The evolution of health system planning is a broad and deep subject with many interrelated components, but share a set of planning practices which if implemented correctly enable powerful, adaptive, data-driven and evidence informed planning to occur, enabling health systems to plan effectively.
Despite establish research clearly demonstrating paths forward towards better health workforce planning, health systems have been slow to adapt these techniques. We believe this is due to the gap between the theoretical and the practical: while published literature covers the theoretical 'what' few if any material available demonstrate the subsequent 'how', indicating a widespread lack of experience in practical health workforce planning transformation. This is exacerbated by an absence of industry tools which has to-date suffered from an absence of focus on the administrative side of health workforce / HHR planning - focusing instead on either clinical-patient experiences (automated triaging, EHR/EMR systems) or frontline operations (scheduling, payroll, time management).
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Stock-flow provider supply modeling enables decision-makers to both understand the current composition of their specific health workforce, as well as anticipate and predict the future supply of healthcare providers based on current levels and their changes overtime. Provider recruitment/inflow and attrition/outflow must be understood, as they are influenced by factors like retirements, new graduates, migration, and changes in work patterns. This modeling approach allows healthcare planners to anticipate potential shortages or surpluses of healthcare professionals by accounting for the dynamic nature of the workforce. By understanding these trends, a health system can make informed decisions on recruitment, training, and policy interventions to ensure that healthcare services are adequately staffed in the future, thereby maintaining or improving the quality and accessibility of care. This foresight is crucial for ensuring that healthcare systems are resilient and capable of meeting population health needs, particularly in the face of changing demographics and evolving healthcare demands.
Health workforce data is critical to the accuracy and effectiveness of stock-flow provider modeling in healthcare planning. This data includes detailed information on the number, distribution, demographics, qualifications, and employment patterns of healthcare professionals. Accurate and up-to-date health workforce data allows for precise calculations of the current “stock” of providers, while also enabling reliable predictions of“flows,” such as incoming graduates, retirements, and migration patterns. Without robust data modeling accuracy erodes, potentially leading to misinformeddecisions potentially exacerbating shortages or creating inefficiencies within the system. High-quality health workforce data supports the identification of trends, gaps, and future needs, ensuring that healthcare planners can develop targeted strategies to maintain an optimal balance of healthcare providers, ultimately safeguarding the accessibility and quality of care for the population.
At a very high level, these are the steps involved in building stock-flow modeling capacity.
Define what provider types your organization currently employs and needs to track. This could include both registered providers as well as unregistered, back office, and/or support staff.
Regions, Locations, Facilities, and even Units may be tracked, trended, managed, and planned separately for. Each location comes with its own set of environmental factors which may need to be considered in a model – this largely depends on your requirements.
Provider specific statuses must be identified and tracked with reporting – including statuses such as fulltime, part time, casual, relief, on-call, and others.
Once the above has been identified preliminary reporting exercises can now occur that collect current-state workforce details pertaining to the above. It is critical thatthis information be both comprehensive and representative.
Build access to workforce data specific to the above – both currently, then historically, at intervals determined for tracking and forecasting (either monthly or quarterly).
Conduct a deeper dive into workforce data to understand key rates such as tenure and retirement rates, attrition trends related to age, tenure, position, and location, and other insights capable of generating a better understanding of your current and future workforce trends.
Assemble stock-flow models based on 1) current state workforce levels, 2) historical and projected inflow and outflow rates and 3) incorporate demographic insights and trends into future state assumptions. Stock-flow models may be specific to 1) provider type and 2) location to account for conditions specific to each environment, which are subject to change.
Solicit input from clinical and operational leads across categories and locations for input to logic and reasonability.Often, rates of inflow and outflow are not well tracked at local levels, so sharing this information may be both enlightening and result in very specific dialogue on what caused previous levels and rates relative to what is current, and what that means going forward.
Stock-flow models must be maintained, updated, and tracked to be useful planning tools. This involves regularly accessing updated data, updating stock-flow actuals compared to projected numbers, and comparing them to determine where variance is occurring. Identified variance should be discussed and potential adjustments to forecasts should be considered. A cadence and process for accessing the data, updating, sharing, and decisioning should be established, with best practices including a clearly defined Standard Operating Procedure (SOP) that includes what is being shared, how often, and with whom. Aggregating workforce details across provider types, categories, and locations offers an ability to identify macro workforce supply trends and insights, while local workforce data enables a drill down to identify specific locations which may be experiencing greater challenges.
Calculating clinical services utilization among populations involves gathering and analyzing data related to the use of healthcare services by individuals within a specific group. The methods and data sources may vary depending on the specific context and the healthcare system in question. Here are general steps to help you calculate clinical services utilization:
Clearly define the population you are studying. This could be a specific demographic group, a geographic area, or individuals with certain health conditions.
Determine the types of clinical services you want to measure. These may include doctor visits, hospitalizations, emergency room visits, preventive screenings, and other healthcare interventions.
Gather data from relevant sources. Common data sources include:
Utilization rates are measures of how often a particular service is used within a specified time period. Common utilization rates include:
To make fair comparisons, adjust utilization rates for age and other demographic factors. This can be done using age-standardization techniques or other relevant adjustments. Specify the time period(s) for which you are measuring utilization. Common timeframes include monthly, quarterly, or annually; the more granular you go the more insight you'll have to day to day trends, however the more detailed your time intervals are more data you must contend with, so establishing an ideal range that offers a combination of both necessary granular details as well as the ability to manage, consolidate, analyze and understand macro trends is important.
If your population is spread across different geographic regions, consider calculating utilization rates for each region separately to identify variations.
Compare your utilization rates to either internal, adjacent, or other jurisdictional benchmarks or standards to assess whether utilization is at, higher, or lower than expected. Compare across units, facilities, service types, and zones - utilization maps can help provide valuable insights into what services are having greater impact, and how those services are changing over time.
Use statistical methods to analyze the data, identify trends, and draw conclusions. Descriptive statistics, inferential statistics, and data visualization tools can be helpful.
Interpret the findings in the context of your research objectives. Report the results clearly, including any limitations of the data or methodology.
Remember that calculating clinical services utilization is a complex process, and it's crucial to have a clear understanding of the population, services, and datasources involved. Additionally, ethical considerations regarding data privacy and consent should be taken into account throughout the process.
This process builds on the preceding steps to analytically measure the number of health human resources (HHR) / health workforce needed to meet clinical and operational services needs. This step involves assessing the healthcare requirements of a population and determining the workforce necessary to deliver those services. The process is complex and may vary based on factors such as the type of healthcare services, the specific roles within the healthcare system, and the unique characteristics of the population. Here are general steps to help guide you through this calculation:
Clearly identify the clinical services that need to be provided. Consider the range of healthcare services required to meet the needs of the population, including primary care, specialty care, emergency services, etc.
Specify the types of healthcare professionals needed to deliver the identified services. This may include physicians, nurses, allied health professionals, support staff, and administrative personnel.
Consider different models of service delivery, such as team-based care, telehealth, or community health worker programs. The chosen model will influence the mix and quantity of health human resources required.
Determine the optimal staffing ratios for each type of healthcare professional based on ndustry standards, best practices, and the specific needs of the population. For example, you might use ratios such as the number of physicians per thousand population or nurses per hospital bed.
Consider Workforce Mix:some text Evaluate the appropriate mix of healthcare professionals. This involves assessing the skills and expertise of different roles and ensuring that the workforce is diverse and capable of meeting the varied needs of the population.
Consider the unique characteristics of the population, such as age, health status, and socioeconomic factors. These factors can influence the demand for certain healthcare services and may impact the required workforce.
Anticipate changes in the population's healthcare needs over time. Consider factors such as population growth, aging, and the prevalence of specific health conditions. Projecting future needs helps ensure that the healthcare workforce can adapt to changing demands.
Consider productivity and efficiency measures to optimize the use of healthcare professionals. This might involve implementing technology, improving workflow processes, or enhancing the scope of practice for certain roles.
There are workforce planning tools and models available that can assist in calculating healthcare workforce needs. These tools often take into account multiple variables and can help estimate the required number of health human resources.
Regularly review and adjust workforce calculations based on ongoing assessments of population health, changes in healthcare delivery models, and advancements in medical technology. It's important to note that workforce planning is an ongoing and dynamic process that requires collaboration among healthcare professionals, policy makers, educators, and other stakeholders. The goal is to ensure that the healthcare workforce is appropriately sized, skilled, and distributed to meet the evolving needs of the population.
Public health utilization indicators in Canada provide insights into the use of healthcare services and the overall health status of the population. These indicators help monitor and assess the performance of the public health system.Some common indicators include:
These indicators help policymakers, public health officials, and researchers understand the strengths and challenges of the public health system. Regular monitoring of these indicators allows for evidence-based decision-making and the development of strategies to improve population health and healthcare delivery. It's important to note that the specific indicators and data sources may vary by province or territory in Canada.
Probing questions to help understand throughput and connection to patient need are detailed below. Insights resulting from these questions help inform modeling approach and assumptions.
Design
Retrospective hospital data were obtained for analysis from the discharge abstracts database (DAD) of the Canadian Institute for HealthInformation (CIHI). It contains sociodemographic, administrative, and clinical data on all hospital episodes in every hospital in all provinces and territories except Quebec (Quebec does not provide data to CIHI). Complete individual-anonymous hospital utilization data (excluding stillbirths) were obtained for 2013–2014 and 2014–2015, the two most recent data years available for this mid-2016 study. Every individual patient had been given a unique number; this number was used to create a third dataset containing all DAD information collected over the last 365 days of life on every inpatient who died in the 2014–2015 year. The SAS program was used to analyze the 2014–2015 and 365-day data, through descriptive–comparative and logistic regression methods.
