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Discussion
VIEWER presents an innovative clinical informatics platform and service designed to advance PHM and integrated care in mental health. The platform can support the management of the full continuum of mental health services across different phases of care, including prevention, diagnosis, treatment, follow-up and care coordination. It aims to comprehensively address the health needs of defined populations by enabling prioritisation of both prevention and management, integrating social and healthcare services, facilitating innovative care delivery models, and the monitoring of complexity and at-risk patient groups. By facilitating this proactive, population-centred approach, VIEWER supports a move away from the reactive care models traditionally prevalent in mental health.22 This shift aligns with ICS principles, aiming to deliver care that is more equitable, effective and sustainable at both individual and population levels.
Unlike other PHM systems that focus narrowly on specific groups (eg, individuals with severe mental disorders), single-modal data sources (eg, administrative data) or isolated clinical tasks (eg, cost auditing),8 23 24 VIEWER takes a more comprehensive approach to addressing the multidimensional needs of diverse patient groups. It uses clinical data analytics, including NLP and visualisation, to integrate multimodal data—particularly extensive clinical text—transforming a large-scale routine EHR data into information that is accessible to clinicians in an interactive way, and highlighting actionable insights with the potential to improve patient outcomes. A user-friendly interface facilitates collaborative design with clinicians, enabling the development of clinically meaningful use cases. These innovations mean VIEWER has the potential to extend the scope and effectiveness of PHM within healthcare services, fostering a clinically led, integrated, data-driven approach to improving population health. A further strength of VIEWER is its agility and customisability—underpinned by open-source, lightweight components18—which ensures adaptability to future shifts in NHS policy and the wider healthcare environment.
There are challenges to adopting this approach more widely. Developing the VIEWER proof-of-concept relied on research funding and significant input from existing clinical informatics capacity within the Trust’s BRC, which provides a level of technical and academic expertise and support not typically available to other healthcare providers. Enabling a move to more proactive and preventative care may be cost-effective in the longer term. However, work towards establishing an operational service and achieving widespread adoption by clinicians requires both substantial additional resourcing to set up and ongoing commitment to running costs. Any resulting cost savings may not necessarily accrue directly to the secondary mental healthcare trust implementing these changes. Piloting of VIEWER has highlighted key learning for wider implementation. First, while VIEWER was designed to support a shift in how services operate, the success of future implementation will depend on teams being adequately prepared and supported to adapt their workflows. The IPS example illustrates how teams with data-driven workflows have been more readily able to adopt a PHM approach. This does, however, require a wider system commitment to shifting to a more proactive, population health-oriented approach reflected in service design, role expectations and job plans. Second, varying levels of digital and data confidence across the workforce impact both adoption and the level of support required. Designing alongside clinicians and piloting with short feedback cycles has been helpful in adapting VIEWER, although involving a greater diversity of clinicians and co-design techniques is needed to improve usability and pilot training and resources. Third, clinical data are inherently complex, and extraction methods, including NLP, have limitations. To ensure safe and effective use, and clinician confidence in tools, clinicians need support in understanding these complexities and attention paid to transparently explaining sources and limitations of data. Finally, the potential for PHM could be greatly enhanced through better data linkage across services within the ICS. Effective mental health tracking requires integrating data from A&E (accident and emergency), acute care, primary care and other sectors to provide a more comprehensive view of patient journeys and service needs.
Beyond these challenges, broader considerations arise when applying this approach in other contexts. Variability in source data fields across Trusts and EHR systems makes standardisation difficult, requiring adaptable frameworks and access to expertise about local systems and health records for integration. While the richness of information in clinical free-text may be more consistent across settings, the transferability of NLP models remains an open question, requiring further evaluation. Finally, the needs and clinical focus of teams can differ widely. For example, a Trust like SLaM, which provides services for a population with a high prevalence of psychosis, may have different priorities than others serving different patient populations. Addressing these variations will be crucial for ensuring adaptability and effectiveness across diverse healthcare environments.
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