Visual analytics for evaluating clinical pathways

Humberto S. Garcia Caballero; Alberto Corvò; Prabhakar M. Dixit; Michel A. Westenberg

doi:10.1109/VAHC.2017.8387499

Visual analytics for evaluating clinical pathways

Humberto S. Garcia Caballero, Alberto Corvò, Prabhakar M. Dixit, Michel A. Westenberg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

17 Citations (Scopus)

330 Downloads (Pure)

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	https://doi.org/10.1109/VAHC.2017.8387499
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/Territory	United States
City	Phoenix
Period	1/10/17 → …

Keywords

Conformance-checking
Event-logs
Process Mining
Sepsis
Visual Analytics

Access to Document

10.1109/VAHC.2017.8387499

authors versionFinal author version , 636 KB

Cite this

@inproceedings{58cee154bee849f3adbdc729c81304a7,

title = "Visual analytics for evaluating clinical pathways",

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.",

keywords = "Conformance-checking, Event-logs, Process Mining, Sepsis, Visual Analytics",

author = "Caballero, {Humberto S. Garcia} and Alberto Corv{\`o} and Dixit, {Prabhakar M.} and Westenberg, {Michel A.}",

year = "2017",

month = oct,

day = "1",

doi = "10.1109/VAHC.2017.8387499",

language = "English",

pages = "39--46",

booktitle = "2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 ; Conference date: 01-10-2017",

}

Caballero, HSG, Corvò, A, Dixit, PM & Westenberg, MA 2017, Visual analytics for evaluating clinical pathways. in 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017. Institute of Electrical and Electronics Engineers, Piscataway, pp. 39-46, 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017, Phoenix, United States, 1/10/17. https://doi.org/10.1109/VAHC.2017.8387499

Visual analytics for evaluating clinical pathways. / Caballero, Humberto S. Garcia; Corvò, Alberto; Dixit, Prabhakar M. et al.
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

T2 - 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017

Y2 - 1 October 2017

ER -

Visual analytics for evaluating clinical pathways

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this