A roadmap to implementing machine learning in healthcare: from concept to practice
BackgroundThe adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1462751/full |
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| author | Adam Paul Yan Adam Paul Yan Lin Lawrence Guo Jiro Inoue Santiago Eduardo Arciniegas Emily Vettese Agata Wolochacz Nicole Crellin-Parsons Brandon Purves Steven Wallace Azaz Patel Medhat Roshdi Karim Jessa Karim Jessa Bren Cardiff Lillian Sung Lillian Sung Lillian Sung |
| author_facet | Adam Paul Yan Adam Paul Yan Lin Lawrence Guo Jiro Inoue Santiago Eduardo Arciniegas Emily Vettese Agata Wolochacz Nicole Crellin-Parsons Brandon Purves Steven Wallace Azaz Patel Medhat Roshdi Karim Jessa Karim Jessa Bren Cardiff Lillian Sung Lillian Sung Lillian Sung |
| author_sort | Adam Paul Yan |
| collection | DOAJ |
| description | BackgroundThe adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.ObjectiveTo provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.Materials and methodsWe present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.ResultsWe show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.DiscussionThese approaches will require refinement over time as the number of deployments and experience increase. |
| format | Article |
| id | doaj-art-fa8390569e504b479bd1b7738338bb06 |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-fa8390569e504b479bd1b7738338bb062025-01-20T05:23:54ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-01-01710.3389/fdgth.2025.14627511462751A roadmap to implementing machine learning in healthcare: from concept to practiceAdam Paul Yan0Adam Paul Yan1Lin Lawrence Guo2Jiro Inoue3Santiago Eduardo Arciniegas4Emily Vettese5Agata Wolochacz6Nicole Crellin-Parsons7Brandon Purves8Steven Wallace9Azaz Patel10Medhat Roshdi11Karim Jessa12Karim Jessa13Bren Cardiff14Lillian Sung15Lillian Sung16Lillian Sung17Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaDepartment of Emergency Medicine, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaDivision of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, CanadaProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, CanadaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, CanadaBackgroundThe adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.ObjectiveTo provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.Materials and methodsWe present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.ResultsWe show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.DiscussionThese approaches will require refinement over time as the number of deployments and experience increase.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1462751/fullmachine learningclinical prediction modelsimplementationclinical utilizationelectronic health records |
| spellingShingle | Adam Paul Yan Adam Paul Yan Lin Lawrence Guo Jiro Inoue Santiago Eduardo Arciniegas Emily Vettese Agata Wolochacz Nicole Crellin-Parsons Brandon Purves Steven Wallace Azaz Patel Medhat Roshdi Karim Jessa Karim Jessa Bren Cardiff Lillian Sung Lillian Sung Lillian Sung A roadmap to implementing machine learning in healthcare: from concept to practice Frontiers in Digital Health machine learning clinical prediction models implementation clinical utilization electronic health records |
| title | A roadmap to implementing machine learning in healthcare: from concept to practice |
| title_full | A roadmap to implementing machine learning in healthcare: from concept to practice |
| title_fullStr | A roadmap to implementing machine learning in healthcare: from concept to practice |
| title_full_unstemmed | A roadmap to implementing machine learning in healthcare: from concept to practice |
| title_short | A roadmap to implementing machine learning in healthcare: from concept to practice |
| title_sort | roadmap to implementing machine learning in healthcare from concept to practice |
| topic | machine learning clinical prediction models implementation clinical utilization electronic health records |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1462751/full |
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