Healthcare AI: From Theory to Practice
Artificial Intelligence (AI) is a set of technologies that includes automated systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. AI in healthcare can apply these systems to then perform administrative or clinical functions, completely augmenting human activity. In the US, healthcare organizations have yet to unlock the complete potential of AI, even though the majority are open to leveraging its extensive capabilities. The opportunity is clearly realized and has been greatly measured.
The Big Picture
AI has a strong reach and can magnify the reach of care by integrating health data across platforms. The 2020 Accenture healthcare AI report found that essential clinical health AI applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026. These cost savings potential is one reason why healthcare organizations will continue to invest in digital solutions to deliver new sources of value over the foreseeable future; whether its to lower the cost of care, improve labor productivity, enable better patient/customer experiences, or another desired outcome.
AI is getting increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. Building off of the Accenture AI report, growth in the AI health market is expected to reach $6.6. billion by 2021, with the market growing more than 10x by 2025. Patients/consumers also want AI, which leads to data security questions and fears. Organizations within the digital health/AI ecosystem will need to work together and in an ethical way, and be secure in how they manage critical information on patients.
Healthcare organizations mostly all agree that AI and machine learning will affect the future of work, therefore, have an opportunity to employ AI to make a systematic shift that affects every process, piece of data, healthcare professional, and patient. While investments and adoption are expected to exponentially grow, organizations must understand the full potential value AI delivers financially, systematically, and organizationally.
AI can help prevent medical mistakes and flag those most at risk for problems. In the January 2020 publication of the scientific journal Nature, researchers found that by using AI technology, there was a reduction in false positives and false negatives when it came to diagnosing forms of breast cancer. Through reduced labor costs alone AI represents a notable opportunity for organizations to better control their bottom line in new payment regard while capitalizing on new growth potential.
Several surveys conducted in 2019 found that the ability to improve or assist in the accuracy of diagnosis and clinical outcomes is the greatest impact AI will have in the short-term on patient care. This same survey also found that of those organizations that have yet to adopt AI, cost remains the most significant obstacle in implementation—followed by a lack of strategic direction” within their organization.
While many American healthcare professionals are not currently using AI, many healthcare professionals would feel comfortable using it for a range of areas, suggesting a general openness to unlocking the full potential of AI in the future. As these and other AI applications gain more experience in the field, their ability to learn and act will continually lead to improvements in precision, efficiency, and outcomes.
AI’s Role in Healthcare and a Path Forward
With much of health and health care moving onto digital platforms, there has been remarkable growth in the amount of information generated. As the conversation on the potential wonders of AI becomes more mainstream, it is easy for misguided fears and optimism to hide its legitimate near-term possibilities. The purpose is to build competency in AI and data science to the point that health care AI presents an assistive benefit to humans rather than replacing them. And of course, the need for privacy and huge amounts of data remains a challenge for AI in healthcare.
Mistaken fears and reporting isn’t the only limitation to AI adoption; low healthcare system literacy is a key barrier for any new technology or service model. This is notably true in the US, where reports are finding over half of the American population ranks low in healthcare system literacy – causing an estimated $4.8 billion annual organizational cost burden for payers. Our next article will cover the role digital health technologies can play in improving healthcare system literacy, also known as social detriments of health.