In particular, Accenture analysis found that electronic health records (EHR) offer an untapped opportunity. By using analytics to create new staffing models from EHR data, healthcare providers could not only see tangible returns but also drive greater efficiencies.
The potential benefits are clear: If analytics can be applied to a common provider cost center such as labor in this way, imagine the wealth of other opportunities that may be uncovered by using analytics throughout other provider operations.
Provider organizations’ staffing decisions are typically based on historical averages. Any unexpected spike in staffing demand generally means overtime costs—extra labor costs that are unplanned and not within the budget. The same is true for an unexpected lull in staffing demand—having excess staff when beds are empty results in excess labor operating costs.
By applying health analytics to historical trend data of patient visits and complexities (such as work hours per unit of service) from EHR and ERP data sources, providers could glean insights that can reduce staff overtime and total labor costs over the next five years.
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Typically, labor costs make up at least 50 percent of a provider organization’s operating costs. Using EHR data in new ways can help predict demand and introduce the smarter use of existing labor to cut the cost of overtime and bring savings through redeploying and using resources more wisely.
When applied to data from EHRs and other sources, health analytics could help provider organizations more accurately assess how many people should be on duty and could better manage staffing and cut overtime—with a direct impact on their operating margins.