Optimizing patient flow with an iBeacon-based in-hospital navigation system: A framework and case study

In recent years, general hospitals have developed rapidly, with their building area and number of departments continuously increasing. This represents an important direction for hospital development [1]. Meanwhile, with the development of medical disciplines, outpatient functional areas have been frequently adjusted, leading to a continuous expansion of hospital layouts and increasingly complex patient routes. These issues pose significant challenges to patient movement within the hospital [2].

In the field of indoor navigation technology, various technical solutions such as Bluetooth, WiFi, and ultra-wideband have been explored and researched [3]. Among them, Bluetooth iBeacon-based technology is convenient to deploy and has a moderate cost [4-6]. However, most existing research focuses on positioning accuracy and technical implementation, and its practical application in hospitals is often disconnected from core business processes, failing to achieve the transformation from “simple static location query” to “dynamic process guidance” [2, 7-9]. To address the above issues, this paper constructs an intelligent in-hospital navigation system and carries out online testing at Shanghai Pudong New Area Zhoupu Hospital. This study makes three key contributions: (1) It designs a process-aware system architecture, which is deeply integrated with the hospital information system (HIS) and links navigation guidance with the real-time progress of patients’ visits, thereby clearly guiding them to the next destination and realizing proactive guidance on “where to go next”. (2) It uses the message push function of WeChat Official Account to automatically send navigation information according to the patient’s subsequent visits [10]. (3) It adopts rule-based dynamic path planning, which can recommend the optimal route for patients and realize one-way traffic control, meeting the relevant requirements of epidemic prevention. This paper combines real application data to fully elaborate on the solution, covering system design, core technologies, on-site deployment and effect evaluation. 

2.1 Overall architecture design

The overall system architecture is shown in Figure 1.

2.1.1 Positioning module

This module includes a positioning device and a positioning terminal (i.e., the patient’s smartphone), primarily employing iBeacon Bluetooth beacon technology [4-6]. To ensure the continuity of indoor positioning, this technology is also combined with the smartphone’s inertial navigation function and an improved pedestrian dead reckoning algorithm [3, 8, 11]. Positioning is achieved through two methods: received signal strength indicator (RSSI)-based fingerprint matching and pedestrian dead reckoning (PDR).

2.1.2 Indoor/outdoor positioning cloud platform

2.1.3 Business logic module (core)

2.1.4 Human-computer interaction module

2.2 Process-aware navigation mechanism

The core innovation of this system lies in the process-aware navigation mechanism, which is consistent with the goal of process optimization in the construction of smart hospitals [1, 2]. Figure 2 shows the system sequence diagram. When the patient information changes in the HIS, the business logic module automatically performs path planning based on the patient’s current location and destination, and actively pushs navigation messages to the patient’s WeChat through the human-computer interaction module [10]. This design effectively realizes the transformation of the service model from “people seeking tasks” to “tasks finding people”.

2.3 Path planning strategy

The path planning is based on an improved algorithm that not only calculates the geometric shortest path, but also incorporates business rule weights (such as prioritizing the planning of accessible pathways and elevators for wheelchair users) and management rules (such as setting up one-way electronic fences during the pandemic) to generate an optimal path with context-aware capabilities. Figure 3 shows the navigation results, which presents examples of optimal paths generated under four different travel preferences (escalator, elevator, stair, and barrier-free access), including the default recommended route.

3.1 System deployment and usage overview

3.2 Visit efficiency evaluation

To quantify the impact of this system on patient visit efficiency, we conducted a retrospective cohort study. Anonymous patient process timestamp data was extracted from the HIS, covering before system implementation (October 2020) and after implementation (October 2024). The typical patient visit process of “payment → examination registration” was selected, and the average time for this process was calculated for all patients before and after the system implementation. After the system implementation, the total time from payment to completion of examination registration decreased by 17%. This is showed in Table 1.

This paper designs an in-hospital intelligent navigation system that is deeply integrated into the entire patient visit process. It has been successfully deployed and tightly integrated with the HIS. This integration achieves process-aware proactive navigation, fulfilling the design goals of a new generation of hospital navigation systems, and also aligns with the core objective of optimizing outpatient process management [2].

This study conducted a retrospective cohort analysis, which provided reliable empirical evidence: patients using the system spent significantly less time finding their way than those who did not use it. The system operates stably and has received positive feedback from users. All of these factors confirm that the solution is feasible, effective, and practical in real medical scenarios.

The main contribution of this study is the successful transformation of a navigation tool: from a technology-driven tool to a patient-centered, process-aware intelligent service system. This achievement aligns with national policies on high-quality hospital development and provides an effective practical case for improving outpatient efficiency and enhancing patients’ medical experience [1, 2].

In the future, we will utilize the trajectory data generated by this system to further study patient flow patterns and explore predictive navigation and crowd management solutions based on artificial intelligence.

Author contributions 

Zhigang Sun: Conceptualization, Methodology, Investigation, Writing—original draft, Writing—review & editing, Software, Data collection, Formal analysis. Feifei Gu: Methodology, Validation. Bei Tian: Supervision, Writing—review & editing, Project consultation. Ming Hu: Supervision, Writing—review & editing, Project administration. All authors read and approved the final manuscript.

Funding

This work was supported by Pudong New Area Outstanding Young Scholars Program (PWRq2024-52).

Data availability

The data used in this study comes from two sources: one is the usage statistics from the in-hospital navigation system backend; the other is the time data of patients’ payment for examinations and test registration extracted from the Hospital Information System (HIS). The time difference between payment and registration was calculated to indirectly reflect patients’ wayfinding time and evaluate the effectiveness of the navigation system. Only anonymized time information was collected, without any personal identifiable sensitive data of patients, ensuring data security and compliance.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

The authors thank Zhoupu Hospital for supporting this research and the staff for their assistance in data collection.

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