Artificial intelligence in healthcare
The journey to smarter health
The journey to smarter health
The widespread data explosion over the last several years is driving a push to develop tools with artificial intelligence (AI) so that companies can use more of their data and extract meaningful insights to help with problem solving and informed decision making.
Healthcare could benefit tremendously from AI and machine learning due to the vast amounts of medical data and other types of information that health organizations collect and maintain. Machine learning could help organizations sort through the large libraries of data they may have to identify what medical data is most meaningful for problem solving and refining their care or business processes. With a deeper level of insight, they could make better decisions or explore new ideas and sources of competitive advantage.
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As healthcare data processing improves, the potential to acquire useful knowledge from large datasets increases. To understand how AI might transform healthcare, let’s explore the capabilities of analytics and AI tools built for health.
In analytics, data is reviewed after an event and predictions are made based on trends. For example, physicians know that factors such as age or medical history can make sepsis infections more dangerous for certain patients and can use that data to inform care.
Machine learning and AI can extend analytics with continuous learning and analyses. For example, an AI program can use massive amounts of depersonalized data from electronic health records to predict which patients are most at risk from a sepsis infection.
Healthcare organizations have so much data that humans can not possibly analyze it by themselves. Deep, continuous analyses with AI and analytics tools can find patterns people miss, which could help enhance services and move research forward.
Healthcare organizations are constantly investigating how they can innovate and keep up with changes in the industry. AI presents many unique opportunities for healthcare, but organizations need to take steps to secure and integrate their data before they start their journey to adopting AI tools.
With an increasing focus on patient outcomes, more incentives exist for system-wide data exchange. But significant challenges still stand in the way of seamless communication and collaboration across healthcare systems. The most daunting challenge is interoperability, or the ability of electronic health records (EHRs) and other healthcare data management systems to exchange information seamlessly.
Once there is a single source of truth and clinicians and care teams are able to quickly and securely access information, organizations may be ready to adopt AI tools that can glean deeper insights from the data.
However, before implementing AI tools, organizations must examine many factors. For example, organizations should seek out technology that is reliable and be prepared to maintain a skilled workforce to operate it. Other considerations include regulatory approval, obtaining user buy-in for new processes and programs, and determining how the solution integrates with existing tools.
There will almost always be challenges associated with adopting newer technologies, but the potential benefits of uncovering insights that could enhance care and business processes should be strongly considered as organizations seek out new ways to improve and compete.
Steps for implementing AI
AI-enabled tools are well suited to navigate the complex data that has been generated from modern clinical care. AI solutions can enable professionals to access insights from multiple sources more quickly, including medical notes entered by physicians, medical images, continuous monitoring data from sensors and more, and apply AI-generated findings for more informed, patient care decisions
Machine learning solutions, of which AI tools are a subset, can also learn the patterns of health trajectories for a vast number of patients and organizations. This can help leaders anticipate future needs and take steps to prepare. This is especially important as health organizations are working to stay on top of incidence trends of particular health conditions and efficiently meet their community’s needs.
AI can identify meaningful relationships in raw data and pullout relevant information without any lapses of attention.With AI, researchers can tackle complex problems that would be difficult –or perhaps almost impossible –for humans to solve alone.
One important way AI may be able to support researchers in life sciences is by streamlining the clinical development journey, including data organization, site selection, recruitment and patient monitoring. For example, AI technologies can help protocol development collaborators identify insights from real-world patient data that are highly relevant to their studies early in the process. That way, protocols can potentially incorporate better approximations of patient availability before the clinical trial moves forward. This advanced capability is helpful to researchers, but also for the patients who need treatments.
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