JMIR Publications

ABSTRACT

Background:

Every hospital manager aims to build a harmonious, mutually beneficial, and steady-state hospital departments. Therefore, it is important to actively explore a hospital department development assessment model based on objective hospital data.

Objective:

This study aims to determine the evaluation indexes of hospital departments through a novel machine learning algorithm, so as to provide a reference for the construction of hospital departments.

Methods:

Data related to the development of a hospital department over the past three years were extracted from various hospital information systems. The resulting dataset was deep mined using neural machine algorithms to assess hospital departments' possible role in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital for assessing the actual work in each hospital department and the impact of each department's development on the overall hospital discipline. The results were used to validate the machine algorithm.

Results:

We performed and modeled a deep machine learning on the hospital system training dataset. Our model successfully leverages the hospital's training dataset to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the ranking set from the department machine algorithm, using the cosine similarity algorithm, showed a good match. This indicated that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.

Conclusions:

Based on their demonstrated accuracy and objectivity, these models provide practical guidance for hospital operations and management. Hospital management researchers may need to focus on establishing machine algorithms for departmental evaluation models based on hospital system datasets.