5 companies using big data to empower recruiting and address human resources challenges in healthcare
Big data, it seems, is everywhere, and continues to get bigger every day. Harnessing the ever-growing body of information is tricky, and predictive analytics—the practice of analyzing data to identify patterns and forecast future outcomes or trends—is one way of creating usable, actionable order out of data-based chaos.
Big Data and Powerful Insight
Healthcare is a data-heavy industry, and there is a constant push to utilize, improve upon and apply the knowledge generated by electronic health records, insurance information, research initiatives and more. Healthcare organizations have begun to incorporate data analytics, including predictive analytics, to assist in this process.
Predictive analytics is also being widely adopted by employers and agencies to accurately plan staffing. When these two trends combine, there is potential to maximize hospital staffing, improve patient care, prevent readmissions, and optimize spending in a tight market.
As Becker’s Hospital Review explains, predictive analytics is already used in a number of non-medical industries and is now beginning to make its way into healthcare. When applied to the workforce planning process, predictive analytics creates and validates forecasting models. These models can project staffing needs up to 120 days in advance. Forecasts are updated weekly, allowing for increasingly accurate staffing predictions as shifts draw closer.
Five Organizations Leading the Charge
DocDelta, a “talent engine for healthcare,” uses predictive analytics to systematize hiring practices in healthcare. According to the DocDelta blog, the company serves staffing firms and hospitals, analyzing data to “understand the job embeddedness of physicians,” including their level of engagement at work, their ties to their current geographical location, and their professional aspirations. This model predicts which clinicians are interested in moving jobs, even before the clinicians have informed others or signaled interest to recruiters or potential employers. This is essentially a proactive tool for recruiters, allowing them to save time and recruiting costs and to get a leg up in the competitive market of physician recruitment.
Data-integration and -management company Actian serves a number of industries, including healthcare. Actian’s healthcare analytics options include a staff optimization feature that forecasts patient volumes and generates staffing plans accordingly. The model emphasizes “patient-optimized” schedules, which Actian claims leads to greater patient and employee satisfaction, minimal wait times, and reduced staffing costs. A downloadable brochure explains the company’s healthcare analytics options in more detail.
AMN Healthcare, a healthcare staffing firm, offers a “workforce solution” that combines predictive analytics and staffing automation. This model combines workforce-demand forecasting, scheduling capabilities, and business intelligence tools, and boasts 97% accuracy in predicting scheduling needs. This predictive modeling can also be combined with AMN’s recruitment services, creating a more comprehensive, data-based approach to staffing. AMN Healthcare and Avantas released a 2016 survey (registration required) and infographic, Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages, that analyzes current challenges in RN staffing and “examines the state of knowledge about predictive analytics in healthcare workforce scheduling and staffing.” AMN’s Healthcare Predictive Analytics page offers additional resources, including white papers and case studies, discussing the use of predictive modeling in healthcare staffing.
Pennsylvania’s University of Pittsburgh Medical Center (UPMC) used predictive analytics to design a cost-effective staffing model. By applying industrial engineering practices, UPMC combined real-time analytics, volume-based staffing, and a “floater pool” of employees to augment its centralized patient- care staff. This approach led to reductions in overtime costs and created a more consistent patient experience. Healthcare Finance offers a slideshow outlining UPMC’s predictive analytics program.
McKesson Technology Solutions offers the McKesson Capacity Planner, a predictive modeling product that obtains real-time data from existing health information software and uses it to match staff with real-time patient demand. The program has three modules, ER, OR, and Regional, and addresses patient demand, workload and resources, potential capacity blockages, discharge management, cost containment, and shift planning. (McKesson Corporation recently completed a merger with Change Healthcare Holdings, creating a new venture known as Change Healthcare. Product migrations will be ongoing.)
Conclusion
In the high-tech, high-pressure environment that defines modern healthcare, the volume of data will only increase as the demand for cost-effective treatment and patient satisfaction continues to grow. Predictive analytics models that leverage the capabilities of big data offer potential solutions to difficult healthcare problems, and effective staffing models are an important piece of the analytics puzzle.
As companies and healthcare systems further embrace predictive modeling solutions, the approaches profiled here (and others like them) hopefully will pave the way for effective and impactful healthcare that benefits patients and clinicians alike.
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