Section 3.2: Forecasting Drug Spend, Labor Costs, and Operational Expenses

3.2.1 The “Why”: Forecasting is Applied Pharmacokinetics for Your Department

In your clinical practice, you are an expert forecaster. When you administer a dose of vancomycin to a patient with renal insufficiency, you are not guessing what the trough level will be; you are making a scientifically-informed prediction. You build a pharmacokinetic model in your head, integrating patient-specific variables—age, weight, creatinine clearance, clinical stability—with the known properties of the drug. You use this model to forecast a future state (the serum concentration) and make a proactive decision to optimize the outcome. This is the essence of clinical excellence: using data to predict the future and act accordingly.

Financial forecasting is the exact same discipline applied to an organizational scale. It is the science of building a financial model for your department, integrating known variables—historical spending, projected patient volumes, drug price inflation, new technologies—to predict a future financial state. A budget is a static snapshot, a target for a single year. A forecast is a dynamic, living projection that must be constantly updated as new data becomes available. Your ability to produce a credible, accurate, and defensible forecast is the hallmark of a sophisticated financial leader. It demonstrates to the organization that you are not simply reacting to your monthly budget reports; you are proactively managing the financial trajectory of your department.

A well-constructed forecast is your most powerful strategic communication tool. It allows you to anticipate problems before they become crises. It provides the data necessary to justify new resources, showing not just what you spent last year, but what you project to spend next year and, most importantly, why. When the CFO asks you why your drug spend is projected to increase by 12% next year, a weak leader says, “Because costs are going up.” An effective, data-driven leader says, “Our baseline spend is projected to increase by 4% due to inflation and a 3% increase in patient days. The remaining 5% is driven by three specific factors: the launch of the new CAR-T cell therapy program, the full-year impact of the new Alzheimer’s drug approved last quarter, and the loss of patent exclusivity on a major biologic offset by a projected biosimilar conversion rate of 60%. Here is the detailed financial model for each of those drivers.” This is the level of mastery this section will help you achieve. We will transform your innate clinical forecasting skills into the robust financial models that command respect and drive strategic decisions.

3.2.2 Masterclass: The Multi-Factorial Drug Spend Forecast Model

Forecasting drug expenditure is the most complex and most critical forecasting task you will perform. The “Last Year + X%” method is unacceptable for a modern pharmacy leader. A credible forecast is not a single calculation; it is the sum of multiple, distinct analyses that are layered on top of each other to build a comprehensive and defensible projection. Your goal is to be able to isolate and explain the financial impact of each individual driver.

Let’s perform a deep dive on how to professionally calculate each component of this model.

Component 1: Establishing the Baseline Spend (The Run Rate)

The foundation of any good forecast is a clean, accurate, and properly analyzed baseline of historical data. Your goal is to determine the “annualized run rate”—what you would spend over a full year if all conditions remained exactly the same as they are right now. This is more complex than simply taking last year’s total spend.

1. Data Extraction: The best practice is to pull the most recent 12 months of drug purchasing data, line by line, from your wholesaler and other primary suppliers. This data should include the NDC, drug name, package size, quantity purchased, and actual acquisition cost for every transaction. Using a full 12 months helps to smooth out random monthly variations.
2. Data Cleansing: Your raw data will be “dirty.” You must perform several cleansing steps:
   • Remove Non-Drug Items: Filter out items that are not pharmaceuticals, such as medical supplies or administrative fees that may be on your invoices.
   • Account for Credits: Ensure that your total spend is net of any credits for returned or expired products. Your total spend should be what you actually paid.
   • Normalize for Price Changes: If there was a significant mid-year price increase on a high-cost drug, a simple sum of past purchases might not reflect its future cost. Advanced analysis might re-price all historical purchases at the current acquisition cost to get a more accurate picture.
3. Analyze the Most Recent Quarter: While a 12-month baseline provides stability, you must also analyze the most recent 3 months of data very closely. This can reveal new trends that are not apparent in the full-year data. Is there a new biosimilar that has only been on the market for two months? Is there a new high-cost drug whose usage is rapidly accelerating? You must compare your 3-month run rate (total spend in the last 3 months, multiplied by 4) to your 12-month run rate. If they are significantly different, you must investigate why and potentially use the more recent data as your true baseline.

Component 2: The Volume Adjustment Factor

Drugs are not consumed in a vacuum; their use is directly tied to patient volume. Your next step is to adjust your historical baseline to account for the hospital’s projected growth or decline. The key is to find the right volume metric, or “unit of service,” that best correlates with your drug spend.

Component 3: The Price Inflation Factor

This factor accounts for the year-over-year price increases from drug manufacturers. Applying a single, flat inflation rate is a common but overly simplistic approach. A professional forecast uses a more nuanced, weighted-average methodology.

1. Segment Your Spend: Do not treat your entire drug spend as a single bucket. At a minimum, segment it into three categories:
   • Brand Drugs (Single Source): These have the highest inflation rate.
   • Generic Drugs: These have a much lower, and sometimes negative (deflationary), inflation rate.
   • Biologics / Specialty Drugs: These often have their own unique, high inflation profile.
2. Assign Category-Specific Inflation Rates: Work with your GPO and supply chain experts to get projected inflation rates for each category. For example: Brands: +8%, Generics: -1%, Biologics: +6%.
3. Calculate the Weighted Average: Your overall inflation factor is the weighted average of these rates, based on what percentage of your total spend each category represents.

   Example: Weighted Inflation Calculation

   A department has a $50M drug spend, broken down as follows:

   • Brands: $25M (50% of spend) with 8% projected inflation.
   • Generics: $10M (20% of spend) with -1% projected inflation (deflation).
   • Biologics: $15M (30% of spend) with 6% projected inflation.

   The calculation is:

   (0.50 * 8%) + (0.20 * -1%) + (0.30 * 6%)

   = 4.0% – 0.2% + 1.8%

   Weighted Average Inflation Factor = 5.6%

   This is a much more accurate and defensible number than simply using a flat 5% for everything. It shows you have done your homework.

Components 4 & 5: New Technology & Strategic Initiatives

This is where your forecast transforms from a mathematical exercise into a strategic document. This is your opportunity to quantify the financial impact of the future. For every significant new drug, new generic/biosimilar, or new clinical program, you must build a specific, standalone financial model, often called a “pro forma.”

Masterclass Playbook: Building a New Drug Pro Forma

Let’s say a new, high-cost medication for refractory heart failure, “CardiaCure,” is expected to be added to the formulary in the first quarter of the upcoming fiscal year.

You will perform this exact same process in reverse for a new generic or biosimilar, calculating the total spend on the brand product and then applying a projected cost reduction based on the generic price and an estimated conversion rate. Each of these pro formas becomes a specific, documented assumption in your final forecast.

3.2.3 Forecasting Labor Costs: A Deep Dive into Your Most Valuable Asset

While the drug budget is larger, the labor budget is often more complex and requires a different forecasting methodology. Your labor forecast is a direct reflection of your staffing model and your operational efficiency. An accurate forecast is essential for justifying new positions and managing your daily productivity.

Fixed vs. Variable Labor: A Core Concept

First, you must understand the difference between your fixed and variable labor pools.

Using Productivity to Forecast Variable Labor

Productivity is measured in Worked Hours Per Unit of Service (WHPUOS). This is a topic we will cover in immense detail in Section 3.4, but for now, it’s important to understand its role in forecasting.

1. Define Your Unit of Service: For pharmacy, a common high-level metric is Adjusted Patient Days (APD).
2. Calculate Your Productivity Target: Your finance department will work with you to establish a target. For example, your target might be 13.0 Worked Hours per APD. This means for every adjusted patient day, you are budgeted 13.0 hours of pharmacy labor.
3. Obtain the Volume Forecast: Get the total projected APDs for the upcoming fiscal year from finance. Let’s say it’s 80,000 APDs.
4. Calculate Total Worked Hours:
   $$ \text{Total Budgeted Hours} = \text{Projected Volume} \times \text{WHPUOS Target} $$ $$ \text{80,000 APDs} \times \text{13.0 WHPUOS} = \text{1,040,000 hours} $$
5. Calculate the Labor Cost: Multiply the total budgeted hours by the department’s average hourly rate (including benefits). This blended rate is provided by finance.
   $$ \text{Total Labor Forecast} = \text{Total Hours} \times \text{Avg. Blended Rate} $$ $$ \text{1,040,000 hours} \times \text{$65.00/hr} = \text{$67,600,000} $$