|dc.description.abstract||Patients increasingly collect lifestyle and health-related data thanks to the explosion of sensors and healthcare applications. Multiple studies have shown that this data can be useful during consultations, resulting in more customised medical services. Moreover, this data permits clinicians to gain an overview of the patients’ conditions and enables patients to gain knowledge about the consequences of their lifestyle choices for their health.
The goal of this thesis is to introduce self-collected health data by patients with diabetes into electronic health record systems (EHRs) and medical consultations. To achieve this goal, this thesis proposes, firstly, to study the state-of-the-art usage of self-collected health data during consultations. The thesis then identifies the acceptance barriers perceived by clinicians, patients, healthcare institutions, and EHR vendors that limit or obstruct the usage of self-collected health data during consultations. Thereafter, the thesis describes the FullFlow clinical decision support system (CDSS) addressing the identified acceptance barriers and finishes by its assessment by clinicians.
The identification of acceptance barriers and the design of the FullFlow CDSS followed a participatory design approach supported by semi-structured interviews and open discussions used during workshops and focus groups involving clinicians, EHR vendors, healthcare institutions and patients with diabetes. The implementation of the FullFlow CDSS followed a combination of Agile and waterfall methodologies supported by a test-driven development approach. The assessment of the FullFlow CDSS relied on a case-study.
Summarising, despite multiple parties being interested in using self-collected health data in a medical context, its usage is still rare and locked into controlled environments, even if some cloud-based solutions permit the integration of this type of data into EHRs. The limited usage of this type of data in medical context can be explained by the numerous acceptance barriers perceived by clinicians, patients, EHR vendors and healthcare institutions. Lack of data reliability, investment costs, lack of practice and training and time consumption are examples of acceptance barriers. The FullFlow CDSS comprises three solutions to address these acceptance barriers: 1) a computer science model for extracting relevant health data and managing data reliability, using a hypothesis-and-test strategy; 2) a dashboard permitting clinicians to consult relevant information regarding patients’ conditions; and 3) an architecture facilitating the integration of patients’ self-collected health data with their health applications in EHRs. The assessment of this system by clinicians showed that this system can integrate self-collected health data during medical consultations and that it would be useful during clinicians’ daily consultations. The next step is to test and verify the developed system in real settings.||en_US
|dc.relation.haspart||Paper 1: Giordanengo, A., Øzturk, P., Hansen, A.H., Årsand, E., Grøttland, A. & Hartvigsen, G. (2018). Design and development of a context-aware knowledge-based module for identifying relevant information and information gaps in patients with type 1 diabetes self-collected health data. <i>JMIR Diabetes, 3</i>(3):e10431. Also available in Munin at <a href=https://hdl.handle.net/10037/14483>https://hdl.handle.net/10037/14483. </a><p>
<p>Paper 2: Giordanengo, A., Årsand, E., Grøttland, A., Bradway, M. & Hartvigsen, G. (2019). Acceptance barriers of using patients’ self-collected health data during medical consultation. In: Granja, C. & Solvol, T. (Eds.): <i>SHI 2019. Proceedings of the 17th Scandinavian Conference on Health Informatics, November 12- 13, 2019, Oslo, Norway, 161, article no 009</i> (50-55). Linköping: Linköpings University Electronic Press. Also available at <a href=https://www.ep.liu.se/index.en.asp>https://www.ep.liu.se/index.en.asp. </a><p>
<p>Paper 3: Giordanengo, A., Årsand, E., Woldaregay, A.Z., Bradway, M., Grottland, A., Hartvigsen, G. … Hansen, A.H. (2019). Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study. <i>JMIR Diabetes, 4</i>(3):e14002. Also available in Munin at <a href=https://hdl.handle.net/10037/16425>https://hdl.handle.net/10037/16425. </a><p>
<p>Paper 4: Giordanengo, A., Bradway, M., Muzny, M., Woldaregay, A., Hartvigsen, G. & Arsand, E. (2017). Systems integrating self-collected health data by patients into EHRs: a State-of-the-art review. In: Martinez, S., Budrionis, A., Bygholm, A., Fossum, M., Hartvigsen, G., Hägglund, M. … Yigzaw, K.Y. (Eds): <i>Proceedings from the 15th Scandinavian Conference on Health Informatics 2017 Kristiansand, Norway, August 29–30, 2017, 145, article no 007</i> (43 – 49). Linköping: Linköping University Electronic Press. Also available at <a href=https://www.ep.liu.se/index.en.asp> https://www.ep.liu.se/index.en.asp. </a> <p>
<p>Paper 5: Giordanengo, A. (2019). Possible usage of smart contracts (blockchain) in healthcare and why no one is using them. In: Ohno-Machado, L. & Seroussi, B. (Eds.): <i>MEDINFO 2019: Health and Wellbeing e-Networks for All</i>, (596 – 600). Also available at <a href=https://doi.org/10.3233/SHTI190292>https://doi.org/10.3233/SHTI190292</a>. <p>
<p>Paper 6: Muzny, M., Henriksen, A., Giordanengo, A., Muzik, J., Grøttland, A., Blixgård, H. … Årsand, E. (2020). Wearable Sensors with Possibilities for Data Exchange: Analyzing Status and Needs of Different Actors in Mobile Health Monitoring Systems. (Manuscript). Accepted manuscript available in Munin at <a href=https://hdl.handle.net/10037/16577>https://hdl.handle.net/10037/16577. </a> Final version published in <i>International Journal of Medical Informatics, 133</i>, 104017, available at <a href=https://doi.org/10.1016/j.ijmedinf.2019.104017>https://doi.org/10.1016/j.ijmedinf.2019.104017. </a>||en_US