A Value Analysis Perspective on a Changing Healthcare Landscape
By Theresa Maas, MSN, RN, CVAHP
Throughout my career in nursing leadership and Value Analysis, I have evaluated countless requests for new medical devices, emerging technologies, and clinical innovations. Most of these evaluations begin with familiar questions surrounding clinical efficacy, safety, financial impact, and operational feasibility.
Recently, however, I have noticed a significant shift. Increasingly, the products and technologies being presented for review incorporate artificial intelligence (AI) or machine learning (ML) capabilities, often in ways that are not immediately obvious during the initial stages of evaluation.
As Value Analysis professionals, we have always served as the bridge between innovation and organizational stewardship. Our role requires us to evaluate not only whether a technology delivers on its intended purpose, but also how its adoption will affect clinicians, patients, workflows, resources, and long-term outcomes.
As AI-enabled technologies become more prevalent throughout healthcare, I believe our profession is entering a new phase where understanding the implications of algorithm-driven tools may become just as important as evaluating their clinical evidence and financial impact.
The promise of AI in healthcare is substantial. These technologies may support earlier diagnosis, enhance clinical decision-making, improve workflow efficiency, and uncover insights that were previously difficult to identify. Yet alongside these opportunities come important questions regarding transparency, governance, cybersecurity, accountability, bias, and ongoing performance monitoring.
For many Value Analysis teams, the challenge is no longer simply evaluating a new technology. It is evaluating the intelligence embedded within it.
A New Reality for Value Analysis Teams
Historically, Value Analysis committees focused on evaluating products based on clinical evidence, safety profiles, pricing, reimbursement considerations, implementation requirements, and stakeholder feedback. While those factors remain essential, AI-enabled technologies introduce an entirely new dimension of assessment.
Unlike traditional medical devices that perform a defined function, AI-enabled solutions often analyze data, generate insights, predict outcomes, prioritize findings, or support clinical decision-making. Some algorithms may even evolve over time as additional data becomes available.
This shift requires Value Analysis professionals to ask questions that were rarely part of our evaluation processes just a few years ago.
Questions such as:
- What type of AI or machine learning is being utilized?
- What data was used to develop and validate the algorithm?
- How transparent is the decision-making process?
- Could bias exist within the model?
- How often is the technology updated?
- What safeguards exist to ensure continued performance?
- What cybersecurity risks should be considered?
- How will clinicians interact with and rely upon the system’s recommendations?
These considerations don’t replace traditional Value Analysis principles. Rather, they expand them.
Looking Beyond the Device
One observation I have made is that AI functionality is not always positioned as the primary selling point of a product. In many cases, it is presented as an enhancement, a workflow feature, or a decision-support capability embedded within a larger solution.
As a result, organizations may focus heavily on evaluating the physical device or software platform while giving less attention to the underlying algorithms driving recommendations and outputs.
How This Can Play Out in Practice
Consider a hospital evaluating smart infusion pumps. The request may initially appear to be for an upgraded infusion device. However, if the pumps incorporate machine learning capabilities to optimize drug libraries or predict potential programming errors, identifying those capabilities early in the Value Analysis process becomes important.
If the presence of AI is not recognized during the initial evaluation, the organization may not identify all the questions that need to be addressed before implementation. AI updates may require validation. Cybersecurity and network requirements may need to be evaluated. Staff training may be needed to address how the technology’s recommendations are generated and used. Ongoing licensing costs may also need to be considered.
More importantly, identifying AI capabilities early allows the appropriate stakeholders to become involved at the right time and ensures that the technology is evaluated through a more comprehensive lens.
AI-enabled technologies can have a range of impacts across the healthcare ecosystem. An AI-enabled monitoring platform may improve the identification of patient deterioration. An imaging solution may help prioritize findings for radiologists. A clinical workflow tool may recommend interventions or identify care gaps.
While these capabilities can provide tremendous value, they also create downstream implications that should be understood during evaluation. Improved detection may increase specialist referrals, additional testing, documentation requirements, staffing needs, or care coordination activities. Workflow efficiencies in one area may create additional workload in another.
As Value Analysis professionals, our responsibility is to understand not only what the technology does, but how it influences the broader healthcare ecosystem.
Expanding the Stakeholder Conversation
The rise of AI is also transforming who should participate in technology evaluations.
Historically, a technology review may have involved clinical stakeholders, supply chain, finance, and operational leadership. Today, conversations increasingly benefit from the involvement of information technology teams, cybersecurity professionals, data governance leaders, compliance officers, clinical informatics specialists, and biomedical engineering.
Many of the questions surrounding AI extend beyond traditional product performance. They involve how patient data is managed, how recommendations are generated, how risk is mitigated, and how performance is monitored over time.
This reinforces something Value Analysis professionals have always known: successful decision-making depends on bringing together the right expertise at the right time.
Balancing Innovation and Governance
Healthcare organizations understandably want to embrace innovation. AI and machine learning have the potential to improve patient outcomes, reduce administrative burden, optimize resource utilization, and support clinical decision-making in ways that were previously unimaginable.
At the same time, innovation should not outpace evaluation.
Value Analysis programs are uniquely positioned to help organizations strike this balance by creating structured, multidisciplinary review processes that assess technologies through both an innovation and governance lens.
The goal is not to slow adoption. The goal is to ensure that organizations fully understand the potential clinical, operational, financial, and strategic implications before implementation.
The Future of Our Profession
I believe AI and machine learning represent one of the most significant shifts Value Analysis professionals have encountered in recent years. As these technologies continue to become embedded within medical devices, software platforms, and clinical workflows, our role will continue to evolve.
The future of Value Analysis will not simply involve evaluating products. It will increasingly involve evaluating data-driven systems that influence patient care, clinician behavior, operational processes, and organizational decision-making.
That evolution presents both challenges and opportunities.
It challenges us to broaden our evaluation frameworks, engage new stakeholders, and develop a deeper understanding of emerging technologies. It also presents an opportunity for Value Analysis professionals to further strengthen our strategic role within healthcare organizations by helping leaders navigate the complexities of AI adoption responsibly and effectively.
A Question Worth Asking Early
As I reflect on the increasing number of AI-enabled technologies entering the healthcare marketplace, one question continues to stand out:
Does this product, device, or technology utilize artificial intelligence or machine learning, and if so, what are the implications?
The earlier we identify AI and machine learning capabilities within a request, the earlier we can engage the appropriate stakeholders, establish the right evaluation criteria, and ensure a more comprehensive review.
This is not about creating barriers to innovation. It is about creating visibility.
Because in the coming years, one of the most important responsibilities of Value Analysis professionals may be ensuring that we are not only evaluating the technology itself, but also understanding the intelligence embedded within it.
About the Author
Theresa Maas, MSN, RN, CVAHP, is Manager – Advanced Decision Support at Staritas. She brings extensive experience in nursing leadership and Value Analysis, with a focus on helping healthcare organizations make informed, evidence-based decisions about clinical technologies and innovation.
