundamental experiences like meaningful interactions, for patients who have lost the ability due to neurological diseases
-Making care more accessible for the underprivileged and developing / poor regions
-Limiting the risks of antibiotic resistance by creating accurate and quicker alerts for healthcare professionals with the help of analytics
-Smart devices and intelligent machines
-Converting Electronics Health Records into reliable risk predictors by leveraging their data for detecting patterns
The opportunities mentioned above exemplify how intricately AI can impact the healthcare industry and optimize procedures related to diagnostics, chronic care management, clinical decision support, and self-care. Although the adoption of AI is still nascent in healthcare, a well-researched approach can do wonders for the industry.
AI in Healthcare Today
More connected data with geographically shifting growth dynamics
Today, the rapid growth of artificial intelligence is on top of minds of healthcare decision-makers, including investors, and even governments. The United States still tops the list by a long shot with the maximum number of completed healthcare AI research studies and trials; however, Asia is witnessing the fastest growth. Leading domestic conglomerates with AI focus on consumer needs are spearheading these efforts. AI Adoption is getting boosted across the globe by gaining clarity on topics like data security, linking of valuable data sets, critical data governance and access issues.
Rapidly rising use cases
There is a constant and exponential growth in applications of AI in healthcare. The use cases range from online symptom checkers to virtual agents carrying out operational / admin related hospital tasks to bionic pancreas for treating dreadful diseases and many more. Some use cases focus on improving healthcare operational activities (for example, bed management); others help in quick detection of certain diseases (for example, specific cancers) or enhancing health with prediction abilities (for example, outcome of micro invasive spine surgery).
Scaling AI in Healthcare
The implementation of AI in healthcare is still growing, and there’s still a long way to realize its full potential. Keeping that in mind, scaling should be done in three phases:
The first phase should include solutions dealing with routine, repetitive, and administrative tasks along with applications based on imaging (for example, radiology). It will optimize healthcare operations and increase adoption while minimizing its disruption.
The second phase should include solutions that focus on:
-Broadly using NLP (Natural Language Processing)
-Seamlessly shifting patients to home-based care (for example, remote monitoring, alerting systems, etc.)
-More specialties (for example, neurology, oncology, etc.)This phase should also include integrated solutions, well-designed strategies to embed AI extensively in clinical workflows.
The third phase should include the implementation of solutions powered by artificial intelligence in clinical practice which should strictly be based on evidence from clinical trials.
The ultimate outcome of these three phases would be the industry’s acceptance of artificial intelligence as an integral part of healthcare. It is essential to implement AI in phases to ensure successful application along with effective adoption.
The fundamental role of healthcare in our society makes it a highly critical sector for applying AI-powered solutions. The implementation of AI in healthcare has to be extremely well-researched, well-designed, and preceded by thorough risk assessment.
If implemented with deliberation and care, AI can act as a crucial tool for the healthcare industry. We are still at nascent stage of AI adoption – the idea of trusting technology with critical procedures of healthcare may seem scary at the moment, but with detailed and evidence-based implementation, its true potential can be realized.
Rashi leads the global service delivery and solution teams and is the site lead offshore at the Hyderabad centere of Dalla, Texas ( US)-headquartered Qentelli, said to be the first technology firm to focus on Continuous Delivery through Orchestrated Engineering. Prior to that she served various roles including developer, tester, project, and program manager for large systems integrators in India, Australia and North America.