Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models
As the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/6/196 |
| _version_ | 1797490784424951808 |
|---|---|
| author | Adele H. Marshall Aleksandar Novakovic |
| author_facet | Adele H. Marshall Aleksandar Novakovic |
| author_sort | Adele H. Marshall |
| collection | DOAJ |
| description | As the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems involving autonomic behaviour and those with no prior clinical feedback, have generally to date had little focus on demonstrating robustness in the use of data and final output, thus generating a lack of confidence. This paper wishes to address this challenge by introducing a new process mining approach based on a statistically robust methodology that relies on the utilisation of conditional survival models for the purpose of evaluating the performance of Healthcare 4.0 systems and the quality of the care provided. Its effectiveness is demonstrated by analysing the performance of a clinical decision support system operating in an intensive care setting with the goal to monitor ventilated patients in real-time and to notify clinicians if the patient is predicted at risk of receiving injurious mechanical ventilation. Additionally, we will also demonstrate how the same metrics can be used for evaluating the patient quality of care. The proposed methodology can be used to analyse the performance of any Healthcare 4.0 system and the quality of care provided to the patient. |
| first_indexed | 2024-03-10T00:38:01Z |
| format | Article |
| id | doaj.art-143f99c987524fb5b0d0c60a474454b7 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T00:38:01Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-143f99c987524fb5b0d0c60a474454b72023-11-23T15:13:08ZengMDPI AGAlgorithms1999-48932022-06-0115619610.3390/a15060196Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival ModelsAdele H. Marshall0Aleksandar Novakovic1School of Mathematics and Physics, Queen’s University Belfast, University Road, Belfast BT7 1NN, Northern Ireland, UKSchool of Mathematics and Physics, Queen’s University Belfast, University Road, Belfast BT7 1NN, Northern Ireland, UKAs the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems involving autonomic behaviour and those with no prior clinical feedback, have generally to date had little focus on demonstrating robustness in the use of data and final output, thus generating a lack of confidence. This paper wishes to address this challenge by introducing a new process mining approach based on a statistically robust methodology that relies on the utilisation of conditional survival models for the purpose of evaluating the performance of Healthcare 4.0 systems and the quality of the care provided. Its effectiveness is demonstrated by analysing the performance of a clinical decision support system operating in an intensive care setting with the goal to monitor ventilated patients in real-time and to notify clinicians if the patient is predicted at risk of receiving injurious mechanical ventilation. Additionally, we will also demonstrate how the same metrics can be used for evaluating the patient quality of care. The proposed methodology can be used to analyse the performance of any Healthcare 4.0 system and the quality of care provided to the patient.https://www.mdpi.com/1999-4893/15/6/196Healthcare 4.0real-time alertsdynamic prognosticsquality of careperformance analyticsconditional survival analytics |
| spellingShingle | Adele H. Marshall Aleksandar Novakovic Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models Algorithms Healthcare 4.0 real-time alerts dynamic prognostics quality of care performance analytics conditional survival analytics |
| title | Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models |
| title_full | Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models |
| title_fullStr | Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models |
| title_full_unstemmed | Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models |
| title_short | Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models |
| title_sort | process mining the performance of a real time healthcare 4 0 systems using conditional survival models |
| topic | Healthcare 4.0 real-time alerts dynamic prognostics quality of care performance analytics conditional survival analytics |
| url | https://www.mdpi.com/1999-4893/15/6/196 |
| work_keys_str_mv | AT adelehmarshall processminingtheperformanceofarealtimehealthcare40systemsusingconditionalsurvivalmodels AT aleksandarnovakovic processminingtheperformanceofarealtimehealthcare40systemsusingconditionalsurvivalmodels |