3 ways artificial intelligence can disrupt healthcare
With expectations for AI rising rapidly, provider organizations may be able to achieve quick benefits from this technology in improving patient care and population health.
New data predicts the market for AI-driven healthcare technologies will exceed $6 billion in just three years. That’s a significant leap from its $600 million valuation just 4 years ago. The surge is being driven largely by growing demand and acceptance among consumers for electronic, data-driven and virtual-based care, and the desire for more convenient, accessible, and affordable care.
While it’s entertaining to speculate on the future of these applications to healthcare, there are several use cases underway today which promise to change the way we think about and deliver care at the individual and population levels. These three areas highlight where AI is already making an impact in the delivery, treatment, and reimbursement of care.
Bio-surveillance. Tracking disease prevalence, treatment methods, and patient response through widespread systematic data collection, analysis, and dissemination has the potential to help us fine tune treatment protocols based on clear evidence of what’s working and what’s not across various disease states and populations.
For example, analyzing bacterial infection patterns and antibiotic resistance can help us prevent the proliferation of diseases like MRSA, which has essentially been created through overuse of antibiotics. By aggregating and analyzing patient data with artificial intelligence, we can detect and address broadscale patterns involved in causation and disease prevalence and help to combat the spread of some of the most common and costly preventable diseases.
Risk assessment and risk adjustment. Using AI to predict risk factors for a given population or individual patient can give us tremendous power to proactively intervene and stop or prevent potential health care threats. While we must be careful not to become too reliant on algorithms, as care patterns are still very individualized and local, we can use predictive assessments to forecast risk.
By analyzing widespread population data, we can identify patterns in otherwise seemingly anecdotal events. Return visits is one example: based on analysis of the historical pattern between conditions, we can predict the likelihood that a patient will return for treatment of the same condition within a specific period of time. If we can predict this pattern, clinicians can conduct proactive outreach to ensure patients follow-up as appropriate, refill their medications, etc. to stay on the path to wellness.
When we can connect data from various care settings, including labs, specialist visits and the like, and analyze both structured and unstructured data using natural language processing, we have even more power to identify risk patterns. Health plans can also use this approach to get a more accurate picture of population health for complete risk identification. By leveraging AI to conduct coordinated retrospective and prospective analysis, organizations can optimize their risk adjustment programs to identify gaps and opportunities to improve both patient and payer outcomes.
Wearables and real-time health assessment. The use of wearables has more than tripled since 2014, and 90 percent of consumers now say they’re willing to share their wearable health data with their medical providers. This has huge potential to give patients and physicians real-time insight into overall health, help to better manage chronic conditions and spot acute conditions immediately before they become serious.
For example, the ability to monitor a cardiac patients vital signs remotely can help spot potential acute event indicators and allow care providers to take intervening measures. One caveat here, however, is that device manufacturers and physicians must be cautious with how they present data to the consumer to avoid causing unwarranted worry and concern. Data must be presented responsibly and with ample patient education in order to avoid confusion and stress.
AI technologies are helping to vastly improve the efficient, effective delivery of care by providing more detailed information to providers while also decreasing their cognitive load. It’s important to acknowledge that AI is—and always should be—a supplement to professional expertise, working alongside a provider to aid in making treatment decisions. In many ways, AI mimics the physician’s thought process and methodology, using tests and known correlations to confirm or deny hypotheses. But, it must still be used wisely by providers who know the patient and his or her lifestyle and environmental factors.
With AI in place as a supplemental technology, patients can also gain greater insight into their own health, find a more appropriate level of care for their needs and help them to be proactive and engaged in managing their own wellbeing.
While it’s entertaining to speculate on the future of these applications to healthcare, there are several use cases underway today which promise to change the way we think about and deliver care at the individual and population levels. These three areas highlight where AI is already making an impact in the delivery, treatment, and reimbursement of care.
Bio-surveillance. Tracking disease prevalence, treatment methods, and patient response through widespread systematic data collection, analysis, and dissemination has the potential to help us fine tune treatment protocols based on clear evidence of what’s working and what’s not across various disease states and populations.For example, analyzing bacterial infection patterns and antibiotic resistance can help us prevent the proliferation of diseases like MRSA, which has essentially been created through overuse of antibiotics. By aggregating and analyzing patient data with artificial intelligence, we can detect and address broadscale patterns involved in causation and disease prevalence and help to combat the spread of some of the most common and costly preventable diseases.
Risk assessment and risk adjustment. Using AI to predict risk factors for a given population or individual patient can give us tremendous power to proactively intervene and stop or prevent potential health care threats. While we must be careful not to become too reliant on algorithms, as care patterns are still very individualized and local, we can use predictive assessments to forecast risk.
By analyzing widespread population data, we can identify patterns in otherwise seemingly anecdotal events. Return visits is one example: based on analysis of the historical pattern between conditions, we can predict the likelihood that a patient will return for treatment of the same condition within a specific period of time. If we can predict this pattern, clinicians can conduct proactive outreach to ensure patients follow-up as appropriate, refill their medications, etc. to stay on the path to wellness.
When we can connect data from various care settings, including labs, specialist visits and the like, and analyze both structured and unstructured data using natural language processing, we have even more power to identify risk patterns. Health plans can also use this approach to get a more accurate picture of population health for complete risk identification. By leveraging AI to conduct coordinated retrospective and prospective analysis, organizations can optimize their risk adjustment programs to identify gaps and opportunities to improve both patient and payer outcomes.
Wearables and real-time health assessment. The use of wearables has more than tripled since 2014, and 90 percent of consumers now say they’re willing to share their wearable health data with their medical providers. This has huge potential to give patients and physicians real-time insight into overall health, help to better manage chronic conditions and spot acute conditions immediately before they become serious.
For example, the ability to monitor a cardiac patients vital signs remotely can help spot potential acute event indicators and allow care providers to take intervening measures. One caveat here, however, is that device manufacturers and physicians must be cautious with how they present data to the consumer to avoid causing unwarranted worry and concern. Data must be presented responsibly and with ample patient education in order to avoid confusion and stress.
AI technologies are helping to vastly improve the efficient, effective delivery of care by providing more detailed information to providers while also decreasing their cognitive load. It’s important to acknowledge that AI is—and always should be—a supplement to professional expertise, working alongside a provider to aid in making treatment decisions. In many ways, AI mimics the physician’s thought process and methodology, using tests and known correlations to confirm or deny hypotheses. But, it must still be used wisely by providers who know the patient and his or her lifestyle and environmental factors.
With AI in place as a supplemental technology, patients can also gain greater insight into their own health, find a more appropriate level of care for their needs and help them to be proactive and engaged in managing their own wellbeing.
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