Big and buzzworthy: How data analytics can help drive improvements in healthcare

Data rich but information poor is the intersection where many healthcare organizations find themselves. With an overabundance of data readily available at their fingertips, health systems have struggled with how to harness their data and unleash actionable insights.

Advancements in technology and the leap into the digital age have catapulted big data into hyper drive, and it seems everyone wants to give it a spin. Data analytics have been widely used in other industries for decades, but has only recently picked up speed among provider organizations.

With the transition to a value-based care model, hospitals and health systems have felt the pressure to find efficient and cost-effective methods for delivering high-quality care – or risk falling behind. This is where big data can add value. That is, if you know how to use it.

Data rich but information poor is the intersection where many healthcare organizations find themselves. With an overabundance of data readily available at their fingertips, health systems have struggled with how to harness their data and unleash actionable insights.

The first step is to understand what “big data” actually means. Traditionally, big data referred to the massive amounts of complex data that couldn’t be processed by traditional software. More recently, it has been used to refer to predictive analytics or other advanced analytics methods that extract value from data. The keyword here is “extract.”

Because big data is so immense, it takes specific technology and analytics methods to make sense of the information and transform it into something valuable. Left alone, big data is simply an immense amount of numbers and statistics. It is only when it is able to be analyzed that it becomes relevant and useful.

Used successfully in various industries such as retail and manufacturing for over a decade, predictive analytics is building a growing fan base in healthcare. One method called time series analysis can be used to analyze past data and look for trends and patterns and make a forecast of events that recur over time. Time series techniques are particularly relevant to forecasting patient in and out flows in a hospital.

Able to predict supply needs and patient readmissions, predictive analytics’ impact on patient care is far-reaching. One area in which advanced analytics can add tremendous value is in the scheduling of care staff. Predictive analytics can help improve staffing problems by accurately forecasting workforce needs weeks in advance of a shift. This ensures the right type of provider is in the right place at the right time to provide patient care.

But data isn’t perfect, and algorithms are not magic. It takes a clear-eyed view to filter out emotional responses to the data to avoid errors and making sure the predictive model is being fed accurate data.

Predictive analytics is a tool to be used in combination with extensive knowledge of staffing strategies; it will not solve all of an organization’s problems alone. Data experts are needed to routinely monitor the predictive model, and functional leaders within the healthcare organization help make sure the model is being applied as intended.

Ready to dive into analytics? To help ensure you set your organization on a path to success, make sure information is readily available to your analytics team. This means enabling access to your data streams, ideally a few years of patient census data and other workload indicators, depending on the area or service line involved. The more years of data provided the more accurate the predictions will be from the start.

While big data has become buzzworthy in the healthcare industry, it is solidifying its presence by offering valuable insights to organizations that know how to leverage it. Provider organizations that have leveraged predictive analytics for scheduling and staffing have achieved outcomes that include increased staff satisfaction scores, improved nurse retention, reductions in their annual labor spending, and decreased the amount of time managers spend on schedule creation and staffing tasks – delivering valuable time back to managers to focus on staff development and patient care.

With continued pressure on provider organizations to improve the patient experience while driving down costs, predictive analytics offers a strategic solution to leverage data to help organizations meet demand.

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