Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach
<p dir="ltr">It has been reported that the healthcare industry is the slowest adopter of artificial intelligence<u> </u>methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-mak...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513547444158464 |
|---|---|
| author | Ahmad A. Abujaber (14586054) |
| author2 | Abdulqadir J. Nashwan (11659453) Adam Fadlalla (9100067) |
| author2_role | author author |
| author_facet | Ahmad A. Abujaber (14586054) Abdulqadir J. Nashwan (11659453) Adam Fadlalla (9100067) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad A. Abujaber (14586054) Abdulqadir J. Nashwan (11659453) Adam Fadlalla (9100067) |
| dc.date.none.fl_str_mv | 2022-09-16T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.imu.2022.101090 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enabling_the_adoption_of_machine_learning_in_clinical_decision_support_A_Total_Interpretive_Structural_Modeling_Approach/29116949 |
| 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 Human society Policy and administration Information and computing sciences Artificial intelligence Machine learning Artificial intelligence Machine learning Total interpretive structural modeling TISM MICMAC Clinical decision-making |
| dc.title.none.fl_str_mv | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">It has been reported that the healthcare industry is the slowest adopter of artificial intelligence<u> </u>methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness. This method paper aims to identify the enablers for adopting ML in supporting clinical decision-making and propose a strategic road map toward boosting the clinicians' intentions to adopt ML as a clinical decision support tool. This paper utilizes the Total Interpretive Structural Modeling (TISM) methodology and the Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision-making. The paper concludes that building an academic foundation, raising awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians' intentions to adopt machine learning in supporting clinical decision-making.</p><h2>Other Information</h2><p dir="ltr">Published in: Informatics in Medicine Unlocked<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/10.1016/j.imu.2022.101090" target="_blank">https://dx.doi.org/10.1016/j.imu.2022.101090</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_11f60dd240a260002175f6bf0a6c00b6 |
| identifier_str_mv | 10.1016/j.imu.2022.101090 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29116949 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling ApproachAhmad A. Abujaber (14586054)Abdulqadir J. Nashwan (11659453)Adam Fadlalla (9100067)Health sciencesHealth services and systemsHuman societyPolicy and administrationInformation and computing sciencesArtificial intelligenceMachine learningArtificial intelligenceMachine learningTotal interpretive structural modelingTISMMICMACClinical decision-making<p dir="ltr">It has been reported that the healthcare industry is the slowest adopter of artificial intelligence<u> </u>methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness. This method paper aims to identify the enablers for adopting ML in supporting clinical decision-making and propose a strategic road map toward boosting the clinicians' intentions to adopt ML as a clinical decision support tool. This paper utilizes the Total Interpretive Structural Modeling (TISM) methodology and the Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision-making. The paper concludes that building an academic foundation, raising awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians' intentions to adopt machine learning in supporting clinical decision-making.</p><h2>Other Information</h2><p dir="ltr">Published in: Informatics in Medicine Unlocked<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/10.1016/j.imu.2022.101090" target="_blank">https://dx.doi.org/10.1016/j.imu.2022.101090</a></p>2022-09-16T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.imu.2022.101090https://figshare.com/articles/journal_contribution/Enabling_the_adoption_of_machine_learning_in_clinical_decision_support_A_Total_Interpretive_Structural_Modeling_Approach/29116949CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291169492022-09-16T12:00:00Z |
| spellingShingle | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach Ahmad A. Abujaber (14586054) Health sciences Health services and systems Human society Policy and administration Information and computing sciences Artificial intelligence Machine learning Artificial intelligence Machine learning Total interpretive structural modeling TISM MICMAC Clinical decision-making |
| status_str | publishedVersion |
| title | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| title_full | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| title_fullStr | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| title_full_unstemmed | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| title_short | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| title_sort | Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach |
| topic | Health sciences Health services and systems Human society Policy and administration Information and computing sciences Artificial intelligence Machine learning Artificial intelligence Machine learning Total interpretive structural modeling TISM MICMAC Clinical decision-making |