For the past year, AI was at the center of conversations throughout healthcare. While the potential for AI to revolutionize healthcare is clear, from care delivery to enhancing operational efficiencies and accelerating research, many organizations are still figuring out where to begin.
Healthcare’s AI Adoption Challenges
Compared to other industries, healthcare is required to take more precautions in AI adoption. The highly regulated nature of our work, and the significant requirements around having supporting evidence for claims or decision-making, remind us that patient safety must always be top of mind.
Every AI model and use-case must be carefully considered. Models must be trained on large, representative datasets that capture a multistakeholder view of the patient. Once the right foundations are set, healthcare leaders and clinicians must adopt human-assisted and transparent AI approaches to ensure responsible implementation.
Additionally, users must meet each output with a certain level of caution as organizations leverage the speed and specialized analytics of these emerging technologies. Where other industries can adopt “auto-pilot” workflows, healthcare professionals must collaborate with their AI “copilot.” AI outputs should be considered as most likely accurate, not as certain, functioning primarily in an assistive modality to augment decision-making for health plans, providers, pharmacists, or researchers.
Yet, there are some areas in healthcare where these systems are already improving clinical and financial outcomes. Massive amounts of data have been properly structured and leveraged with a co-pilot approach to transform how healthcare works.
Here are four areas where AI is making noticeable improvements in healthcare.
#1: Automating Medical Record Reviews
For health plans, medical record reviews (MRR) are crucial for risk adjustment performance and improving member care. MRR is typically a tedious, costly process. It requires significant resources and manual human review which can hinder risk score accuracy and lead to worse health outcomes, higher costs, and false positives – records that seemingly have conditions to code, but are actually not qualified for risk adjustment.
Until now, this has been the only way to catch data discrepancies between medical documentation and claims data. However, AI and ML technologies are replacing the manual, error-prone nature of MRR with a better approach, combining clinical intelligence with natural language processing (NLP) to perform reviews faster and with greater accuracy.
This combined power of AI and NLP can analyze targeted member medical records and identify when intervention is needed, eliminating false positives – which health plans lose significant resources on each year. With NLP and ML-powered solutions, health plans can now reduce costs spent on MRR by focusing their team on true positives to improve risk score accuracy and member outcomes.
#2: Identifying and Addressing Costly Coverage Errors
For providers, claims payment in the back-end of their revenue cycle is largely dependent on front-end accuracy. But when patient coverage is missing or incorrect, access to care is delayed, back-end denials increase, and it takes extra resources to correct claims for payment.
AI is helping providers get their revenue cycle started on the right foot, turning eligibility verification from inefficient and error-prone to a quick, more accurate, and automated process. AI-powered submissions separate good eligibility inquiries from those with missing information, sending only the inquiries with all required information to health plans. Health plans get cleaner batches of inquiries to verify, and incorrect inquiries are sent back to the provider to update.
Applying AI and ML to eligibility verification empowers providers to correct costly mistakes and remove barriers to patient care. They get the information they need, while patients enjoy a better experience.
#3: Optimizing Medication Adherence
For pharmacies and hospitals, non-adherence to medication is costly, accounting for 10% of hospitalizations and 16% of healthcare spending. For patients, it weakens the effectiveness of their care plan.
The challenge with medication adherence is there’s no single mechanism. Patients may not be following their care plan for a variety of reasons, ranging anywhere from medication costs or lack of transportation to the pharmacy, to negative side effects or simply forgetting to take their medication.
Pharmacists, already pressed for time to consult patients, must take a unique approach with every patient to reduce the costs of non-adherence and improve patient care. AI is helping them monitor and optimize medication adherence by analyzing relevant patient data, such as health history and socioeconomic characteristics, and matching that data with the applicable prescription or treatment plan information. The result: a probability of patient adherence predicting whether patients will refill their prescriptions on time or not, and recommendations around adherence programs targeted for the patient, thus giving pharmacists greater efficiency throughout their day and more time to spend on patient consultation.
#4: Harnessing the Power of Generative AI
Generative AI can transform administrative and clinical processes throughout healthcare by analyzing and summarizing large volumes of data. Already, there have been examples of generative AI helping identify conditions and diagnoses, augmenting decision-making for clinicians, pharmacists, or providers.
Large language models’ ability, scale, and speed are driving invaluable efficiency in healthcare, empowering treatment providers to spend more time with patients. It’s making vast amounts of data easily accessible, keeping decision-makers informed and focused on the person in front of them. AI can also help keep users informed on treatment requirements and best practices for care.
AI Success Depends on the Breadth, Depth, and Quality of Data
Keeping up with the rapid adoption of AI starts with well-laid data fundamentals. The transformative impact of AI hinges on the quality of data on which models are built, paired with the appropriate use-case. As large language models accelerate the use of AI and ML, healthcare organizations must implement AI models responsibly and ensure robust data architecture, data cleanliness, and of course, strict data governance.
As more AI and ML applications are deemed safe and reliable for care settings, the industry can improve healthcare outcomes and economics at scale. AI can help users achieve more, faster – and ultimately, improve the patient care journey throughout the care continuum.
About the Author
Rajesh Viswanathan serves as the Chief Technology Officer for Inovalon. In this role, Mr. Viswanathan leads and is responsible for all aspects of the Company’s technology strategy, design, development, testing, production, infrastructure, operation, security, and maintenance.
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