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Clinical intervention effect of computer-aided therapy based on software engineering on patients with schizophrenia

Published online by Cambridge University Press:  27 October 2023

Donghu Gu  and
Haiyan Tan
Donghu Gu*
Affiliation:
Yunnan Technology and Business University, Kunming 651701, China
Haiyan Tan
Affiliation:
Yunnan Technology and Business University, Kunming 651701, China
*
*Corresponding author.
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Abstract

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Background

Schizophrenia (SCH) is a serious mental disorder of unknown aetiology, with clinical symptoms involving sensory perception, thinking, emotions, behavior, and other aspects. When symptoms occur repeatedly and the condition persists, the patient’s life and learning abilities are significantly impaired and gradually exhibiting cognitive impairment. The treatment effectiveness and rehabilitation process for SCH are often complex and diverse. This study developed a computer-aided treatment system using software engineering methods to evaluate its impact on the clinical intervention effectiveness of schizophrenia patients.

Subjects and Methods

A total of 300 patients with schizophrenia were selected for the experiment and divided into an experimental group and a control group, with 150 patients in each group. The experimental group received a 6-month intervention using a computer-aided treatment system, while the control group received traditional treatment methods. Evaluate using the Positive and Negative Symptom Scale (PANSS) and record the changes in scores before and after the intervention in both groups. The process data was analyzed using SPSS.

Results

After 6 months of treatment, the PANSS score of the experimental group significantly improved, with a statistical difference compared to the control group (P<0.05). The treatment compliance of the experimental group was also significantly improved, with higher patient satisfaction.

Conclusions

Computer-assisted therapy based on software engineering has shown positive effects in clinical interventions for schizophrenia and can serve as a beneficial supplement to traditional treatment methods. Future research needs to further improve the usability of the system and explore its applicability in different types of mental disorders and treatment stages.

Acknowledgement

Science Research Fund Project of Yunnan Education Department in 2019 (No. 2019J0999).

Type
Abstracts
Information
CNS Spectrums , Volume 28 , Supplement S2: Abstracts of the 3rd International Conference on Psychiatry and Neurorestoratology (ICPN2023) October 27-28, 2023 Zhengzhou, China , October 2023 , pp. S113
Copyright
© The Author(s), 2023. Published by Cambridge University Press