September 20, 2017 | Written by: Watson Health
Categorized: Blog Post | Value-Based Care
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Healthcare Payers today can increasingly leverage proven, off-the-shelf methodologies in their own technology environments to help accelerate business insights and transformation. In Part 1 of this blog series we explored ways that payers interested in leveraging this new approach to enterprise analytics can evaluate the maturity of their enterprise data warehouses (EDWs), where analytics are integrated. In Part 2 we turn our attention to the topic of data management and how payers can institute a series of quality checks to evaluate the completeness and validity of the data.
An audit, balance and control (ABC) framework should be in place to identify and limit the impact of missing data, and you’ll want to determine acceptable thresholds when data is missing. Establishing these thresholds and benchmarks by field will focus the data warehouse team on areas that need further improvement, as well as allow users to contemplate the impact incomplete data could have on their analytics and reporting.
It’s important to conduct validity checks on fields that should contain standard codes or elements, and compare recorded values to lists of possible valid values for that field. When these validity checks flag unexpected values, you can establish the validity of the nonconforming code. If new values have been added to the coding scheme, it might be necessary to update of the conversion program or code lists.
Consider conducting reasonableness checks to ensure the data makes sense. For example, look at the relationship between two or more related columns, or between a column and benchmark data, to confirm they are reasonable. Examples of reasonableness checks include ratio of surgical services to total services, percentage of non-specific diagnosis codes, and ranges of average cost per service by procedure code.
Click here to read Part 3 of the blog series, which explores how payers can develop their enterprise analytics roadmap to help them prioritize and roll out analytic resources and initiatives across the business.