dc.contributor.author |
Mansour, Nashat |
|
dc.contributor.author |
Danas, Konstantinos |
|
dc.contributor.author |
Nammour, Fadi |
|
dc.date.accessioned |
2018-05-17T06:26:29Z |
|
dc.date.available |
2018-05-17T06:26:29Z |
|
dc.date.issued |
2018-05-17 |
|
dc.identifier.isbn |
978-1-5090-3370-6 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10725/7835 |
|
dc.description.abstract |
Patient information in healthcare organizations is distributed across several systems and data silos. Clinicians make decisions based on data in patient health records. Improving the efficiency of decision-support requires collective knowledge of all patient information. The classical approach of linking patient data from many databases into one data warehouse poses various problems when it comes to building clinical analytics. An implementation of the Performance Measurement and Management approach used in Engineering and Business is adapted to healthcare scenarios, and a new system is developed that allows clinicians that are not technical professionals to develop, test and apply custom analytics to patient health data. Part I of this paper is an introduction to the problems and current situation in healthcare data analytics. Part II states the aim and objectives. Part III explains the system design and its modular components. Part IV presents the results of three performance indicators evaluated through the system, and evaluates the system through technical and clinical usability methods. Part V concludes and discusses future work. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.title |
CorporateMeasures |
en_US |
dc.type |
Conference Paper / Proceeding |
en_US |
dc.title.subtitle |
a clinical analytics framework leading to clinical intelligence |
en_US |
dc.author.school |
SAS |
en_US |
dc.author.idnumber |
198629170 |
en_US |
dc.author.department |
Computer Science and Mathematics |
en_US |
dc.description.embargo |
N/A |
en_US |
dc.keywords |
Performance measurement and management |
en_US |
dc.keywords |
Clinical analytics |
en_US |
dc.keywords |
Business intelligence |
en_US |
dc.keywords |
Patient health records |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.1109/HealthCom.2016.7749451 |
en_US |
dc.identifier.ctation |
Nammour, F., Danas, K., & Mansour, N. (2016, September). CorporateMeasures: A clinical analytics framework leading to clinical intelligence. In e-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on (pp. 1-6). IEEE. |
en_US |
dc.author.email |
nmansour@lau.edu.lb |
en_US |
dc.conference.date |
14-16 Sept. 2016 |
en_US |
dc.conference.place |
Munich, Germany |
en_US |
dc.conference.title |
2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) |
en_US |
dc.identifier.tou |
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php |
en_US |
dc.identifier.url |
https://ieeexplore.ieee.org/abstract/document/7749451/ |
en_US |
dc.orcid.id |
https://orcid.org/0000-0002-3603-8284 |
en_US |
dc.publication.date |
2016 |
en_US |
dc.author.affiliation |
Lebanese American University |
en_US |