Abstract
Background:
The daily increasing amount of health data from different
sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in personalized patient therapy.
Objectives:
The goal of this work was to consider such an infrastructure, implemented in a smart registry for heart
failure, as a comparative method for the analysis of health data.
Methods:
We analyzed to what extent the dataset of a study on the telehealth program HerzMobil
Tirol (HMT) can be reproduced with the data from the smart registry.
Results:
A table with 96 variables for 251 patients of the HMT publication could theoretically be replicated from the smart registry for 248 patients with 80 variables. The smart registry contained the tables to reproduce a large part of the information, especially the core statements of the HMT publication.
Conclusion:
Our results show how such an infrastructure can enable efficient analysis of health data, and thus take a further step towards personalized health care.
The daily increasing amount of health data from different
sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in personalized patient therapy.
Objectives:
The goal of this work was to consider such an infrastructure, implemented in a smart registry for heart
failure, as a comparative method for the analysis of health data.
Methods:
We analyzed to what extent the dataset of a study on the telehealth program HerzMobil
Tirol (HMT) can be reproduced with the data from the smart registry.
Results:
A table with 96 variables for 251 patients of the HMT publication could theoretically be replicated from the smart registry for 248 patients with 80 variables. The smart registry contained the tables to reproduce a large part of the information, especially the core statements of the HMT publication.
Conclusion:
Our results show how such an infrastructure can enable efficient analysis of health data, and thus take a further step towards personalized health care.
Original language | English |
---|---|
Title of host publication | dHealth 2023 |
Editors | Bernhard Pfeifer, Günter Schreier, Martin Baumgartner, Dieter Hayn |
Publisher | IOS Press BV |
Pages | 242-247 |
Number of pages | 6 |
Volume | 301 |
ISBN (Electronic) | 978-1-64368-387-4 |
ISBN (Print) | 978-1-64368-386-7 |
DOIs | |
Publication status | Published - 2 May 2023 |
Event | dHealth 2023 - 17th Annual Conference on Health Informatics meets Digital Health - Apothekertrakt / Schönbrunn Palace, Vienna, Austria, Vienna, Austria Duration: 16 May 2023 → 17 May 2023 https://dhealth.at/ |
Publication series
Name | Studies in Health Technology and Informatics |
---|---|
Publisher | IOS Press |
Volume | 301 |
ISSN (Print) | 0926-9630 |
ISSN (Electronic) | 1879-8365 |
Conference
Conference | dHealth 2023 - 17th Annual Conference on Health Informatics meets Digital Health |
---|---|
Abbreviated title | dHealth 2023 |
Country/Territory | Austria |
City | Vienna |
Period | 16/05/23 → 17/05/23 |
Internet address |
Research Field
- Exploration of Digital Health
Keywords
- Verification
- smart registry
- data-driven healthcare
- clinical decision support
- chronic heart failure