Modeling workflows: Identifying the most predictive features in healthcare operational processes.
Limited resources and increased patient flow highlight the importance of optimizing healthcare operational systems to improve patient care. Accurate prediction of exam volumes, workflow surges and, most notably, patient delay and wait times are known to have significant impact on quality of care and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0233810 |
| _version_ | 1818734206961844224 |
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| author | Colm Crowley Steven Guitron Joseph Son Oleg S Pianykh |
| author_facet | Colm Crowley Steven Guitron Joseph Son Oleg S Pianykh |
| author_sort | Colm Crowley |
| collection | DOAJ |
| description | Limited resources and increased patient flow highlight the importance of optimizing healthcare operational systems to improve patient care. Accurate prediction of exam volumes, workflow surges and, most notably, patient delay and wait times are known to have significant impact on quality of care and patient satisfaction. The main objective of this work was to investigate the choice of different operational features to achieve (1) more accurate and concise process models and (2) more effective interventions. To exclude process modelling bias, data from four different workflows was considered, including a mix of walk-in, scheduled, and hybrid facilities. A total of 84 features were computed, based on previous literature and our independent work, all derivable from a typical Hospital Information System. The features were categorized by five subgroups: congestion, customer, resource, task and time features. Two models were used in the feature selection process: linear regression and random forest. Independent of workflow and the model used for selection, it was determined that congestion feature sets lead to models most predictive for operational processes, with a smaller number of predictors. |
| first_indexed | 2024-12-18T00:01:41Z |
| format | Article |
| id | doaj.art-2569e75693cb433bb923eb0ac034af70 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T00:01:41Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2569e75693cb433bb923eb0ac034af702022-12-21T21:27:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01156e023381010.1371/journal.pone.0233810Modeling workflows: Identifying the most predictive features in healthcare operational processes.Colm CrowleySteven GuitronJoseph SonOleg S PianykhLimited resources and increased patient flow highlight the importance of optimizing healthcare operational systems to improve patient care. Accurate prediction of exam volumes, workflow surges and, most notably, patient delay and wait times are known to have significant impact on quality of care and patient satisfaction. The main objective of this work was to investigate the choice of different operational features to achieve (1) more accurate and concise process models and (2) more effective interventions. To exclude process modelling bias, data from four different workflows was considered, including a mix of walk-in, scheduled, and hybrid facilities. A total of 84 features were computed, based on previous literature and our independent work, all derivable from a typical Hospital Information System. The features were categorized by five subgroups: congestion, customer, resource, task and time features. Two models were used in the feature selection process: linear regression and random forest. Independent of workflow and the model used for selection, it was determined that congestion feature sets lead to models most predictive for operational processes, with a smaller number of predictors.https://doi.org/10.1371/journal.pone.0233810 |
| spellingShingle | Colm Crowley Steven Guitron Joseph Son Oleg S Pianykh Modeling workflows: Identifying the most predictive features in healthcare operational processes. PLoS ONE |
| title | Modeling workflows: Identifying the most predictive features in healthcare operational processes. |
| title_full | Modeling workflows: Identifying the most predictive features in healthcare operational processes. |
| title_fullStr | Modeling workflows: Identifying the most predictive features in healthcare operational processes. |
| title_full_unstemmed | Modeling workflows: Identifying the most predictive features in healthcare operational processes. |
| title_short | Modeling workflows: Identifying the most predictive features in healthcare operational processes. |
| title_sort | modeling workflows identifying the most predictive features in healthcare operational processes |
| url | https://doi.org/10.1371/journal.pone.0233810 |
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