According to a 2019 research, more than 40% of health care executives believe artificial intelligence will have the greatest influence on operational performance in the next three years. But AI isn’t only for doctors. Any repeated, labor-intensive contact with paperwork, such as billing, legal, security, and compliance, is a potential for AI application. Without artificial intelligence technology, these sorts of transactional operations are generally performed by people, which takes longer, costs more, and is more prone to error.
Let’s look at three potential AI security and compliance use cases.
Contract Lifecycle Management
Thousands of business contracts, ranging from leasing agreements to physician affiliations to purchase contracts, must be managed and reviewed by health care systems. However, due to the volume and complexity of a large number of agreements, doing a yearly assessment is practically difficult. However, AI may now assist in areas such as:
If AI technology could be used to analyze hundreds of contracts, human resources might be shifted to more essential, strategic activities and decision-making.
Vendor Risk Management
Similarly, utilizing AI to answer these sorts of queries, an annual review of business associate agreements (BAAs) may be completed more quickly:
However, vendor risk management needs more than simply an AI tool to evaluate documents. Health care companies should first inventory all BAAs and assess the inherent risk of each business associate using protected health information (PHI). For the average health system, this inventory might include hundreds or even thousands of contracts – utilizing AI for this evaluation process would undoubtedly be an efficient way.
Once the inherent risk has been identified, rank each BA from highest to lowest risk. Artificial intelligence technology may assist in automating the determination of which contracts and BAs require additional audit controls, while a governance, risk, and compliance (GRC) software solution can be used to manage action plans to decrease risk and avoid breaches.
To combat possible security breaches, security, privacy, and IT teams currently employ a number of technical solutions, such as honeypots and email filters. AI may go a step further by examining behavioral anomalies that may signal a possible PHI security issue. For example, machine learning may check an EHR database to see whether someone is signing on to the system in the middle of the night, whereas normal login hours are between 9:00 a.m. and 5:00 p.m. Modeling various risks in this manner allows businesses to put themselves in the shoes of possible attackers, knowing where they could hit and where holes in the system or network may lie.