The future of healthcare innovation over the next decade will be shaped by insights from clinician big data and AI, otherwise known as workforce intelligence. While clinical discovery is often the first thing that comes to mind, workforce data and the intelligence derived from it represent a lesser-known but equally important place in the future of medicine.
How we shape healthcare’s workforce, peer networks, and processes comes down to how we use data to surface meaningful information. Leaders must leverage data inside and outside their organizations for healthcare systems to thrive, let alone survive in an arena of extreme supply and demand limitations among doctors, advanced practice providers, allied health professionals, and nurses.
Workforce Shortages Are the New Normal
According to the U.S. Census Bureau projections, by 2030, every Baby Boomer will be 65 or older, meaning that one out of every five U.S. citizens will be of retirement age, increasing the use of healthcare services nationwide. Meanwhile, the Association of American Medical Colleges warns that the U.S. could face a shortage of up to 124,000 physicians by 2034, with more than two of five of today’s active physicians aging over 65 years in the same period. Exacerbating this problem is the growing nurse shortage that threatens access to care for millions of Americans.
Typically, when healthcare and big data are mentioned in the same sentence, it’s in reference to patient data. When attempting to maximize the patient experience, patient data is a logical first step in addressing the needs of patients. However, the use of patient data, AI, and automated treatment remains a controversial subject. On the other hand, clinician data entails fewer roadblocks, holding the key to the discovery of more efficient workforce models to address the existential challenges of workforce shortages, burnout, and attrition.
Healthcare’s Money Ball Moment
In 2003, Billy Beane, Oakland Athletics general manager, greatly enhanced his strategy for selecting players to create a winning baseball team by altering how he perceived available data. That “Moneyball” strategy could be healthcare’s home run. Data is available, and health systems must change their perception of it to provide the best outcomes for patients and clinical workforces alike.
Beane was successful because he and his team amassed a massive amount of data, analyzed it, and paid attention to the trends. They operated in a place of scarcity and discovered how to build a great team with minimal resources. The U.S. healthcare system’s issues, like workforce shortages and unmanageable workloads, deserve a fresh approach with a similar strategy.
Clinician Big Data Helps Clinicians and the Organizations They Work For
Clinician big data applies millions of data points to construct a mosaic of the clinical workforce. Much like a phenotype, this data tells a detailed story of a clinician’s career history, including education, training, licensures, employers, facility affiliations, procedural experience, and the patient population traits they have served, benefiting the clinician and organization they work with.
So, how can big data play a role in addressing healthcare workforce issues?
- Optimizing the Workforce You Have: Healthcare organizations, particularly those that operate across many service lines, license types, locations, and treatment modalities, struggle to keep track of the ever-changing dynamics of their workforce. Leveraging data to align supply and demand in their communities is crucial for efficiently distributing limited resources, preventing over-utilization and burnout, and managing attrition. As the executive director of strategy development for the Mayo Clinic recently stated in a podcast, “We won’t be able to recruit our way out of a workforce shortage.”
- Recruiting a Smart Way: When recruiting is the last option, applying workforce analytics to understand the gaps and specific clinician profiles needed to fill them is vital to an efficient growth strategy. Even better, organizations that recruit candidates they know to match those profiles significantly improve their ability to build well-aligned care teams for their patients.
- Reducing Onboarding Time and Cost: Collecting information and forms from clinicians and the primary sources who must verify their credentials is a slow, painful, and expensive process, costing healthcare organizations thousands of dollars a day in lost productivity, according to a study conducted by The Health Management Academy. Using big data and intelligent form automation reduces delays in credentialing and enrollment.
- Improving Access to Care: Optimizing the workforce means optimizing patient access to care. When clinicians are used more efficiently in roles aligned with their work history and career goals, patients receive elevated care, bettering their experiences. Proper workforce utilization also allows health systems to hire strategically – in an anticipatory manner based on healthcare trends. Access to clinician data enables health systems to staff appropriately before the increase in demand, which means shorter wait times for patients. This capability ensures health systems executives can retain patients while positively engaging clinicians.
In closing
In reimagining the landscape of healthcare’s potential, the focus must shift from merely accumulating data to actively leveraging it –– especially in the realm of clinician data. The staggering volume of underutilized data is key to resolving today’s critical healthcare issues. Billy Beane and the Oakland Athletics may still be waiting for their championship, but that season changed more than just one team –– it revolutionized a sport. Healthcare stands at a similar precipice, ready to transform its approach by embracing clinician big data.
The crux lies in recognizing the multidimensional nature of clinician data beyond traditional qualifications. By dissecting work histories, individual experiences, and aspirations, healthcare systems can weave together a comprehensive tapestry of their workforce. Embracing this holistic view empowers decision-makers to implement micro-credentialing strategies, optimize workforce management through data-informed decisions, and proactively align clinicians with roles that match their expertise and ambitions. This strategic use of clinician data ensures more efficient patient care. It primes healthcare systems to expertly navigate workforce shortages, fostering a symbiotic relationship between patient access, clinician satisfaction, and operational excellence. As the horizon of healthcare expands, the careful application of clinician data becomes apparent –– not just as an opportunity but as an imperative pillar for the future of a resilient, patient-centric healthcare ecosystem.
About Charlie Lougheed
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.
