Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper
<h3>Background</h3><p dir="ltr">Machine learning (ML) models can enhance patient–nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513534199595008 |
|---|---|
| author | Mutaz I. Othman (21186827) |
| author2 | Abdulqadir J. Nashwan (11659453) Ahmad A. Abujaber (14586054) |
| author2_role | author author |
| author_facet | Mutaz I. Othman (21186827) Abdulqadir J. Nashwan (11659453) Ahmad A. Abujaber (14586054) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mutaz I. Othman (21186827) Abdulqadir J. Nashwan (11659453) Ahmad A. Abujaber (14586054) |
| dc.date.none.fl_str_mv | 2025-04-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/nop2.70195 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimising_Nurse_Patient_Assignments_The_Impact_of_Machine_Learning_Model_on_Care_Dynamics_Discursive_Paper/30405883 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning health care machine learning nurse competencies nurse–patient assignment patient outcome |
| dc.title.none.fl_str_mv | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Machine learning (ML) models can enhance patient–nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managing staffing and establishing flexible workforce solutions.</p><h3>Aim</h3><p dir="ltr">This discursive paper discusses the application of ML in optimising patient–nurse assignments within healthcare settings, considering various factors such as staff skill mix, patient acuity, cultural competencies and language considerations.</p><h3>Methods</h3><p dir="ltr">A discursive approach was used to optimise nurse–patient assignments and the impact of ML models. Through a review of traditional and emerging perspectives, factors such as staff skill mix, patient acuity, cultural competencies and language‐related challenges were emphasised.</p><h3>Results</h3><p dir="ltr">Machine learning models can potentially enhance healthcare patient–nurse assignments by considering skill integration, acuity level assessment and cultural and language barrier awareness. Thus, models have the potential to optimise patient care through dynamic adjustments.</p><h3>Conclusion</h3><p dir="ltr">The application of ML models in optimising patient–nurse assignments presents significant opportunities for improving healthcare delivery. Future research should focus on refining algorithms, ensuring real‐time adaptability, addressing ethical considerations, evaluating long‐term patient outcomes, fostering cooperative systems, and integrating relevant data and policies within the healthcare framework.</p><h2>Other Information</h2><p dir="ltr">Published in: Nursing Open<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/nop2.70195" target="_blank">https://dx.doi.org/10.1002/nop2.70195</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_61fc22f7d2bbfb20a2e8685cf6b22a2d |
| identifier_str_mv | 10.1002/nop2.70195 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30405883 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive PaperMutaz I. Othman (21186827)Abdulqadir J. Nashwan (11659453)Ahmad A. Abujaber (14586054)Health sciencesHealth services and systemsInformation and computing sciencesMachine learninghealth caremachine learningnurse competenciesnurse–patient assignmentpatient outcome<h3>Background</h3><p dir="ltr">Machine learning (ML) models can enhance patient–nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managing staffing and establishing flexible workforce solutions.</p><h3>Aim</h3><p dir="ltr">This discursive paper discusses the application of ML in optimising patient–nurse assignments within healthcare settings, considering various factors such as staff skill mix, patient acuity, cultural competencies and language considerations.</p><h3>Methods</h3><p dir="ltr">A discursive approach was used to optimise nurse–patient assignments and the impact of ML models. Through a review of traditional and emerging perspectives, factors such as staff skill mix, patient acuity, cultural competencies and language‐related challenges were emphasised.</p><h3>Results</h3><p dir="ltr">Machine learning models can potentially enhance healthcare patient–nurse assignments by considering skill integration, acuity level assessment and cultural and language barrier awareness. Thus, models have the potential to optimise patient care through dynamic adjustments.</p><h3>Conclusion</h3><p dir="ltr">The application of ML models in optimising patient–nurse assignments presents significant opportunities for improving healthcare delivery. Future research should focus on refining algorithms, ensuring real‐time adaptability, addressing ethical considerations, evaluating long‐term patient outcomes, fostering cooperative systems, and integrating relevant data and policies within the healthcare framework.</p><h2>Other Information</h2><p dir="ltr">Published in: Nursing Open<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/nop2.70195" target="_blank">https://dx.doi.org/10.1002/nop2.70195</a></p>2025-04-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/nop2.70195https://figshare.com/articles/journal_contribution/Optimising_Nurse_Patient_Assignments_The_Impact_of_Machine_Learning_Model_on_Care_Dynamics_Discursive_Paper/30405883CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304058832025-04-23T09:00:00Z |
| spellingShingle | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper Mutaz I. Othman (21186827) Health sciences Health services and systems Information and computing sciences Machine learning health care machine learning nurse competencies nurse–patient assignment patient outcome |
| status_str | publishedVersion |
| title | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| title_full | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| title_fullStr | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| title_full_unstemmed | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| title_short | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| title_sort | Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning health care machine learning nurse competencies nurse–patient assignment patient outcome |