Healthcare organizations can improve decision-making and productivity, as well as increase revenue, by using business intelligence tools to tap their own data.

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In general, there are two types of business intelligence tools. Predictive analytics tools use data mining to make predictions about events and identify trends, whereas nonpredictive analytics retrospectively summarize and organize data. In the healthcare realm, predictive analytics can be used for things such as finding missing charges and estimating costs. Predictive tools can be integrated into health information systems that are already in use, and ideal solutions should be customizable to the healthcare facility.

Increasing revenue

Using predictive analytics can help organizations automatically analyze patient accounts for correct coding and charges by using data that are already in the healthcare information system. This not only reveals patterns in accounts, but also improves the productivity of nurse auditors, who can target their medical record reviews to prescreened accounts.

Automating processes

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Predictive analytics can also automate credit resolution and the detection of underpaid claims.

When it comes to credit resolution, predictive models use data and groupings from the healthcare organization's information systems, including expected payments and claim information, to aggregate an account's information and find out why there is a credit balance. This process can be automated.

Underpaid claims are another area in which hospitals can leverage predictive analytics tools to increase revenue and improve productivity. In one case, a 800-bed health system automatically analyzed historical data and found $20 million in contract violations that occurred before it started using the predictive model. Since then, the model has found another $2 million per month in new underpaid claims. Moreover, the model identified the cause of the violation, allowing staff members to review claims and submit appeals more quickly.