Artificial intelligence (AI) appears in a broad spectrum of technologies in varying forms and degrees, from smartphones, wearables, retail apps, autonomous vehicles, social media, and even smart TVs.
It transforms how we interact, consume information, and purchase goods and services. Healthcare is no exception!
The impact of AI in healthcare through natural language processing (NLP) and machine learning (ML) is transforming care delivery and patient journey in ways never before possible.
Its application has a vital role in enhancing patient engagement, quality, and access to care in healthcare. It improves provider and clinician productivity, accelerates the speed at which new pharmaceutical treatments can be developed while reducing the cost, and personalizes medical treatments by leveraging analytics to mine the enormous amounts of noncodified clinical data that currently exist.
Creating patient profiles
As a method to convert human language into a structured and understandable format that computers can then use to perform various computational analysis, the use of natural language processing (NLP) is particularly important in healthcare where there is still a significant amount of clinical information being documented via a variety of unstructured methods, including dictation, typing, and writing. Even though this unstructured “free text” can provide valuable information to a human who reads it, any valuable information contained within it cannot be presently analyzed and used by a computer until it has been codified and structured.
Here is where NLP comes in. It allows free text information entered into the patient record to be turned into potentially useful data that a computer can use. NLP applied in a clinical setting can convert the information from transcribed history and physical dictation into data representing the patient’s problem list, medication list, allergies, past medical and surgical history, family history, and social history. When supplemented with the use of speech-to-text applications that convert spoken words into text, NLP can be used to turn dictated speech into structured and codified information that is usable by a computer application.
Effective diagnoses, treatment, and prevention
At its core, machine learning (ML) is a branch of AI that uses algorithms to parse data, learns from it, and then makes a determination or prediction. This learning capability enables systems to act without any explicit programs involved. In healthcare, the technology can enable faster and more accurate analysis of massive quantities of health data from various sources (e.g., research and development, physicians and clinics, non-physician clinical workers, wearables, patients, etc.) and unearth insights for more effective prevention of illness and better treatment of individuals, as well as populations.
ML also has the potential to be used for a variety of health care goals, including better drug discovery and manufacturing, clinical trials and research, improved accuracy of radiology and radiotherapy diagnoses and treatments, the development of smarter electronic health record (EHR) and health information exchange (HIE) systems, and the prediction of epidemic outbreaks.
Better patient self-service
Patient self-service emphasizes the patients’ choice and convenience in rapidly and easily completing tasks such as scheduling appointments, paying bills and filling out or updating forms, and using devices such as phones, tablets, and laptops. Implementing self-service programs helps hospitals to realize benefits such as reduced cost, reduced patient waiting times, fewer errors, easier payment options, and increased patient satisfaction.
ML and NLP further increase the convenience and efficiency of patient self-service with virtual health assistants (VHAs) and chatbots that can interact with the patients and complete simple administrative tasks and medication refills at anytime and anywhere. Patient self-service can also streamline several administrative tasks like registration, appointments, payment collection, and billing, freeing staff to do higher-level work.
Reduced time and cost for drug discovery
Pharmaceutical development has historically been a long and expensive process. It has many layers to the process of drug discovery and development. The sheer volume of tests to understand the biological systems and their adverse reactions to compounds keeps the costs high and the pace slow. ML, coupled with NLP, machine vision, and image analysis is well suited to sort through thousands of pages of research results to make the process more efficient.
The AI system then draws connections between relevant data points and can narrow the number of candidate molecules by an order of magnitude. Many drug companies are using AI to study the deep chemistry of drug interactions and to probe entire biological systems to see how a drug might affect a patient’s tissues. By analyzing large amounts of data and using machine vision, AI promises to help reduce the time and cost of drug discovery by identifying candidate molecules.