โก Quick Summary
This study evaluated the use of automated clinical coding in cancer registries across the United Kingdom and Europe, revealing that 12.8% of registries currently utilize automation technologies like natural language processing and machine learning. The findings highlight the efficiency of automation, with 87% of respondents reporting its effectiveness in data collection.
๐ Key Details
- ๐ Survey Participants: 23 out of 117 cancer registries
- ๐งฉ Technologies Used: Natural language processing, machine learning
- โ๏ธ Main Application: Automating data collection from pathology reports
- ๐ Efficiency Reported: 87% of respondents found automation efficient
- ๐ Data Quality Improvement: 26.1% reported enhanced data quality
๐ Key Takeaways
- ๐ค Automation is being increasingly adopted in cancer registries.
- ๐ 12.8% of registries currently employ automated coding methods.
- ๐ง Natural language processing is a primary technology used for automation.
- ๐ 73.3% of registries automate data collection from pathology reports.
- ๐ก 87% of respondents view automation as an efficient solution.
- ๐ 52.1% of registries still manually verify automated processes.
- ๐ ๏ธ Further development of algorithms is needed for complex tasks, according to 74% of respondents.
- ๐ Potential for global application of these technologies in cancer registries.
๐ Background
The integration of automated clinical coding in cancer care represents a significant advancement in the management of clinical data. By utilizing artificial intelligence and statistical methods, healthcare providers can transform unstructured clinical data into structured codes, enhancing the quality and accuracy of data collections. This shift not only saves resources but also accelerates research efforts in oncology.
๐๏ธ Study
The study involved an online survey distributed to members of the United Kingdom and Ireland Association of Cancer Registry and various European cancer registries. The aim was to assess the current state of automation in cancer registries and gather user opinions on its effectiveness. Data analysis was conducted using Microsoft Excel to summarize the responses.
๐ Results
Out of the 117 cancer registries surveyed, only 23 responded, with 15 registries (12.8%) implementing automation. The majority of these registries (73.3%) utilized automation primarily for data collection from pathology reports. Notably, 87% of respondents found automation to be efficient, while 26.1%52.1% of registries still performed manual checks on automated processes, indicating a need for further development in algorithmic capabilities.
๐ Impact and Implications
The findings of this study underscore the potential of automated clinical coding to enhance cancer care data management. While automation offers significant advantages, the ongoing role of human oversight remains crucial. As the technology evolves, it is essential to ensure that automated systems are validated and refined to maximize their effectiveness in cancer registries worldwide. This could lead to improved data quality and more efficient research processes, ultimately benefiting patient care.
๐ฎ Conclusion
The exploration of automated data collection in cancer registries reveals a promising yet cautious approach to integrating technology in healthcare. While current implementations show efficiency and potential for improved data quality, further research and development are necessary to validate these systems. The future of cancer care may very well hinge on the successful integration of automation, but the human element will continue to play a vital role in this transition.
๐ฌ Your comments
What are your thoughts on the integration of automation in cancer registries? Do you believe it will enhance data quality and efficiency? Let’s engage in a discussion! ๐ฌ Share your insights in the comments below or connect with us on social media:
Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.
Abstract
BACKGROUND: Automated clinical coding can use statistical or artificial intelligence-based technology to transform unstructured clinical data into clinical codes. These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research.
OBJECTIVE: To evaluate the use of automated clinical coding in the United Kingdom (UK) and European cancer registries.
METHOD: An online electronic survey was formulated to evaluate the use and user opinion of automation within cancer registries. The survey was distributed to members of the United Kingdom and Ireland Association of Cancer Registry and the European cancer registries. Data analysis was performed using Microsoft Excel 2015ยฎ version 15.13.3 in order to summarise the results.
RESULTS: Twenty-three of the 117 cancer registries responded to the distributed survey; 15 (12.8%) cancer registries used automation within their registry, mainly in the form of natural language processing or machine learning. Most of the sampled registries (73.3%) used these technologies to automate data collection from pathology reports; 87% of respondents reported automation as efficient; and 26.1% reported improved data quality; 12 (52.1%) of cancer registries still manually checked all the automations; and 17 (74%) respondents believed that the algorithms for difficult tasks require further development.
CONCLUSION: Various computer-based algorithms have been used for automated clinical coding in the UK and European cancer registries in the past few decades; however, to date there are no published data to validate its use. Further research and development of these technologies is needed to ensure external validity and maximise the potential use within other cancer registries globally.Implications for health information management practice:It is clear that while automation can be advantageous in areas of clinical coding, the role of the “human” (HIMs and clinical coders) in coding and classifying registry data, and in overseeing the transition, will be required for some time yet.
Author: [‘Roman M’, ‘Ali S’, ‘Ibrahim N’, ‘Dobbs TD’, ‘Hutchings H’, ‘Whitaker IS’]
Journal: Health Inf Manag
Citation: Roman M, et al. Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe. Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe. 2025; (unknown volume):18333583251378962. doi: 10.1177/18333583251378962