Adopted health technologies have created an overwhelming amount of personal health data for providers and patients. Using this data for better diagnosing and outcomes has been the greatest challenge. Will AI start to prove meaningful in analyzing all this data for the better? Here is what the experts have to say. And check out all our 2023 predictions.
More providers and payers will deploy artificial intelligence and machine learning in 2023 to understand and map the massive amounts of data flooding in from multiple sources, including wearables and clinical-grade home-based devices. Data analytics will become more accessible across multiple user interfaces, which will provide clinicians with contextualized information at the point of care.
Rosemary Kennedy, Chief Health Informatics Officer, Connect America
Predictive analytics and artificial intelligence (AI) will play a more prominent role in identifying at-risk individuals and facilitating real-time interventions. For example, a remote patient monitoring platform with an AI-enabled virtual health assistant that can engage with patients via text message to answer questions, deliver positive reinforcement, and provide reminders to take readings of their vital signs. This critical data is automatically recorded in their patient portal and accessible by their provider, providing valuable clinical insight to identify patients who may require an adjustment in therapy, who are struggling with medication and refill compliance, or who may be at risk for a fall. By utilizing this technology, providers can facilitate earlier interventions and better support aging populations so they can live safely and well in their homes.
More healthcare organizations will try to increase quality of care and patient safety in 2023 by improving the quality of data available in EHRs. The inability of provider organizations to accurately identify patients and match them to their records can lead to misdiagnoses, inappropriate and even dangerous treatments or procedures, administrative inefficiency and avoidable utilization costs. I expect to see providers and labs turn to intelligent technologies to reduce patient duplications and other issues that impact data quality.
AI is usually discussed in terms of moon shots, but it can have a big impact when applied to practical, down-to-earth challenges. For example, even after decades of digitization, dirty data still plagues clinicians due to systems that don’t talk to one another. As healthcare organizations consolidate or switch EHRs, we expect to see greater use of AI-based tools that maintain the integrity of medication data as it moves from one system to another. Considering what could happen if 10 mg of medication is mistaken for 1.0 mg, it’s clear that sometimes small mistakes can carry big risks.
Healthcare organizations are navigating rapid transformations in key areas, including business model, care delivery model, and market positioning. With continued margin pressure in 2023, we expect to see acceleration in the use of healthcare data to optimize clinical capacity and service lines, find new revenue streams, and discover new medical advances. In 2023 we will see healthcare providers embrace technology that makes healthcare data easily accessible and usable for their teams. Data, and effective use of data, will be the new currency of successful healthcare performance.
In 2023, I hope to see more disruptive healthcare collaborations and the use of AI to truly solve our most complex industry challenges. Despite significant investment over the years, the healthcare industry has not made as much progress as we have all hoped. AI vendors have promised transformational changes, yet the clinician’s day-to-day routine remains largely the same. Successful AI companies will focus on addressing the most visible and tangible pain points first and do so by collaborating across sectors and stakeholders to realize the power of AI.
The volume of data being produced is at an all-time high. Recent years have seen a proliferation in different types of AI being used in healthcare – mostly confined to the research domain. In 2023, we will see increased migration from AI algorithms developed and published in research to clinical practice and decision support. With the value of AI becoming ever clearer, there has also been a shift in attitude and perception from many clinicians – from fear (will AI make me redundant?) and distrust (the machine can’t get it right every time) to acceptance of AI as an enabler and augmenter of clinicians. I expect 2023 to bring more stories of the successful union of human and artificial intelligence making a difference in clinical practice.
In 2023 we will see continued adoption of AI technologies in all areas of healthcare–from diagnostic use cases, like image analysis, to predictive use cases for future events to improve patient outcomes. Natural Language Processing (NLP), a branch of AI, will continue to see increased adoption, helping to solve problems dealing with the enormous amounts of unstructured data produced by the healthcare industry. The healthcare use cases for NLP are numerous: from identifying cohorts for training new AI models, to surveillance of AI models compared to human performance. As NLP technology continues to evolve, it will provide deeper insights into the states of health and well-being of patients. Some implementation and technology challenges remain, but both technology and market forces are producing the motivation and creativity for vendors and purchasers alike to solve these issues and launch a golden age of AI in healthcare.