Health insurers are relying more on advanced analytic tools as they move from reimbursement services through to provider management, care management, and care delivery services. The sheer volume and complexity of healthcare data can create bias, hinder analysis, and impair decision making. Inadequate data is costly because it is time-consuming to work with and often expensive to address.
Fortunately, data quality tools can identify specific areas of improvement to help actuaries and insurers carry out advanced operational and clinical analytics. In this paper, Milliman’s Joanne Buckle and Natasha Singhal highlight such a tool to assure data quality is properly vetted prior to actuarial analysis. The data quality tool was applied to data collected from five different private medical insurance (PMI) insurers in the United Kingdom. The authors also discuss ways that high-quality PMI data can be used to achieve decision confidence.