Thursday, Nov 9, 2023

Transforming Healthcare Workforce Management with AI and Predictive Analytics

Jackie LarsonExecutive Vice President, Advisory Solutions, Aya Healthcare

HLTH

Staffing is a critical component of ensuring quality patient care. For the past two years, hospital CEOs have ranked workforce challenges as their top concern. One of their most pressing issues lies in the realm of labor costs, which is the largest category of spend for hospitals. This financial burden has reached critical levels, significantly straining hospital finances. As healthcare leaders balance the care and cost conundrum, the power of predictive analytics and artificial intelligence (AI) emerges as a transformative force to help optimize costs and labor utilization.


Taking the guesswork out of workforce and capacity planning


Traditionally, the number of nursing resources needed is based on the number of beds or the average census. It’s a little more than just guessing as it doesn’t factor emerging needs, productivity timelines for workers such as new graduates, contractors, international workers and other variables. This guesswork approach can result in several challenges:

  • Understaffing: Without accurate volume predictions, healthcare facilities may find themselves understaffed during peak demand periods, jeopardizing patient care and staff morale.
  • Overstaffing: Overestimating staffing can lead to unnecessary labor costs, and sending workers home regularly can affect the financial health of the providers.
  • Mismatched staffing: Failing to match appropriate healthcare professionals' skills and competencies with patient needs can compromise the quality of care and patient safety.
  • Burnout: When staffing is inconsistent and unpredictable, healthcare professionals may face burnout, impacting both their well-being and the quality of care they provide.


Harnessing the power of predictive analytics and AI incorporates the analysis of thousands of data points from historical hospital census data, weather patterns, the health status of the service area and a hospital’s existing EMR for patient acuity and diagnoses. Hospitals can utilize this data to predict patient volumes more precisely with more than 90% accuracy to build future demand-driven labor budgets and develop hiring plans from 12-18 months out. 


Taking the guesswork out of scheduling


While many advanced enterprise scheduling systems exist, no solution shows the complete staffing schedule (including internal and external contingent resources), department activity (including census) and ADT. Advanced algorithms can provide guidance to create an optimal schedule based on precision forecasting with alerts for high/low census or under/overstaffing, such as with Aya Healthcare’s workforce AI. 


AI-generated staffing can create ideal schedules in three easy steps: 

  1. Predict Future Patient Volume: Intelligently predict the number of physicians and nurses needed to meet future patient demand.
  2. Distribute Resources: Improve efficiencies by scheduling existing provider resources where appropriate rather than relying upon contract labor. 
  3. Optimize Schedules: Achieve an optimal schedule that balances provider preference, organizational policy and regulatory constraints.

AI-generated staffing models with hour-by-hour volume predictions represent a transformative approach to healthcare workforce management. Across multiple metrics, Aya’s Workforce AI-generated staffing models outperformed manually generated schedules to identify significant savings opportunities. 


Taking the guesswork out of contingent labor 


While the pandemic contributed to the market growth of travel nursing, hospitals in our current market should re-evaluate their contingent labor utilization. When used to cover short-term needs such as seasonal demands or staff leaves, temporary labor can serve as a strategic tool in your workforce toolbox to improve employee engagement and quality of care by giving providers much-needed support. 


To effectively manage contingent labor utilization and spend, healthcare systems must leverage technology and market analytics to formulate bill rate strategies. Because of Aya’s size and scale as the largest talent software and staffing company in the U.S., clinician engagement data from Aya’s platform provides deep analysis predicting which pay packages clinicians will transact on, providing real-time, market-validated pricing. No other model can use this strategy at the scale of Aya, and, without this guidance, healthcare leaders are merely guessing rates and often overpay. 


Through detailed analysis of contingent labor demand, available talent supply and clinician engagement data, Aya Healthcare provides insights on predicted job market performance before the job is posted. Job market insights provide key data and recommendations to help hospital managers maximize candidate engagement to optimize fulfillment and costs.


While predictive analytics are not new, AI-generated staffing models take predictive analytics to the next level. In this era of workforce challenges, innovative healthcare leaders should not eliminate tools from their workforce toolbox. Instead, they should use all available tools strategically that maximize workforce capacity and reduce labor costs to maintain a premium level of care for patients and the community.


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