Abstract
Falls are one of the major health concerns for the elderly people. These falls often result in severe injuries which lead into huge medical expenses. Over the recent years, many ICT based fall detection and fall prevention solutions emerged to address the risk factors associated with falls. However, despite of these research studies, predicting the likelihood of falls still remains as a huge challenge in both medical and IT research domains. Data related to these risk factors being scattered among different healthcare providers can be attributed as a main reason for this challenge. This is further amplified by healthcare providers being reluctant to disseminate the data beyond their entities due to the security and privacy concerns. However, in recent years, blockchain has been proven as a promising technology to address the security and privacy challenges in healthcare data exchange as it provides a shared, immutable, and transparent audit trail for accessing data. Therefore, in this paper, we are going to propose a conceptual blockchain based fall prediction model leveraging smart contracts and FHIR (Fast Healthcare Interoperability Resources) standard to identify the elderly people who are at a higher risk of falling.
| Original language | English |
|---|---|
| Title of host publication | ACSW'19 - Proceedings of the Australasian Computer Science Week Multiconference |
| Subtitle of host publication | Sydney, NSW, Australia — January 29 - 31, 2019 |
| Editors | Ying Wang, Dale Patterson |
| Place of Publication | New York NY USA |
| Publisher | Association for Computing Machinery (ACM) |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450366038 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | Australasian Computer Science Week Multiconference 2019 - Sydney, Australia Duration: 29 Jan 2019 → 31 Jan 2019 http://www.acsw.org.au/ |
Conference
| Conference | Australasian Computer Science Week Multiconference 2019 |
|---|---|
| Abbreviated title | ACSW 2019 |
| Country | Australia |
| City | Sydney |
| Period | 29/01/19 → 31/01/19 |
| Internet address |
Keywords
- Blockchain Technology
- Fall Prediction
- Healthcare
- Smart Contracts
Cite this
}
Towards a blockchain based fall prediction model for aged care. / Rupasinghe, Tharuka; Burstein, Frada; Rudolph, Carsten; Strange, Steven.
ACSW'19 - Proceedings of the Australasian Computer Science Week Multiconference: Sydney, NSW, Australia — January 29 - 31, 2019. ed. / Ying Wang; Dale Patterson. New York NY USA : Association for Computing Machinery (ACM), 2019. 32.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Towards a blockchain based fall prediction model for aged care
AU - Rupasinghe, Tharuka
AU - Burstein, Frada
AU - Rudolph, Carsten
AU - Strange, Steven
PY - 2019
Y1 - 2019
N2 - Falls are one of the major health concerns for the elderly people. These falls often result in severe injuries which lead into huge medical expenses. Over the recent years, many ICT based fall detection and fall prevention solutions emerged to address the risk factors associated with falls. However, despite of these research studies, predicting the likelihood of falls still remains as a huge challenge in both medical and IT research domains. Data related to these risk factors being scattered among different healthcare providers can be attributed as a main reason for this challenge. This is further amplified by healthcare providers being reluctant to disseminate the data beyond their entities due to the security and privacy concerns. However, in recent years, blockchain has been proven as a promising technology to address the security and privacy challenges in healthcare data exchange as it provides a shared, immutable, and transparent audit trail for accessing data. Therefore, in this paper, we are going to propose a conceptual blockchain based fall prediction model leveraging smart contracts and FHIR (Fast Healthcare Interoperability Resources) standard to identify the elderly people who are at a higher risk of falling.
AB - Falls are one of the major health concerns for the elderly people. These falls often result in severe injuries which lead into huge medical expenses. Over the recent years, many ICT based fall detection and fall prevention solutions emerged to address the risk factors associated with falls. However, despite of these research studies, predicting the likelihood of falls still remains as a huge challenge in both medical and IT research domains. Data related to these risk factors being scattered among different healthcare providers can be attributed as a main reason for this challenge. This is further amplified by healthcare providers being reluctant to disseminate the data beyond their entities due to the security and privacy concerns. However, in recent years, blockchain has been proven as a promising technology to address the security and privacy challenges in healthcare data exchange as it provides a shared, immutable, and transparent audit trail for accessing data. Therefore, in this paper, we are going to propose a conceptual blockchain based fall prediction model leveraging smart contracts and FHIR (Fast Healthcare Interoperability Resources) standard to identify the elderly people who are at a higher risk of falling.
KW - Blockchain Technology
KW - Fall Prediction
KW - Healthcare
KW - Smart Contracts
UR - http://www.scopus.com/inward/record.url?scp=85061270592&partnerID=8YFLogxK
U2 - 10.1145/3290688.3290736
DO - 10.1145/3290688.3290736
M3 - Conference Paper
AN - SCOPUS:85061270592
BT - ACSW'19 - Proceedings of the Australasian Computer Science Week Multiconference
A2 - Wang, Ying
A2 - Patterson, Dale
PB - Association for Computing Machinery (ACM)
CY - New York NY USA
ER -