I N D U S T R Y I N V E S T I G A T I O N
Often , medical tests are required to give underwriters better insight into the health and underlying health risks of the person they are insuring . These tests inevitably slow the underwriting process down , bringing substantial cost and decision time delay to the insurer , which ultimately creates a poor customer experience .
Harnessing the constantly increasing data availability
Technology disruption , in particular , digitalisation of health records , API-based solutions , Artificial Intelligence ( AI ) and Machine Learning ( ML ), self-administered and cost-effective testing capabilities such as remote photoplethysmography ( rPPG ) and genomic sequencing are changing the fundamentals of underwriting .
The shift to electronic health records is becoming a game changer . As broader sets of health data become more readily available digitally , the ability for the insurer to view historic consumer health records exponentially increases . This , in turn , enables them to better understand the risk they are underwriting . Data sets becoming more readily available include doctor , clinic and hospital visits , medication prescriptions and usage , pathology lab results , as well as data from diagnostic and wearable devices . Access to this data is not without challenges , due to its fragmented , unstructured and distributed
ADVANCEMENTS IN AI AND ML ARE SIGNIFICANT ENABLERS IN THE UNDERWRITING PROCESS .
nature , as well as the time and cost of setting up data partnerships with providers .
Here again , technology plays a vital role . API environments are integrated now in even basic IT systems , as are often found in medical and other health practices . Integrating these disparate data sets into a health platform – such as the solution that Alula provides – enables stakeholders to share and exchange enriched data sets which , outside of the insurance vertical , enhances care , diagnosis and clinical decision-making . The data which can be retrieved through integration includes clinical diagnoses , known chronic conditions , medications taken , as well as historic records for blood pressure , BMI , cholesterol , glucose levels and other important blood markers . Existing consumer data , which the insurer may have , is also extracted to be combined with the new , external data sets .
A key element that must be considered when considering use of electronic health data for insurance purposes is the consent
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