Many hospital planners remain skeptical that demand forecasting solutions can actually improve their operating margins by reducing labor costs and average length of stay (ALOS). These hospitals are already doing all they can to address staffing costs, and they correctly believe that a static forecast – one that predicts census over the next few weeks or months – cannot have a significant effect on the bottom line.
Full-spectrum capacity planning systems, when integrated with the frontline manager's daily workflow and coordinated with workforce management and patient flow systems, can produce significant returns on investment. Hospitals have realized 15% or greater reductions in ALOS, as well as 10% reductions in care hours per patient day. By developing a proactive culture driven by transparent, trusted forecasts, organizations can tightly control their costs by reducing ALOS and premium pay – while also improving their patients' overall experience.
For more than a decade, many workforce management systems have included various degrees of census forecasting ability. Yet despite vast improvements in the field of demand forecasting, most of these systems can still only produce simplistic forecasts. Other vendors “predict” patient-specific staffing needs a few days out, using a moving average patient census for the past several weeks. This approach is more accurately described as care planning, which does not help hospitals manage staffing costs proactively.
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Predictive Analytics for Capacity Planning: All Forecasts Are Not Created Equal
Most forecasting tools use 3+ years of historical census data to establish a baseline patient census forecast. Static forecasts stop there, producing a one-shot prediction. More robust tools build on that baseline, using multiple forecasting methods that incorporate all relevant variables. There are five key methods that together produce the caliber of continuous forecasting needed to impact the bottom line:
- Predictive Modeling: The use of historical data on patient volumes, arrival patterns and the like to determine causal factors behind patient demand and flow. Commercial analytics platforms and business intelligence products cannot easily produce a patient demand forecast because they typically don't take into account these and other key inputs.
- Algorithmic Modeling: The use of averages, distributions and dependent data relationships in past activity to create rules for producing a forecast. Accuracy analyses should be based on an absolute daily comparison of the forecast to what actually happened. A model that compares its average monthly census to the average actual census may artificially inflate its accuracy claims.
- Pattern Identification: The art of identifying trends and seasonal or day-of-week patterns in historical data, removing anomalies and combining patterns into a base forecast. This forecast can then be modified to reflect upcoming changes.
- Scenario Modeling: The art of refining a base forecast with global and specific adjustments reflecting upcoming changes. Global adjustments can be made to such variables as inpatient/outpatient mix or length of stay in order to make the forecast more representative.
- Simulation: The ability to combine arrival data with predictive analytics about expected patient movement to assess the impact of various staffing scenarios on patient flow, census and capacity. Care pathways can be combined with case booking or block scheduling data to create a simulation across departments.
By integrating a full-spectrum predictive analytics tool with your organization's routine workflows, your hospital can enable a cycle of real-time, continuous capacity planning that will revolutionize the way you deploy resources – and help you save considerable money. Our white paper,
Predictive Analytics for Capacity Planning: All Forecasts Are Not Created Equal, will help you become better informed about the market.
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