Personalized medicine, patient-centric care, telemedicine, digital imaging, digital pathology and AI-driven disease management are driving massive unstructured data growth in the healthcare industry.
Healthcare is one of the largest industry creators of data. Roughly 30% of the world’s data volume is generated by the healthcare industry, and this will grow to 36% by 2025, according to research compiled by RBC Capital Markets.
Consider common medical files such as lab slides, X-rays, MRIs and CT scans. These everyday files take up petabytes of high-performing storage. Regulations often require their retention for several years. Clinicians may need to review some images again months later, so IT can’t hide them in a dusty basement tape archive. Dictation and nursing notes also contain patient data that’s valuable for data mining projects which organizations need to improve patient outcomes and develop personalized medicine programs.
Common data management challenges:
Managing data growth is a large initiative in healthcare. The global healthcare data storage market size is expected to grow from $4.17 billion in 2021 to $9.23 billion in 2026, according to The Business Research Company. Beyond data volumes, there are many different systems and clinical file types as technologies and protocols evolve. This complexity makes it laborious to search for specific files, meet compliance challenges and manage storage costs. Most healthcare providers are under tight budgetary constraints following the pandemic and ongoing industry pressures to lower the cost of care.
The AI Opportunity
The healthcare industry is on the brink of revolutionizing patient care. Two decades ago, electronic health records were still rare. Today, digitization has accelerated quickly with mobile apps, wearables, telemedicine and the integration of AI technologies into daily practice:
- AI is delivering more accurate, faster analysis of common scans such as mammograms, cardiograms and colonoscopies.
- AI is also behind intelligent alerting systems for community health, such as an environmental health crisis tracked to ER patients from the same location.
- AI and big data analytics are helping medical leaders create holistic care plans by analyzing demographic and social data for patients with a particular condition and delivering better preventive care by analyzing chronic disease data.
- Generative AI solutions are reducing the paperwork burden of clinicians and even improving communications between physicians and their patients.
These programs depend upon timely access to the right data sets for real-time analysis. They need unstructured data management solutions that can deliver efficient ways to discover, tag, and move data to the right storage tiers as needed to support changing needs.
AI’s positive impact on healthcare and the dangers of data bias and incomplete data.
How unstructured data management helps:
Unstructured data management solutions help healthcare organizations lower the overall cost of data storage (including backups and disaster recovery) by 70% or more through intelligent analysis and placement of files. This frees up money for critical analytics programs required to maintain profits, high standards of care, grants and funding and patient satisfaction.
The right unstructured data management solution can also bring deep analysis to data, allowing managers and researchers to understand data usage, easily locate and use or move data as needed and avoid compliance issues. Automated workflow capabilities create more efficient ways to find data, copy or move it to an AI tool for analysis, tag the results with metadata and then archive or delete the original data once the AI has finished.
Case in point: St. Luke’s Health
Komprise customer St. Luke’s Health reduced capacity on its flash array by maintaining excellent performance for the data that people are using and moving data that hasn’t been touched in three years to lower-tier storage.
As covered in this article:
“The new technology also positions St. Luke’s well for healthcare and medical advances. A new type of digital pathology technology, for instance, generates extremely large file types. The data is initially very active until it’s read and a report is issued. After that, the large files probably won’t be needed too often. The IT team can now set up a policy to send those files to the archival storage tier after a specified period.”
In our next post in this industry unstructured data management series, we will take a closer look at the public sector, which must be cost-effective with IT spend while delivering new digital initiatives to improve safety and services for citizens.
Read the post: Why Unstructured Data Management Matters: An Industry View
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