Big Data Analysis Center, Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
School of Foreign Studies, Xi'an Medical University, Xi'an, China.
JMIR Form Res. 2024 Sep 4;8:e54638. doi: 10.2196/54638.
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.
每家医院的管理者都旨在建立和谐、互利和稳定的科室。因此，探索一种基于客观医院数据的医院科室发展评估模型非常重要。
本研究旨在使用一种新颖的机器学习算法来识别医院科室的关键评估指标，为医院管理中的战略规划和资源配置提供见解。
从各种医院信息系统中提取了过去 3 年医院科室发展的数据。使用神经机器算法对所得数据集进行挖掘，以评估医院科室在医院发展中的可能作用。使用问卷咨询熟悉医院的高级专家，评估每个医院科室的实际工作以及每个科室的发展对整体医院学科的影响。我们使用该问卷的结果来验证机器学习算法计算的科室风险评分的准确性。
对医院系统训练数据集进行了深度机器学习和建模。该模型成功地利用了医院的训练数据集来学习、预测和评估医院科室的工作和发展。使用余弦相似算法和 Pearson 相关分析对问卷结果与科室机器学习算法的风险排名集进行比较，结果匹配良好。这表明，基于医院系统客观数据的科室发展评估模型和风险评分具有较高的准确性和客观性。
本研究表明，我们的机器学习算法为医院科室发展提供了一种准确和客观的评估模型。通过统计分析验证，模型的风险评估与专家意见高度一致，凸显了其可靠性和指导医院管理决策的潜力。
