Digital Health – What does it really mean? There are many descriptions, but it’s about how to use modern IT technology in healthcare. An example could be the intelligence in Professor Max Jair Ortiz Catalan’s thought-controlled prosthesis, but more often it refers to image processing, electronic medical records, clinical decision support with and without AI/ML, telemedicine solutions or solutions for remote monitoring and follow-up, for example in the home.
Bengt Arne Sjöqvist, Professor of the Practice Emeritus in Digital Health and active in the research group [email protected] – Remote and Prehospital Digital Health at Bio-Medical signals and systems, at the Department of Electrical Engineering, Chalmers, says:
“For us, it’s about us wanting to offer different forms of clinical decision support in the entire care chain from an emergency incident or a home monitoring for further treatment in hospital, at home or at a health care centre. The decision supports can be AI-based, video conferences or traditional telemedicine”.
There are many reasons why we need to work with Digital Health. Above all, one needs to look at the problems and challenges of healthcare today and in the future.
“Care must find new ways of working as more people live longer with more illnesses while at the same time the availability of personnel is reduced – the resources will not be enough. In the end, it can be said that it is about streamlining for the benefit of healthcare staff and patients, and that healthcare is given the opportunity to use its resources more efficiently to handle the load. At the same time, Digital Health also provides tremendous opportunities to achieve better care results and outcomes in most areas. The possibilities with digital resources in healthcare are endless”.
“If we look at the area we are researching in “remote and prehospital digital health”, our motto is to “increase the precision in every single decision” with the help of clinical decision support. Our main tools are then “data fusion” where we aggregate patient related information from many different sources of various kinds – vital data, checklists, medical records, images etcetera. This amount of data then forms the basis for our analysis and, for example, risk predictions. The working method has several advantages such as more individualised, objective, and equal decisions”.
There are several clinical challenges in healthcare, including the fact that we have a growing aging population leading to significantly increased costs if we are to deliver healthcare in the same way as now and at the level we wish. For example, it has been estimated that around 80 percent of healthcare costs for an individual are in the last 10-20 years of life.
Bengt Arne Sjöqvist believes that the change that has now begun where more care will be given in homes requires the help of more mobile teams. More cooperation and information exchange between different healthcare providers, but also more qualified decision support for those who must deal with the large panorama of conditions they encounter, is necessary to achieve the desired social effect.
Acute myocardial infarction, stroke, trauma, and sepsis affect over 100,000 individuals each year with a mortality rate of over 20,000. Of these, roughly over 50 percent have their first care contact with pre-hospital care, mainly ambulance care. With the implementation of digital tools, many improvements can be made.
The vision for the [email protected]
– Remote and Prehospital Digital Health group is to increase the precision in all patient related decisions to prevent errors in assessment, patient prioritization and management.
The research group [email protected] – Remote and Prehospital Digital Health at Bio-Medical signals and systems. Photo: Chalmers
The research group works with methods and solutions to improve remote healthcare and pre-hospital/mobile care by using data fusion, clinical decision support including artificial intelligence (AI), telemedicine and innovative user interaction. The latter means designing solutions that increase usability “in the field”, for example through voice control and speech synthesis for data entry and presentation of results, suggestions for measures or alternative care processes. Several projects are based on a generic structure for data fusion, interoperability and decision support called ASAP (Acute Support, Assessment and Prioritizing. Some project examples:
Remote support (no care personnel attending at incident site)
- ASAP Home/Autumn Leaves; Automatic fall detection in the home as well as data fusion and decision support to optimize rapid response and processing from municipal care and regional prehospital healthcare.
- COPE (Connected Occupant Physiological Evaluation), DRIVER (Driver physiologic monitoring for Vehicle Emergency Response) and Syncope (feasibility study); Complementary projects based on non-intrusive vital data monitoring and analysis of drivers with applications in automotive and health / healthcare including detection of drowsiness, sudden illness or in connection with traffic accidents
- TEAPAN (Traffic Event Assessment Prioritizing and Notification); an ASAP similar concept in road safety where data from vehicles and other sources are aggregated and analysed to enable optimal resource allocation of “blue light resources” as well as information to ambulances etcetera when “en-route”.
Point of Care support (care personnel attending at incident site)
- ASAP Trauma; data fusion and decision support in acute trauma. OSISP (On-Scene Injury Severity Prediction), DRP (Dynamic Risk Prediction) and OD (Optimal Destination) are different AI-based decision support systems that are being developed.
- ASAP Stroke; the same basic concept as ASAP Trauma but within acute stroke care. In particular, image analysis of image and video for objective assessments of e.g. pupillary movements will be studied in addition to other decision support.
- VIPHS (Video support in the PreHospital Stroke chain); Video-streaming and conference that through real-time consultation between ambulance and stroke specialist enables more informed decisions regarding direct transport to stroke centers for “long-distance” patients who are candidates for thrombectomy (can today only be done in university hospitals).
- Talk2Me; voice control and speech synthesis for data entry to decision support and result presentation, suggestions for measures or alternative care process.
- Talk2Me; voice control and speech synthesis for data entry to real-time decision support and result presentation, suggestions for measures or alternative care process.
When care moves home
When care moves home is an initiative from three different departments at Chalmers: Electrical Engineering, Technology Management and Economics, and the Centre for Healthcare Architecture (CVA). The ambition is, through collaboration within and outside Chalmers, take a broader interdisciplinary and multidisciplinary approach to the social transformation within health care that has now begun – often referred to as “Omställningen” (the transition). If this is to succeed, in addition to the right technology, system innovation in healthcare and an understanding of the challenges of the transformation are also required. If this is not managed, the technology and process improvements will not be able to land properly in the operations and produce the desired positive effects for patients and society. Through joint research, knowledge sharing and utilization projects, the initiative will contribute to the transition being successful.
Several research projects in the field of Digital Health are ongoing at Electrical Engineering. First project that is showcased is [email protected]
– Remote and Prehospital Digital Health and in future articles we will dive deeper into one of the following research projects:
- Computer vision
- Microwave diagnostics and treatment
- Bone conduction
- Bionics and pain
- Adaptive physiotherapy
- Health informatics
Text: Sandra Tavakoli