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BACKGROUND with the widespread application of computer network systems in the medical field, the plan-do-check-action (PDCA) and the international classification of diseases tenth edition (ICD-10) coding system have also achieved favorable results in clinical medical record management. However, research on their combined application is relatively lacking. Objective: it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding. Material and Method: a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. AIM To study the adoption of network and PDCA in the ICD-10. METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. RESULTS In the 3, 6, 12, 18, and 24 months of PDCA cycle management mode, the coding accuracy and medical record completion rate were higher, and the coding time was lower in the observation group as against the controls (P < 0.05). The satisfaction of coders (80.22% vs 53.45%) and patients (84.89% vs 51.72%) in the observation group was markedly higher as against the controls (P < 0.05). CONCLUSION The combination of computer networks and PDCA can improve the accuracy, efficiency, completion rate, and satisfaction of ICD-10 coding.
BACKGROUND with the widespread application of computer network systems in the medical field, the plan-do-check-action (PDCA) and the international classification of diseases tenth edition (ICD-10) coding system have also achieved favorable results in clinical medical record management. However, research on their combined application is relatively lacking. Objective: it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding. Material and Method: a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. AIM To study the adoption of network and PDCA in the ICD-10. METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. RESULTS In the 3, 6, 12, 18, and 24 months of PDCA cycle management mode, the coding accuracy and medical record completion rate were higher, and the coding time was lower in the observation group as against the controls (P < 0.05). The satisfaction of coders (80.22% vs 53.45%) and patients (84.89% vs 51.72%) in the observation group was markedly higher as against the controls (P < 0.05). CONCLUSION The combination of computer networks and PDCA can improve the accuracy, efficiency, completion rate, and satisfaction of ICD-10 coding.
The present research focused on combining Particle Swarm Optimization (PSO) based hybrid deep learning models to classify heart disease images and patient sequences. This study employs Convolutional Neural Networks (CNNs), including VGG 16, VGG 19 and ResNet 50, as well as Recurrent Neu-ral Networks (RNNs), whereby their performance is optimized by PSO to im-prove the accuracy in diagnosing heart disease from CT images together with associated medical history. The models experienced a significant increase in classification performance, using manual hyperparameters tuning by PSO. The combined algorithm PSO with VGG 19 and the RNN model performed best, achieving a precision of 97.78% and becoming the highest recall on testing. The model that we propose uses the modern feature extraction of VGG 19 and an RNN to take into consideration the sequential nature of data, making it very accurate while keeping loss minimal. PSO with VGG 16 and RNN model is also another robust performance with an accuracy of 94.5%.
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