Digitalisation is rapidly advancing into the clinic. But clinician perspectives and insights have at times been neglected in technological innovations and implementations, leading to solutions that do not work well within existing workflows and processes, and add additional burdens within already strained clinical workloads. Can clinician and patient perspectives be considered more closely in the design and development of digital tools?
Medical knowledge and decision support face challenges in integration and translation of evidence into practice through all the steps from primary research to clinical implementation. The medical evidence base grows exponentially and can be fragmented. It can be difficult to identify, assess, evaluate and gain an overview of all the evidence relevant to a given topic. The process of manual synthesis of evidence and translation into practice is slow and laborious. Can synthesis, translation and implementation be accelerated through digital tools?
While there have been many recent advances in the algorithms and technologies used in clinical decision support, these have been slow to translate into usable systems that are implemented in practice and have a measurable impact on health outcomes. This may be in part because of gaps between what the technologies can offer and the contexts in which successful implementations need to be delivered. Can design and implementation considerations be harnessed to develop a new generation of decision support tools that bridge the gap between technology and clinical need?
The overarching objectives of the research of the Medical Knowledge and Decision Support group are to accelerate translation and integration of evidence into the clinic and to ensure that technological progress is aligned with the needs of clinicians.
- Evidence Synthesis: Knowledge-driven approaches to support partial automation of medical evidence synthesis and thereby accelerate the translation of evidence into implementation in the clinics.
- Semantic (Meaningful) AI: Semantic, meaningful or human-centered artificial intelligence approaches are built around meaningful categories that humans understand. They combine symbolic and sub-symbolic approaches to machine learning and reasoning, and can be used to reduce manual documentation burdens, provide human-friendly explanations, and accelerate the availability of clinical data for subsequent research towards personalised medicine and learning health systems.
- Clinician Experiences (Decision Support): Understanding and amplifying clinician experiences of and expectations of decision support and electronic health information systems, and matching those to available technological capabilities and opportunities in order to strategically steer future technological developments towards greater clinical impact and benefis to patients in practice.
- Clinician and Patient Experiences (Medical Knowledge and Evidence): Understanding clinician and patient experiences of the wider medical knowledge and evidence ecosystem, in particular differences in perspectives between different clinicians (interdisciplinarity/interprofessionality) and between clinicians and patients, and how these different perspectives are negotiated in shared decision-making.