One of the largest expenses of any healthcare delivery system is for care providers. Increasingly, medical facility administrators and healthcare policy setters have realized that they can improve healthcare workers’ efficiency by providing them the tools to work smart rather than working long and hard. And the tool most heavily relied upon is Artificial Intelligence (AI). Not only does AI help improve efficiencies of front-line healthcare workers, but AI-driven eLearning also helps trainers and learners master some of those efficiency drivers.

AI – a Third Dimension to The Rescue

Today, a combination of human intelligence and medical devices and equipment delivers healthcare to countless patients throughout the world. Adding a third dimension to the mix, AI, helps human intelligence accomplish more than it can today. Here are some unique ways that AI is improving efficiencies in a myriad of healthcare delivery situations:

  • More Precise Pathology Decision Support

Pathologists are the source of over 75% of all patient data, including scanned imagery. These images are often critical for making accurate and timely diagnosis that leads to better patient outcomes. Currently, physicians, surgeons, and other diagnostic professionals conduct most diagnosis, that rely on digital images, manually – through visual interpretation of those images.

Digital pathology, in conjunction with AI analytics engines, changes all that. AI drills down to the pixel level of those images, while quickly identifying anomalies and unique features of interest. This not only improves productivity of healthcare workers but leads to a more accurate diagnosis.

  • Turning “Smart” to Genius Medical Devices

Clinics and hospitals rely on a wide range of “smart” devices today. In most cases, these are a network of disparate technologies that, on their own, function well to deliver individual health care needs. By overlaying an AI element onto these devices, healthcare workers analyze the disparate sets of Big data they generate in real-time. This not only brings efficiency to the healthcare environment, but it dramatically improves patient outcomes and reduces recovery times.

  • Turning Voice and Video into Records

Healthcare workers spend a fair amount of time creating healthcare records and processing related paperwork. Physicians mouth key comments during complex examinations, while surgeons speak as they proceed with critical procedures. Some medical teams also wear head cams during routine procedures and examinations. AI can take all that data, index, catalog and collate it to automatically provide a contiguous set of Electronic Health Records (EHRs) that would otherwise chew up hundreds of hours to manually (by humans) produce.

  • Matching Patients with Healthcare Providers

One major area of inefficiencies in the healthcare sector, where AI is playing an increasing role, is in matching patients with the right healthcare service providers. Individual practitioners first spend a lot of time and effort studying a patient’s records, their test results and diagnosis reports, before making a referral or recommendation for additional care.

AI can streamline and expedite that process by anticipating additional caregiving needs based on real-time analysis of records, tests, and clinical reports. Additionally, AI’s Big Data engines can go a step further, to ensure personalized care and better caregiving outcomes, by matching healthcare facility capabilities, and individual healthcare workers’ experience and qualifications to pair them with patients.

  • Natural Language Processing (NLP)

When someone can’t understand the grocery list their spouse/partner scribbled, they commonly jest that it’s written like “a physician’s prescription!”.

Natural-Language-Processing (NLP)

SOURCE: ThePost24

Even as healthcare professionals increasingly use digital devices to input their short-hand notes and prescriptions, there’s a chance that support staff, like therapists and nurses, might get it wrong when implementing or interpreting treatment protocols. AI, with NLP engines, take unstructured data (such as a healthcare worker’s notes) and turns them into a set of well laid-out and easy to understand medical records.

  • Going Beyond Reviewing EHRs

Healthcare providing facilities are busy. While physicians, surgeons, and nurses exercise diligence in reviewing patient records before embarking on a procedure or treatment, that protocol can sometimes fall short. AI and machine learning can take reviewing EHRs a step further, by quickly analyzing years’ worth of EHRs, spotting trends and proactively identifying potential symptoms and risks to the care provider. AI can also flag potentially harmful treatment protocols based on analysis of past patient behavior, reaction and outcomes gleaned from medical records.

  • Proactive Predictable Bedside Care

The integration of AI, machine learning and Big Data make continual bed-side monitoring by healthcare providers a thing of the past. Intelligent interfaces, hooked up to a bedridden patient, feed vital data and signals to physicians and nursing staff who go about their daily routine – making the rounds, tending to emergencies and seeing a stream of patients.

By analyzing hundreds (if not thousands) of data points simultaneously, at high speeds, the AI-assisted interfaces can proactively predict signs of sepsis, strokes or seizures. Such applications free physicians and nursing staff to focus on more value-added healthcare delivery functions instead of around-the-clock bedside monitoring.

  • Making Healthcare Reachable to All

Many parts of the world, even within the western world, don’t have easy access to highly-valued healthcare skills – like pathologists or consulting surgeons. The use of AI, combined with machine learning engines, can help minimize that gap. Healthcare institutions are delegating preliminary diagnostic functions – like flagging potential lung infection based on analysis of an x-ray – to AI technologies. Thereafter, if needed, and only in select cases, a “human” healthcare specialist may step in to continue diagnosis and treatment protocols.

  • BCI for Better Outcomes for Complex Neurological Diseases

Neurological tests generate inordinate amounts of data, requiring specialists and support teams to spend a lot of time analyzing and decoding signs and symptoms. By attaching a machine to someone with a complex neuro condition, through brain-computer interfaces (BCI), and then letting AI interpret and decode neural signals, neurologists free themselves from tedious number-crunching and pattern recognition. Instead, that time is better spent brainstorming about optimal treatment protocols with colleagues and other specialists.

  • Bringing Health-on-The-Go to Life

Not only are healthcare workers extremely busy, but their patients too lead full and demanding lives. Wouldn’t it be ideal – and more efficient – if healthcare professionals could deliver high levels of pro-active care and support to their patients, saving professional time and avoiding a patient visit? Well, by integrating AI into implantable and wearable devices, that scenario is now a reality. The intelligence engines continually monitor vital signs of the patient remotely, and alert healthcare workers of a need for intervention only if needed.

AI and Healthcare eLearning

So, how could AI assist healthcare eLearners leverage the many benefits outlined above? The answer: Through intelligent learning. AI-assisted learning is also known as adaptive learning (AL) or personalized learning. In this model, AI algorithms analyze how learners consume online learning content, and then adapts the content to the learner based upon their (eLearners’) choices.

The algorithms can also adapt training delivery based on a learner’s preferences, skill levels, and learning styles. For example, though two healthcare workers take the same course, one might see more video content on (say) first aid techniques, while the other may entirely skip the first aid module and directly start the module on advanced defibrillator use.

AI-supported adaptive learning is also a powerful tool in the hands of trainers. The extensive data, generated through algorithmic analysis produces informative reports that trainers use to assess learner progress. Where the learner is readily grasping a concept, no action may be necessary. However, if reports indicate a learner is struggling with a difficult concept or idea, timely intervention delivers additional support required.

The Way of the Future

It’s not a matter of IF, but when AI is more prevalent in the healthcare arena there’ll be a sea change in healthcare workers’ efficiencies. AI applications, such as automated imaging analysis, NLP, integrated telehealth technologies, machine learning, and pathological clinician support, will not only bring efficiency to healthcare but also result in better patient outcomes.

On the eLearning front, Zoomi a KDG client, has unleashed the power of AI-driven adaptive learning, predictive learning and machine learning in delivering real-time adaptive learning experiences to eLearners. This not only ensures better learning outcomes for learners but delivers higher training ROI to hospitals and healthcare facilities.