A process mining and text analysis approach to analyse the extent of polypharmacy in medical prescribing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

A process mining and text analysis approach to analyse the extent of polypharmacy in medical prescribing. / Weber, Philip; Backman, Ruth; Litchfield, Ian; Lee, Mark.

The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018). IEEE Xplore, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Weber, P, Backman, R, Litchfield, I & Lee, M 2018, A process mining and text analysis approach to analyse the extent of polypharmacy in medical prescribing. in The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018). IEEE Xplore, The Sixth IEEE International Conference on Healthcare Informatics , New York City, United States, 4/06/18. https://doi.org/10.1109/ICHI.2018.00008

APA

Vancouver

Author

Bibtex

@inproceedings{d785b6fa1e4148a4aebbc4888516358b,
title = "A process mining and text analysis approach to analyse the extent of polypharmacy in medical prescribing",
abstract = "Prescription of conflicting concurrent medications, polypharmacy, is recognised as a significant problem in the UK, but its extent is not fully known. We combined a process mining approach with text analytics, to discover prescription processes for patients from five primary care sites in the UK WestMidlands. Free-text prescription instructions were combined with online knowledge about drug interactions to reveal that almost 62% of patients were prescribed with medication with some level of interaction, during a two year period. We describe a novel domain-specific approach to reduce the complexity of the mined processes, which will nevertheless be applicable to other flexible environments such as knowledge work. We also highlight difficulties encountered in accessing, interpreting and processing the data, which may be significantly mitigated through wider adoption of a mindset of curating data for automated analysis.",
author = "Philip Weber and Ruth Backman and Ian Litchfield and Mark Lee",
year = "2018",
month = jul,
day = "26",
doi = "10.1109/ICHI.2018.00008",
language = "English",
booktitle = "The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018)",
publisher = "IEEE Xplore",
note = "The Sixth IEEE International Conference on Healthcare Informatics , ICHI 2018 ; Conference date: 04-06-2018 Through 07-06-2018",
url = "http://hpr.weill.cornell.edu/divisions/health_informatics/ieee_ichi.html",

}

RIS

TY - GEN

T1 - A process mining and text analysis approach to analyse the extent of polypharmacy in medical prescribing

AU - Weber, Philip

AU - Backman, Ruth

AU - Litchfield, Ian

AU - Lee, Mark

PY - 2018/7/26

Y1 - 2018/7/26

N2 - Prescription of conflicting concurrent medications, polypharmacy, is recognised as a significant problem in the UK, but its extent is not fully known. We combined a process mining approach with text analytics, to discover prescription processes for patients from five primary care sites in the UK WestMidlands. Free-text prescription instructions were combined with online knowledge about drug interactions to reveal that almost 62% of patients were prescribed with medication with some level of interaction, during a two year period. We describe a novel domain-specific approach to reduce the complexity of the mined processes, which will nevertheless be applicable to other flexible environments such as knowledge work. We also highlight difficulties encountered in accessing, interpreting and processing the data, which may be significantly mitigated through wider adoption of a mindset of curating data for automated analysis.

AB - Prescription of conflicting concurrent medications, polypharmacy, is recognised as a significant problem in the UK, but its extent is not fully known. We combined a process mining approach with text analytics, to discover prescription processes for patients from five primary care sites in the UK WestMidlands. Free-text prescription instructions were combined with online knowledge about drug interactions to reveal that almost 62% of patients were prescribed with medication with some level of interaction, during a two year period. We describe a novel domain-specific approach to reduce the complexity of the mined processes, which will nevertheless be applicable to other flexible environments such as knowledge work. We also highlight difficulties encountered in accessing, interpreting and processing the data, which may be significantly mitigated through wider adoption of a mindset of curating data for automated analysis.

U2 - 10.1109/ICHI.2018.00008

DO - 10.1109/ICHI.2018.00008

M3 - Conference contribution

BT - The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018)

PB - IEEE Xplore

T2 - The Sixth IEEE International Conference on Healthcare Informatics

Y2 - 4 June 2018 through 7 June 2018

ER -