Using Electronic Medical Record Data for Revenue Optimization

Electronic medical record (EMR) platforms generate a wealth of information that can be used to optimize revenue for hospitals. The trick is knowing if your EMR is capturing the data you need, how to optimize data capture if it is not, and then knowing how to analyze the data to develop an action plan. Anesthesia services are a great example of how to leverage EMR data to maximize revenue.

As a use-case example, let us take a hospital that is preparing to negotiate an anesthesia contract with external anesthesia groups. One aspect of those negotiations will be how much anesthesia coverage will be provided, when, and what subsidies may be required for that coverage. In this scenario, the contracted anesthesia group will bill and collect on anesthesia services but will need additional subsidies for call coverage. An asset in the negotiation will be knowing when and where services associated with anesthesia generate revenue for the hospital. The EMR comes into play by leveraging historical data to identify how to optimize anesthesia coverage.

The most basic data towards this purpose is start and stop times. For example, wheels into the operating room (OR) and out of the OR could be used. Anesthesia start and stop times are another set of useful data points. By pairing these data with location information, a detailed map of when and where anesthesia services are being utilized can be generated. An example of a question that can be answered with these data is, how often are we using anesthesia services overnight? Based on that answer, how much anesthesia coverage should we subsidize overnight?

In addition to when and where cases are going, who and what in terms of revenue are the next data variables. Who refers to what service line or provider. For example, when a certain location was in use, what surgeon, or what service line was using it? From there, tracing how the cases requiring anesthesia services generate revenue for the organization is key. An example would be, when we do sedated MRI cases with anesthesia, how much revenue do those cases generate outside of billing collected by anesthesia? How much are we charging for the MRI, for the services of the radiologist, etc.? In pediatric anesthesia, a great example is cases where the patient is admitted postoperatively to the intensive care unit (ICU). Revenue may not be generated by surgical and anesthesia services, but a significant amount of revenue may be generated by the care provided in the ICU. One challenge to executing this strategy is that financial data can be difficult to associate with data from the EMR.

Financial data in terms of billing and collections is usually not granular enough to be useful and often requires optimization. For example, money in and out of departments like surgery, anesthesia, ICU, etc., may be treated like a bucket: we know how much goes in and how much comes out, but we aren’t tracking the details of what goes in. While challenging, investing the time to optimize billing and collection data down to the granular level of who, when, and where allows for looking at these services through the lens of who, when, and where are we generating revenue. This information can then be used to optimize not only anesthesia coverage, but in other areas such as block times for surgeons and service lines, in a way that ensures return on assets like operating rooms, as well as return on investments like subsidies for anesthesia services.


Copyright Michael Lambert