Recent News: DNAMIC announced Number 1 of the Top Costa Rica Custom Software Developers by Clutch.co – Learn More Details
Menu
+1(347) 991-9855
+1(347) 991-9855
Recent News: DNAMIC announced Number 1 of the Top Costa Rica Custom Software Developers by Clutch.co – Learn More Details
$22.44 billion
There is no denying the hype surrounding AI. It is reinventing patient care, changing industries, and has the potential to make our organizations faster, smarter, and more effective. AI has enormous potential in the medical field and life sciences, from forecasting patient outcomes to streamlining clinical trials.
Grand View Research shows that the global AI in healthcare market generated $22.44 billion in 2023 and is projected to increase at a compound annual growth rate (CAGR) of 37.5% from 2024 to 2030, reaching a whopping amount of $208.2 billion. The growing need for improved accuracy, efficiency, and patient outcomes is driving this expansion.
Consulting firms like McKinsey & Company estimate that AI could automate up to 30% of healthcare tasks, from diagnostics to administrative work, helping reduce costs and improve care. They estimate AI could save the U.S. healthcare system up to $360 billion annually by enhancing efficiency and streamlining operations.
But for many organizations, there’s a frustrating gap between potential and reality. You have heard about AI’s power, and you are excited about the possibilities, but when you try to implement it, you hit a wall. Initiatives stall, teams get stuck, and projects never move beyond the idea or a very short pilot phase.
So, what’s really stopping you from harnessing AI’s full power? Is it your technology, your team, your partners, or something else entirely?
Building my data technology consulting firm from the ground up taught me the answer is simpler than you think: Success with AI starts with understanding the roadblocks. Let’s break them down.
All of the difficulties healthcare and life sciences organizations face attempting to implement AI fall into one of two basic categories:
• Internal Barriers (You Control Them): You can solve these issues within your organization if you know where to look.
• External Barriers (You Depend On Others): These are limitations caused by regulations, your technology ecosystem, partners, or third-party providers.
These challenges are within your reach to fix but often require changes to your people, processes, or technology.
Simple, low-risk AI tools, like automated reporting dashboards or internal chatbots, can often be deployed without waiting for leadership approval. The question is: Have you started? Basic training in granular but powerful topics like these can make a big difference.
Even with the best technology, an unprepared team will slow progress. If your team is buried in complicated reports or your clinicians don’t trust AI recommendations, it’s time to invest in training and redirect their efforts.
A critical but often underestimated barrier is the skills gap and lack of digital maturity. While 86% of healthcare organizations use AI, the HIMSS and Medscape 2024 report highlights major gaps in workforce readiness and digital maturity, stressing that AI success depends as much on people as technology.
Legacy systems and disconnected databases prevent AI from doing its job. If your team spends more time fixing spreadsheets than analyzing insights, your technology stack needs a serious upgrade.
Hospitals face a major challenge balancing current operations with digital transformation. The AHA notes that success will require bold action, not small steps, to shift from outdated systems to AI-ready, connected care models.
These challenges depend on your partners, vendors, and technology ecosystem. Ignoring them will stall even the most promising AI initiatives.
In healthcare and life sciences, compliance isn’t optional. Regulations like HIPAA and GDPR create hurdles for AI adoption. The solution? Work with partners who understand compliance and build privacy-first AI models.
Data privacy is critical. As AI adoption grows, so do concerns about handling patient data. Regulations like HIPAA and GDPR require strict safeguards, making privacy-first AI essential.
Want to integrate AI into your patient portals or clinical trials? It won’t happen if your technology providers are stuck in the past. If your vendors can’t support modern AI tools or real-time data sharing, it’s time to rethink those relationships.
Here’s the biggest barrier of all: If your company doesn’t have a centralized place to store and process data, like a data warehouse or data lake, AI simply can’t work. Without a unified data platform, your AI models operate blindly, basing their decisions on incomplete, outdated, or fragmented information.
This last point is usually the underlying limitation. So let’s address it.
Most companies fail to implement AI not because they lack ideas or ambition but because they lack data readiness. AI is only as good as the data it’s trained on. And without a modern data architecture, such as a Databricks lakehouse or AWS-powered data warehouse, your AI initiatives will stall before they even start.
A real-world example of overcoming this challenge comes from one of our clients, who faced fragmented data and stalled AI initiatives. By building a centralized, scalable data infrastructure, they unlocked AI-driven insights and accelerated growth.
Read the full case study and learn what’s possible for your organization.
Think of it this way:
So why would you even consider deploying AI without a modern data infrastructure?
When you ask these basic yet powerful questions, the path to move forward suddenly looks clearer.
What does a better journey look like?
To overcome these barriers and unlock the power of AI, your first step isn’t a model or a chatbot; it’s your data.
Why? AI needs fast, secure access to your clinical records, patient data, and research results, all in one place. Use proven platforms to unify your data and make it AI-ready.
AI is useless without accurate data. Ensure your data is complete, consistent, and compliant with healthcare standards.
Connect your electronic health records (EHR), research databases and patient engagement tools into one ecosystem.
Build an agile approach around learning; outline a roadmap of the future state of your company with AI, break it down into smaller pieces, prioritize, assign small teams to work on clear goals, define cycles, evaluate progress, communicate results, and iterate.
Johns Hopkins researchers developed AI models that predict ICU delirium risk with up to 90% accuracy, using data from more than 100,000 ICU stays. These tools are designed to help clinicians intervene earlier and improve patient outcomes in critical care settings.
AI has the power to transform healthcare and life sciences, reducing costs, improving outcomes, and accelerating discoveries. But without the right data infrastructure and process, you’ll remain stuck in pilot mode while competitors race ahead.
The good news? You don’t have to solve everything overnight. Start by solving the problem that unlocks everything else: your data.
Written on Forbes by
Arturo García is the CEO of DNAMIC – AI & Data Solutions.
Healthcare
Bioinformatics
Genomics
Pharmaceuticals
Biotechnology
Medical Devices
Health Informatics
HealthTech
DNAMIC | Databricks Data Solutions is a premier data services and technology company dedicated to transforming the healthcare and life sciences industries through data-driven AI solutions.
San Diego, California, United States
Healthcare
Bioinformatics
Genomics
Pharmaceuticals
Biotechnology
Medical Devices
Health
DNAMIC | Databricks Data Solutions is a premier data services and technology company dedicated to transforming the healthcare and life sciences industries through data-driven AI solutions.
San Diego, California, United States
This will close in 0 seconds
This will close in 0 seconds
This will close in 0 seconds
This will close in 0 seconds
