Four applications of AI in healthcare
Healthcare is more complex today. Stretched by increasing costs1, a global deficit of health workers2 and operational inefficiencies, the need to address demand for high-quality and efficient care is urgent. For healthcare IT leaders, the goal of operational efficiency looms large alongside the pressure to do more with the huge volumes of data they collect enterprise wide.
A health data insight deficit
While AI has been around for decades, most healthcare organizations are still at the very beginning of their AI journeys. AI is now gaining traction in healthcare because of its ability to help generate insights from large amounts of data – offering a much-needed helping hand to overburdened staff.
The amount and granularity of the stored digital medical and healthcare data a CIO oversees has increased exponentially, but just a fraction of it is being used to improve the efficiency and quality of care. This huge growth in volume and diversity is a concern for health IT leaders because the speed of data accumulation is far outpacing the ability to analyze it.
“What healthcare providers have is incredible data, but very few insights,” says Roy Smythe MD, Chief Medical Officer for Health Informatics, Philips Healthcare. “And what clinicians really want is insights – to tell them what they really need to know. For example, among the 2000 diabetics in their patient population, who are the 10 that they need to bring in for a different intervention? Those are insights that they need.”
AI applications in healthcare
The potential of AI to improve the healthcare delivery system is limitless. It offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise. Getting all aspects of AI-enabled solutions right requires extensive collaboration between clinicians, data scientists, interaction designers, and other experts. Here are four applications of artificial intelligence to transform healthcare delivery:
1. Improve operational efficiency and performance
On a departmental and enterprise level, the ability of AI to sift through large amounts of data can help hospital administrators to optimize performance, drive productivity, and improve the use of existing resources, generating time and cost savings. For example, in a radiology department, AI could make a difference in the management of referrals, patient scheduling, and exam preparations. Improvements here can help to enhance patient experience and will allow a more effective and efficient use of the facilities at examination sites.
2. Aiding clinical decision support
AI-enabled solutions can help to combine large amounts of clinical data to generate a more holistic view of patients. This supports healthcare providers in their decision making, leading to better patient outcomes and improved population health. “The need for insights and for those insights to lead to clinical operations support is tremendous,” says Dr. Smythe. “Whether that is the accuracy of interventions or the effective use of manpower – these are things that physicians struggle with. That is the imperative.”
3. Enabling population health management
Combining clinical decision support systems with patient self-management, population health management can also benefit from AI. Using predictive analytics with patient populations, healthcare providers will be able to take preventative action, reduce health risk, and save unnecessary costs.
As the population ages, so does a desire to age in place when possible, and to maximize not only disease management, but quality of life as we do so. The possibility of aggregating, analyzing and activating health data from millions of consumers will enable hospitals to see how socio-economic, behavioral, genetic and clinical factors correlate and can offer more targeted, preventative healthcare outside the four walls of the hospital.
4. Empowering consumers, improving patient care
As recently as 2015 patients reported physically carrying x-rays, test results, and other critical health data from one healthcare provider’s office to another3. The burden of multiple referrals, explaining symptoms to new physicians and finding out that their medical history has gaps in it were all too real. Patients now are demanding more personalized, sophisticated and convenient healthcare services.
The great motivation behind AI in healthcare is that increasingly, as patients become more engaged with their own healthcare and better understand their own needs, healthcare will have to take steps towards them and meet them where they are, providing them with health services when they need them, not just when they are ill.