Healthcare decision-making depends on accurate, connected information, yet many systems still operate with fragmented data and limited interoperability that slow clinical and administrative responses. These gaps compromise treatment decisions and reduce user satisfaction with digital services, underscoring the need for an approach that embeds digital tools into everyday care. Smart Product Service Systems bring together intelligent products, digital platforms and value-added services to address this disconnect by aligning technology with practice. An analytical framework developed using Smart PSS clarifies which attributes influence data management and organisational performance the most, offering direction for teams seeking to reduce silos and strengthen service outcomes.
Why a Framework Was Needed
Digital tools can personalise treatment, improve information flow and widen access, but without robust governance and interoperability they create isolated data streams that undermine enterprise analytics. A business model that redesigns workflows around shared data pathways is therefore essential for translating potential into routine use. Smart PSS answers this need by combining intelligent, connected products with e-services and adaptable architectures to support data-driven care, remote supervision and timely interventions. Prior research pointed to five recurring aspects—intelligent products, service realisation, stakeholder collaboration, data management and organisational performance—yet the specific attributes linking them to measurable outcomes remained unclear. The framework reduces this gap by mapping how these aspects interact and by prioritising the criteria with the greatest leverage on data quality and operational results.
How the Framework Was Built
A literature review generated 47 candidate criteria. A panel of 33 experts with experience in healthcare service, digital transformation and medical technology then participated in a two-round assessment to refine and structure the model. Using the Fuzzy Delphi Method, the panel validated 27 criteria at a threshold of 0.596, covering five aspects: intelligent products, stakeholder collaboration and communication, data management and responsibility, service realisation and organisational performance. The Fuzzy Decision-Making Trial and Evaluation Laboratory method then converted expert influence judgements into a causal diagram, distinguishing drivers from outcomes and quantifying interdependencies among aspects and criteria. This hybrid approach provided both consensus on what matters and an analytical view of how those elements act on one another.
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The research was grounded in a health system context marked by uneven digital capacity and application sprawl, where data are fragmented across specialties, departments and locations. By situating exercise in this environment, the framework sought to reflect the operational realities that can blunt transformation efforts when organisational performance and data management are not addressed alongside platforms and applications.
What Drives Improvement
The causal mapping placed intelligent products, stakeholder collaboration and service realisation in the driver group, while data management and organisational performance appeared as effects influenced by those drivers. Intelligent products exerted the strongest influence on data management, with moderate effects on service realisation and organisational performance, and a minimal effect on stakeholder collaboration. Collaboration and communication showed a moderate effect on data management and a weaker direct effect on organisational performance. Service realisation moderately influenced data management but had only a weak direct effect on organisational performance.
Within the 27 validated criteria, six stood out as the most influential for practice: smart repair, monitoring and early warning, synchronised transactions, information integration and interaction, data quality and organisational readiness. These linkage criteria represent practical levers for improving Smart PSS execution, because they connect technical capability with process outcomes across the system.
Managerial implications emphasise proactive maintenance and operational continuity. Smart repair capabilities use real-time sensing and analytics to detect early signs of malfunction, schedule timely maintenance and reduce equipment downtime. Monitoring and early warning enable earlier responses to emerging risks and contribute to more responsive care pathways. On the coordination front, synchronised transactions and information integration strengthen interoperability across partners and processes, reducing duplication and delays. Elevating data quality enhances transparency and reliability in service delivery, supporting analytics and decision-making. Finally, organisational readiness—spanning technical infrastructure, programme design and the interaction of technologies, people and systems—supports adoption and helps translate service-level improvements into wider performance gains.
The analysis also indicates that better front-line experiences alone do not guarantee organisation-wide improvement. Service realisation strengthens data integrity and availability, yet its direct contribution to high-level performance is limited unless service-level data flows are integrated within broader structures addressing ethical use and staff competence. This places renewed emphasis on data responsibilities and workforce capability as necessary complements to intelligent products and engagement platforms.
An analytical Smart PSS framework clarifies how intelligent products, collaboration and service realisation drive improvements in data management and, in turn, organisational performance. For operational leaders, focusing on predictive maintenance, real-time monitoring, transaction synchronisation, information integration, data quality and organisational readiness offers a practical route to reduce fragmentation and strengthen outcomes. The model provides a validated set of attributes and a causal map to prioritise investment and design choices for digital health transformation. The authors note that the criteria were drawn from prior studies and that the expert-based assessment reflects a context-specific panel, which may limit generalisability, witnessing the value of broader and longitudinal applications in future work.
Source: Healthcare Analytics
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