Data analytics isn’t anything new. But precede that term with “big” and people get excited. 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.
Living in a time in which we’ve grown accustomed to having (and demanding) information immediately at our fingertips, organizations have recognized the value in analyzing their data, which can often be done in real-time. Analyzing big data sets can help find correlations to something previously inaccessible, which could help improve business outcomes, and even health outcomes.
Conventionally, big data referred to the massive amounts of complex data that couldn’t be processed by traditional data processing application software. More recently, “big data” has been used to refer to predictive analytics or other advanced data analytics methods that extract value from data.
Because big data is so immense, it takes specific technology and analytical methods to make sense of the information and transform it into something valuable. Left alone, big data is simply a vast amount of information. It is only when it is able to be analyzed that it becomes relevant and useful.
Making predictions is one such method to gain value from the availability of big data. Predictive analytics have been used successfully in various industries such as retail and manufacturing for over a decade, and 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 identify inventory needs and predict 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 up to many days 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.
Staffing and scheduling problems are known to frustrate staff, and negatively impact patient care. Without proper forecasting tools, unit managers are left to trust their gut about what staffing needs will be weeks in advance. As the shift gets closer, this often causes last-minute chaos of either scrambling to find resources or calling people off – both of which are major staff dissatisfiers, perpetuating the cycle of burnout and turnover.
Provider organizations that have used predictive analytics for nurse 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 – which delivers valuable time back to nurse managers to focus on patient care and staff development.
While big data has become a buzzword in the healthcare industry, it is solidifying its useful presence by offering valuable insights to organizations that know how to leverage it. With continued pressure on provider organizations to improve the patient experience while driving down costs, predictive analytics offers a strategic approach to leverage data to help organizations meet demand. Predictive analytics is able to churn raw data into meaningful and actionable insights that allows hospitals and health systems to stay ahead of the curve. Advanced data analytics delivers beneficial outcomes in a variety of areas within healthcare, ultimately enhancing patient care.