4.6.2026 Care Pathway Automation in Practice: Lessons from Helsinki University Hospital’s Cancer Care Implementation.
This article is a reflection on a Presentation by Finland’s Largest Hospital District, HUS: Results and Future Opportunities of Care Pathway Automation. Author Juha Nieminen is Global Head of Healthcare at Digital Workforce and a member of the executive leadership team.
At a recent healthcare IT event in Helsinki, HUS representatives Administrative Chief Physician Meri Utriainen and Planning Specialist Johanna Pakarinen shared their experiences of automating end-to-end care pathways. The case example focused on a breast cancer follow-up solution, for which Digital Workforce serves as the contracted supplier.
When a customer shares two years of production experience, it is worth listening carefully. I was particularly interested in three things: the tangible results achieved through automation, the unexpected effects of implementation, and the extent to which HUS believes this operating model can be applied to other care pathways. In this article, I reflect on the key observations and lessons I took away from the presentation.
The presentation examined HUS’s breast cancer follow-up solution from three perspectives:
• the initial challenge
• measured production outcomes
• scalability and potential use cases
Challenge: The Administrative Burden of Care Pathways
Breast cancer follow-up is HUS’s largest long-term post-treatment monitoring programme. After completing active treatment, thousands of patients remain under specialist follow-up, with monitoring programmes that can extend for up to ten years.
The follow-up pathway consists of recurring activities such as imaging, laboratory tests, outpatient appointments, symptom assessments, and patient communications. Managing these activities across a large patient population requires extensive coordination, creating a significant administrative burden and increasing the risk of delays and backlogs. HUS openly described how the COVID-19 pandemic further highlighted the need for better operational control, forecasting, and resource management.
From the patient perspective, follow-up should be timely and predictable. For clinicians and care teams, however, delivering that experience requires extensive coordination, communication, and administrative effort. Once again, this case highlights that a significant portion of healthcare’s productivity challenge stems not from clinical decision-making, but from orchestrating the fragmented workflows, communications, and administrative activities that enable care delivery.
How The Problem Was Addressed
HUS sought to shift towards a model in which the entire care pathway is designed upfront, with automation orchestrating its execution and escalating to clinicians only when clinical input or decision-making is required.
In addition, there was a clear ambition to give patients greater involvement in their own care planning by enabling self-service appointment booking and allowing them to choose their follow-up approach—either symptom-driven or scheduled routine visits.
Measured Impact and Results
HUS replaced a manual operating model with a single configurable care pathway process that orchestrates patient flow and automates administrative tasks.
The solution has been in production for two years. Here are the key figures shared by Meri and Johanna:
- 6,909 patients on an automated care pathway
- 95% of all manual tasks in patient follow-up automated
- 52% of patients chose symptom-based follow-up, leading to a significant reduction in nursing visit volumes annually
- 47% reduction in inbound calls
- Over a three-week measurement period, automation executed 6,768 tasks, with only 1.2% escalated to clinicians
A particularly notable finding relates to the reduction in inbound calls. HUS had expected demand to increase as outpatient visits decreased, based on the assumption that patient uncertainty would grow. However, the opposite occurred. When follow-up is timely and patients have clarity on what will happen next, the need for additional contact is significantly reduced.
For patients, care pathway automation is experienced as timely communication, self-service appointment booking, and selection of follow-up mode. Communication channels remain unchanged—automation executes tasks through HUS-defined channels and can also use traditional channels such as letters where necessary.
The main value of care pathway solutions lies in reduced waiting times and delays, more reliable and timely follow-up, and enabling clinicians to focus more on patients requiring urgent clinical attention.
Scaling The Impact
Although the results presented by Meri and Johanna were impressive, a more interesting question is how widely the same operating model can be applied across other care pathways. In long-term patient monitoring, similar structural patterns tend to repeat, suggesting that HUS’s experience is not limited to a single patient group.
The presentation also identified several emerging use cases, including medication monitoring in dermatology and neurology, imaging-based follow-up in other cancer types, and monitoring of genetic risk carriers and meningioma patients. Further opportunities were highlighted in care coordination between specialist and primary care, such as secondary prevention of coronary artery disease events.
Based on HUS’s experience-based estimates, the scalability potential of the model is significant:
- Over 95% of suitable patient flows can be transitioned to automated pathways
• Over 95% of tasks within these pathways can be handled by automation
• Over 95% of imaging findings are classified as non-actionable and do not require intervention
• Approximately 50% of patients prefer symptom-based contact over scheduled follow-ups
Surprises and Key Learnings
At the end of the session, Johanna and Meri reflected on key lessons from the breast cancer follow-up implementation. Three particularly important insights stood out to me:
“One directive, ten years” framework
Replacing periodic decision-making with a single configurable workflow is key to scalability. The care pathway is implemented as a core template, with variations defined through parameters for different diseases, patient groups, and care plans.
47% reduction in inbound calls as an unexpected outcome
Healthcare automation initiatives are often justified by cost savings and efficiency gains. However, the most significant benefits are frequently those that cannot be predicted in advance. In this case, freed capacity was greater than expected, and more focus on change management could have improved early utilisation of that capacity.
“No rocket if a bicycle is enough”
The breast cancer solution is based on algorithm-driven process automation, not AI. It does not make clinical decisions and is not a medical device; instead, it orchestrates and automates scheduling and administrative tasks, while also managing work coordination in a single seamless flow.
A key lesson is that AI should not be used where simpler automation is sufficient. Instead, it should be applied where it adds real value. HUS identified applicable areas such as document processing, imaging, structured data capture, and referral handling.
HUS is also quite advanced in this area. Digital Workforce has been involved in HUS’s AI-based referral triage solution, which processes and classifies more than 300,000 specialist care referrals annually.
The key principle is simple: first design the process, then select the most appropriate technology for each step. In many cases, the best outcome is achieved through a combination of automation and AI, supported by strong orchestration of the overall system.
Summary
After leaving the session, I reflected on how much of healthcare’s productivity challenge is still driven by fragmented processes, limited coordination, and the burden of communication and administrative work. HUS’s experience shows that these tasks can be extensively automated without shifting clinical decision-making to technology.
As clinicians’ time is freed for clinical work and care pathways become more transparent, predictable, and timely, the benefits extend to patients, professionals, and organisations alike. In my view, transforming care pathways by leveraging automation and advanced process orchestration to create seamless workflows that deliver the core objective—better care and better outcomes at lower cost—is set to become one of the key development directions in healthcare in the coming years.
HUS’s example is compelling: two years in production, 6,909 patients on an automated pathway, and 6,768 tasks in three weeks—only 1.2% of which required manual intervention. While these results are significant for a single patient group, the real impact lies in the scalability of the model across other pathways and organisations.
Key Learnings:
- Automation delivers the greatest value at the level of orchestrating entire care pathways
- The most significant benefits are not always predictable in advance
- Change management is as important as the technology itself
- Many care pathways share a common underlying process logic, enabling solutions to be scaled
- AI is not a universal solution; automation and AI each have their strengths and can be used together. The starting point should always be clear process design
Author: Juha Nieminen is Global Head of Healthcare at Digital Workforce and a member of the executive leadership team. He has over two decades of experience in sales leadership and business development across healthcare, IT, and other industries. In his current role, he focuses on healthcare process automation and care pathway solutions. He holds a Master of Science in Engineering (Industrial Engineering and Management).
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