Best AI Process Automation Use Cases for Healthcare

The World Bank statistics show that the world healthcare expenditure is rising in a rather stable manner with only slight anomalies, for example a significant rise during the Covid-19 pandemic in 2020, from time to time. The industry as a whole, be it private sector or public institutions, is in a constant search of cost optimizations and efficiency improvements, as decision makers need to deliver desired outcomes to shareholders and taxpayers respectively. And the situation is not getting better, as the healthcare industry suffers from bottlenecks that tend to hamper efforts to promote AI adoption in healthcare overall. The solution lies in cooperating with only the top custom AI agent consultancies for healthcare, who are equipped to provide end-to-end support with expertise from strategy through implementation. Partnering with a boutique AI agency, such as Vstorm, will provide the technological know-how required to bridge the gap between real.

TL;DR

• Healthcare sees an increasing demand for healthcare professionals and diminishing supply due to aging population and long training time required
• Significant portion of modern healthcare work is not patient-focused, but administrative
• AI Agents can be helpful in administrative tasks like appointment management, prescription management and document processing
• Healthcare is a highly regulated industry and the application of new solutions needs to be compliant and secure

Key bottlenecks faced by the healthcare AI market #

Apart from the technical or societal challenges healthcare needs to tackle, like the effects of an increasingly sedentary lifestyle or new pathogens emerging, there are more mundane problems which may be solved in a systemic way.

Increasing complexity of diagnostics

It was not a full hundred years between the first blurred image of Wilhelm Conrad Roentgen’s wife’s hand and the first MRI scanner , showing how modern diagnostics and treatment techniques rapidly evolve. These marvels of medicine require not only expensive equipment, but also a trained staff to man it and use it properly. And this leads to the next challenge.

Not enough physicians

According to McKinsey data , the US faced a shortage of up to 64,000 physicians at the end of 2024 and is expected to see the shortage of 86,000 physicians by the end of 2036. The problem is not limited to the US, as medical professional training is expensive and time-consuming. Also, the global population is aging, with the percentage of people over 65 rising from 5% in 1960 to 10% in 2024 , creating even greater demand for healthcare professionals. With rising demand and low supply, their time becomes ever more valuable, but its allocation can raise doubts.

Paperwork

As research from the University of Amsterdam shows, up to 35% of time spent by a physician with a patient is used for filling out paperwork. This means at least one out of every three hours of doctor’s work time is reserved for paperwork, not for actual care. Yet Healthcare is not only about physicians performing treatment on patients, albeit that is the core of it. There is also tremendous need for this paperwork, be it typical business day-to-day in keeping the lights on, legal, or facility maintenance, which creates administrative overhead that needs to be funded using money that could be assigned to saving lives. But all these challenges, and many more, can be tackled by a wise and strategic application of tailored agentic AI solutions to reduce healthcare administrative costs.

“The opportunity lies in using agentic systems to harness the intelligence currently trapped in healthcare data. Doing so could provide the ability to scale multi-disciplinary reasoning, collaboration, and process automation to support care providers so they can spend more time with their patients.”

• Dr. Taha Kass-Hout, MD, MS ; Global Chief Science and Technology Officer of GE HealthCare in “How agentic AI systems can solve problems in healthcare today,” December 10, 2024

Processes to automate with AI for healthcare #

AI Agents are versatile tools that can support healthcare processes in multiple way, either reducing or even eliminating the burden of radius tasks that wastes the time of valuable staff (think about this one-out-of-three hours of each physician’s work), or supporting healthcare professionals in doing their work more efficiently. In this context, it means providing clinical decision support to help reduce pain or save another's life. Interesting applications include (but are not limited to):

New treatment tools and processes

Agentic AI and neural networks are perfect tools to analyze and process vast amounts of data, often in contexts that are extremely challenging for humans to comprehend. This creates new opportunities to streamline patient care, reduce bottlenecks, or increase the comfort of treatment.

Vstorm case study – GlucoActive:

Vstorm has a proven track record of being helpful in custom AI development specializing in healthcare solutions. GlucioActive is a research and development startup focused on delivering medical care products for people with diabetes. The device the company produces uses laser beams to get through the skin and measure glucose levels without the need to collect blood samples. Vstorm proudly contributed to this project by providing all LLM and AI development and knowledge necessary to make the vision a reality. More detailed information can be found in the GlucoActive case study .

Appointment management

Medical Group Management Association data shows that no-shows cost the healthcare industry up to $150 billion each year. This wastes not only medical professional’s time, but also blocks a place that could have been used by another patient. Smarter and more agile management of appointments is the perfect use case for agentic AI, which could contact patients, update queues and ensure the effective use of time of physicians and healthcare professionals, rather than waiting for a patient who never appears.

Prescription management

Prescription drugs can deliver miracles of modern medicine, from antibiotic therapies preventing deadly infections, to vaccines, to cancer and AIDS treatment drugs. Yet when it comes to patients struggling with multiple diseases (for example, due to old age or an unhealthy lifestyle) drugs may interact with each other, causing unwanted side effects. Research published in the International Journal of Clinical Pharmacy shows that up to 45.1% of elderly patients using two or more drugs at once had at least one hospital admission related to Adverse Drug Reactions (ADR). The research also cites data showing that ADRs are the fifth most common cause of death. Artificial Intelligence in healthcare, capable of connecting, analyzing and processing immense amounts of multimodal data, can find a perfect application in supporting patient’s pharmacological therapy, ensuring as few ADR’s as possible while maximizing results.

Healthcare document processing

As mentioned above, the healthcare industry faces a physician shortage and an increasing demand for healthcare givers at the same time. Yet time allocation is imperfect, with every one out of three hours with each patient being spent on paperwork. Agentic AI assistants can take a huge part of this burden off the physician’s shoulders, handling the paperwork and documentation.

Vstorm Case study – multi-channel AI Agent for personalized appointments

A NASDAQ-listed healthcare provider from the US was looking for a way to improve the efficiency of appointments with healthcare professionals. To do so, the company was looking for the best of the best AI consulting companies for healthcare automation to develop a system that scans through all the information available about the patient, be it medical records, doctor’s notes, or history of prescriptions, enriched with information that the agent extracted from the patient in conversation. Using this data, the system prepared a brief for a doctor to be used before the meeting, saving time while keeping the quality of the care. More information can be found in our Multi-channel AI Agent for personalized appointments in Healthcare case study.

Challenges in AI in healthcare processing #

Building the best tailored RAG-based solutions for healthcare requires not only technical expertise, but also a deep and contextual understanding of the business background. Common pitfalls include:

• Lack of talent – not only healthcare itself is struggling with talent shortage. Hiring a skilled engineer, knowledgeable about LLMs, RAG, and the specifics of the healthcare industry in order to know what is applicable and what is not, can be a difficult thing to achieve.
• Compliance and regulatory environment – healthcare is a highly regulated industry, with HIPAA in the US, GDPR in the EU, and various other local regulations, the industry requires more care and gives less freedom than many other industries.
• Technological understanding – with the hype around the Artificial Intelligence, it is easy to overtrust and overestimate the capabilities of modern AI. It is a trap that can end up costing a lot of money, time and resources. And it may be one of the main reasons why 95% of generative AI project pilots fail .
• Vision-to-market fit – last but not least, just because something is possible does not mean it is useful. This principle applies to AI projects in healthcare also, with some looking brilliant on paper and in business presentations, yet lacking usefulness in daily operations.

Summary – finding the right AI healthcare partner #