While the benefits of big data analytics for health insurers are increasingly well known, the unique nature of the health insurance marketplace does create challenges that require careful and strategic work by insurers and their partners to address.

Probably the most challenging of all the relevant issues is the matter of regulatory compliance. This can be driven by a complex and shifting set of state and federal regulators, requiring constant attention to the latest developments.

The most critical of these regulatory regimes is the Health Insurance Portability and Accountability Act, or HIPAA. While most consumers are familiar with HIPAA’s privacy protections, far fewer are aware of its concurrent data security protections. However, for health insurer IT teams and their partners, the security rules are often more substantive and challenging than their better-known privacy counterparts.

For instance, HIPAA’s security rules require organizations handling electronic records containing personal health information (e-PHI) “maintain reasonable and appropriate administrative, technical, and physical safeguards for protecting e-PHI” and,

• Ensure the confidentiality, integrity, and availability of all e-PHI they create, receive, maintain or transmit;
• Identify and protect against reasonably anticipated threats to the security or integrity of the information;
• Protect against reasonably anticipated, impermissible uses or disclosures; and
• Ensure compliance by their workforce.

Each of these four broad requirements generate significant – but not insurmountable – challenges for big data analytics in health insurance. And, as always, the devil is in the details when it comes to designing data analytics implementations in fast-moving technical and shifting regulatory environments to fuzzy standards such as “reasonably anticipated” threats.

In addition to regulatory challenges such as HIPAA, one other pervasive challenge for big data analytics in health insurance is data integrity. This isn’t new to health care, but poor-quality can cause “garbage in, garbage out” issues in an analytics environment.

For instance, patient-reported data has long been understood to have significant quality challenges. This happens for a variety of reasons but requires special handling nonetheless. Additionally, the nature of the patient-provider relationship means that while systems are usually excellent at capturing events – illnesses, injuries, diagnoses, prescription refills, claims, etc. – they are not as good at identifying when there is no problem. Is a policyholder who’s not generating claims and not engaging with providers actually healthy, or are they simply not accessing the health care they should? Was the patient who’s no longer refilling their prescription cured of their condition, or can they just not afford the copay?

The variety of stakeholders in play in health care can also create issues with data integrity, as data sources of widely varying sophistication and quality risk being aggregated into datasets without the necessary system for weighting if necessary.

All these challenges and more make health insurance a difficult – but still deeply rewarding – environment for big data analytics. If you’re interested in learning more about how we’ve successfully dealt with all of these issues for health insurance clients, contact us to find out more.