Constructing a Hospital Department Development–Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments
Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. This study aims to use a novel machine learning algorithm to identify key eva...
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| Veröffentlicht in: | JMIR formative research Jg. 8; S. e54638 |
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| Abstract | Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.
This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management.
Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm.
Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.
This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions. |
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| AbstractList | BackgroundEvery hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. ObjectiveThis study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. MethodsData related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department’s development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. ResultsDeep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital’s training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. ConclusionsThis study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions. Background:Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.Objective:This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management.Methods:Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department’s development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm.Results:Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital’s training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.Conclusions:This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions. Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions. Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.BACKGROUNDEvery hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management.OBJECTIVEThis study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management.Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm.METHODSData related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm.Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.RESULTSDeep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.CONCLUSIONSThis study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions. |
| Author | Yan, Ziqiang Han, Junying Tai, Jiaojiao Zhang, Meng Li, Yang Liu, Jingkun Yang, Hongjuan |
| AuthorAffiliation | 1 Big Data Analysis Center Honghui Hospital, Xi'an Jiaotong University Xi'an China 2 School of Foreign Studies Xi'an Medical University Xi'an China |
| AuthorAffiliation_xml | – name: 2 School of Foreign Studies Xi'an Medical University Xi'an China – name: 1 Big Data Analysis Center Honghui Hospital, Xi'an Jiaotong University Xi'an China |
| Author_xml | – sequence: 1 givenname: Jingkun orcidid: 0000-0001-8538-9075 surname: Liu fullname: Liu, Jingkun – sequence: 2 givenname: Jiaojiao orcidid: 0009-0009-9824-7995 surname: Tai fullname: Tai, Jiaojiao – sequence: 3 givenname: Junying orcidid: 0009-0001-9011-0291 surname: Han fullname: Han, Junying – sequence: 4 givenname: Meng orcidid: 0000-0002-0983-9692 surname: Zhang fullname: Zhang, Meng – sequence: 5 givenname: Yang orcidid: 0009-0003-0026-2609 surname: Li fullname: Li, Yang – sequence: 6 givenname: Hongjuan orcidid: 0009-0007-8514-5258 surname: Yang fullname: Yang, Hongjuan – sequence: 7 givenname: Ziqiang orcidid: 0009-0007-4538-0405 surname: Yan fullname: Yan, Ziqiang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39230941$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1039/b907946g 10.1016/j.arth.2019.06.017 10.1155/2017/9674712 10.7326/0003-4819-152-8-201004200-00009 10.1111/j.1365-2044.2006.04875.x 10.1016/j.ijsu.2008.12.027 10.1016/j.revinf.2019.09.012 10.1167/tvst.9.2.14 10.3389/frai.2021.740817 10.1111/joim.12822 10.6133/apjcn.202309_32(3).0007 10.1038/nbt1206-1565 10.1145/3345120.3345147 10.1093/bioinformatics/btq170 10.2196/33600 10.1177/0840470415614842 10.1186/1472-6947-10-16 10.1016/j.cjtee.2020.09.007 10.1111/1468-0009.12511 |
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| Copyright | Jingkun Liu, Jiaojiao Tai, Junying Han, Meng Zhang, Yang Li, Hongjuan Yang, Ziqiang Yan. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.09.2024. 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Jingkun Liu, Jiaojiao Tai, Junying Han, Meng Zhang, Yang Li, Hongjuan Yang, Ziqiang Yan. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.09.2024. 2024 |
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| SubjectTerms | Algorithms Cluster analysis Clustering Communications systems Correlation analysis Datasets Departments Hospital Departments - organization & administration Humans Information systems Machine Learning Multimedia Nomograms Original Paper Principal components analysis Questionnaires Ratings & rankings Referral and Consultation Support vector machines Surveys and Questionnaires Workloads |
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| Title | Constructing a Hospital Department Development–Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments |
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