The Goal: Healthcare and Life Sciences companies know the value of data and analytics to solve problems and improve outcomes. With the risks of reimbursement shifting, hospitals and other providers to shift their focus from disease treatment to wellness and prevention, saving huge sums on medical expenditures and improving the quality of life. Life Science companies spend billions on new discoveries, but improving the time to market while boosting safety in during the testing phase remains a major unmet goal.
The Requirements: Most scientists recognize the importance of sharing data online in an open fashion. While HIPAA violation concerns are often cited as a reason for not sharing data, federal agencies are working to ensure that entities know this is not the case. Gathering raw data for analytics and machine learning from multiple sources within your organization and from partners outside your organization is required.
The Challenge: Patient data is sensitive, and each data owner may allow it to be combined and commingled but only under rigid guidelines. The challenge that strict regulatory prohibition poses in the integrated care setting is that patients frequently do not have a relationship with all of the providers among whom information sharing should be coordinated. Sensitive data, such as patient identifiers, procedures, results, price, cost and other details cannot be exposed directly to analysts but must be available for the analytics.
The Problems: Conventional systems can be configured to allow users to expose or change data owned by another party. If data Owner A shares raw data with Owner B, using a conventional system, then Owner B can use the combined data sets in violation of Owner A’s rules. Legal agreements cannot be relied upon to prevent data misuse. Most data sharing projects get stuck as a result. Reliance on 3rd party services do not provide the capability for self-service analytics, and do not provide the data quality and transparency required to ensure the analytics are generating accurate results.
The Myngl Concordance Solution: Secure Analytic Containers work differently than conventional systems: providing decentralized controls to each data owner that unify data security, governance and compliance to each data owners separately and simultaneously. Users, admins, even Myngl staff, are blocked from direct source data access to change or override owners’ rules. This removes vulnerabilities and prevents data risks, misuse, and breaches.
Analysts can make use of sensitive data without exposing it. Secure Analytic Containers provide analysts with a secure analytic sandbox utilizing open-source Zeppelin Notebooks, SQL, Python, TensorFlow and other powerful tools. Analytic jobs are separately authorized by data owners to make use of restricted data. Automated scheduling is orchestrated via microservices and results are segregated from source data. This allows analytic apps to use data elements which are hidden from direct Subscriber view.