Using Clinician Big Data to Alleviate a Struggling Workforce

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Clinician big data has the power to transform healthcare organizations, but its potential has remained mostly untapped. While hospitals produce 50 petabytes of siloed data annually, 97% of it goes unused, the World Economic Forum reports. Healthcare is missing significant opportunities to inform supported decision-making with a deeper understanding of care patterns and utilization through the analysis of clinician data. 

Using clinician data as a strategic foundation for success has never been more imperative. Healthcare is experiencing a major staffing crisis––further amplified by the pandemic. More than 145,000 U.S. healthcare providers—including 71,309 physicians—left the workforce between 2021 and 2022. These clinicians, vital to healthcare operations, include nurse practitioners, physician assistants, physical therapists, and social workers. On the nursing front, the U.S. Bureau of Labor Statistics projects that more than 275,000 additional nurses will be needed by 2030.

While organizations have begun to refocus efforts on clinician recruitment in response, the pool of applicants remains limited, and quick and efficient onboarding and deployment remain challenging. Age-old, industry-standard approaches are no longer enough. There are only so many schools from which to draw clinicians and only so many clinicians willing to embrace the circumstances of the current environment. Healthcare leaders are doubling down on retention efforts and increasing the existing workforce capabilities within the walls of their facilities. Challenged with ongoing financial performance pressures, these same leaders are left with the realization that delayed onboarding results in the lost revenue potential of around $6,000 per day per physician opening. Clinician big data is the key to optimizing the existing workforce, and is critical for engaging clinicians in smarter, more efficient care delivery.

Rethinking data usage in healthcare 

When “big data” is mentioned in the healthcare space, it’s often in reference to patient data. Over the past decade, patient data has made leaps and bounds to improve outcomes and reduce costs. But with workforce shortages and burnout threatening the progress made in healthcare, it’s time to focus on the enormous amount of clinician “big data” available in our healthcare systems.

There must be an intentional shift that acknowledges this potential to facilitate the application of clinician data in the healthcare environment. This culture shift embracing big data, which has already occurred in so many other industries, has the potential to solve many of the most pressing workforce problems in healthcare.

A more efficient supply chain of healthcare workers

Drain from administrative inefficiencies is common. Of all healthcare spending, it’s estimated that 15% to 30% is administrative. For example, workforce optimization focusing on network development, clinician recruitment, credentialing, and enrollment is historically a manual, time-consuming endeavor. Harnessing clinician big data will significantly improve these processes and workforce operations.

Below are ways the collection and secure curation of clinician data—and its subsequent analysis—offers a proactive means of understanding and optimizing the workforce: 

  • It’s time for healthcare systems to hire more data scientists. Health systems must pair the advancements in big data networks, AI, and predictive models with individuals trained to leverage and integrate these technologies into the decision fabric of their organizations. 
  • Workforce analytics on large data sets can provide insights on complex and distributed workforces across departments and facilities—and, to some degree, across competitor facilities by leveraging claims data sets. Understanding supply and demand trends among clinician groups and geographic locations will reveal critical ways to utilize and strategically expand existing workforces. 
  • “Clinician Phenotyping” is the process of studying the workforce by evaluating their credentials, experiences, and behaviors holistically to predict performance, burnout, and attrition. This will become increasingly important to managing healthcare costs and effectiveness.   
  • Innovative organizations can leverage large data networks to automate and streamline manual processes that waste precious resources across clinician and administrator populations alike. By infusing data into the application process for privileging and enrollment, healthcare systems can activate talent faster and safer, saving time and money. 

In Conclusion

In an industry with crippling attrition and shortages, organizations that streamline processes for their clinicians can ensure a competitive advantage. These health systems attract and retain top talent—then allow them to work at their best in a well-suited environment—based on the understanding and analysis of their provided information during enrollment. The more you understand any situation, the better you can address its challenges. There has never been a more opportune time to collect and analyze as much data as possible on the clinician population to address the need to close the people supply and demand gap in healthcare. 

About the Author

Charlie Lougheed is the CEO and co-founder of Axuall, a workforce intelligence company built on a national real-time Clinician Data Network that enables healthcare organizations to create more efficient care networks while reducing onboarding time by over 70 percent. 

Lougheed co-founded and co-funded Explorys, now IBM Watson Health, in 2009 as a spin-off from Cleveland Clinic. Explorys became the leader in healthcare big data and value-based-care analytics, spanning hundreds of thousands of healthcare providers and over 60 million patients across the United States. Having amassed the World’s largest clinical data set, Explorys went on to serve the payer, life sciences, and pharmaceutical sectors by providing real-world evidence and insight for product planning, research, health economic outcomes research, and safety.

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