Business Intelligence of Mckinsey & Company
McKinsey payor client’s revenue became dependent on how accurately healthcare providers coded the diagnosis and treatment of their patients. McKinsey client was struggling to ensure its fair share of the fund allocation, due to pervasive problems with inconsistencies in the diagnosis codes entered into the system for chronic disease patients. When the McKinsey project began, the client was not able to combine data effectively to identify instances of incomplete ICD (International Statistical Classification of Diseases) coding. Data were stored across many silos, which prevented the focused analysis needed to improve coding quality. McKinsey proposed applying a business intelligence solution to identify insufficient coding for selected chronic diseases and to identify individual providers with below average coding quality. McKinsey team identified three key steps to improve coding quality: First, established a unified database and an automated business intelligence solution that would enable high-performance data analysis across the different parts of the organization. Second, design and validate medical rules for the identification of likely cases of imcomplete coding, based on historical data. Third, prepare and execute quarterly campaigns to identify and reach out to physicians with higher levels of inaccurate coding. The new tools and processes triggered additional revenues for McKinsey client of more than $150 million per year. The enhanced IT capability now enables our client to gain valuable insight from high-performance data analysis across the whole organization.