Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 7, 2019
Date Accepted: Apr 8, 2020
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Application of Artificial Intelligence Assisted Follow-up System in Postoperative Follow-up of Orthopedic patients
ABSTRACT
Background:
Clinical follow-up work is an indispensable component of a clinical treatment. With the development of deep learning algorithms, certain follow-up assignment can be completed by artificial intelligence (AI). We developed an AI assisted follow-up system which can simulate human voice and select an appropriate follow-up time for quantitative, automatic and personalized patient follow-up. Meanwhile, patient feedback can be automatically collected and voice information can be converted into text data in real time.
Objective:
The aim of this study is to explore the efficiency and cost-effectiveness of AI assisted system in postoperative follow-up.
Methods:
The AI assisted follow-up was used in orthopedic ward of Peking union medical college hospital since April 2019. A total of 270 patients were followed up through AI assisted follow-up system. Besides, 2656 patients were followed up by phone call manually. The baseline characteristics, telephone connection rate, information collection rate, call duration, feedback collection rate and feedback composition are compared between two groups of patients.
Results:
There was no statistically significant difference in age, gender, disease and other baseline characteristics between the two groups. There was no significant difference in telephone connection rate and feedback rate between the two groups. The average session duration in AI assisted follow-up group was 87.73±39.54 seconds, which was shorter than the average time required by manual follow-up. The feedback rate in AI assisted follow-up group was higher than in manual follow-up group (2.5% vs 10.3%, P < 0.001). The composition of feedbacks was different in two groups. Feedbacks in AI assisted follow-up group mainly included nursing, health education and hospital environment, while feedbacks from manual follow-up group were mostly medically related.
Conclusions:
The follow-up capability of AI assisted system was not inferior to traditional manual method. The application of AI assisted follow-up system in ward management is able to help save manpower and time for telephone operators, and obtain more patient feedbacks.
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