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Background <p>Robust data privacy and management policies are becoming increasingly important in veterinary medicine. Oncology patient records, in particular, are often complex, as patients may consult with a wide range of specialists (surgery, medical oncology, radiation oncology), visit different clinics, and receive a variety of treatments. Subsequently, the oncology patient record is often complex, multi-institutional, and challenging to harmonize.&#xa0;</p> Methods <p>We describe a comprehensive framework for data management in a veterinary setting, focusing on large academic environments, oncology, and artificial intelligence. </p> Results <p>Veterinary hospital data privacy practices require assessing broadly applicable basic security measures and veterinary-specific details, including consideration of provisions for handling diverse animal species, addressing who owns the data in the practice, protected owner data, employee privileged data, and third-party partner use. Implementing effective data privacy policies entails considering multiple data encryption, access controls, and data anonymization, along with adopting robust technological infrastructure, cloud-based storage solutions, and secure communication channels. Compliance with data privacy demands secure storage technology, policies, regular audits, staff training, and engagement with legal counsel to ensure data confidentiality and accountability. Developing a data management policy for sharing data with external entities emphasizes formal agreements, data de-identification, when necessary, secure transmission, access controls, compliance verification, and intellectual property protection. Distinctions between commercial parties and academic institutions highlight the varying data use purposes, ownership rights, and confidentiality requirements, necessitating tailored data-sharing approaches while maintaining data privacy and security.</p> Conclusions <p>Challenges in implementing data privacy policies stem from the need for standardized policy frameworks, data classification standards, and clear guidelines for data sharing and consent management. Leveraging advanced security technologies offers prospects for enhancing data protection against cyber threats. Standardized privacy regulations and data privacy education can foster awareness and compliance among veterinary professionals, ensuring responsible data management practices.</p>

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Veterinary oncology data management in the era of artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月14日

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  • S. Pu,
  • M. Thompson,
  • S. Ross,
  • P. S. Basran

摘要

Background

Robust data privacy and management policies are becoming increasingly important in veterinary medicine. Oncology patient records, in particular, are often complex, as patients may consult with a wide range of specialists (surgery, medical oncology, radiation oncology), visit different clinics, and receive a variety of treatments. Subsequently, the oncology patient record is often complex, multi-institutional, and challenging to harmonize. 

Methods

We describe a comprehensive framework for data management in a veterinary setting, focusing on large academic environments, oncology, and artificial intelligence.

Results

Veterinary hospital data privacy practices require assessing broadly applicable basic security measures and veterinary-specific details, including consideration of provisions for handling diverse animal species, addressing who owns the data in the practice, protected owner data, employee privileged data, and third-party partner use. Implementing effective data privacy policies entails considering multiple data encryption, access controls, and data anonymization, along with adopting robust technological infrastructure, cloud-based storage solutions, and secure communication channels. Compliance with data privacy demands secure storage technology, policies, regular audits, staff training, and engagement with legal counsel to ensure data confidentiality and accountability. Developing a data management policy for sharing data with external entities emphasizes formal agreements, data de-identification, when necessary, secure transmission, access controls, compliance verification, and intellectual property protection. Distinctions between commercial parties and academic institutions highlight the varying data use purposes, ownership rights, and confidentiality requirements, necessitating tailored data-sharing approaches while maintaining data privacy and security.

Conclusions

Challenges in implementing data privacy policies stem from the need for standardized policy frameworks, data classification standards, and clear guidelines for data sharing and consent management. Leveraging advanced security technologies offers prospects for enhancing data protection against cyber threats. Standardized privacy regulations and data privacy education can foster awareness and compliance among veterinary professionals, ensuring responsible data management practices.