Hybrid Patient Record – Supporting Hybrid Interaction in Clinical Wards

Houben, Steven; Schmidt, Mathias; Frost, Mads; Bardram, Jakob E.

doi:10.4108/icst.pervasivehealth.2015.259507

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Many publicly available applications offer symptom tracking/logging functionalities8 or clinician-patient technologies9. In contrast to the numerous works in symptom loggers and clinician-patient technologies [185][186][187][188][189][190][191][192][193][194], there exists little research which explores patient matching. As we discussed in Chapter 3, many of these works propose patient matching to aid clinicians with the diagnosis of rare conditions [195][196][197][198].…”

Section: Prototype Implementationmentioning

confidence: 99%

Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey

Nielsen

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People with a rare condition face several hurdles throughout their odyssey to obtain a diagnosis. This odyssey lasts several years and involves frequent referrals and misdiagnoses, often resulting in permanent and severe consequences on patients’ health. In addition, patients feel unheard by their healthcare providers and isolated from their peers who ‘just don’t understand’. The UK Strategy for Rare Diseases states that patients can play a significant role in their diagnosis if given suitable resources. However, patients with rare diseases feel that they lack the support they need. This thesis explores the role that technology can have in addressing this gap in support.Within this context, this thesis spans a range of topics, from human-centred design approaches to generating data and presenting a new methodological approach. Through a human-centred approach, we characterise the needs of rare disease patients, thus opening the research space to include previously unmet support needs. In addition, we identify limitations with existing measures of success and highlight the importance of a reduction in the time of diagnosis for rare disease pre-diagnostic technology. This provides the basis the simulation-based methodological approach that we develop. The simulation-task aimed to mirror the information seeking tasks that rare disease patients undertake. To do this, we curate data that is representative of a rare disease patient’s perspective, both in terms of the terminology used and the stage in which symptoms and clinical findings are discovered. In addition, we curate a pre-diagnostic patient matching prototype that is designed around rare disease patients’ needs and demonstrate that (in comparison to two search engines) our application shows greater potential to: aid clinical experiences; facilitate empathetic support networks; and provide better facilitation of information-seeking. All of these contributions stem from a critical examination of the experiences that rare disease patients go through on their journeys towards diagnosis and aim to pave the way for future research within this area.

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