Together, we’ll delve into the potential of intelligent automation to reshape the future of healthcare.

Crystal Broj (Digital Transformation Leader, Medical University of South Carolina)

How do you see AI and machine learning changing the way healthcare organizations handle data and clinical workflows? Could you share any real-world examples where this has resulted in significant improvements in patient care?

AI and machine learning are transforming healthcare, making it easier to manage data and improve clinical workflows. These tools can sift through massive amounts of data, providing clinicians with insights that lead to better decisions, less manual work, and quicker interventions.

For example, machine learning can make appointment scheduling smarter, helping patients get in faster while reducing wait times. AI-powered virtual assistants are sending messages to help patients confirm/cancel/reschedule visits, cutting down on no-shows and then allow them to update all preregistration paperwork before attending the appointment. AI language models even handle phone calls 24/7, allowing patients to confirm, cancel, or reschedule appointments and get answers to common questions. This frees up staff to focus on more complex cases.

We’re also testing AI pilots that can review patient charts to find care gaps or suggest screenings, helping providers engage patients proactively. While AI is exciting, we see it as a co-pilot for our team, not a replacement. AI is another valuable team resource, but it doesn’t make any care decisions without a “human in the loop.” AI can close care gaps, inform patients about clinical trials, and assist providers in diagnosing and referring patients to specialists or new treatments.

These applications are boosting patient engagement, speeding up diagnoses, and making clinicians' work more efficient—leading to better outcomes and higher patient satisfaction.

Healthcare is often a sector where regulatory and compliance concerns slow down the adoption of emerging technologies. How do healthcare institutions navigate these regulatory frameworks while still pushing forward with innovative automation solutions?

Balancing innovation and compliance is key in healthcare, and it starts with careful planning and following regulations. Here’s some ideas to think about:

• Create an AI Compliance Team Early: It’s crucial to involve compliance and legal teams right from the start. A separate AI Governance team can ensure AI tools are built with any existing or upcoming regulations in mind. Early collaboration helps identify and fix potential issues before they escalate. AI tools need their own checks for security, fairness, and effectiveness.
• Use Pilot Programs: When testing new AI, use small-scale pilots. Pilots help address compliance step-by-step, adjusting based on real-world results without affecting broader operations. Be prepared to let pilots fail—if the solution doesn’t meet expectations, it’s okay to start over.
• Keep Clear Audit Trails: Automated tools should track every action to ensure transparency. This helps during audits and measures project success, showing what works and what might need adjustments.

By following these steps, healthcare organizations can innovate while keeping patient safety and data privacy at the forefront, ensuring AI solutions are fair, safe, and effective.

Automation inherently involves data exchange across systems, which brings up concerns about data privacy and security. What best practices should healthcare organizations follow to protect sensitive patient data while implementing automation solutions?

Protecting patient data during automation requires strong security measures and careful monitoring. Here are some simple steps healthcare organizations should take:

• Encrypt Data: Use end-to-end encryption to protect data both when it’s moving and when it’s stored.
• Control Access: Use role-based access control so only authorized users can see specific data, reducing the risk of leaks.
• Do Regular Audits: Regular checks help find and fix security issues in automated systems, keeping data safe.
• Limit Data Use: Collect and use only the necessary data, lowering the risk of breaches and protecting patient privacy.
• Monitor Constantly: Use real-time monitoring to catch unusual data activity quickly and have a response plan ready in case of a breach.

These steps help healthcare organizations build trust in automation while keeping patient data safe and secure.

Looking ahead, what do you think are the biggest challenges and opportunities for intelligent automation in healthcare over the next five to ten years? How can healthcare leaders prepare to maximize the benefits of this digital transformation?

In the next 5-10 years, intelligent automation in healthcare will bring big challenges but also exciting opportunities. Here’s what to expect:

Challenges:

1. Old Systems: Many healthcare organizations will continue to use legacy systems that don’t integrate easily with new tech. Integrating automation can be tough, often requiring custom fixes or costly updates. Review new systems to see how they will integrate with your existing ones and to see if necessary APIs are available.
2. Changing Jobs: Automation will change roles in healthcare. Routine tasks might be automated, but new roles will need new skills. Some staff may resist, so it’s important to include them early, explain why change is happening, and show the benefits.

Opportunities:

1. Better Patient Care: Automation can speed up diagnoses, improve workflows, and let care team members spend more time with patients.
2. More Efficiency: Automation can make scheduling, billing, and administrative tasks faster and easier, reducing costs and wait times while improving patient access.
3. Predictive Care: By analyzing data, automation can identify risks sooner, allowing providers to act quickly and offer more personalized treatment.

Preparing for the Future:

• Choose Flexible Tools: Invest in automation that can grow with the organization. Avoid too many small solutions; focus on partners that can support multiple needs.
• Encourage Innovation: Create a culture that supports trying new things. Involve all team members to ensure solutions are practical and effective.
• Manage Change Well: Use clear communication, training, and ongoing feedback to help team members adapt to automation.

By addressing these challenges and embracing these opportunities, healthcare leaders can make care more efficient, accessible, and patient-centered.

Richard McGregor (Leader of Digital & Performance Initiatives, Cumbria Health)

Having led digital transformation in Cumbria Health, what challenges did you encounter while implementing AI in primary and urgent care services?

Implementing AI across any healthcare area has its challenges however, in primary and urgent care specifically the correct use case is key and similar to climbing Mount Everest the conditions have to be correct from the clinical pathway design to the clinicians and other associated staff key to a successful clinical process.  The biggest challenges I have encountered has been during the business change and specifically ensuring user adoption.  Clinicians can at times be hesitant to adopt new technologies sometimes due to the fear of the unknown but more often due to the loss of control.  They become risk averse and resistant if they feel that the new technology will affect how they interact with patients or where clinical pathways are being modified and therefore, change their processes some of which will have been in place since they qualified.

I feel that a lot of early technological changes were forced upon rather than collaboratively and some clinicians still bear those scars.  More generally I feel that until recently there has been a lack of understanding as to what AI is and that in actual fact, we have been using it in our daily lives as consumers whether this be using voice assistants such as SIRI or Google and Apple maps.  More general challenges in implementing have been around Information Governance and Data Protection although by adopting a collaborative approach from the outset with the right people can mitigate any delays that this would normally take assuming of course the technology you are implementing has had data protection by design at its core.

How have wearable technologies integrated with AI helped improve patient care and monitoring in Cumbria Health?

We have had a number of very successful implementations of wearable technologies and AI, probably the most exciting of which has been a wearable patch which monitors vitals in real time and provide full ECG analysis and identification of heart arrythmias.  The product has been used as a means of being able to provide hospital level monitoring for patients at home and therefore reduced bed days, avoided hospital admissions for a range of clinical specialties, improved the care experience for patients, carers, families and staff working within our Hospitals.  Following a proof of concept project, we will be starting shortly to utilize the ECG analysis as an alternative to the ECG holter for which there are significant waiting times for results across the country. We have also utilized wearables and AI to run a number of primary care services namely around diagnosing hypertension through the use of a 7 day health monitoring pathway of blood pressure which has directly resulted in attendance at practices and faster response to unknown health conditions due to the ability to screen at scale.

In Cumbria Health’s journey toward digital transformation, how has predictive analytics helped improve patient outcomes or resource management?

Predictive analytics are one of if not the most important component of our digital transformation and one of the earliest benefits we have seen from the introduction of digital technologies particularly in our unscheduled care services where demand is variable dependent upon a range of factors whether this be pressures in other parts of the healthcare system, seasonal trends, local events etc.

Utilising our business data, we are able to predict at a site level and by time of day, the expected demand to our services with a 98% accuracy achieved over 6 weeks.  The predictive algorithm is based upon a range of internal and external data and is automated with no human intervention required other than an approval for any significant outliers outside of our control limits for the forecast.

We staff according to demand and during times of finite health care resource and budgets, predictive analytics ensure that we are ensuring a safe service for our staff and patients as well as ensuring that we protect the British tax-payers pound.

How can intelligent automation be used to improve access to healthcare in remote or underserved communities?

We provide services to the 2nd largest county in England by land size and one of the most sparsely populated with several remote and rural communities with technology a key enabler in providing care to our patients.  We have been providing video consultations to our patients since 2016 and our first remote monitoring service was available at scale in 2019.  Video consultations amount to 12% of total activity within our unscheduled care services which accompanied with electronic prescribing significantly reduces the carbon footprint of our patients as well as reduces the time in systems.  From a remote monitoring perspective, almost 10,000 patients have benefited from our primary care remote monitoring services as well as a transient care home population of over 2,000 spread across 94 residential and nursing homes.

We have also implemented an online symptom checker system which accounts for almost a fifth of all cases received from the national 111 service.

We are also in the process of implementing a test at home service for lipids and a health check questionnaire which will automate the NHS health check process thus reducing healthcare appointments, system and travel time for patients, speeding up diagnosis processes and reducing the burden on pathology departments.

David Labajo Izquierdo (Head of Digital Innovation, Siemens Healthineers)

With your experience at Siemens Healthineers, how do you see intelligent automation transforming diagnostic processes in healthcare?

As Head of Innovation & Special Projects Digital in Southern Europe at Siemens Healthineers, I believe that intelligent automation is fundamentally transforming diagnostic processes in healthcare by increasing efficiency, accuracy, and personalization.

Leveraging AI and machine learning, our company is enhancing clinical workflows, reducing variability in diagnoses, and enabling faster, data-driven decision-making.

One of our strongest areas, and one that is significantly transforming how diseases are diagnosed and clinical decisions are made, is imaging diagnostics. Some of our scanners incorporate intelligent automation to streamline imaging workflows, instantly adjusting parameters to achieve optimal results, thereby reducing manual intervention. Also, once the exam has been done, our AI solutions are analysing the exam to support professionals in identifying lesions and abnormalities in a much faster and efficient way. This leads to greater reproducibility and consistency in outcomes.

In this case, and in the other AI-driven solutions we promote, automation means less time spent on manual activities and more time interpreting meaningful insights, which directly impacts patient outcomes.

How do you both see the role of machine learning and AI enhancing decision-making in clinical settings?

More than 90% of medical decisions today rely on laboratory results, diagnostics imaging and pathology lab outcomes; key to identify patients at risk of developing complex diseases, such as cancer, or early detection when the disease is already starting to be developed.

Multimodal AI, integrating holistic patients’ data, can identify patients at risk and predict the early presence of a disease, even before any noticeable physical symptom appear.

By integrating AI into the main diagnostics workflows like laboratory, imaging and pathology, together with relevant patient information, will take us to disease-specific predictive models to help physicians to focus on areas of concern, possible risks or potential diagnoses for the patient, improving efficiency, productivity and what’s more important, health outcomes for the patients.

In this regard, at Siemens Healthineers, we are actively leveraging machine learning and computational reasoning in the development of new tools to support decision-making. One example, and one of the most pioneering in the industry, is the digital twin: a technological solution that uses AI to simulate human organs and provide more accurate diagnoses. With its help, doctors can increasingly predict how a person health evolves, how the body behaves when diseases may appear, how they progress, and what the most effective treatment is.

Siemens Healthineers operates at the cutting edge of healthcare technology. What role do you see robotic process automation playing in reducing administrative workloads?

As I’ve been saying, robotic process automation is an essential tool for reducing administrative workloads and improving operational efficiency while improving healthcare outcomes. The robotic automation of process allows healthcare providers to automate routine, repetitive tasks such as data entry, billing, appointment scheduling, and claims processing, which significantly reduces administrative burdens on staff and frees up time for more value-added activities including patient-facing interactions.

In the case of laboratory and pathology diagnostics, automation can help manage tasks like sample sorting and routing, ensuring faster and more accurate processing, and thereby reducing the need for manual intervention. This approach minimizes human errors, particularly in data handling, and ensures that administrative processes are reliable and efficient.

Additionally, with regard to healthcare professionals' workflows, automation speeds up patient registration, billing reconciliation, and even inventory management, helping providers reduce the time spent on paperwork, minimize delays, and focus more on patient care.

Ultimately, this supports a more efficient healthcare system where automation complements human expertise to deliver faster, more accurate, and patient-centered care.

How can intelligent automation be used to improve access to healthcare in remote or underserved communities?

‘Access to care’ is a global challenge, even in advanced countries where smaller communities often lack resources and skilled professionals. Intelligent automation has immense potential to improve access to healthcare by using Digital Technologies to break the barriers of physical distance, and bring the right professionals, with the right tools, to each patient and citizen. At Siemens Healthineers, we are leveraging AI-driven tools and digital solutions to bridge these gaps and enhance care delivery, even in regions with limited infrastructure.

One of the key ways this is being achieved is through remote diagnostics and telehealth. With some of our AI-powered imaging solutions, we are able to reduce the need for highly trained radiologists to be present on-site, providing the capability for remote experts to review and interpret results, and improving diagnostic speed and accuracy in underserved areas. It’s the information moving to the right professional, and not the patient needing to move to where the professionals are.

Furthermore, laboratory automation is making it easier for clinics in remote regions to handle routine diagnostic tests more efficiently. By automating sample processing, data entry, and analysis, our devices reduce dependency on skilled laboratory staff. This means that local healthcare providers can deliver faster test results, which is critical for timely interventions in resource-limited settings.

The era of interconnectivity we live in, also opens the door to streamlining administrative tasks, such as patient registration and electronic health record management, which can be critical in areas with minimal staffing. Automation helps ensure that healthcare providers can focus on patient care rather than administrative burdens, speeding up the delivery of care.

In regard with this, our Teamplay Digital Health Platform facilitates the remote monitoring of patients and data-sharing between healthcare providers, enabling better management of chronic diseases and continuity of care across distant geographies. By connecting underserved communities to larger healthcare networks, intelligent automation helps extend care beyond physical borders.

As we look ahead to the future of intelligent automation in healthcare, what key advancements or innovations do you believe will have the most significant impact on both healthcare providers and patients in the next decade?

I think that in the coming years we will witness vast changes in healthcare thanks to artificial intelligence and automation, but I would like to summarize into 2 topics:

1) AI-driven medicine: The wide application of artificial intelligence to all patients’ real world data including genomic information from Next-Gen sequencing, will surely bring the desired Personalized and Precision Medicine we have been talking about during the last years. We will see improved, faster and more accurated diagnosis; together with the right interventions, treatments and therapies for each individual, to achieve better health outcomes, better quality of life, within much more efficient healthcare systems, all of that through the power of Data and AI.
2) Virtual Care: Thanks to automation, robotics and AI, we will be able to bring the right care at the right moment to each patient and citizen:

• Healthcare companions based on automation and AI will be able to bring a first line of care directly to each individual in real time no matter where they are, will be able to triage and decide what should be done next; multiplying the access to care for everyone.
• Advanced analysis based on Real World Data for each patient, will automate the right care program for each individual.
• Robotics applied not only to surgery but also to other care activities like home-care therapies or rehabilitation
• Remote Patients Monitoring, bringing the individualized care to every patients’ home, instead of making the patients to go to hospitals or primary care centers; monitoring the evolution of each patient and detected wrong disease evolutions even before the symptoms are visible for both patients and professionals.

However, this will never eliminate the need for healthcare professionals, but rather the contrary, will provide them with more tools to focus their knowledge, skills and time into the most important and complex cases and decisions.

Looking ahead, what do you think are the biggest challenges and opportunities for intelligent automation in healthcare over the next five to ten years? How can healthcare leaders prepare to maximize the benefits of this digital transformation?

Artificial Intelligence and Automation, will for sure open the door for a Personalized and  Predictive care. I expect to see how our healthcare systems, currently mainly built on top of acute-care organizations and processes, will use AI-driven solutions to not only improve diagnostics but also proactively predict patient outcomes by analyzing vast datasets, enabling earlier interventions, and highly individualized treatments and therapies based on real-time patient data, reducing hospital readmissions and enhancing long-term health management in a real personalized medicine approach. We will measure our healthcare systems, not by how many procedures or interventions have been made, but by how many have been prevented and how many years of healthy life have been improved for our citizens.

--Issue 66--