Step-Therapy Policies: How to Model Cost Impact Before P&T Approval

Step therapy formulary policies (often called “fail-first” protocols) require patients to try a preferred, lower-cost medication before a more expensive drug is covered. These policies are widely used by payers to control rising drug costs (one review found nearly 40% of health plan drug policies include step therapy). With prescription drugs accounting for over 10% of total healthcare spending, P&T (Pharmacy and Therapeutics) committees increasingly consider step therapy as a cost-containment tool.

Before a P&T committee approves a new step-therapy rule, however, decision-makers need a clear forecast of its financial impact. This is where robust cost impact modeling comes in. A well-built model can project budget savings, identify potential cost offsets, and ensure that patient outcomes remain acceptable. In this guide, we explain how to model the cost impact of step-therapy policies in a clear, data-driven manner, highlighting what data to gather, which assumptions matter, and how to interpret and present the results. Along the way, we’ll share best practices that showcase MedReb8’s expertise and the value of its data module in supporting these analyses.

Why Model Step Therapy’s Cost Impact Before Approval

Implementing a step-therapy protocol is a significant formulary decision. While the primary goal is to reduce drug spend by steering utilization to cheaper first-line therapies, there may be downstream consequences. For example, delays in effective treatment could lead to higher medical costs (if patients experience complications) or higher patient out-of-pocket costs and dissatisfaction. P&T committee members and payer stakeholders must balance cost savings against such risks. By modeling the cost impact in advance, you can:

Quantify Expected Savings: Estimate how much pharmacy spend will decrease by requiring the lower-tier drug first. Studies consistently show substantial drug cost savings from step therapy due to greater use of lower-cost alternatives. A model provides concrete dollar figures for these savings.
Identify Cost Offsets: Determine if reduced drug spending might be offset by other costs like more doctor visits, hospitalizations, or administrative burdens. (Notably, many evaluations have found no significant increase in hospital or ER utilization under step therapy, but this can vary by therapy class.)
Assess Patient Impact: Understand how many patients will need to “step through” therapy and if some might discontinue treatment. Savings can come not only from using cheaper drugs, but also from some patients never progressing to the costlier drug (due to improvement or sometimes therapy abandonment). It’s important to gauge these scenarios for both cost and clinical implications.
Support Data-Driven Decisions: A credible financial model, grounded in evidence, gives the P&T committee confidence. It shows that recommendations are based on solid analysis, not just cost-cutting instincts. This fosters transparency and trust in the decision.

In short, upfront modeling ensures that when you bring a step-therapy proposal to the committee, you can clearly articulate the anticipated budget impact and address questions about patient outcomes or hidden costs. Next, we delve into the modeling process step by step.

Data Gathering: Laying the Foundation for the Model

The first phase is collecting the right data. A cost impact model is only as good as its inputs. Here’s what to gather before you start building:

Population and Utilization Data: Identify the patient population affected by the step therapy policy. How many patients would be subject to the “fail-first” requirement per year? Use your health plan or hospital’s data (e.g. claims, EHR, or utilization reports) to get baseline numbers. Determine current prescribing patterns: how many patients start on the targeted drug versus alternatives, and how often second-line (step-up) therapy is needed. For example, if the policy is for rheumatoid arthritis biologics, gather data on how many patients start biologics annually and what therapies they use first. This provides the baseline volume for your model.
Drug Cost and Rebate Information: For each drug involved (first-line and subsequent options), collect cost data. This should include list prices (like Average Wholesale Price or Wholesale Acquisition Cost) and net costs after rebates or discounts. Step therapy works by favoring a less expensive drug, but “cost” should account for rebates and negotiated rates, since a higher-priced drug might have a big rebate. Include dispensing fees or other pharmacy costs if relevant. MedReb8’s data module can be invaluable here: it aggregates up-to-date drug pricing and rebate data, ensuring your model reflects the true net cost of each therapy option. For instance, MedReb8’s platform can provide current negotiated prices and rebate percentages for the drugs in question, rather than relying on outdated averages.
Clinical Efficacy and Step Success Rates: Gather evidence on the effectiveness of the first-line therapy and how often patients “fail” it. Key assumptions include the step-through rate (what percentage of patients will require the second-line drug after trying the first). Clinical trial data or published studies can inform this (for example, if 30% of patients don’t respond to Drug A and need Drug B). Also note the typical time on first-line therapy before switching (e.g., 2 months trial). These efficacy and failure rates are critical assumptions in the model.
Medical Utilization and Outcome Data: Consider any differences in other healthcare utilization between therapies. Does delaying the higher-cost drug lead to more hospitalizations, ER visits, or other medical costs? For many drug classes, research hasn’t found major increases in downstream medical costs due to step edits, but it depends on the condition. If you have disease management data or literature on outcomes (e.g., uncontrolled disease leading to hospitalization), note those figures. Even if qualitative, they help anticipate indirect or clinical outcome costs (discussed more below).
Administrative and Operational Costs: Implementing step therapy often involves prior authorizations or exceptions processes. Estimate the administrative cost per case (staff time, physician call-backs, etc.) for processing these. It might be modest, but if the volume is high, it adds up. Also consider patient and provider time (while harder to monetize, it’s part of the overall impact).

By gathering these data points, you set up all the inputs needed for modeling. Tip: Organize the data clearly in a spreadsheet input section (population counts, drug unit costs, adherence rates, etc.). That makes it easy to adjust assumptions later and see how results change. Ensure sources for each input are documented. P&T members may ask where numbers came from. At this stage, a data platform like MedReb8 can streamline collection, by pulling in claims data and cost benchmarks automatically, saving you manual work and improving accuracy.

Key Assumptions to Define Upfront

With data in hand, the next step is to define the key assumptions for your model. Assumptions are needed where exact data is uncertain or variable. Here are the critical ones to consider in a step-therapy cost model:

Step Therapy Protocol Details: Clearly define the rule being modeled. For example, “Patients must try Drug A (generic) for 8 weeks before Drug B (brand) is covered.” Specify the number of steps (sometimes there could be multiple step levels), and any allowed exceptions. This sets the framework for how patients flow through therapy.
Percentage of Patients Starting Therapy: If some patients who need treatment never start it (perhaps due to step requirements or other barriers), decide how to handle that. Some models assume a certain non-initiation rate (maybe 5–10% of patients drop off and receive no therapy if the step barrier is in place). This can produce savings (fewer people on expensive drugs) but at a potential clinical cost. Use past experience or literature to estimate if applicable.
First-line Success/Failure Rate: As noted, assume what proportion of patients will be adequately treated with the first-step drug versus those who will progress to the next step. For instance, if historical data or trials suggest 50% respond well to Drug A and the rest need Drug B, use those figures. This drives the volume that moves to the costly drug.
Time Horizon for Analysis: Decide if you’re modeling one year of impact, multiple years, or a per-month figure. Many P&T budget impact models use an annual timeframe or a 1-3 year projection. Choose a period that aligns with budgeting cycles. If multi-year, you may need to incorporate trends like population growth or drug inflation, but keep it manageable (often a “constant population” static model is acceptable for short horizons).
Unit Costs and Inflation: For cost per prescription or per patient, decide if you will hold prices steady or include inflation. Often, budget models use current costs and assume they remain constant (for simplicity), especially over a short horizon. If a price increase is expected (or a generic entry that will drop price), you can include that assumption explicitly.
Rebate and Discount Assumptions: If using net cost, include rebate percentages. For example, assume Drug B has a 20% rebate off wholesale cost. If you don’t know exact confidential rebates, you might use averages or consult a data source. Clearly state these assumptions; they heavily influence the outcome. Excluding rebates, for instance, could overstate the savings of switching to a cheaper drug if the expensive drug had big rebates (one study noted exceptions in antipsychotics where ignoring rebates changed the savings outcome).
Adherence and Persistence: Consider if step therapy might affect medication adherence. Will patients be less likely to stick with treatment if forced to switch? If so, you might assume a slight drop in days on therapy, which affects cost. On the flip side, if a patient finds the first-line ineffective for too long, there could be a gap or overlap in therapy that adds cost. Decide if your model needs to factor this in or if it’s negligible for the scenario.
Clinical Outcomes Differences: As an advanced consideration, if there is evidence that delayed access to the second-line drug worsens outcomes (e.g., more disease flare-ups), you might incorporate a cost for that (perhaps an increased probability of a hospitalization or extra doctor visits). If no solid data, you might assume no difference in clinical outcomes for simplicity, but be prepared to discuss qualitatively with the committee.

Each assumption should be documented and, where possible, informed by data or literature. Sensitivity analysis is also wise: identify which assumptions (e.g., failure rate or drug cost) have the biggest impact on savings, and consider showing best-case and worst-case scenarios based on varying those. This helps stakeholders understand the range of possible outcomes and builds confidence that the model isn’t a single-point estimate.

Building the Cost Impact Model: Structure and Calculations

With inputs and assumptions defined, now you can build the spreadsheet model to calculate the cost impact. A logical, transparent structure is key. Here’s a suggested approach:

Baseline Scenario (No Step Therapy): First, model the current or baseline scenario without the step policy change. Calculate how much is being spent on the drug(s) today. For example, say 1,000 patients are eligible annually. If no step therapy, perhaps 700 end up on Drug B (expensive) right away and 300 use Drug A (cheaper) first (perhaps due to physician choice or other utilization management). Multiply those patient counts by the annual cost of their therapies to get total current cost. Include any relevant medical costs in this scenario (if, say, without step edits some patients get optimal control and avoid hospitalizations, but you would include status quo outcomes cost here).

New Scenario (With Step Therapy): Next, model costs under the proposed step therapy policy. Now, assume most patients must start on Drug A. Perhaps out of 1,000 patients, 900 will try Drug A first (with a smaller 100 qualifying for exceptions to go straight to Drug B, if applicable). Of those 900, assume X% (say 50%) succeed on Drug A and don’t need Drug B, while the remaining 450 fail and move to Drug B after some delay. This means in the new scenario, costs incurred include:

Cost of Drug A for all 900 patients (for the trial period each).
Cost of Drug B for the 450 who needed it (possibly for part of the year if they switch mid-year).
Any additional costs due to failure/switch (e.g., an extra doctor visit to adjust therapy).
Administrative costs: e.g., processing 900 prior auths for Drug B or exceptions requests.
Potential outcome costs: e.g., if delay caused some complications in those 450 (this could be minor or omitted if negligible).

Sum these up to get the total cost under step therapy for the year.

Calculate Impact: Now compare the two scenarios. The difference (Baseline cost – New cost) is the net cost impact (savings) of implementing step therapy. You can present this as a total dollar amount (e.g., “Projected annual savings: $500,000”) and also as a percentage of baseline spending (e.g., “a 10% reduction in drug spend for this category”). For health plans, it’s useful to translate this into per member per month (PMPM) savings as well, if applicable (total savings divided by total plan member-months). That puts the impact in context of premiums or budget.

Break Down the Components: It’s very useful to show the breakdown of costs in each scenario:

Pharmacy costs for Drug A and Drug B.
Medical costs (if included, like hospital/ER costs).
Admin costs.
Patient cost share (if you want to note that patients might pay more or less out-of-pocket; not a cost to plan, but could be mentioned qualitatively).

A table could be created in the model summarizing these components for baseline vs new scenario. For example:

Cost Component	Baseline (No ST)	With Step Therapy	Difference
Drug A Costs	$X	$Y	+$Δ
Drug B Costs	$M	$N	–$Δ
Total Pharmacy Cost	$A	$B	-$S (savings)
Medical Costs	$P	$Q	+$Δ?
Admin/Operational	$0 (no ST)	$R (PA costs)	+$R
Overall Total Cost	$T1	$T2	-$S_net

In this hypothetical table, you’d plug in values from your model. The idea is to illustrate which elements drive savings (likely Drug B costs drop significantly) and which add costs (Drug A usage rises, plus admin overhead, maybe slight medical changes).

Spreadsheet Structure: On a practical level, structure your Excel (or Google Sheets) model with clarity:

An Inputs tab where all key data and assumptions are listed in one place (population, costs, rates, etc.). This can include toggle cells for scenario (e.g., you could even make a dynamic model where a cell like “Step therapy on/off” changes assumptions of distribution).
A Calculations tab that references the inputs and computes the scenario costs step by step (preferably split into baseline and new).
A Summary tab or section that draws the comparison and highlights the key results (like the table above, plus any charts).

Readers or committee members might not see the whole spreadsheet, but this organization ensures auditability. If someone asks “what if only 30% fail first-line instead of 50%?”, you can quickly adjust that input and show the revised outcome. Designing the model in a modular way will also make it easier to reuse for future formulary decisions. Pro tip: Consider adding brief documentation within the spreadsheet (comments or a legend) explaining each section. It demonstrates diligence and makes it easier for others to review.

MedReb8’s expertise in this domain means such models can even be pre-built or templated. In many cases, vendors provide spreadsheet-based budget impact models to clients. You should expect a well-structured workbook with clearly labeled inputs and automated calculations. If building your own, mimic the professional approach: transparency, version control for assumptions, and perhaps have a colleague validate the formulas. The goal is to have a reliable tool that can stand up to scrutiny during the P&T meeting.

Direct Costs: Focusing on Drug Spend Savings

When presenting your analysis, it’s helpful to break down the direct costs, primarily the drug spend, as a distinct category. This is usually the largest driver of financial impact with step therapy policies. Key points to consider and model in the direct cost section:

Medication Cost Differential: This is the core of direct savings. Calculate how the mix of drugs changes. For example, in the baseline, many patients might be on an expensive brand drug; with step edits, a majority use a lower-cost generic first. The savings per patient who can be kept on the cheaper drug is essentially the cost difference between the two options (factoring dosage and duration). Summing across all such patients yields the gross pharmacy cost reduction. Make sure to use net costs (post-rebate) for accuracy. If Drug B costs $5,000/month and Drug A $500/month, and 100 patients avoid Drug B due to success on A, the direct savings are substantial (e.g. ~$450k per month in that simplistic example before rebates).
Utilization Reduction (if any): Sometimes step therapy not only substitutes drugs but also reduces overall utilization. The earlier literature noted that one source of savings was reduced initiation. Some patients never start the expensive drug at all. If your assumption is that a subset of patients will forego treatment rather than step through, that reduction in prescriptions should be counted as a cost saving (with the obvious caveat of potential health impact). Clearly highlight if any savings are coming from fewer patients being treated, as this might draw questions or concerns.
Pharmacy Budget vs Medical Budget: Direct costs here typically refer to pharmacy benefit spend. Clarify that your direct cost analysis covers the drug budget. If there are medical costs related to drug administration (for example, if the step therapy involves an infused drug vs an oral drug, infusion costs or monitoring might differ), you can note those here or in the indirect section. But usually, direct = drug acquisition cost.
Include All Relevant Drug Costs: Don’t forget to include the cost of the first-line therapy itself in the new scenario. Sometimes people focus only on savings from less of Drug B, but you must subtract the added spend on Drug A. The net drug cost savings is [Cost of Drug B avoided] minus [Cost of Drug A used instead]. In many cases, Drug A is much cheaper, so the net is still a big savings, but quantify it. For instance, “We anticipate $2 million less spending on Drug B, offset by $0.3 million more spent on Drug A , yielding $1.7 million net pharmacy cost savings.”
Time Component: If patients use Drug A for a trial period (say 2 months) before moving to Drug B, consider the partial-year cost. A common modeling approach is to annualize costs for simplicity (assuming steady state, some patients on A for part of year, etc.), but you can also do a more granular calculation of first 2 months on A, next 10 months on B for those who fail. Ensure consistency and document the approach. Over a one-year horizon, approximations are fine if they don’t distort the total too much.
Rebate Implications: Mention in analysis if the step therapy might affect rebate revenue. Sometimes health systems get rebates on expensive drugs; if those drugs are used less, rebate income drops. Your net cost calculation should have already accounted for net cost, but it may be worth noting to the committee: “Our cost figures are net of rebates, so the savings already reflect any lost rebate on Drug B usage reduction.” This signals that you’ve thought of both sides of the ledger.

In the report or presentation, the direct cost findings can be highlighted as the tangible benefit of the policy: “By implementing step therapy, we project a direct drug cost savings of X% for this therapeutic class, primarily due to increased use of [cheaper drug] over [expensive drug].” Reinforce that this is a recurring annual savings if the population and usage remain similar, which can be quite impactful for the budget.

Indirect Costs: Operational, Patient, and System Impacts

Beyond the obvious drug dollars, indirect costs should be considered to give a full picture. These are costs not directly tied to purchasing the medications but arising as a side-effect of the policy. Including them in your model (even if qualitatively) demonstrates a thorough analysis:

Administrative Costs: Implementing step therapy means more work in utilization management. Estimate the cost of processing prior authorizations (PA) or step therapy override requests. For example, if each PA takes 30 minutes of a pharmacist’s time and that costs $X, and you expect 200 requests a year, that’s an operational cost of $X * 200. It might be small relative to drug costs, but it’s a real cost to the health plan or hospital pharmacy department. Also consider the cost of provider offices’ time dealing with step edits, while not the payer’s expense directly, it’s part of the healthcare system burden. Some organizations factor in these to decide if the juice is worth the squeeze. Note: If these admin costs are borne by a different department than pharmacy, it’s still good to mention them, as someone in the P&T might raise it.
Patient Out-of-Pocket and Satisfaction: From a purely financial modeling view for the payer, patient cost-sharing isn’t an expense to the plan, but it’s wise to acknowledge it. Step therapy might force patients to pay for a medication that ultimately doesn’t work (co-pays for Drug A, then Drug B). If Drug A is cheaper and maybe a lower copay tier, patients could save money, or they might incur extra co-pays by trying multiple therapies. You can qualitatively mention this: “Patients may face multiple co-pays if required to cycle through therapies, which is an indirect cost to them and could affect adherence.” This is more of a consideration than something you put a dollar on in the model, but it shows you’re considering patient impact.
Productivity or Lost Time: In some cases, delaying effective therapy might cause patients to have more sick days or reduced productivity (for employer-based plans, this could be a concern). While hard to quantify and often outside the scope of a P&T committee’s purview, you could note it if relevant. For instance, if modeling a step policy for migraines, one might mention that if headaches are not controlled due to step edits, there could be productivity losses. These indirect, societal costs are usually only mentioned in broader economic analyses, but listing them signals comprehensive thinking.
Impact on Other Services: Indirectly, step therapy could shift costs to other treatments or services. For example, if a patient can’t get a certain medication, do they use more physical therapy, or need more labs or imaging while on a less effective med? Those costs might appear in the medical claims. If you have data or anecdotal evidence, incorporate it. Perhaps “During the step therapy period, patients might require one extra specialist visit for monitoring, which adds an estimated $X per patient.” Multiply by number of patients and include as an indirect medical cost.
Technology or Implementation Costs: If the policy requires any new software rules, provider education, or system configuration (for instance, updating the EHR or pharmacy system to enforce the step), there could be a one-time cost. Often it’s minimal (just a formulary update), but if significant, include it. Most likely, this is negligible, but it’s part of the full cost of adopting the policy.

In many step therapy cases, indirect costs are relatively small compared to drug cost savings, but they are the “hassle factor” that can erode some of the benefit. By quantifying them, you ensure the P&T committee sets realistic expectations. For example: “We anticipate about $50,000 a year in additional administrative workload costs to manage this policy, which is far outweighed by the $500,000 in drug savings, but it does require resource planning.”

It also underscores why efficient tools are valuable (an electronic system that automates step edits can reduce administrative burden). This subtly highlights the value of solutions; for instance, MedReb8’s data-driven formulary tools could help streamline identifying and tracking these cases, minimizing overhead.

Clinical Outcome Costs: Considering the Effects on Outcomes

Finally, a comprehensive model should address clinical outcome costs like the potential impact (positive or negative) that the step therapy protocol might have on patient health outcomes and the associated costs of those outcomes. While step therapy is intended to be clinically safe (patients still get an effective therapy, just in a sequence), it’s important to consider scenarios such as: What if forcing the cheaper drug first leads to poorer disease control for some patients? Will that incur additional costs down the line?

Key considerations for clinical outcome costs:

Hospitalizations and Emergency Care: If the condition being treated has acute events (e.g., asthma attacks, seizures, flare-ups of a disease) that could increase if therapy is delayed or suboptimal, those events carry costs. Check any evidence from literature or internal data. For instance, did exacerbations increase under step therapy in asthma or did relapse rates go up in mental health conditions? Many studies have not found a statistically significant increase in hospital or ER usage due to step therapy protocols, but this can depend on the condition’s urgency. Incorporate an estimated cost if you believe, say, 5 extra hospitalizations might occur in a year because of slower control, or be ready to explain that clinical monitoring will mitigate this risk.
Disease Progression or Complications: Some diseases can worsen irreversibly if optimal therapy is delayed (for example, rheumatoid arthritis joint damage, or uncontrolled diabetes complications). If applicable, mention the potential long-term cost of such progression. Often, P&T decisions focus on short-term budget impact, but high-cost complications are a red flag. You might state, “Our model assumes no long-term difference in disease progression within the one-year horizon of analysis. We will monitor outcomes to ensure the step policy isn’t causing increased complications.” This flags the issue and provides reassurance.
Therapy Effectiveness and Outcomes: If the first-line therapy is less effective on average, patients might have lower quality of life or clinical metrics for a period. While quality of life doesn’t directly translate to immediate cost, it’s part of the value assessment. Some advanced models convert health outcomes to costs (e.g., using quality-adjusted life years), but that’s usually beyond what a P&T needs for budget impact. Instead, focus on tangible outcome-related costs like those above.
Non-Medical Costs from Outcomes: In certain cases, if patients do worse, there could be non-medical financial impacts (e.g., a patient disabled from disease progression might need social services). These are typically out of scope for a formulary decision, but knowing the full picture can inform a balanced discussion.

In your spreadsheet, you might not have a precise dollar value for clinical outcome costs unless you have robust data. It’s acceptable to handle some of this qualitatively or with ranges. For example, you could present: “Clinical Outcome Impact: No significant increase in medical service use is expected based on published evidence. If anything, a small uptick in outpatient visits (approximately $10,000 total) might occur for managing patients who fail first-line therapy.”

This way, the committee sees that the major financial considerations are in the drug costs, and that patient health isn’t being ignored. It also underscores the importance of monitoring outcomes after implementation. A data-driven organization will review if hospitalizations or adverse events increase, and adjust policy if needed.

MedReb8’s data capabilities can assist here as well, by tracking clinical outcome metrics alongside spend. For example, the MedReb8 module could help analyze hospital admission rates before and after the policy, ensuring that cost savings are not coming at the expense of patient health. Emphasizing such a feedback loop reinforces the message that data-driven decisions and adjustments are part of the process.

Presenting Results Clearly to the P&T Committee

After all the number-crunching, how you present the results is crucial. P&T committee members are busy professionals (pharmacists, physicians, executives) who need the bottom line quickly, but also the confidence that due diligence was done. Here are tips for clear presentation:

Use Executive Summaries: Start your report or presentation with a concise summary. For example: “Implementing step therapy for [Drug Class] is projected to save approximately $1.7 million in annual drug costs (a 15% reduction in that category’s spend), with minimal impact on other medical costs. No significant adverse outcomes are anticipated, based on available data. The net savings, after accounting for administrative efforts, is about $1.6 million annually.” In a few sentences, this tells the committee what’s at stake.
Visualize the Data: Include a simple chart or graph if possible. A bar chart comparing Baseline vs Step-Therapy Scenario Costs can immediately show the reduction in pharmacy spend and slight changes in other costs. Or a pie chart of cost components might highlight the drug cost slice shrinking. Visual aids help make the point clear and are more engaging than tables alone. (In this text format, we can’t show the actual chart, but you would have one in slides or handouts.)
Highlight Key Figures and Assumptions: In the presentation, call out the critical numbers: the net savings, the percentage of patients affected, the failure rate used, etc. Also, explicitly note if any assumption is a key driver. For instance, “If the failure rate were higher than our assumed 50%, savings would actually increase; if it were much lower, savings shrink. We tested a range from 30%–70% failure and the net savings stays positive in all cases.” This preempts questions about uncertainty and shows that you’ve done sensitivity checks.
Use Clear, Jargon-Free Language (where possible): While the audience is technical, clarity is still king. Explain abbreviations the first time. For example, “PMPM (per member per month) impact on premiums”. Use terms like “savings” or “additional cost” rather than only saying “negative impact” or “positive impact” which could be misinterpreted. For example, say “an additional $50K in admin costs” instead of “a negative $50K impact” to avoid confusion.
Be Honest and Balanced: Acknowledge any downsides or limitations. If the model assumes something uncertain, mention it: “This analysis assumes no increase in hospitalization rates; if that assumption doesn’t hold, the cost savings would be somewhat reduced. However, evidence and our clinical judgment suggest this is a reasonable assumption.” By stating this, you build credibility. P&T members don’t expect perfection, but they do expect transparency.
Focus on Decision-Relevant Metrics: Tailor the results to what the committee cares about. Common metrics include total budget impact, % budget change, cost per treated patient, and perhaps impact on premiums or departmental budget if relevant. If the P&T is at a hospital, maybe frame how the savings free up budget for other pharmacy initiatives. If a health plan, frame in terms of population impact or premium.
Prepare for Q&A: Often, after your clear presentation, the committee will ask questions. Having the model handy (or backup slides) to dig into details if needed is helpful. You might include an appendix or have notes on things like “What if we don’t implement this policy? (Answer: then drug spend likely increases X% next year based on trend.)” or “How will we monitor success? (Answer: track monthly drug spend and any changes in utilization or appeals).”

By emphasizing clarity and context, you ensure the committee understands not just the numbers, but the meaning behind them. That meaning should be that this step therapy policy is a financially sound move that has been carefully evaluated. This is also an opportunity to subtly reinforce MedReb8’s role: for instance, you could mention, “We will leverage MedReb8’s data dashboards to regularly report on the policy’s impact, so the committee can see real-world results against this projection.” This highlights a value-add without a direct sales pitch.

Spreadsheet Model Structure: What to Include for DIY or Vendor Models

Many readers might want to build their own simple model or at least know what to expect if they partner with a vendor like MedReb8 for a more sophisticated analysis. A well-structured spreadsheet model for step-therapy cost impact should include the following elements:

Input Section: A clearly labeled area (or tab) for all key inputs. This includes population size, prevalence or number of eligible patients, drug unit costs (list and net), baseline utilization rates (e.g., market share of each drug without ST), assumed step therapy adherence rates, failure rates, and any cost per event (hospitalization, admin) assumptions. Each input should have a description and source if possible (e.g., “Drug A cost = $500/month [Source: 2025 formulary data]”).
Logic for Patient Flow: The model should mimic the patient journey under each scenario. For example, in the Step Therapy scenario, you might have a mini-calculation: “Out of 1,000 patients: 100 exempt → go straight to Drug B; 900 start Drug A → of those, 450 fail and move to Drug B, 450 stay on Drug A.” These flows should be computed with formulas referencing the input rates (so if you change the fail rate, the numbers recalc automatically). This can be done in a section of the calc tab or separate sections for each scenario.
Cost Calculation: For each scenario, calculate drug costs = (patients on Drug A * cost of A * duration) + (patients on Drug B * cost of B * duration), etc. Do similarly for other cost components (e.g., “hospitalizations = number of hospital events * cost per event”). It’s often helpful to break it down by per-patient cost and multiply by number of patients, so it’s clear. For instance, “Per patient cost in scenario 1 = $Y, in scenario 2 = $Z, difference = $Δ; then multiply by patient count for total budget impact.”
Summary Outputs: A section that pulls together total costs in each scenario and the differences. This is where you’d show total pharmacy cost baseline vs with ST, total medical cost baseline vs with ST, and net change. Including calculated metrics like % change and PMPM is useful here. A best practice is to highlight key outputs in bold or a different color in the sheet so they stand out.
Scenario Comparison Toggle (Optional): If you want a single interactive model, you can build a toggle or dropdown (e.g., 0 = no step therapy, 1 = step therapy) that feeds into formulas. However, many prefer side-by-side scenario columns for transparency. Either approach is fine if it’s well documented.
Sheet for Charts (Optional): If presenting to others, the spreadsheet can include a simple chart or two (linked to the summary outputs) that you can copy into slides. For instance, a line chart of cumulative cost over time if multi-year, or a column chart of cost breakdown.
Documentation: Top-notch models include a README or a notes section describing the model’s purpose, methodology, and any limitations. If you expect others to use or audit the sheet, consider writing a brief description of how to use it. In a vendor-provided model, expect a cover page or notes tab with this info.

The structure above mirrors what you’d get from a professional tool. In fact, MedReb8 often supplies clients with well-structured Excel models as part of their data module outputs, so stakeholders can adjust assumptions and see impacts. The idea is that the spreadsheet is not a black box. It’s a transparent decision support tool. Clarity and organization in the model not only aid accuracy but also make it easier to present and defend your findings (you can point to each part of the sheet if questions arise).

By building or using a spreadsheet model with this clear structure, even a complex analysis becomes understandable. A P&T member could follow the flow from inputs to outcomes, which increases their confidence in the analysis. It also makes updates straightforward (for example, if drug prices change next quarter or new data on outcomes emerges, you can plug it in without redoing the whole model).

Below is a quick checklist summarizing the steps and components of modeling a step therapy policy’s cost impact. This can serve as a handy guide as you work through your analysis or review a model from a vendor:

Define the Scope: Clearly outline the step therapy policy (which drugs/conditions, who is affected, timeline).
Gather Data: Collect patient counts, current utilization rates, drug costs (list and net), and any outcomes data for the relevant therapy area.
Set Key Assumptions: Determine failure/success rates, time on therapy, expected behavior changes (non-initiation, adherence), and any cost factors like admin or complication rates.
Build Baseline Scenario: Calculate current costs without the policy (pharmacy spend on each drug, etc., plus relevant medical costs).
Build New Scenario: Calculate costs with step therapy in place (including first-line and second-line drug costs, additional admin efforts, etc.).
Compare and Calculate Impact: Compute net savings or costs by comparing scenarios; include breakdown of pharmacy vs medical vs admin changes.
Validate with Sensitivity: Test the model with varying key assumptions (e.g., higher or lower failure rates) to ensure the outcome remains reasonable and identify which factors influence results most.
Document and Present: Prepare a summary of results, noting assumptions and methodology. Use clear visuals or tables to communicate the projected impact to the P&T committee.
Plan for Monitoring: Indicate how you will track real-world results post-implementation (utilization shifts, spend, outcomes) to verify the model’s predictions and adjust if needed, highlighting a continuous data-driven approach.

Data-Driven Decisions and the MedReb8 Advantage

In conclusion, modeling the cost impact of step-therapy policies before P&T approval is an essential exercise in responsible formulary management. By diligently gathering data and making transparent assumptions, you can forecast how a step therapy protocol will affect both the budget and patient care outcomes.

This proactive analysis empowers P&T committees to make informed, data-driven decisions rather than taking a “wait and see” gamble. The process we outlined, from data collection and assumption setting to building the model and communicating results, ensures that all stakeholders understand the rationale for the policy and have confidence in its financial prudence.

Importantly, the goal is not just to cut costs, but to do so while maintaining quality of care. A nuanced model that considers direct savings, indirect effects, and clinical outcomes will help strike that balance. It reinforces that the P&T committee’s decision is grounded in evidence and aligned with both fiscal responsibility and patient well-being.

Finally, leveraging the right tools can make this complex task much easier. MedReb8’s expertise in pharmaceutical data aggregation and formulary analytics means that much of the heavy lifting, from obtaining accurate cost data to structuring scenario models, can be streamlined.

MedReb8’s data module is designed to support exactly these kinds of analyses, providing reliable insights at your fingertips. By partnering with such experts or using robust data tools, organizations can not only build better models faster, but also continuously monitor and refine their formulary strategies over time.

In the ever-evolving landscape of healthcare costs and drug innovation, step-therapy policies will continue to be a key lever for payers. Approving them should never be a shot in the dark.

With thorough pre-implementation modeling and ongoing data-driven evaluation (and partners like MedReb8 to assist), P&T committees can confidently implement step therapy where it makes sense, achieving cost savings and preserving the quality of patient care. Data-driven decision-making is the cornerstone of effective formulary management, and it’s a philosophy that ultimately benefits patients, providers, and payers alike

References

Pharmacy Benefit Management Institute (PBMI). Trends in Drug Benefit Design Report
Centers for Medicare & Medicaid Services (CMS). National Health Expenditure Data
Express Scripts Drug Trend Report. Step Therapy Protocols and Drug Spend: Analysis and Insights
Agency for Healthcare Research and Quality (AHRQ). Evidence-based Practice Center Reports
Institute for Clinical and Economic Review (ICER). ICER’s Reference Case for Economic Evaluations
American Medical Association (AMA). Prior Authorization and Utilization Management Reform Principles
University of Kentucky College of Pharmacy, Brenda R Motheral. Pharmaceutical step-therapy interventions: a critical review of the literature. (PMID: 21348547, PMCID: PMC10437762, DOI: 10.18553/jmcp.2011.17.2.143)