Previously submitted to: JMIR Human Factors (no longer under consideration since Sep 10, 2025)
Date Submitted: Sep 12, 2024
Open Peer Review Period: Sep 13, 2024 - Nov 8, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Study of Pre-hospital Medical Services Complaints: A Thematic Analysis Based on Shanghai Health Hotline
ABSTRACT
Background:
As a crucial component of the modern healthcare system, pre-hospital medical services have become increasingly significant with the advancements in medical technology and evolving medical concepts. However, pre-hospital medical services face numerous challenges and difficulties. To enhance the quality of pre-hospital medical services, it is essential to identify the current deficiencies from the perspective of the demand side, analyze and monitor their concerns, and provide data support for future improvements.
Objective:
This study aims to conduct text mining on the content of complaint work orders related to pre-hospital medical services recorded in the 12320 Health Hotline, with the goal of gaining an in-depth understanding of the key concerns and issues expressed by the public regarding these services.
Methods:
We obtained complaint work orders related to pre-hospital medical service from the 12320 health hotline, spanning from June 1, 2016 to December 31, 2019. Using the Latent Dirichlet Allocation (LDA) topic model, we identified the themes mentioned in these complaint work orders and analyzed their prevalence. The themes identified by the LDA model were further refined and categorized through manual verification. Detailed analyses and discussions were conducted for each complaint theme.
Results:
Among the 2287 complaint hotline orders related to pre-hospital medical services, the LDA model identified 12 distinct themes, each with specific subject terms and prevalence values. Through manual verification and summarization, we consolidated these into 6 main complaint themes: (1) ambulance response and dispatch, (2) service quality and attitude, (3) Poor coordination between pre-hospital and in-hospital medical services, (4) pre-hospital medical services cost issues, (5) medical documentation issues, and (6) hotline management.
Conclusions:
Utilizing an intelligent LDA topic model combined with manual thematic summarization, this study successfully identified the primary concerns of public complaints regarding pre-hospital medical service in Shanghai from the 12320 Health Service Hotline. These findings highlight key challenges within the service process and provide data support for future improvements. By analyzing and monitoring hotline data, we gained a deeper understanding of public expectations and the difficulties they encounter with pre-hospital medical services.
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