Decision support tools
Flagship tools delivered to date include mobile apps and websites for polypharmacy, antimicrobial prescribing and gastrointestinal care pathways, and conversion of elements of chronic pain and polypharmacy guidance into person-specific, intelligent decision support integrated into the context of individual patient records in primary care systems. These are currently being evaluated in operational test environments.
Cross-cutting outcomes supported by all these decision support tools include improving self-management, shared decision-making between patient and professional, and co-ordination of care around individual patient needs. These are all priorities for Realistic Medicine.2
Implementation and evaluation framework
To design decision support solutions and implementation approach to maximise impact, we have developed a sociotechnical evaluation framework to identify the barriers and enablers to implementation.3
To keep implementation focused on impact of decision support on key outcomes, we aim to integrate enablers and barriers into an outcomes chain based on evidence of cause–effect relationships. Indicators are specified for key enablers and outcomes at multiple levels and are measured throughout iterative improvement cycles. This will enable us to continuously improve our decision support tools and implementation approach.
Technology architecture
Key aspects of the co-ordinated national technology architecture developed through the decision support programme to date are illustrated in figure 1:
Figure 1Emerging thinking around a co-ordinated technical architecture, underpinned by assurance for safety, quality, ethics, information governance and information security. This brings together decision support from dispersed organisations to deliver consistent high-quality care to individual patients across all stages of their journey. For example, a patient with low back pain may need knowledge-based decision support for self-management, prescribing, other therapies and referral management in primary care, as well as rules-based imaging requesting and data-driven AI decision support to interpret imaging results in secondary care.
(1) Shared, open repository of quality assured decision support models, algorithms and content. This will provide a ‘once for Scotland’ single source of truth for decision support. Algorithms can be shared, re-used, localised, combined, and integrated into different clinical systems and applications through an integrated service layer. A national co-ordinating framework has been developed which outlines the intention to converge over time on open digital standards for decision support.
(2) A spectrum of decision support tools—informational to intelligent. The existing evidence base indicates that maximum impact is delivered by intelligent, knowledge-based expert systems. These integrate decision support algorithms with electronic health record systems and proactively push patient-specific calls to action to the user.4
At the same time, learning from developments so far indicates that it is important to continue to support freestanding and informational solutions such as mobile apps and websites. These simpler solutions are quicker to implement at scale and NHS Scotland survey results indicate that they are widely used in health and care services. For the medium-term to long-term future, we are also exploring next-generation data-driven artificial intelligence methods where the potential is great and the evidence still emergent.
(3) A self-service model. To enable health and care services to build quality-assured decision support solutions to support outcomes identified as priorities in their own organisations. These tools are currently being piloted in early adopter organisations, accompanied by skills training for key staff.