Opportunities Found in Learning Healthcare Systems

The desire to prevent medical errors, enforce best practice guidelines and limit inefficiency is shaping the healthcare industry’s future. To solve these problems, industry leaders are moving to Learning Healthcare Systems (LHCS) for real world evidence. LHCS are created by feeding the data gathered in the provision of patient care back into the healthcare system that was used to guide the patient’s treatment. Unfortunately, the use of individual patient data to inform research also puts the data at risk of violating patient privacy. For LHCS to be effective, privacy must be managed when sharing data.

In order to share protected health information, legal requirements require that patients give consent for their information to be used for secondary use. Barring consent, the data must be de-identified as per the HIPAA privacy rule. There are two methods of de-identification, Safe Harbor and Expert Determination. One method of de-identification has been proven to protect privacy and comply with legal requirements while still providing quality, granular data. This paper will outline de-identification methods and make a case for Expert Determination as the optimal approach to building a LHCS.

 

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