Developing future interfaces for Digital health data

Digital healthcare technologies provide a multitude of opportunities for improvements in healthcare service delivery. The adaptation of digital medicine, artificial intelligence (AI) and robotics into healthcare provision has the potential to improve the accuracy of diagnoses, treatments and the efficiency of care (Topol Review, 2019). The goal of digital technologies is not to replace the expertise of clinical professionals but rather support and augment them in their activities and decision-making processes. The time gained through the use of such tools should enable the staff to spend more time with patients and provide better care (Topol Review, 2019).
High-quantity, unnecessary or incorrect information (eg. flashy visualisations, incorrect scaling, non uniform colour maps) can easily lead to cognitive overload and have a
detrimental effect on one's ability to interpret and compare different sources of information.
Digital health interfaces augmenting clinician's decision making should be designed in such a way to extend human capabilities and compensate for their weaknesses. Such visualisation techniques can be combined with other technologies such as interactive visualisations, speech recognition, wearable technologies and predictive analytics to promote effective data extraction. This project seeks to develop novel interfaces for digital health to optimise the clinician's interactions with data, with a focus on how to make burgeoning health data more accessible to clinicians by providing them with new tools and ways of interacting with it. The following steps are identified to achieve the aims of the
project:
1. Identifying needs and establishing requirements to augment current practices.
2. Developing design prototypes that meet these needs and incorporate knowledge
of human cognition to extend capabilities and compensate for weaknesses.
3. Building iterative versions of the designs by effectively combining different
technologies.
4. User testing to assess the effectiveness of resulting tools throughout the process.

Initially, qualitative approaches (interviews, focus groups) to better understand user needs and current issues they are facing will be utilised. Observational methods will be used for early prototypes (eg., interactive visualisations, speech recognition software, wearable technologies) to see how the technology is manipulated in context, confirming requirements and exploring ideas. Predictive analytics with machine learning may also be used to produce personalised treatment algorithms and risk prediction. Quantitative research methods will prove more useful during the later stages and experimental studies using synthetic patient data or historical data will be run to measure the effectiveness of the interfaces in improving the decision making process. In addition, user surveys will be conducted to assess the interfaces in terms of usability principles.