Health systems are now facing a new challenge - they must adapt to new ways to strategically plan service delivery. For decades, health systems have struggled to adopt health workforce planning systems that can effectively understand how many of what provider is required where and when, and how that changes over time. While individual methodologies and approaches to planning have been developed, the industry lacks any kind of robust, best-practice based, standardized approach to health workforce planning. This is despite the fact that provider services are often compartmentalized into work units and administered repeatedly - presenting ideal conditions for demand forecasting. Basic supply-based management tools, systems, and processes that can determine how much of what provider a system will have when and where are not widepsread (if used at all); population-health needs-based modeling to determine provider demand is even rarer. Despite myriad planning and forecasting technology and methodologies available and used across several other industries, healthcare organizations have struggled to plan effectively.
Now, healthcare organizations are facing the realization that health systems planning must improve. To understand how many of what you need where and when and how that changes over time is not just a requirement; not knowing this information is sure to lead to system failure, staff shortages, service outages, and poor patient outcomes. An inability to effectively understand either supply or demand components of health system services delivery leads to things like long wait times, high provider turnover, burned out, disgruntled or unhappy staff, high agency and/or overtime costs, and a host of other issues. These are now commonplace across most health systems in North America.
Any health workforce planning strategy must start with an effective way to understand current provider staff; this includes how many of what provider exists now, of what type and kind they are, and how that changes over time. Key metrics may include number of headcount, positions, and full time equivalents (FTE) for each type (ie full time, part time, casual, relief, etc), plus line of sight to recruitment/inflow and attrition/outflow levels. Understanding these levels enables rate-based forecasting to occur; reviewing historical data on inflow and outflow levels and rates allows a planning team to build supply-forecasts that can anticipate how many of what provider may be available where and at what point in time in the future. As the composition of workforce as well as its recruitment and attrition rates are likely to change by location and provider type, having this information on-hand and available is critical to taking the first step in effective health workforce planning.
While supply management and modeling is a prerequisite to effective health workforce planning planning cycles, the ability to quantitatively understand how many of what provider you is required where and when, and how that changes over time based on a variety of inputs, is the next major step in building proactive health workforce planning strategies. While methodologies have been developed and published that demonstrate various approaches to provider demand modeling, common components of a demand model include provider utilization and frequency, clinical practices and approaches, patient access to services, population demographics and needs, and the intersection between supply and demand.
An integrated approach to planning that accounts for demands across the entire system enables decision makers and planners to understand and coordinate the full scope of impacts that various strategic plans have on the system. This ensures that the most pressing or highest need or impact initiatives are prioritized, enabling the health system as a whole to work collectively and in the same direction. Necessary to achieve this integrated way of working is a strong governance model, capable and knowledgeable leadership, and access to timely data and analytics to help drive discussions, prioritization, and decision making.
