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Conference Paper  

Leveraging Web Scraping and API Integration for Efficient 
Medical Device Data Management 

Agapi Konstantina Liontou1,*, Spilios Zisimopoulos2 and Aris Dermitzakis1 

1 Biomedical Technology Unit, Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece. 
2 Institute of Biomedical Technology, Patras, Greece. 

* Corresponding Author Email: liontou.kon@gmail.com 

ABSTRACT

Accurate identification and management of medical devices is of particular importance to ensure patient safety and regulatory 
compliance within healthcare systems. This paper presents a comprehensive exploration of medical device data retrieval, focusing 
on the integration of web scraping and Application Programming Interface (API) technologies. The utilization of Unique Device 
Identifiers (UDIs) and the Global Medical Device Nomenclature (GMDN) system is emphasized to enhance device authentication, 
attribute verification, and accurate categorization. 

This paper introduces a state-of-the-art code implementation that combines web scraping techniques and API integration to 
address the challenges of retrieving and verifying device information. The code facilitates both access to data and healthcare 
professionals and stakeholders to make informed decisions based on reliable and up-to-date information. This is a significant 
and defining advance in the field, offering a powerful solution that is innovative as well as vital. 

The paper concludes by discussing the potential impact of these developments on patient safety, regulatory compliance, and 
the overall advancement of healthcare technology. In addition, the importance of accurate device identification, the role of UDIs 
and GMDN, and the significance of the provided cutting-edge code are highlighted, providing valuable insights into the field of 
medical device data retrieval. 

Keywords—Web scraping, Medical device data management, API integration.

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

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INTRODUCTION

In today’s data-driven landscape, the demand for effec-
tive data extraction techniques is critical. Patient safety, 
regulatory compliance, and informed decision-making 
hinge on the acquisition of accurate and up-to-date in-
formation about medical devices. However, obtaining this 
information can be a daunting task, given the disparate 
origins and formats of available data. 

This paper examines the challenges associated with 
medical device data management and introduces a com-
prehensive methodology, proposing a combination of web 
scraping methods with the integration of Application 
Programming Interfaces (APIs), with a particular focus 
on two key elements: Unique Device Identifiers (UDIs) 
and Global Medical Device Nomenclature (GMDN) terms, 
pivotal for the identification and categorization of medi-
cal devices. Our objective is to present an integrated ap-
proach for gathering, validating, and employing medical 
device data from the AccessGUDID (Global Unique Device 
Identification Database), which acts as the authoritative 
source of device information, laying the foundation for 
our methodology. In addition, our methodology relies on 
the web-Praxis Medical Equipment Management Soft-
ware (MEMS), developed by the Institute of Biomedical 
Technology (INBIT)1, whose data can be accessed by 
appropriate healthcare unit users. A dataset of medical 
device information was provided for the purpose of this 
study. AccessGUDID database on the other hand is freely 
accessible. 

The significance of swift medical equipment (ME) 
identification in today’s fast-paced healthcare environment 
cannot be overstated. Particularly in the context of recalls 
and field safety notices (FSNs) issued by manufacturers, 
which contain vital information about affected device 
types, rapid identification plays a crucial role. Clinical 
engineers are entrusted with the immediate and appro-
priate response to these notices, ensuring the correct 
course of corrective actions or equipment withdrawals 
to prevent adverse events. 

To address this pressing need, our developed software 
serves a dual purpose. It aids in the validation of ME within 
a hospital’s inventory, ensuring precise matching with the 

corresponding UDI codes. UDI serves as a standardized 
system for identifying and tracing medical devices Addi-
tionally, it facilitates the accurate classification of ME into 
GMDN groups, a critical facet of the UDI system. GMDN 
serves as a standardized system for classifying medical 
devices worldwide. It is noteworthy that recalls and FSNs 
frequently include UDIs for affected devices, emphasiz-
ing the growing importance of UDI-based traceability. 
Moreover, according to MDR 2017/7452, the traceability 
of devices by means of a UDI system should significantly 
enhance the effectiveness of the post-market safety-related 
activities for devices. 

To address the complexities of gathering and verifying 
device-related data, this paper discloses an innovative code 
implementation. We combine web scraping techniques 
with AccessGUDID (Global Unique Device Identification 
Database) API integration, presenting a powerful solution. 
Our code adeptly extracts imperative device details from 
the AccessGUDID website and validates this informa-
tion with data provided by web-Praxis. Additionally, our 
code integrates seamlessly with the AccessGUDID API, 
simplifying the retrieval of device-specific information 
dependent on UDIs, ensuring compliance with data usage 
regulations, and increasing the reliability and accuracy 
of the retrieved data. 

In summary, this paper delves into the intricate realm 
of medical device data management, leveraging advanced 
techniques for data extraction and integration. Our inte-
grated approach not only promises to enhance patient 
safety and regulatory compliance but also equips healthcare 
professionals with the tools needed for informed decision-
making in an increasingly dynamic healthcare landscape. 

BACKGROUND 

In the modern era, accurate and timely management 
of medical device data is essential for various stakehold-
ers in the healthcare industry. In this section, we talk 
about the background of the key elements of this paper’s 
methodology: Unique Device Identifiers (UDIs) and Global 
Medical Device Nomenclature (GMDN), in addition to an 
overview of web scraping and Application Programming 
Interface (API) integration. 

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53 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

APIs, on the other hand, serve as intermediaries 
between software applications, enabling seamless com-
munication and data exchange. 

 Web Scraping vs. API

Web scraping, with its capability to access data from 
multiple web pages and repositories, excels in collect-
ing large volumes of heterogeneous data efficiently. It 
offers flexibility in data collection and analysis, making 
it a valuable tool in domains such as computer vision 
and natural language processing. 

APIs, on the other hand, provide a structured and 
controlled means of accessing specific data from applica-
tions or software. While they offer advantages such as 
standardized interfaces and faster data extraction, they 
also come with limitations in terms of functionality and 
access to a single website or predefined functions.5, 7, 8

METHODS AND MATERIALS 

Python for Web Scraping

Python is a highly favored choice for web scraping due 
to several key reasons. Firstly, Python is a very popular 
programming language because of its simplicity and 
learnability, facts that make it accessible even to those 
with little programming experience. Its extensive com-
munity support ensures readily available assistance for 
overcoming coding challenges. 

Furthermore, Python excels in web scraping because 
of the readability of the code. Python code is designed 
for easy understanding, promoting clear and concise pro-
gramming practices. This readability not only enhances 
productivity during development but also facilitates 
code understanding, even when revisiting it after some 
time has passed. This attribute promotes more efficient 
code maintenance and facilitates code reuse.8 For these 
reasons, Python is used in all the software we developed. 

Our Code

We present an integrated code solution that   combines 
web scraping via Beautiful Soup (Version 4, Python Li-
brary for HTML and XML Parsing, Crummy, Cambridge, 
MA, USA)  with AccessGUDID API integration. Tailored 

Unique Device Identifiers (UDIs)

Unique Device Identifiers or UDIs, are alphanumeric 
codes for medical devices, offering a standardized global 
means of identification. They enable precise tracking from 
manufacturing to post-market surveillance. UDIs encode 
key device information like manufacturer, model, and 
production date. Their implementation has significantly 
advanced healthcare, enhancing patient safety, regulatory 
compliance, and supply chain management. UDIs empower 
healthcare professionals to quickly access device data, 
identify recalls, and respond to adverse events effectively. 3

Global Medical Device Nomenclature (GMDN)

The Global Medical Device Nomenclature (GMDN) 
is an internationally recognized system for naming and 
categorizing medical devices. It offers a structured clas-
sification system that facilitates global communication 
in healthcare. GMDN codes categorize devices based on 
their purpose, structure, and operation, enabling precise 
comparisons. GMDN adoption has streamlined regula-
tions, research, and product development in the medical 
device industry. It promotes consistency in terminology 
and categorization, as well as seamless compatibility and 
cooperation among healthcare stakeholders for sharing 
accurate device information.4

Web Scraping and API Integration

    In the contemporary era, extracting data from various 
web sources has become essential for various domains, 
including healthcare. In this section, a foundational un-
derstanding of the main data extraction methods: web 
scraping and Application Programming Interfaces (APIs), 
is provided. Web scraping, also known as web extraction 
or harvesting, involves the automated extraction of data 
from websites and their subsequent storage for analysis or 
retrieval. This method, widely recognized for its efficiency 
and accuracy, has evolved significantly over the years. 
Modern web scraping tools have become versatile, capable 
of parsing markup languages, integrating with computer 
vision and natural language processing techniques, and 
simulating human browsing behavior.5,6 

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for healthcare professionals, this tool simplifies access, 
validation, and analysis of device data based on UDIs and 
GMDN names while ensuring data compliance. 

A key aspect of our methodology involves cross-refer-
encing data derived from web scraping with information 
from Praxis, our primary data source. This information 
is structured in an Excel file and has been collected by 
humans, checked, and entered into the Praxis database. 
This quality control step identifies disparities in device 
attributes, improving data accuracy. For example, dis-
crepancies in GMDN names trigger further investigation 
to address updates or errors. 

Our comprehensive validation process involves veri-
fying GMDN names, selecting the most prevalent name 
when multiple names exist, and aligning device attributes 
across sources, ultimately enhancing the reliability of 
medical device data. 

Data Import

To begin data retrieval, the code begins by importing 
data from an Excel file provided by Web Praxis. This file 
includes critical information about medical devices and 
the specific columns of interest include General Group, 
Specific Group, Manufacturer, Model, and Comments, 
where Unique Device Identifiers (UDIs) are often found. 
The initial Excel file includes 279 records, representing 
distinct medical devices. 

UDI Extraction

Next, the code extracts the 14-digit UDIs from the 
appropriate field (if any because not all records have a 
registered UDI). This step is crucial for subsequent op-
erations as UDIs serve as the primary key for accessing 
device information. 

Web Scraping and API Integration

The heart of the methodology lies in its ability to com-
bine web scraping and API integration for comprehensive 
data collection. Web scraping extracts data based on UDIs, 
while API calls are made to the AccessGUDID database to 
retrieve detailed device information. This hybrid approach 
ensures that even devices without readily available UDIs 
can be identified and analyzed. 

Data Validation and Presentation 

As data is retrieved, the code simultaneously validates 
and cross-references it to ensure accuracy and reliability. 
The data are then organized and presented in a structured 
format for further analysis and reporting. 

Data Cross-Referencing

For enhancing data reliability, the code employs cross-
referencing. It verifies device attributes like GMDN terms 
by comparing data from Praxis with data obtained through 
web scraping or API integration. This process ensures 
consistency across multiple sources and is a vital quality 
control step. 

The aforementioned accomplishments were realized 
through the utilization of the following libraries: requests 
for making HTTP requests to interact with web services 
and retrieve data from web servers, pandas for data 
manipulation, tkinter for creating a GUI, re for regular 
expressions, time for time-related operations, and Beauti-
ful Soup for web scraping purposes to pull the data out of 
HTML and XML files, as it helps in navigating, searching, 
and modifying the parse tree. Beautiful Soup creates a 
parse tree from page source code that can be used to 
extract data easily. 

RESULTS

The results were quite satisfactory. Out of the 279 
records initially provided, we found UDI matches for 193 
records (69% success rate). By implementing a complex 
search based on the company name and brand name 
(this was done because there were cases that we did not 
have a UDI—device type match a priori), we were able 
to identify 14 more records which means 207 records in 
total (74% success rate). 

The output generated by the code is presented in a 
structured Excel format (Figure 1), providing a compre-
hensive overview of matched UDIs, associated company 
names, brand names, GMDN names, cross references, 
and definitions. 

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55 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

demonstrate a fairly accurate recording of medical equip-
ment, with correct UDIs, GMDN names, and definitions. 

Matching all 279 medical records cannot be possible 
due to inaccuracies inherent in the databases. This creates 
issues for our model, often leading to many closely related 
outcomes. Even for a human observer, distinguishing 
between these outcomes is extremely challenging. So, 
reaching a perfect 100% match rate is impossible and 
would demand a lot of human effort from an experienced 
eye, potentially even reviewing unrelated results. Hence, 
a 74% match rate is considered satisfactory under these 
circumstances. 

Overall, the output provides a comprehensive overview 
of the matched UDIs, associated company names, brand 
names, GMDN names, cross-references, and definitions, 
allowing for further analysis and verification of the re-
corded medical equipment data. 

In general, the implemented code combining web 
scraping techniques and integration with the AccessGU-
DID API has proven to be an essential tool for retrieving, 
validating, and analyzing medical device data based on 
UDIs and Global Medical Device Nomenclature (GMDN) 
names. 

The need for web scraping arises from the vast amount 
of device information available on websites like Access-
GUDID. Web scraping enables the efficient extraction of 
specific data elements, such as company names, brand 
names, GMDN names, cross-references, and definitions, 
from complex HTML structures. By automating the data 
retrieval process, this method provides instant access 
to the most up-to-date device information, ensuring ac-
curacy and timeliness. 

Integrating an API (in this case, the AccessGUDID API) 
further enhances the code’s functionality and reliability. 
By leveraging the API, the code establishes a secure con-
nection to the comprehensive device database provided 
by AccessGUDID, ensuring compliance with data usage 
policies, facilitating seamless data retrieval, and enhanc-
ing the reliability and accuracy of the obtained device 
information. 

Moreover, the use of UDIs plays a crucial role in device 
identification and traceability. UDIs provide a standardized 
system for the unique identification of medical devices, 
ensuring many benefits for both patient and equipment 
safety. The code’s ability to cross-check device data with 

Where: 

1st column: UDI, UDIs are listed. It includes both the 
UDIs that were initially matched and the “N/A” symbol 
for equipment that had no UDI in the initial data and 
required an advanced search. 

2nd column: Company Name as registered on the website. 

3rd column: Brand Name as registered on the website. 

4th column: GMDN Name as registered on the website. 

5th column: GMDN Cross Reference. The term “same” 
is displayed for records that have the same GMDN Name 
on both the website and the initial Excel data. For records 
that are not the same, a different description is provided. 
This description was obtained from the initial data. 

6th column: GMDN Definition as registered on the 
website for the devices that were “identified” by the UDI. 
For the devices that we did not know the UDI beforehand, 
and we made an advanced search, we had many results. 
So, the GMDN definition was obtained by examining the 
first page of search results on the website generated by 
the advanced search. We identified all the GMDN names 
and applied a sorting algorithm to select the names that 
appeared most frequently.

FIGURE 1. Format of the output Excel File. 

DISCUSSION

The high success rates in both types of searches in-
dicated that the data provided by web-Praxis, although 
challenging to manage due to its unstructured format, 

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UDIs and GMDN names ensures the authenticity of devices, 
verifies their attributes, and allows for accurate catego-
rization. This cross-checking process allows healthcare 
professionals to identify and address potential discrep-
ancies, ultimately ensuring patient safety and improving 
informed decision-making. 

 
CONCLUSION

In conclusion, the combination of web scraping, API 
integration, and the use of UDIs addresses the challenges 
of accessing, validating, and analyzing medical device 
data. The implemented code streamlines these processes, 
enabling users to efficiently retrieve reliable device in-
formation, verify device characteristics, and eventually 
make informed decisions. The code can be periodically 
used to ensure the fidelity of UDI codes and GMDN group 
classification of ME in a medical equipment inventory, 
to address the dynamic nature of the above systems, 
and to update the information for new ME types added 
to the inventory. As technology evolves, more and more 
advancements in web scraping and API integration will 
contribute to even more efficient and accurate device 
data management and analysis in the healthcare industry. 

Μany suggestions can be taken into account for even 
greater effectiveness of the solution we proposed. Firstly, 
validating and cleaning the input data is significant for 
ensuring data quality in general. Additionally, automating 
code execution as well as batch processing capabilities 
can improve efficiency, especially when dealing with large 
volumes of data. Moreover, integration with healthcare or 
inventory management systems is significant, in order to 
synchronize data and improve decision-making capabilities. 

Collaborations and partnerships with regulatory bod-
ies, healthcare institutions, or manufacturers can facilitate 
data sharing and drive industry-wide improvements in 
device identification and data management practices. 
Moreover, continuous data monitoring is recommended 
both to periodically retrieve and update device informa-
tion from reliable sources so as to maintain data accuracy 
and relevance. Finally, comprehensive documentation 
and user support materials is necessary to be provided, 
as they would help users to use the code effectively and 
maximize its potential. 

ACKNOWLEDGMENTS 

We would like to acknowledge the invaluable contribu-
tion of the web-Praxis software, as a primary data source 
for this research. 

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3. Unique Device Identification System (UDI System), FDA. 
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