Abstract
Digital platforms in healthcare institutions enable tracking and recording of patient care pathways. Besides the Electronic Health Records (EHRs), the event logs from Hospital Information Systems (HIS) are a very efficient source of information, from both operational and clinical point of view. Process mining allows comparison of a patient care pathway with the event log(s) from HIS, to understand how well the reality as depicted in the event log fits the expectation as modeled using a care pathway. In this paper, we present SepVis, a visual analytics tool which aims to fill the gap in current process-centric applications by looking at patients' pathways from a clinical point of view. We demonstrate the utility of SepVis in selected use cases derived by the guidelines in the management of sepsis patients.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 39-46 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538631874 |
| DOIs | |
| Publication status | Published - 1 Oct 2017 |
| Event | 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 - Phoenix, United States Duration: 1 Oct 2017 → … |
Conference
| Conference | 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 1/10/17 → … |
Fingerprint
Keywords
- Conformance-checking
- Event-logs
- Process Mining
- Sepsis
- Visual Analytics
Cite this
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Visual analytics for evaluating clinical pathways. / Caballero, Humberto S. Garcia; Corvò, Alberto; Dixit, Prabhakar M.; Westenberg, Michel A.
2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017. Piscataway : Institute of Electrical and Electronics Engineers, 2017. p. 39-46.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Visual analytics for evaluating clinical pathways
AU - Caballero, Humberto S. Garcia
AU - Corvò, Alberto
AU - Dixit, Prabhakar M.
AU - Westenberg, Michel A.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Digital platforms in healthcare institutions enable tracking and recording of patient care pathways. Besides the Electronic Health Records (EHRs), the event logs from Hospital Information Systems (HIS) are a very efficient source of information, from both operational and clinical point of view. Process mining allows comparison of a patient care pathway with the event log(s) from HIS, to understand how well the reality as depicted in the event log fits the expectation as modeled using a care pathway. In this paper, we present SepVis, a visual analytics tool which aims to fill the gap in current process-centric applications by looking at patients' pathways from a clinical point of view. We demonstrate the utility of SepVis in selected use cases derived by the guidelines in the management of sepsis patients.
AB - Digital platforms in healthcare institutions enable tracking and recording of patient care pathways. Besides the Electronic Health Records (EHRs), the event logs from Hospital Information Systems (HIS) are a very efficient source of information, from both operational and clinical point of view. Process mining allows comparison of a patient care pathway with the event log(s) from HIS, to understand how well the reality as depicted in the event log fits the expectation as modeled using a care pathway. In this paper, we present SepVis, a visual analytics tool which aims to fill the gap in current process-centric applications by looking at patients' pathways from a clinical point of view. We demonstrate the utility of SepVis in selected use cases derived by the guidelines in the management of sepsis patients.
KW - Conformance-checking
KW - Event-logs
KW - Process Mining
KW - Sepsis
KW - Visual Analytics
UR - http://www.scopus.com/inward/record.url?scp=85050126682&partnerID=8YFLogxK
U2 - 10.1109/VAHC.2017.8387499
DO - 10.1109/VAHC.2017.8387499
M3 - Conference contribution
AN - SCOPUS:85050126682
SP - 39
EP - 46
BT - 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -