You're in charge of data retention in healthcare. What tools will help you manage it best?

Based on my experience, the most labor-intensive task is determining the criteria according to which data needs to be stored or no longer needs to be kept. Often in this matter, they are guided by the requirements of market regulators. However, an important aspect of this issue is the collection of business requirements. For example, could there be a situation where, 5 years after a transaction took place, a bank customer will request information about it from the bank? For instance, as proof in a property dispute in court. Or after 7 years? Thirdly, an additional criterion in this matter is the correlation between the cost and the value of storing information, which can also be a nontrivial task.

BigID could help with end to end ILM/DLM programmes. Enabling full inventory, retention and classification of all data across the enterprise.

Instead of of policies to help with data retention, industry audits are in my opinion a better way to ensure an organization understands why a data retention policy is required. For instance, finance audits, government audits are examples of how a data retention policy can help to ensure these audits are passed and the organization does not incur heavy penalties thus starting the business case for why this policy should be created and implemented: not only in principle, but in a ongoing effort.

As per my experience in healthcare, data retention policy starts with identifying and categorizing the type of data based on its criticality and frequency of usage. Regulatory requirements further determine the retention policies. Some of the tools which we regularly use for data retention policies are (AWS): - Data arrival, data deletion, audit trails through data lifecycle Management - data encryption policies - access control policies - data classification policies - backup and disaster recovery solutions - data governance solutions (profisee, informatica)

The key to effective and optimal data retention policy is having a very strong data governance framework in the organization. This will in turn drive policies and rules to define the data retention policies. Based on the organizations technology landscape, and then doing a best fit analysis, a tool can be selected that is effective for the organization terms of cost and TCO, without compromising on the security and effectivity.

I would slightly rephrase this point. A data inventory is essentially a data catalogue at the earliest, simplest stages of its evolution. Certainly, having a ready-made application that reflects data sources will greatly simplify data retention tasks. However, unfortunately, it often happens that there is no finished data catalogue and there is no expectation of one, yet a decision needs to be made here and now about deleting unnecessary data.

Este ponto deveria ser o primeiro. N?o há como criar uma Política sem antes saber exatamente quais dados s?o tratados. O Inventário serve, inclusive, para embasar decis?es sobre necessidade do tratamento. Além disso, "dados na área de saúde" é apenas um indicativo, n?o podendo ser presumido quais dados s?o esses e suas reais finalidades.

This is number one. If you don't know what you have then you have no idea what to do with it. Retention, deletion etc. have to come after you know what you have.

Data Governance Tools: Implement a robust data governance platform to catalog, classify, and track all healthcare data across your organization. This helps understand what data you have, where it resides, and its purpose. Popular options include Informatica, and OneTrust. Automated Data Discovery Tools: Leverage tools that automatically scan your systems to identify and catalog healthcare data assets. This helps discover hidden data and ensures a comprehensive inventory. Consider solutions like Alteryx, or Collibra Discovery & Mapping. Metadata Management Solutions: Employ a metadata management system to capture detailed information about your data. Explore tools like Collibra and Informatica.

Since it’s is to do a lot with handling critical data and specially PII information , my first three priorities would be data documentation and inventory, data Security and good retention policy tool .. these all tie up to a good governance of health care data.

In my experience, this piece is crucial for initiating the enforcement of data retention policies. It's important to remember that the more data you possess, the greater the risk you incur. Effective planning hinges on having a clear understanding. Relying solely on DLP (Data Loss Prevention) is insufficient as it primarily targets endpoints, while data often seeks liberation. The perimeter has been reshaped with users now defining the boundary Be data source agnostic, strive to consolidate your visibility on these fronts It’s worth noting that 80% of organizational data is unstructured. While structured data in databases is straightforward to query and regulate, it's imperative to address the vast majority of data that remains uncharted

There would be something to categorize! With ready-made criteria for data retention and a catalog, at least at the "data inventory" stage, assigning labels is not the most difficult problem. In practice, it turns out that the organization almost has nothing. But at the moment, the storage is already overflowing, and there is no money to buy new hardware in the budget.

Data classification in the healthcare industry is crucial because it ensures the confidentiality, integrity, and availability of sensitive patient information. By categorizing data based on its level of sensitivity, healthcare organizations can implement appropriate security measures to protect patient records from unauthorized access and breaches. This not only complies with regulatory requirements like HIPAA but also fosters trust between patients and healthcare providers, ensuring that personal health information is handled with the utmost care and respect. Effective data classification also streamlines the management and retrieval of health records, enhancing operational efficiency and improving patient care outcomes.

Tools such as Microsoft Azure Purview enable companies to define policies and apply them to all data based on rules. This can reduce the organisation's efforts with regards to data labelling/classification, retention, processing and access policies.

For me this is one of the most underutilized and misunderstood areas. While a Data Catelogue is excellent at providing details of what data you have and where it is stored and used, I find that classifications often lack utility. Classifying data correctly and then ensuring you have policies (retention and security) that are tied to those classification is essential.

Data archiving stands out as the premier tool for overseeing data retention in healthcare due to its capability to provide extended storage for inactive or historical patient data. These solutions commonly include functionalities like compression, encryption, and indexing, thereby enhancing storage efficiency and maintaining data integrity over prolonged periods.

Enterprise Content Management (ECM) systems: These platforms offer centralized storage, organization, and access to various types of healthcare data, including patient records, medical images, and administrative documents. They facilitate secure archiving, version control, and retrieval based on retention policies. Cloud-based archiving solutions: Cloud storage offers scalability and cost-effectiveness, allowing you to archive large datasets without investing in on-premise infrastructure. Secure cloud platforms provide robust security and compliance features. Hybrid archiving solutions: Combining on-premises and cloud storage can provide a balance between cost, security, and performance.

Efficient data archiving, in my opinion, involves seamless transitions of inactive data to secure storage. Tools with advanced indexing and retrieval mechanisms, along with data compression, optimize storage space. Personally, I find that integrating such technologies reduces costs associated with long-term data retention.

Ao implementar práticas robustas de prote??o de dados, como criptografia e controle de acesso, as institui??es de saúde podem n?o apenas cumprir regulamenta??es, mas também construir confian?a com os pacientes. Isso n?o apenas resguarda informa??es sensíveis, mas também contribui para o avan?o da pesquisa médica, promovendo inova??o enquanto preserva a integridade e confidencialidade das informa??es pessoais.

As data gravity will decide the future of enterprises and states, data retention will be more important with anonymized option rather than purging the same as AI based decisions may need historic data for accurate and practical decisions. therefore, data retention infrastructure and solutions in any verticals is necessary to be adopted. However, Cloud based solutions in preferable as per my experience, the gravity of data will be augmented due to digital transformation of current eco system as whole.

There is no single solution for managing enterprise data retention. A framework balancing regulatory requirements, business needs, and IT costs is necessary. Oversight for data privacy, security, governance, and legal compliance is key to mitigate breach risks, fines, and reputation damage.

Understanding the applicable regulations, audit policies, data retention/deletion requirements from the legal perspective n from any other data controller perspective (3rd party data used in our eco system) would be a great starting point to ensure the policy/procedure and retention timelines are upto date. Data inventory using an automated data discovery tool and enforcement through classification and lifecycle management are most effective. Periodic internal audits would help with the process efficiency and compliance.

Health data is so sensitive since it involves sensitive information about the patients. Retention of data should be done per aspects of data protection principles e.g. data integrity, lawful basis as per data subjects' rights, transparency, and proper data storage. Having proper technical and organizational safeguards in place

Data retention and deletion is important but it’s more important to understand the underlying usage of the data along with the retention period both from consumption as well as from origin perspective. Sometimes data is no longer required to carry out Purpose A by Consumer A but the same data is required for Purpose B by same or another consumer. So it is imperative that access can be revoked for Consumer A but data still can be retained in an organisation. Also sometimes the organisation do need to use the data for legitimate purposes where certainly data is required for longer period than standard data retention policy of the platform. So the purpose of usage is also one of the driving factors to decide on data retention and deletion .

Data deletion is a sensitive topic that needs careful consideration and detailed and structured process, that would identify the "Why and How". Numerous times items are deleted, only to realize soon after that it was incorrectly deleted. It is important that Governance drive such a step, in that what the reason identified is for such a step, due diligence is also performed to ensure that the need or activity is not flawed.

For the very nature of healthcare data, that involves the management and preservation of patient sensitive information, organizations must implement robust retention policies that align to data privacy principles. This must ensure that data is collected and stored for legitimate purposes, using appropriate security measures and always in accordance with subject rights. This process involves data minimization principles, data not retained for longer than necessary, security and confidentiality of data must be ensured and protected, patients should be able to access their data, manage their accuracy or request their erasure. And, most of all, healthcare providers must be able to demonstrate compliance and be accountable for any noncompliance.

Regular data retention audits help healthcare organizations assess compliance with internal policies, industry regulations, and legal requirements governing data retention and privacy. Audits identify gaps in data management practices, ensure adherence to retention schedules, and demonstrate accountability to patients, regulatory authorities, and stakeholders.

Audits are keys to understand where you are and what to do to comply. This is the mandatory step when startingany compliant activities. So it is for data retention. Dedicated consulting companies are runnnig such audits per their own referential that have been built with their experience. When choosnig those question their framework and methodologies so that it is compatible with your business. In fact this will be something you will built upon it your strategy. Whiich means you will regularly call them to update your audit and assess your compliancy.

Effective data retention in healthcare involves a strategic selection of tools. Integrated systems like HIM and EHR, coupled with specialized solutions such as Data Classification Engines and Data Loss Prevention, contribute to efficient data management and compliance. Backup solutions, MDM systems, and metadata tools ensure data integrity, while HIE platforms facilitate secure information exchange. Audit and compliance tools offer continuous monitoring, enabling healthcare organizations to establish a comprehensive and compliant data retention strategy.

Embrace the principle of keeping only the information you really need and getting rid of anything extra as part of your data governance strategy. By focusing on collecting only essential information, you not only streamline your data management but also strengthen your adherence to privacy laws and minimize security risks.

I would say that data retention is a separate discipline within the family of data management. Most likely, there will be few specialists in data retention: 1-2 for a huge corporation. The uniqueness of the tasks that a data retention manager performs lies in the fact that they combine the expertise of a business analyst and a technician who, on the one hand, can gather data storage requirements and, on the other hand, is well-versed in how this can be technically implemented using the tools available to the database administrator.

Valuable contributions, especially in Healthcare and BFSI domains, may require specific considerations. Not every organization is willing to risk its reputation for minimal compliance or regulation. Review the organization historical events and look for unusual data requirements to avoid potential risks from external threats. Overplanning is key in some contexts; if not your IT/data team will be in need to rebuild archived data that it might not be even on the same format over the years.

In addition to the Data Retention policies and procedures, the entity should also consider implementing data lifecycle monitoring controls that would enable them to identify data across different lifecycle stages and proactively manage data handling and retention. A simplified illustration of such control would be having data dashboards and reports or implementing Data Loss Prevention tools.