Many countries—regardless of their income level—are facing the same growing crisis: a shortage of health care workers. The World Health Organization estimates that by 2030, the global health sector will be short more than 11 million professionals. This shortage doesn’t just strain hospitals and clinics, it directly impacts access, quality, and outcomes for patients everywhere.
One possible solution? Artificial intelligence.
AI can’t replace doctors or nurses, but it can help them do their jobs better, faster, and with less administrative burden. It can reduce the time spent on documentation, streamline operations, and even improve clinical decision-making. That’s why so many health care organizations are turning to AI not just as a tech trend, but as a strategic priority.
According to McKinsey, 85% of health care leaders in the U.S. are already implementing or actively developing generative AI initiatives.
At Inclusion Cloud, we’re seeing this shift play out in two major areas:
- The bureaucratic and operational side, where paperwork and inefficiencies slow down care
- The clinical and research front, where AI is helping providers detect illness earlier, engage patients more meaningfully, and accelerate innovation
1. Bureaucracy: The Hidden Burden in Health Care
Let’s start with the paperwork.
Health professionals in the U.S. spend an average of 1.77 hours per day on documentation alone—and that doesn’t even include time spent on insurance, billing, or managing patient records. Between navigating EHRs, complying with coding standards, processing claims, and logging clinical notes, a significant portion of their day is pulled away from what matters most: caring for patients.
This growing administrative burden is taking a visible toll. Burnout among physicians and nurses is on the rise, and patient satisfaction is declining—not because providers aren’t doing their best, but because the system demands too much of their time and attention. In many cases, the doctor-patient interaction is filtered through a screen, with clinicians forced to type and document instead of focusing fully on the person in front of them.
AI is beginning to help shift that balance:
Ambient Listening
Nuance DAX, a solution developed by Microsoft-owned Nuance, is helping reduce documentation time by converting doctor–patient conversations into structured clinical notes in real time. Atrium Health—one of the largest health systems in the U.S., with 40 hospitals across several states—has integrated DAX Copilot into its primary care practices. Reports show it saves an average of 7 minutes per appointment, enabling up to five additional visits per day.
But these gains in efficiency also bring up important questions around privacy and consent. Ambient listening tools require that patients agree to have their conversations recorded, and providers must clearly communicate how the data will be used, stored, and protected.
Automated Billing and Claims
Mayo Clinic has actively implemented AI-driven solutions to optimize its revenue cycle management. According to leaders at a 2024 HFMA conference, Mayo built 34 “virtual workers”—automated workflows powered by AI—that now handle millions of revenue-related tasks. These systems streamline claims submission, coding accuracy, and denial management—all while integrating smoothly with existing workflows.
In addition, a study published in Annals of Emergency Medicine involved Mayo Clinic’s Department of Emergency Medicine and showed that AI models using natural language processing could predict evaluation and management billing codes for emergency department visits with upwards of 92% accuracy for high-complexity.
Predictive Scheduling and Patient Flow
Cleveland Clinic is redefining hospital logistics through an AI-powered Virtual Command Center built in collaboration with Palantir Technologies. The system integrates real-time and historical data to optimize patient flow, staffing levels, and operating room (OR) scheduling across multiple facilities.
At the core of this platform are three AI-enabled modules:
- Hospital 360 tracks bed occupancy and forecasts capacity, improving patient transfers and reducing bottlenecks.
- Staffing Matrix predicts patient volume and aligns nurse schedules accordingly, helping nurse managers plan shifts with greater accuracy and less manual effort.
- OR Stewardship identifies scheduling opportunities, predicts case needs, and aligns surgical resources.
These systems replace the manual, fragmented approaches previously used—such as Excel files, phone calls, and emails—and have proven to save hours of administrative work while increasing agility and responsiveness.
Automated Patient Communication
Hospitals using Google Cloud’s AI tools are automating patient touchpoints such as appointment reminders, insurance verifications, lab results notifications, and follow‑ups using chatbots or voice assistants. These systems plug directly into Electronic Health Records (EHRs), ensuring that every message is personalized—references the patient’s name, appointment history, and unique health needs.
EHRs are digital versions of patients’ medical charts. They store treatment notes, test results, billing information, insurance coverage details, and more. By integrating AI with EHRs, hospitals can automate outreach, offer real-time scheduling adjustments (e.g., prioritizing urgent cases), and ensure communications reflect each patient’s care context—without overloading staff.
The result? Lower call-center volumes and better patient experiences. But it’s essential that systems are monitored to maintain message clarity and that any escalations (e.g., for urgent issues) are smoothly transferred to human staff.
Our work in Health Care & Pharma
Before we get into how AI is reshaping patient care, it’s worth sharing some of what we’ve seen up close.
At Inclusion Cloud, we’ve been working closely with healthcare and pharmaceutical organizations as they move through their digital transformation journeys—modernizing legacy systems, automating workflows, and strengthening their data foundations.
We helped Roche implement a solution to streamline electronic credit invoicing across multiple SAP environments. At Sanofi, we rolled out invoice automation across eight countries, improving productivity and standardizing processes for local teams. And with Bayer, we accelerated the delivery of mission-critical applications by assembling agile teams tailored to their internal development needs.
All these projects have something in common. To truly innovate and tackle the daily challenges we see—staff shortages, rising system complexity, and the need to improve quality of care—you need clean data, integrated systems, and the talent to make it all run smoothly.
You can explore more examples in our case study section, but for now, let’s move to the front lines—where AI is already starting to support doctors, researchers, and patients in new ways.
2. Patient Care and R&D: From Reactive to Preventive
While most of today’s AI implementations in hospitals focus on administrative efficiency, clinical use cases are starting to show some of the most promising results.
Surprisingly, some diagnostic models have already outperformed human doctors in certain tasks. A 2024 JAMA Network study found that large language models trained on clinical datasets provided more accurate and empathetic diagnostic answers than physicians in multiple specialties. This highlights the need for both technology and workforce development to fully realize the potential of physician–AI collaboration in clinical practice.
This is where AI shows real potential: supporting doctors by analyzing vast amounts of data—lab results, imaging, historical records—and surfacing patterns or correlations that might take humans hours or days to detect, if ever.
These systems still need clinicians in the loop. But with proper oversight, they can reduce diagnostic error, speed up discovery, and free medical professionals to focus more on human care.
That said, clinical AI comes with a heavy responsibility. These models rely on sensitive health data—and unlike administrative automation, their outputs can directly affect medical outcomes. That’s why proper governance, data quality, and patient consent are critical topics we’ll explore more deeply in the next section.
Here are some of the most advanced real-world examples of AI supporting diagnosis, care, and discovery today:
Fast Triage
In emergency medicine, every second counts. That’s why the Yorkshire Ambulance Service in the UK uses AI to triage calls more efficiently. The system analyzes real-time data—like the caller’s description, tone of voice, and symptom keywords—alongside historical patterns to prioritize the most urgent cases.
This AI support allows dispatch teams to make faster, more informed decisions and ensures that life-threatening situations are addressed immediately. In trials, the model helped improve response times for cardiac arrest and stroke calls by flagging subtle signs that human operators might miss.
Early Detection
Preventive care is often more effective and less costly than reactive treatment—and AI is becoming a critical ally here. Organizations like AstraZeneca and the UK Biobank are using machine learning models to scan millions of anonymized medical records, looking for early indicators of chronic diseases like diabetes, heart conditions, or kidney failure.
These models can detect risk factors even before symptoms appear, allowing physicians to recommend interventions sooner. For instance, AI can identify abnormal biomarker patterns or lifestyle-related flags in EHRs that could signal pre-diabetes long before traditional diagnosis methods would.
Smarter Imaging
Google Health’s research is pushing the boundaries of what AI can detect in medical images, and who can benefit from those insights.
From lung scans to mammograms and even external eye photos, AI models are being trained to identify subtle signs of disease that might be missed by the human eye. One standout example is Google’s AI model for breast cancer screening, which demonstrated performance on par or better than radiologists in retrospective studies published in Nature. The model is now being tested in real-world settings with Northwestern Medicine to reduce false positives and accelerate time to diagnosis.
In collaboration with the Mayo Clinic, Google is also developing tools that use AI to plan radiotherapy treatments more efficiently. By helping segment tumors and surrounding tissue, these models aim to reduce time spent on manual prep, freeing up clinicians to focus more on patients.
Other examples include:
- Lung cancer screening: Google’s deep learning models helped detect early signs of cancer, even when tumors were missed in traditional workflows.
- Anemia detection from eye images: A study published in Nature Biomedical Engineering showed AI could estimate hemoglobin levels using simple photographs—pointing to new, non-invasive screening possibilities.
- Diabetic biomarkers from external eye photos: Instead of requiring specialized retinal imaging, AI models could predict conditions like diabetic retinopathy or kidney issues from standard external eye images, helping expand access to care in underserved areas.
These advances are still under research and clinical evaluation. But they hint at a future where AI helps radiologists and specialists interpret images faster, more accurately, and in ways that reduce diagnostic delays for patients.
AI won’t replace clinical judgment, but it can uncover patterns across millions of images and data points, making diagnostics more scalable and precise.
Drug Discovery
Traditionally, identifying a new drug candidate takes years of trial and error in the lab. But with AI, that timeline is shrinking. Harvard’s Zitnik Lab built an AI model that combined chemical compound libraries with gene expression data to identify potential therapies for Parkinson’s disease.
The result? Promising candidates that would have taken months or years to find surfaced in a matter of weeks. By simulating biological reactions and mapping how molecules interact at scale, AI can accelerate the pace of biomedical innovation.
At AstraZeneca, researchers are using AI across 70% of small molecule projects to design compounds faster and with higher precision. One technique, inspired by reinforcement learning, helps chemists test thousands of molecular structures virtually and choose the best candidates before any lab work begins. This not only speeds up discovery but also improves the chances that a drug will succeed in clinical trials.
AI is also helping design more complex therapies, like antibodies. Instead of screening thousands manually, researchers can now analyze hundreds of millions of antibody sequences per experiment, paving the way for faster, more targeted treatments for diseases like cancer and autoimmune disorders.
For patients, this means a shorter wait for effective therapies and a better shot at personalized medicine. At Genentech (part of Roche Group), AI models are trained to predict the most effective cancer vaccine for each patient by analyzing unique tumor mutations. This “lab-in-a-loop” system rapidly iterates between AI prediction and lab validation—bringing promising therapies to the clinic faster and with greater accuracy.
Clinical trials are also being reimagined. AstraZeneca uses AI to scan electronic health records and identify ideal participants, improving trial design and reducing time to recruitment. Even tasks like counting coughs in respiratory studies—once done manually—are now automated using AI-trained audio models, freeing up valuable time and improving consistency.
In the end, AI is changing how patients experience care—by enabling earlier detection, more effective treatments, and the hope that tomorrow’s cures will arrive much sooner.
Remote Monitoring
At Mount Sinai Health System in New York, AI-powered tools are transforming how clinicians monitor patients outside the hospital—especially those managing chronic conditions or recovering from surgery. These systems continuously analyze patient data, including vitals from wearables, self-reported symptoms through mobile apps, and real-time metrics from connected devices.
If an AI model detects early signs of trouble—such as increasing heart rate variability, abnormal blood oxygen levels, or a pattern of missed medications—it automatically triggers an alert to the patient’s care team. This enables proactive intervention, often days before a human would have noticed the change.
For doctors and nurses, this means they no longer need to rely solely on periodic check-ins or patient calls. Instead, they get timely, data-driven signals that help them prioritize care, respond faster, and personalize treatment adjustments based on dynamic trends. This approach reduces burnout by filtering noise and focusing clinical attention where it’s needed most.
For patients, it offers peace of mind. They can recover in the comfort of their homes while still being “digitally watched over” by their healthcare providers. It reduces unnecessary visits, helps avoid serious complications, and empowers patients to play a more active role in their health.
Remote monitoring isn’t just a convenience—it’s a shift toward more continuous, preventive, and personalized care. And with AI as the engine behind it, the healthcare system moves from reactive to truly anticipatory.
The Challenges Ahead
Despite the remarkable promise of AI in health care, real-world implementation is anything but simple. From our experience supporting digital transformation in hospitals, life sciences companies, and health systems, these are the core challenges and opportunities they open up when addressed strategically:
1. Data Integration
The challenge: AI needs large, clean, and connected datasets to work effectively. But in many health systems, patient data is scattered across disconnected platforms—ranging from paper records and Excel files to outdated EHRs. Clinical, billing, imaging, and pharmacy data often live in separate silos, making it difficult for AI to find patterns across the full patient journey.
The consequence: Incomplete or inconsistent data leads to inaccurate predictions and missed opportunities to intervene early. Worse, if AI models are trained on biased or partial datasets, they can perpetuate inequality in diagnosis and care.
The opportunity: Cloud-based data lakes, standardization frameworks (like FHIR), and platform integrations (e.g., ServiceNow, Salesforce Health Cloud) can streamline and unify fragmented data. This paves the way for more comprehensive AI models, supports regulatory compliance, and ultimately leads to smarter, faster decision-making across the care continuum.
2. Privacy and Consent
The challenge: Health care data is among the most sensitive and heavily regulated. HIPAA, and other frameworks require strict controls over who can access, share, and analyze patient information—especially when data is used to train or deploy AI models.
The consequence: If privacy protocols are overlooked, hospitals risk reputational damage, legal consequences, and loss of public trust. Patients may also be less willing to opt in to data-sharing programs that are essential for advancing AI research.
The opportunity: Techniques like federated learning (where data stays on-premises while models are trained across systems), differential privacy, and zero-trust architectures can help maintain compliance while still enabling powerful AI applications. Transparent consent models also build patient confidence and open doors for more collaborative innovation.
3. Model Bias and Validation
The challenge: If training datasets don’t represent diverse populations—in terms of race, gender, age, socioeconomic status, or geography—the resulting tools may deliver skewed or unsafe results.
The consequence: Biased algorithms in health care can lead to misdiagnoses, under-treatment of vulnerable populations, or overconfidence in flawed recommendations. This is especially dangerous when AI influences clinical decisions or treatment prioritization.
The opportunity: Rigorous validation processes, independent audits, and synthetic data generation can help detect and reduce bias before deployment. Building diverse datasets through collaborations with public health institutions and global partners is key to ensuring equity and generalizability across populations.
4. Workforce Readiness
The challenge: Many physicians, nurses, and administrative staff haven’t been trained to interpret or act on AI outputs. Even well-designed tools can go unused if they’re poorly understood, or if clinicians don’t trust the technology behind them.
The consequence: Without proper adoption, even the best AI solutions remain underutilized. It can also lead to a dangerous overreliance on automation, or unnecessary skepticism that stalls progress.
The opportunity: Training programs, change management strategies, and clinical-AI liaison roles are essential to bridge the gap. When paired with intuitive interfaces and real-time explanations (“explainable AI”), clinicians can begin to see AI as a true partner in care—not a black box.
Equally important is hiring the right technical talent. Organizations need engineers and specialists who can build, integrate, optimize, and maintain these AI systems—ensuring they perform reliably, scale effectively, and stay aligned with clinical needs.
5. Integration with Legacy Systems
The challenge: Hospitals rely on critical platforms like Epic, Cerner, and Allscripts for everyday operations. Any new AI tool must plug into these ecosystems without disrupting workflows, compromising data accuracy, or increasing cognitive burden on staff.
The consequence: If AI implementation slows down existing processes or demands too much from IT teams, adoption grinds to a halt. Fragmented tooling can also frustrate clinicians and create more harm than good.
The opportunity: AI orchestration platforms, API-first integrations, and purpose-built healthcare middleware (e.g., MuleSoft, IntegrationHub) can smooth interoperability. The key is aligning AI tools with existing care pathways, so they become invisible accelerators.
Final Thoughts
The global health care talent shortage is real, and it won’t be solved overnight. The World Health Organization estimates we could be short 10 million health workers by 2030. Meanwhile, the demand for care keeps growing as populations age and chronic conditions rise.
That’s where AI can help. Not only by taking the administrative burden off our shoulders, but also by giving us back the time we need to provide more precise and human-centered care.
And by making operations more efficient and helping clinicians focus on what matters most, AI gives us a way to do more with the resources we have. In fields like radiology, we’re already seeing AI models match or even exceed the accuracy of specialists in early detection and triage. And that’s just the beginning.
But unlocking this potential doesn’t happen on its own.
Health professionals need training and trust in the tools. Behind the scenes, you also need the right people to build, integrate, and maintain the systems that make AI work. And you need a team that understands how to do this in a way that’s secure, ethical, and aligned with your strategy.
At Inclusion Cloud, we help health care and pharma organizations prepare their data, connect their systems, and support their AI journey with certified, senior tech specialists. If you’re exploring how to bring AI into your organization, we’d love to help.
Let’s schedule a discovery call and talk about what you need to move forward!