7 Effective Ways Providers and Payers Improve Healthcare with Sensitive Data
Whether it’s complying with regulatory requirements or protecting the privacy of people represented in all kinds of data, you know that there are plenty of reasons to be cautious. And if you’re struggling to safely unlock the value across all your structured and unstructured data, you’re not alone.
Despite the challenges, you know that the value of healthcare data is too great to ignore. Organizationally, you’ve seen how value generated through data sharing, research collaborations, and even data commercialization can fuel critical investments. As a provider, you’ve seen how population health, precision medicine, and predictive analytics can transform our future. As a payer, you recognize the importance of driving value-based care through a clear view of patient outcomes.
But these benefits can be out of reach without the right technology and expertise. For example, you may struggle to parse complex structured data, free text, medical images and/or genetic information. Or perhaps you’re facing brittle privacy solutions that limit your ability to use all your data assets with speed, scale, and agility. And with personal data or PHI buried in free text, image pixels, and the combined linkage of otherwise safely de-identified data, it’s no wonder that some organizations are hesitant to explore the full potential of their data.
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At Privacy Analytics, we create innovative solutions for business and healthcare leaders that turn sensitive information about people into data that is timely, usable, and compliant. We’ll ensure your organization can unleash maximum value from all your structured and unstructured data without running afoul of privacy and data protection laws worldwide.
For US healthcare providers and payers, we automate rigorous statistical analyses of data to provide HIPAA Expert Determination with speed and scale, giving you the agility to link, combine, and recombine complex structured and unstructured data elements for widespread benefits.
For European health systems, we combine various privacy-enhancing technologies to empower safe and responsible uses of patient data. You’ll have auditable proof there is no reasonable basis to identify individual people in the information – allowing you to accelerate innovation while proving GDPR compliance and privacy protection.
For healthcare organizations worldwide, we have informed over 20 regulatory guidelines and standards for data privacy and enablement, giving you a trusted foundation to build data pipelines and platforms that will drive transformative improvements in healthcare.
We’ll design a complete solution for your organization that scales to your needs, operating context, and regulatory jurisdictions. You’ll get statistical proof of privacy protection while enabling the richest data possible for many important use cases.
Need help cutting through the complexities of data and regulation? Looking to build your own data platform or de-identification solution? Want a partner that bridges the disciplines of privacy/legal, data science, and technology?
Our team of experts can work with you to develop tailored solutions and practical guardrails for the safe and responsible use of structured and unstructured data. In addition, we offer expert third-party validation to ensure your existing technology and processes for data are compliant, as well as privacy engineering to roll up our sleeves with you.
Does your initiative require data de-identification or Expert Determination under HIPAA? Do you need a trusted third party to link and render complex data non-identifiable under applicable regulations?
Our experts select and manage the right technology options from our Privacy Analytics Platform to get you the results you need, at the speed and scale your business depends on.
Benefit from the flexibility and extensibility of our data privacy and enablement technology by integrating the Privacy Analytics Platform – or key modules of our commercial software – behind your own firewall so that data never leaves your environment.
Pseudonymize, de-identify and/or anonymize data at scale, with the flexibility to create differentially private datasets and synthetic data for your . Visualize the re-identification risk contribution of different elements in your data to make informed decisions on protecting privacy.
We support cloud-native deployments on Azure, AWS, and Google Cloud as well as on-premises and are a prioritized, Marketplace transactable, and technically validated Microsoft Azure Partner.
“Along the way, Privacy Analytics shared best practice recommendations with us on how to protect patient privacy, making suggestions in areas beyond just the de-identification plan.”
Director, neuroMuscular ObserVational Research (MOVR) Data Hub at Muscular Dystrophy Association
“(Privacy Analytics) gives us the ability to not only share this data but make sure that the privacy of patients and providers is protected and that is absolutely key for us. Not only for researchers but for external stakeholders as well.”
Director of Customer Relationship Management and Data Access, Alberta Health
“Privacy Analytics software provides an objective measure. It identifies what the risk is and I know there is a proven methodology to back it up. It’s insurance that we are not going to inadvertently disclose information that we shouldn’t.”
Dr. Ann Sprague,
Project Advisor at Better Outcomes Registry & Network (BORN) Ontario
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Situation: California’s Consumer Privacy Act inspired Comcast to evolve the way in which they protect the privacy of customers who consent to share personal information with them.
Situation: Integrate.ai’s AI-powered tech helps clients improve their online experience by sharing signals about website visitor intent. They wanted to ensure privacy remained fully protected within the machine learning / AI context that produces these signals.
Situation: Novartis’ digital transformation in drug R&D drives their need to maximize value from vast stores of clinical study data for critical internal research enabled by their data42 platform.
Situation: CancerLinQ™, a subsidiary of American Society of Clinical Oncology, is a rapid learning healthcare system that helps oncologists aggregate and analyze data on cancer patients to improve care. To achieve this goal, they must de-identify patient data provided by subscribing practices across the U.S.
Situation: Needed to ensure the primary market research process was fully compliant with internal policies and regulations such as GDPR.
Situation: Needed to enable AI-driven product innovation with a defensible governance program for the safe and responsible use
of voice-to-text data under Shrems II.
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