Artificial intelligence (AI) is transforming healthcare. From accelerating drug discovery to improving cancer screening, AI has the potential to radically improve patient outcomes while reducing costs. However, successfully implementing AI in healthcare remains challenging.
In this post, we’ll explore the key benefits AI offers healthcare as well as the main barriers to adoption. We’ll also look at practical steps health systems can take to overcome those challenges and drive value from AI investments today and into the future.
The Promise of AI in Healthcare
AI refers to computer systems designed to perform tasks normally requiring human intelligence. In healthcare, AI applications analyze complex medical data to uncover insights that improve everything from clinical decision-making to hospital operations.
According to Accenture, key clinical AI applications could potentially create $150 billion in annual savings for the US healthcare economy by 2026.
At the same time, the global healthcare AI market is expected to grow from $6.9 billion in 2021 to $67.4 billion by 2027, an impressive CAGR of 46.2%, according to Reports and Data. This growth will be driven by both increasing adoption and AI solutions tackling more impactful healthcare challenges.
Broadly, AI offers three main benefits to healthcare:
1. Better Outcomes for Patients
AI is improving practically every aspect of patient care.
In diagnostics, AI algorithms analyze medical imaging and patient data to detect diseases earlier and more accurately. Studies show AI can diagnose certain cancers and interpret medical images as well as experienced clinicians. AI is also proving highly effective at analyzing pathology slides to detect cancer.
By accelerating diagnosis and improving accuracy, AI helps get patients the right treatments sooner. Equally importantly, AI reduces misdiagnoses which lead to harmful treatments or delays in proper care.
In pharmaceutical research, AI algorithms analyze data from clinical trials to determine which new therapies are most promising. Other AI systems scan vast databases of research to uncover potential new drug candidates. By automating and enhancing pharmaceutical R&D, AI will accelerate the development of innovative, life-saving treatments.
Once patients are diagnosed, AI helps ensure they receive the best care. AI chatbots, for example, can provide personalized education and counseling, helping patients better manage chronic conditions. Other systems analyze electronic health records (EHRs) along with guidelines to recommend tailored treatment plans for individual patients.
During procedures, surgeons are leveraging AI and robotics to perform minimally invasive surgery with greater precision. Early research suggests AI-assisted surgery leads to better outcomes for certain procedures.
Post-discharge, AI chatbots and remote monitoring technology allow patients to get follow-up care and assistance managing recovery. Together, this new wave of AI-enabled treatment is helping patients recover faster and live healthier lives.
2. Lower Costs
With healthcare spending rising unsustainably, AI’s potential to cut costs is generating tremendous excitement.
According to Accenture, key AI tools could save up to $150 billion per year by reducing administrative waste, preventing unnecessary treatments, shortening hospital stays, and more. Specific examples include:
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Administrative automation: AI is improving backend processes such as scheduling, documentation, billing and claims processing. Bots and other automation tools optimize these activities to reduce overhead costs.
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Reduced length of stay: AI helps healthcare providers diagnose conditions quicker and deliver more effective treatments. This allows patients to leave the hospital sooner, significantly cutting costs.
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Fewer unnecessary procedures: By enabling earlier, more accurate diagnoses, AI prevents patients from receiving unnecessary surgeries and other treatments. This improves outcomes and reduces risks associated with inappropriate procedures.
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Better resource allocation: AI analytics optimize use of high-cost resources like OR time and imaging equipment by ensuring patients who need them most get prioritized access.
While cost savings potential is substantial, realizing it will require healthcare organizations to carefully implement AI to maximize impact. We’ll explore keys to success later in this article.
3. Improved Clinician Workflows
In addition to directly helping patients, AI is making clinicians’ jobs easier.
Doctors spend significant time on EHR data entry and other administrative tasks. AI-powered digital scribes listen in on patient visits to automatically document key details. Other tools like conversational bots handle appointment bookings and medication refills, allowing doctors to focus on patient care.
For analysts, AI can automate the most tedious parts of their workflow. Algorithms flag anomalies in data and surface relevant insights without requiring manual queries or statistical analysis. Data scientists can then more efficiently investigate these high-potential leads rather than combing through data themselves.
By automating mundane tasks, AI lets skilled medical professionals devote their expertise to where it matters most – improving the health and lives of patients.
Challenges of Implementing AI in Healthcare
While AI holds tremendous promise, successfully applying it in healthcare remains difficult. Hospitals, insurers, and other health industry organizations face 3 main challenges when deploying AI:
1. Data Scarcity and Bottlenecks
Training accurate AI models requires massive amounts of high-quality, well-organized data. Unfortunately, such data remains in short supply in healthcare.
Hospitals frequently have data trapped across multiple siloed IT systems. Important information also sits buried in doctors’ notes and medical images rather than structured databases.
Privacy restrictions rightfully limit sharing of patient data. But this further exacerbates data scarcity. For example, cancer screening algorithms need access to a wide variety of patient scans to learn how to detect tumors. Yet data sharing between hospitals is nearly impossible today.
Finally, key healthcare data like readings from clinical trials is private and proprietary to pharmaceutical companies. AI startups often struggle getting access to enough data to prove the value of their solutions.
Together, these bottlenecks mean that even the most advanced AI algorithms struggle reaching their potential in healthcare. For instance, AI diagnostic accuracy for certain cancers is highly inconsistent, with some tools performing very well and others quite poorly depending on the data available for training.
2. Integration Difficulties
Hospitals use a vast array of IT systems for everything from clinical systems to financial and operational software. Connecting new AI algorithms into these fragmented environments is challenging.
Similarly, many AI solutions rely on modern cloud platforms. Integrating these with legacy hospital IT infrastructure can prove problematic.
Without seamless integration, it’s difficult for hospital staff to access and act on the insights AI provides. Poor integration also hinders collection of data back into AI platforms for improved model training.
Solving these IT challenges is crucial. One study found lack of integration between AI and existing health IT systems was the top barrier to adoption, outranking even AI’s high costs.
3. Organizational Hesitance
Though excitement about AI’s potential is high, healthcare organizations remain rightly skeptical about practical benefits and wary of disruption.
Doctors and other clinicians are often cautious about adopting new technologies. Earning clinician trust is crucial, as years of research make clear that technologies which don‘t actively engage doctors and nurses fail to drive meaningful improvements in care delivery.
Beyond trust, many healthcare workers fear AI will replace them. Even if unfounded, such perceptions can breed resistance to AI projects. Change management and internal communications must be handled delicately to overcome this hurdle.
On the financial side, hospitals and insurers are wary of investing in AI that fails to deliver projected ROI. Proving the hard dollar value of AI – whether through pilot projects or other means – is key to convincing decision makers.
How to Overcome AI Challenges in Healthcare
While barriers exist, lessons and best practices are emerging for successfully deploying AI in healthcare:
1. Start with Targeted Pilots
The most effective approach is implementing AI incrementally via a series of targeted pilot projects.
Pilots should focus on alleviating specific pain points, such as improving diagnosis for a costly condition or automating an inefficient administrative workflow. Avoid massive, open-ended AI initiatives until you‘ve built internal buy-in and experience.
Target areas where AI can augment clinicians’ expertise and workflows, rather than replace them, to minimize organizational resistance. Similarly, look for pilots that can quickly start generating ROI to prove out the benefits of AI and build support for expansion.
2. Address Data Needs Upfront
Data scarcity will hinder AI at scale. Alleviate bottlenecks early and often:
- Work with clinicians to identify and digitize data trapped in analog formats like medical images and doctors’ notes. Structure this info for use in training algorithms.
- Modernize IT infrastructure, implementing standardized data models, APIs, and modern tools like FHIR to unify data access.
- Partner creatively with AI vendors on data sharing to overcome limitations, e.g. by training models on proprietary data, testing solutions locally then installing for your exclusive use.
3. Take an Open Platform Approach
An open AI platform, built on public cloud infrastructure, solves many integration headaches. Open platforms provide:
- A centralized data layer bringing together disparate apps and data sources via modern APIs and formats like FHIR. This enables unified data access for AI.
- A way to readily deploy AI solutions with pre-built connectors into existing IT systems like EHRs and PACS. This reduces integration costs.
- Scalability and accelerated innovation via a cloud-based environment designed for rapidly deploying and iterating on AI apps.
Equally importantly, open platforms engage clinicians. Solutions that tightly embed into existing clinical workflows and data access points maximize adoption.
4. Train Clinicians on AI Capabilities
Doctors, nurses, and other staff require training both on using AI tools as well as new capabilities the technology enables. For example, if deploying an AI-assisted diagnostic system, clinicians need education on its performance, optimal usage scenarios, and how to interpret its output.
Continuing education helps clinicians build trust in AI systems by improving transparency into how solutions work and when they deliver the highest value. Hands-on training also boosts user adoption.
5. Create an AI Advisory Board
Form a steering committee of clinical and operational leaders along with AI and IT experts to oversee your overall AI strategy. The group provides critical guidance on where AI can drive maximum impact as well as inputs on change management, communications, and more.
An advisory board lends further credibility to AI initiatives and helps get buy-in across the organization. Including clinicians ensures AI projects target true pain points and integrate effectively into real-world provider workflows.
The Future of AI in Healthcare
While still early, AI in healthcare is gaining momentum. In coming years, we will see AI transition from cutting-edge pilots into standard care.
Here are three predictions on how healthcare AI will evolve through the 2020s:
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AI will increasingly focus on population health. Today most AI applications target individual conditions and encounters. The future is AI that looks holistically across entire patient populations to drive better outcomes, lower costs, and greater equity.
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Hybrid AI + Clinician teams will become the norm. Rather than competing with doctors, most impactful AI solutions will be designed to tightly integrate with and augment human expertise. The greatest advances will come from combining AI and clinical strengths.
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Healthcare‘s competitive balance will shift. AI success requires data, talent, and modern IT systems. Large academic hospitals and payer-providers will adopt AI fastest, gaining an edge over smaller providers. But public cloud platforms may allow creative health companies to compete. New partnerships will emerge.
The bottom line is that AI holds enormous potential to transform nearly every facet of healthcare. But thoughtfully addressing key barriers like data access, integration and change management is crucial to realizing that potential in the real world. Healthcare organizations that invest now in building their AI foundations will gain a lasting competitive advantage as artificial intelligence reshapes medicine in the years ahead.