What does the AI-enabled future look like for Healthcare? Top three changes to expect.
Artificial intelligence (AI) has crossed the threshold from an emerging trend to accepted technology. It’s now being commonly used across industries, healthcare included. As adoption increases and investments spur innovation, it’s a good time to understand where it’s all leading.
So, where is AI taking us? Or better framed, where do we want to take AI? As AI continues to flourish, where could the “care” in healthcare be by 2030? What are the biggest changes we’ll see?
1. Predictive Care
At present, we have years of big data analytics experience and the infrastructure to support it. Yet, we are not mining that data in all the ways we could – not by a very long shot.
It’s estimated that companies only use or tap into about 1% of the data they have and store.1
Data silos are still a reality, so we’re not getting the synergy of seeing the entire picture. AI will help us access and aggregate more data, medical and non, from a growing number of locations, sources, and devices. Increasingly sophisticated analytics will untangle the complexities and make clear the subtleties across huge data volumes — to reveal patterns that help solve diagnostic and treatment puzzles. AI will allow health systems to deliver more targeted, proactive, and predictive care.
AI will also enable a deeper understanding of the social determinants of health (SODH)2 like birthplace, housing, diet, workplace, and income that the World Health Organization has linked to differences in people’s health status. We will be able to anticipate the risk for chronic diseases influenced by SODH, and reduce the incidence of diabetes, congestive heart failure, and COPD.
2. Connected Care
In addition to transforming what care is possible and anticipating when to deliver it, AI will help redefine where care is given. In the coming decade, large hospitals that treat a broad range of diseases will give way to decentralized hubs, with hospitals taking on acutely ill patients and highly complex procedures. Less urgent cases will be treated in smaller facilities like retail or specialty clinics, same-day surgery centers or at home care.
AI will help us bring these locations together under one digital umbrella, centralizing infrastructure and data analytics. We will use clinical and location data to send patients where they can be best cared for and direct healthcare professionals where they are most needed. Instead of physical location, the care delivery network will be driven by the interconnected needs of patients and clinicians. Moving forward, AI and connected care in this manner will ultimately increase patient access to care.
3. Patient-Centered Care
In 2022, competing priorities and strained resources continue to compromise patient satisfaction, even as person and community engagement is a performance pillar under the 2022 CMS Hospital Value-Based Purchasing (VBP) Program.3 We also face high levels of physician burnout that beg for a turnaround.
The fully realized AI-powered healthcare network will:
- Shorten patient wait times
- Optimize staff workflows
- Lighten the administrative load for physicians
Patients will benefit from AI-integrated clinical practices and augmented diagnostic and surgical skills. By 2030, AI will enhance our ability to learn from all patients, diagnoses, and procedures, helping us continually create experiences that satisfy patients and those who care for them.
Some of the 2030 vision is already happening. AI capabilities are being used from cancer detection in imaging, to patient flow in emergency departments. Outside the hospital, AI helps reduce hospital admissions by identifying at-risk groups who can benefit from pre-emptive primary or community care.
Enabling AI for The Future of Healthcare
AI is poised to enhance the field of healthcare moving forward, but it can only do so with the right resources in place. To work efficiently, AIs require either on-premises data center infrastructure to store large amounts of data or a robust cloud computing environment paired with quick response resources at the edge of computing. For larger data files, some hospitals prefer on-premises storage over the cloud, especially those who want an AI roll-out at scale and prefer a capital expense over an operational expense model.
Transformative system AIs in healthcare can and do run efficiently on a range of Lenovo solutions like the ThinkStation P920 Data Science Workstation, the ThinkStation PX and the ThinkStation P7 – all powered by Intel® Xeon® platforms.
The ThinkStation PX with 4th Gen Intel® Xeon® Scalable processors was developed to provide power and reliability for customized performance. As the most advanced dual-processor workstation, it’s ideal for the highest-performing workflows including rendering, simulation, visualization, deep learning, and AI. Superior performance and design features on the ThinkStation PX as well as the ThinkStation P7 with Intel® Xeon® W processors include Flex Trays, blind connection, Tri-Channel cooling with innovative air baffle design.
AI is increasingly being recognized as the wave of the future enhancing, and not replacing, human capabilities as we move towards 2030 and beyond.
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