Around the US, healthcare organizations are struggling with the transition to value-based care, rising costs, and uneven quality despite being one of the most expensive healthcare in the world. The current generation of commercial AI technology is rule-based and doesn’t understand the context and semantics required to capture patient risks accurately. They are built using legacy stack, so they are rigid and unable to meet organizations’ need for modular workflows.
A common use of artificial intelligence in healthcare involves NLP applications that can understand and classify clinical documentation. NLP systems can analyze unstructured clinical notes on patients, giving incredible insight into understanding quality, improving methods, and better results for patients.
Truly Innovative Organizations in healthcare today rely on accurate DL models to provide value to their customers. RAAPID.AI has developed their state of the art NLP (Natural Language Processing) technology that runs on top of Deep Learning algorithms.
Sophisticated tasks like predicting medical diagnosis, auto-suggesting HCC codes for reimbursement and capturing complete patient risk involve tens of thousands of data sets & nonlinear relationships among variables. In these cases, it’s complicated to use the data to its the best effect i.e. to optimize the predictions. In other cases such as recognizing objects for medical imaging, a programmer can’t even develop rules to describe the features to be looked for. For example – How can a programmer write a set of rules, to work in all situations that can describe and detect abnormalities in common imaging tests, such as chest x-rays?
Now, what if we can transfer the complication of making difficult predictions i.e feature specification & data optimization, from the programmer to the program itself? This is the promise and potential of modern AI in healthcare. AI consists of a set of advanced technologies that make machines (hardware or software) capable of doing highly complex tasks effectively, tasks that would otherwise require intelligence if a person were to carry those out. . However, progress has been limited because algorithms to deal with many real-world healthcare problems are far too complex for humans to program by hand.
Separating Hype from Reality
To describe the various applications, we can broadly classify AI in healthcare into the following buckets.
- Clinician assistants (or Clinician-oriented)
- Virtual health assistants (or Patient-oriented)
- AI for healthcare revenue (or Operational-oriented)
Clinician Assistants:
Applications in this segment aim to enhance clinicians’ performance on cognitive tasks, such as therapeutic or diagnostic decisions. They are a key means today by which therapeutic and diagnostic advancement can be translated into practice. It transforms the ways of doing things among clinicians, because of the fundamental shift from the clinician being in near-complete cognitive control into something else.
Virtual health assistants:
This segment is geared towards helping the patient governing their health. It uses technologies such as augmented reality, speech and body recognition, and cognitive computing to build a virtual encounter between a patient and a health assistant. The goal of such applications is to develop relationships with patients by empathizing and conversing with them using real language. This is a growing segment as highlighted by the Global Market Insights study that the market for virtual assistants will grow at a rate of 35% from 2024.
AI for healthcare revenue:
Applications in this segment leverage AI to automate manual processes, optimize the efficiencies, eliminate waste and remove administrative burden in a revenue cycle. Accenture study estimates AI’s potential benefits of $18 billion by 2026 in healthcare Administrative and workflow assistance. According to experts at Forbes, “The impact of AI investments in healthcare is realized first in the operational & administrative side of the healthcare system rather than the clinical side”. Reason being, clearly defined opportunities exist, as the value of AI’s impact and ROI can be precisely tracked due to improved staff productivity, reimbursement and cash flow of the healthcare organizations.
Conclusion
Healthcare leaders need to start getting their feet wet in the AI wave or else they might be left behind and will be forced to play a catch-up role. That is a scarier proposition for any healthcare leader due to various forces already crippling the healthcare organizations such as rising costs, razor-thin operating margins, declining reimbursement and value-based future.
