The emergence of artificial intelligence (AI) has had and will continue to have a transformational impact on the healthcare industry. What’s more, the COVID-19 pandemic is accelerating the pace of this evolution as more providers, payors, and industry leaders see such technology as not just beneficial but essential.
Reports suggest that the AI health market could reach $6.6 billion in 2021. As we embrace this technology in clinical research, medication and therapy management, robotic surgery, and drug development, it helps to reduce costs, improve outcomes, and relieve workers of repetitive, time-consuming tasks.
When most people hear the words “artificial intelligence,” they first think of chatbots, or programs designed to ask and answer questions based on key words and phrases. However, the capabilities of AI – which isn’t a single technology, but rather a collection of applications and programs – extend far beyond this basic example.
In fact, rule-based expert systems are some of the most relevant and practical AI applications for long-term and post-acute care (LTPAC) practitioners. These systems consider “if-then” propositions to organize data, and they are commonly used in electronic health record (EHR) platforms to offer clinical decision support.
Machine learning, which is a statistical means of fitting models to data and using data to create computer algorithms to find patterns. The most common form of machine learning in health care is “precision medicine,” which uses data to predict what treatments, medications, and interventions are most likely to result in successful outcomes for a particular patient. This type of AI also helps determine whether a patient will acquire a specific disease based on their medical history, risk profile, and broader comparative data.
Natural language processing, which includes applications like speech recognition, text analysis, and other activities related to language. Health care professionals mainly use this AI technology to create, interpret, and classify clinical documentation and published research.
Robotics, including both physical robots and automatic robotic processes, the latter of which is generally used for administrative purposes. In the healthcare industry, robotics are used for tasks like updating and managing patient records and billing.
The implications of AI technology on patient care and quality outcomes are significant. For instance, AI provides the ability to reduce diagnostic and therapeutic errors by comparing large data sets against complex criteria, such as with a drug utilization review. These screening systems can be used to generate alerts that might be missed with traditional clinical decision support systems.
Consider this example. A physician meets with a patient, gathers their history and conducts a physical, and records this information in their EHR. AI then takes these details, analyzes them, and — based on clinical evidence and multiple sources of data — helps confirm the diagnosis and suggests what medications are most likely to be safe and effective for the patient. It also identifies the person’s risk of other illnesses or experiencing problems like a fall. Better yet, these smart diagnostics occur within the current workflow without adding complicated or time-consuming steps.
Thanks to the assistance of AI, the practitioner and their team can implement better targeted interventions with a lower risk of complications, medication-related problems, and emergency room visits or hospitalizations. (And this example utilizes just one of the many applications of AI technology.)
Looking ahead, we expect to see the use of AI increase in efforts to improve the safety and efficacy of medication prescriptions, as well as help clinicians make more targeted disease and medication management decisions and recommendations.
Additionally, we will see health care professionals employ AI technology to predict health risks and outcomes across large groups as part of population-based health. For instance, by using patients’ medication histories from EHRs and other sources, AI can help determine the risk of overdosing from a prescribed drug like an opioid. From there, we can recommend interventions based on risk.
By helping to improve accuracy and reduce errors, AI will also reduce avoidable expenditures, including costs related to emergency room visits and hospitalizations. AI-enabled dosage error reductions alone could lead to billions of dollars in savings annually. In the world of value-based medicine, this cost reduction will be essential to the success of health systems, hospitals, LTPAC facilities, and practices worldwide.
As we continue to move toward population health, AI will play a key role in promoting adherence to treatment plans and ensuring that patients have the support they need in their communities once they are discharged from LTPAC facilities. For instance, AI equipped with a scheduling application reduces patient wait times by sending them updates two hours before their appointments about when exactly they will be seen.
Finally, at a time when we expect practitioners to provide more documentation than ever, AI technology effectively integrated into EHRs can boost productivity and reduce stress simultaneously. For example, we can extract required clinical data easily from the clinical note in the EHR and submit it at the same time as the electronic prior authorization.
When using AI in this way, prior authorization becomes a decision support tool for the prescriber. An evidence-based algorithmic program factors in the therapy policy and restrictions (including the formulary). It then pulls patient-specific data and information related to a patient population with similar characteristics to suggest therapy recommendation options to the prescriber.
As with the best technology, AI has the potential to re-humanize medicine, enabling physicians and other practitioners to spend more time on direct patient care and less time on paperwork and administrative tasks. If utilized well, it will contribute significantly to the triad of quality care, cost efficiency, and patient, family and team satisfaction and communication, which is the cornerstone of value-based medicine and population health.
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