How AI is revolutionizing healthcare back office

AI-powered healthcare has been a prominent topic in recent years. Every day, we learn about innovative ways AI can contribute to disease detection and support surgical procedures. However, what about its potential to enhance operational efficiencies within healthcare back office systems and applications? Let's delve deeper into this subject.

The AI revolution in healthcare is a topic that could easily fill a 500-page book. Machine learning, powered by big data, is tackling diagnostic challenges by improving accuracy and efficiency. It supports decision-making with deep learning techniques such as data mining, which recognize important disease patterns in huge data sets. So far, AI has proven highly effective in predicting various types of cancer and demonstrated greater accuracy than clinicians.

AI also plays a crucial role in optimizing drug dosing and predicting adverse events, promoting patient safety and treatment efficacy. AI algorithms enable personalized healthcare plans and drug dosages and help predict future health trends and risks of chronic diseases. It may sound like science fiction, but it will soon be our reality. However, what about leveraging AI for back office functions and supporting healthcare providers in their daily routines? It’s not a secret that healthcare workers have been struggling with inefficiencies in back-office systems for years. Now, with AI in work tools and internal applications, there’s a chance to automate some time-consuming tasks and improve both the staff’s efficiency and well-being. Let’s have a closer look at this topic.

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First, it seems that AI has the potential to alleviate the global staffing crisis in the healthcare sector and improve personnel management. One of the most important applications here is the integration of chatbots and virtual assistants, which serve as the first point of contact between healthcare providers and patients.

Often, healthcare professionals are stuck attending calls, completing administration-related tasks, and some trivial activities that could be resolved automatically with minimal human intervention by introducing chatbots. For example, more and more healthcare providers use chatbots to schedule appointments. Users can interact with the chatbot to find available time slots, book appointments and receive confirmation details, streamlining the appointment scheduling process and freeing employees’ time.

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A question arises: How do patients feel about talking to robots about their health condition? Research shows people have a mixed attitude towards AI as their healthcare provider, depending on the context, type of AI system, and participants’ characteristics. Some surveys have indicated that people are generally willing to use or interact with AI for health-related purposes such as diagnosis, treatment, monitoring, or decision support, yet other studies show that people still prefer human healthcare practitioners over AI, especially for complex or sensitive issues such as mental health, chronic diseases, or end-of-life care.

In one of the surveys, 60% of participants expressed discomfort with providers relying on AI for their medical care . However, the same study found that 80% of Americans would be willing to use AI-powered tools to help manage their health. Experts draw attention to the fact that patients often trust medical staff unconditionally – and this trust is vital in healthcare. This is why it’s crucial to build a relationship with AI, too – which will take time and depend on various factors, such as patients’ age, gender, education level, cultural background, and previous experience with technology. And the patients’ mixed attitude is natural and similar to users' general feelings about AI-powered apps.

Coming back to the main topic – chatbots are not the only way to relieve the medical staff from redundant tasks. AI can also help doctors and nurses collect relevant patient data, easing the cognitive burden on caregivers. While many health facilities already use Electronic Medical Record (EMR) systems to store patient information, they have led to increased data entry burdens. A survey from 2022 revealed that 63% of US physicians experience burnout, partly due to heightened documentation demands. And that’s precisely the place for AI solutions that integrate with EMR and automate data input. They can help doctors and medical staff find relevant information go through large sets of results whenever it’s necessary, and organize medical records, reducing the time needed to find and retrieve information.

For example, Doctolib, a French tech company valued at nearly €6 billion, has just announced plans to introduce AI assistants in medical appointments to help doctors save time on note-taking and information filtering. This is part of the company's ongoing efforts to enhance patient care and streamline healthcare processes. Doctolib is also in talks with British doctors and wants to expand into the US.

As a side note, the general understanding of these technological improvements is not that it will replace humans. As for now, machine-learning systems work on a narrow range of tasks and will need close supervision for years to come. This means that people are still central to the process. Still, regarding the healthcare staffing shortages worldwide, AI can be a powerful tool for employees to do more satisfying work instead of spending time on tedious tasks.

AI also transforms healthcare by helping providers quickly identify issues and suggest the best actions. For instance, it can recommend effective treatments or medications for individual patients, making faster and more accurate decisions. Large language models can extract and summarize key information from large volumes of data, such as patient referrals, lab results, or imaging results, and integrate them into EMR systems, and thanks to robotic process automation tools, these transfers can be done without human involvement. Such activities have the potential to improve patient care and reduce administrative costs of healthcare facilities.

Moreover, AI-driven task management systems can prioritize and allocate tasks based on urgency, complexity, and individual workloads. They can assist in insurance claims processing, prescription fulfilment, HR and administration tasks such as recruitment, screening and onboarding.

In summary, AI-powered solutions can help humans perform many tasks and automate routine processes. This means that healthcare staff can focus on critical responsibilities, which can improve their satisfaction. And in the end, streamlined back-office operations contribute to better patient care.

There is one more aspect of back-office applications related to healthcare – and it’s about the challenges AI brings to the discussion. First, medical data is highly sensitive information, so all internal systems must have comprehensive cybersecurity strategies and robust security measures to protect patient data and critical healthcare operations.

Second, in most countries, we still lack ethical guidelines and standards for AI algorithms and their use in clinical decision-making; this area remains a grey zone. Many experts stress that we need more investment in research and development and more collaboration between healthcare organizations, AI researchers, and regulatory bodies.

Finally, we can see that AI technology is currently more in the experimental phase. Healthcare providers already use it in internal software, such as HCA Healthcare, which operates 180 hospitals and around 2,300 outpatient care services in the UK and the US. The network is currently working on Gen AI to improve patient handover between nurses. In 2024 and 2025, we will see more examples and real-world use cases, especially in areas that reduce administrative burden, help clinicians find information easily, support healthcare call centre agents and ultimately help organizations operate more efficiently.

LinkIn this context, many see AI and automation as low-hanging fruit in the future of health and a new way of work, especially with a declining number of doctors, nurses, and societies getting older.

Previously, we have streamlined back-office processes in media companies, leading to significant cost optimization, faster work and more productivity. If you’re interested in how we did it, here’s a link to the case study we’re most proud of. Make sure to take a look.