The healthcare industry is undergoing a transformative period in which new and innovative technologies, such as artificial intelligence (AI), are revolutionizing standard processes to improve efficiency, enhance patient experiences, and develop cost-saving measures. This presents exciting opportunities for investors, but also some challenges that must be considered before making a significant investment.
Here are a few different scenarios for how AI is expected to be deployed in healthcare moving forward.
Behavioral Health, Interoperability and eConsent: Meeting the Demands of CMS Final Rule Compliance
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Operational
In the short term, AI has immediate applicability within the administration and operation of a hospital or healthcare system. Think in terms of supply chain and inventory management within a hospital. For example, AI can be used to ensure a surgical room is correctly stocked with appropriate inventory based on the series of surgeries scheduled to be performed in the room that day. Additionally, inventory could be automatically tracked with an algorithm that is able to assess a surgeon’s needs and send instructions to a robot that can pick up and deliver appropriate supplies to the surgical room at the beginning of a day. Another scenario would be an algorithm automatically assessing the cost of supplies from multiple vendors and immediately analyzing which vendor delivers the best product at the lowest cost and promptly ordering the appropriate inventory – all done without human intervention.
Data security
With the evolution of digital technology and massive amounts of big data being generated daily, health systems will need AI in order to keep track of clinical data. Most breaches within a healthcare system occur because an insider deliberately or inadvertently accessed data they should not have. From a security perspective, AI technology could determine whether someone within the system is inappropriately accessing data and flag the incident before there is an actual breach. There are already early stage companies with technology offerings to address this specific need. What health systems need to evaluate prior to deciding on a vendor is which technology is most suited to their particular environment, depending on the complexity of their workflow.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
Clinical care delivery
From a clinical care delivery perspective, AI is already presenting itself, albeit in the early stages, with the ability to provide clinical decision support (CDS) to physicians. With the amount of data, research and literature published on a daily basis, it’s humanly impossible for a specialist to digest it all. However, a sophisticated AI system can be designed to scan the latest and greatest data, combine research with known medical information on a patient, and provide the overseeing physician with options on optimum care for this patient – creating a truly personalized experience. There are companies that currently offer CDS software in a wide variety of clinical indications, ranging from OBGYN to cardiology, that help clinicians go through a decision tree to provide the most appropriate and optimal care to patients.
Additionally, we are now in a phase where technology has surpassed the ability of a human to diagnose disease accurately, so it’s very possible we will see the impact of AI in patient diagnoses within the next several years. For example, in pathology, traditional training involves visual pattern recognition by looking at human tissue through a microscope. Today, diseases are being detected at earlier stages, to the point where in many situations the tissue is caught at the cusp of a disease inception. Identification of disease at early stages is critical, particularly for cancer and certain devastating autoimmune diseases. This is an area where AI can make a huge impact as sophisticated algorithms are now able to facilitate this process more efficiently and accurately.
Patient Experience
In the digital-driven environment, customers come first – and in healthcare, it’s the patient experience that counts. Hospitals and health systems are increasingly forced to consider people not just as patients, but also as customers. This is more so today with the myriad choices patients have in choosing physicians and facilities. With several hospitals vying for the same customer, providing excellent service is key.
From a patient engagement perspective, AI creates the ability for hospitals and health systems to personalize the patient’s experience from the moment they walk into a hospital. This includes everything from providing them with education about their condition and what they can expect from a care delivery perspective to helping them navigate a very complex healthcare ecosystem. Imagine signing in at the front desk, and you are given a tablet that you keep throughout your hospital visit. This tablet immediately pulls up all of your information from multiple sources – both hospital and non-hospital – and then through an interactive process, you are guided through your entire hospital experience, from clinical care to billing and insurance, to education and entertainment, all while awaiting your procedure. This creates a customized, comfortable environment, making the patient experience as close to “at home” as it can get.
Care coordination
Over the next few years, AI can be applicable in care coordination, particularly outside the hospital setting once a patient has been discharged. To ensure patients have a seamless experience, hospitals are now responsible for both the inpatient and outpatient journeys. Additionally, capturing patient data after discharge, including activities of daily living, clinical and non-clinical interactions with family members and additional caregivers, allows a more robust data set leading to greater predictive accuracy. The best way to manage patients once they leave the hospital setting is to accurately predict what types of catastrophic incidents might put patients back into the hospital. With AI support and coordination mechanisms, patients, caregivers and care coordinators will have the ability to remain ahead of an adverse event with forms of pre-emptive intervention.
The Challenges
The critical challenges that should be anticipated are industry regulations that may limit the full potential of AI technology, as well as understanding what the best practices are when taking information gleaned from AI and utilizing it in an appropriate and optimal way.
From a clinical perspective, the biggest challenge that physicians are going to face – and in fact are already experiencing – is that they are no longer solely in charge of delivering medical care. Today, they have to answer to the patient, who is sometimes armed with more knowledge about their condition than the specialist. And tomorrow, they may have to respond to an AI application that actually does know more than the specialist. However, in both cases, the doctor still has to be the one to assess the information provided and deliver the appropriate care, which sometimes might differ from what the AI technology recommends.
From a diagnostic perspective, physicians are already facing the challenge of whether to deliver care or not due to disease detection now occurring at earlier stages; there exists a time when the tissue may not actually be diseased yet and is therefore “indeterminate.” In those cases, the biggest issue is whether to do something or whether it’s best left alone. There are many studies that have demonstrated that in some cases it’s best to leave the human body to heal itself and that medical intervention is not always appropriate. This becomes even more critical when the use of AI leads to accurate diagnoses earlier in the lifecycle of a disease. At some point, human judgment is a lot more valuable than any insights AI can provide.
Moving Forward
As artificial intelligence continues to develop its platform, there’s no question we’ll see a wider adoption of this technology across businesses to improve processes and streamline efficiencies, especially in healthcare. With the help of existing, yet rudimentary AI, we have already started to take steps towards personalized clinical care. In the future, AI technology can play a pivotal role in reimagining patient engagement by personalizing the entire patient care journey. And while the possibilities for investors are growing each day, it’s important to conduct thorough due diligence to assess the potential challenges that may arise as AI technology continues to be introduced to the industry.
Photo: ANDRZEJ WOJCICKI, Getty Images
