Question 1

Why do so many new prescriptions drop off when a prior authorization is required?

Accepted Answer

Many new prescriptions fail when a prior authorization is required because the PA process adds delays, paperwork, and administrative complexity that patients and providers often struggle to complete quickly. Long approval times, missing documentation, payer denials, and high out-of-pocket costs frequently cause patients to abandon therapy before the first fill. Without proactive outreach, automated PA support, and real-time visibility into case status, these friction points lead to significant drop-off at the start of treatment. Healthcare providers often lack the administrative capacity to pursue complex PA requirements, and patients facing delays may simply not fill the prescription or switch to a competitor product with easier access.

Question 2

How can we systematically track step therapy fail-first requirements across hundreds of health plans?

Accepted Answer

You can track step therapy fail-first requirements across hundreds of health plans by bringing formulary and utilization management data into one standardized database. Pull policy details from payer documents and data vendors, then normalize them so every plan follows the same structure. Use coverage fields that clearly flag step therapy and prior authorization by plan, product, and line of business. This lets you map each drug to its required steps, compare rules across payers, and see access differences at national and regional levels. Then add two layers: policy-change alerts that tell you when rules change, and outcome linkage that shows how step edits affect real outcomes like time-to-therapy, abandonment, and overall access by connecting policy data to claims and patient journey data.

Question 3

How can I detect which payers increased access restrictions over the last quarter?

Accepted Answer

You can detect where access restrictions increased in the last quarter by comparing payer-level utilization management metrics against the previous quarter, including prior authorization rates, step therapy enforcement, rejection patterns, and formulary status changes. Claims data, formulary intelligence sources, and hub activity can reveal spikes in PA volume, growing denial rates, or new fail-first requirements. By automatically highlighting plans where these indicators moved negatively, analytics makes it easy to pinpoint which payers tightened access and quantify the impact on prescriptions and patient progression. This quarterly comparison approach reveals both the magnitude of change and which specific payer policy shifts are responsible, allowing teams to prioritize response efforts on the payers creating the biggest new barriers.

Question 4

How can we determine which prior authorization criteria are responsible for the highest rejection rates?

Accepted Answer

You can identify which prior authorization criteria cause the most rejections by analyzing denial codes, documentation requirements, and clinical criteria within payer PA responses to see which rules are most frequently unmet. By linking rejection reasons to specific criteria such as missing step therapy evidence, unsupported diagnosis codes, insufficient lab results, or incomplete paperwork, you can pinpoint the drivers of failed PAs. Layering this analysis with claims and hub data highlights which criteria create the biggest barriers to patient access, helping teams target education efforts, documentation improvements, and payer-specific strategies. This analysis often reveals that a small number of criteria account for the majority of rejections, allowing focused interventions that can dramatically improve approval rates.

Question 5

How can I measure the impact of prior authorization and step therapy requirements on our brand's prescription volume?

Accepted Answer

You can quantify the impact of prior authorizations and step edits by comparing prescription volume for patients exposed to these requirements against those who are not, using claims and hub data to track drop-off at each step of the access process. By measuring PA submission rates, approval times, denial patterns, and step therapy failures and linking them to TRx, NBRx, and first-fill outcomes, you can calculate how many prescriptions were delayed or lost due to utilization management barriers. Utilization management refers to access rules that can delay or block a fill even when a prescription is written. This analysis highlights the true volume impact of access restrictions and identifies where targeted interventions such as improved PA support, provider education, or payer negotiations can recover starts and reduce abandonment.

Question 6

How can we measure time-to-therapy across payers and identify where delays occur?

Accepted Answer

You can measure how long it takes for patients to start therapy under different payers by tracking the timestamps between key steps in the access journey and comparing these intervals across plans: prescription, benefits verification, prior authorization, approval, and first fill. Claims, hub, and specialty pharmacy data reveal where delays occur, such as slow PA processing, high denial rates requiring resubmission, or long patient outreach cycles to complete documentation. By benchmarking each payer's average time-to-therapy and highlighting outliers, analytics makes it easy to identify which plans create the biggest access bottlenecks. This analysis often shows that seemingly similar payers can have dramatically different processing speeds, with some approving PAs in days while others take weeks, directly impacting patient abandonment and brand performance.

Question 7

What analytics platform can track real-time prior authorization approval rates by payer?

Accepted Answer

Prior authorization approval rates can range from 30% to 80% depending on the payer, but many manufacturers only see aggregate denial data monthly or quarterly, making it impossible to respond quickly to deteriorating access. Real-time tracking requires pulling together data from hub services, specialty pharmacies, and claims reversals into one view with daily or weekly refresh cycles. Tellius can connect these different data sources through its semantic layer, giving daily visibility into PA approval rates by payer and flagging when approval performance starts to deteriorate so teams can respond quickly. This real-time capability is critical because PA approval problems compound rapidly, and waiting for quarterly claims means intervening after significant volume has already been lost to abandonment.

Question 8

Can AI automatically read payer policy documents and tell us what changed in PA criteria or step edits?

Accepted Answer

Yes, if you treat payer policy PDFs as a real data source. In access, rules often change in the documents first while claims and dashboards usually show it later. The process involves collecting the latest policy docs including formularies, medical policies, utilization management updates, and bulletins from approved sources. Then turn PDFs into usable text using document parsing, and extract structured policy facts with natural language processing and large language models to pull fields like PA required status, step therapy sequence, clinical criteria including diagnosis and labs, and effective dates. Reconcile across sources by cross-checking with vendor feeds, hub and specialty pharmacy signals like denials and time-to-therapy, and internal intelligence. Agentic monitoring can detect and summarize changes automatically by comparing new documents to old versions and flagging meaningful changes, providing a change log, a rules table that can be joined to outcomes data, and an audit trail showing which document text supported the change.

Question 9

Why does it take so long to measure the market access impact of a new competitor launch?

Accepted Answer

It often takes 2-3 months to assess the market access impact of a new competitor because the data that reflects real payer and patient behavior, such as adjudicated claims, rejection patterns, formulary updates, copay changes, and PA trends, lags behind the launch date. Payers typically take weeks to update policies and implement new coverage rules, pharmacies adopt changes at different speeds across their networks, and early claims don't show mature utilization or switching behavior until sufficient volume accumulates. Until enough data accumulates across these sources showing actual prescription patterns and access friction, analytics teams cannot reliably evaluate how a competitor's entry affects access, pull-through, and patient conversion. This delay means brands often miss the critical early window to respond to competitive access threats.

Question 10

Why do similar formulary positions yield different results across payers?

Accepted Answer

Similar formulary positions can yield different results across payers because real-world access depends on more than the listed coverage tier. Performance is shaped by variations in utilization management rules, PA approval rates, step therapy enforcement, pharmacy networks, patient cost-sharing, and operational efficiency of each plan. Even when two payers label coverage the same way, differences in adjudication behavior, benefit design, and member demographics can produce very different speed-to-therapy, abandonment, and overall access outcomes. For example, two payers might both list a drug as preferred with PA required, but one might approve 70% of PAs in 3 days while the other approves only 40% after 2 weeks, leading to dramatically different prescription volumes despite identical formulary status on paper.

Question 11

How can I quickly identify which payers contributed to this month's TRx or NBRx decline?

Accepted Answer

You can find the payers driving a TRx or NBRx decline by breaking the drop into payer-level contributions and comparing this month to prior periods. First, attribute the decline by payer by splitting TRx and NBRx by payer and plan, then rank payers by how much they contributed to the total drop. Second, diagnose what changed inside those payers by checking shifts in new starts, approvals versus rejections, and utilization management barriers like prior authorization and step therapy for the top negative payers. Third, layer in the likely reasons by overlaying payer contribution with signals like formulary or tier changes, claim rejection spikes, and higher patient cost-share from copay increases. This three-step approach gives you both who caused the decline and why, so you can target follow-up actions with the specific payers that matter most rather than implementing broad programs that miss the actual problem sources.

Question 12

Can we identify which payers drive actual scripts versus just counting covered lives?

Accepted Answer

Yes, you can identify which payers drive real prescriptions by looking at what happens inside each plan, not just how many lives are covered. Measure by payer and plan the TRx and NBRx volume showing actual scripts and new starts, approval rates showing how often patients get through access barriers, and patient progression showing how many move from script to approval to first fill to refills. Claims and hub data show which payers convert eligible patients into starts, how often PA or step therapy blocks patients, and where adherence and persistence stay strong or drop. Compare real prescription flow to the plan's covered lives: some payers have fewer lives but high pull-through and truly drive volume, while others look good on paper with lots of covered lives but produce low real-world scripts because restrictions or cost-share block uptake.

Question 13

How can I compare my formulary position vs. competitors across major payers?

Accepted Answer

For each big payer, you need to know three basics: which tier your drug is on, what extra rules apply like prior auth or step therapy, and what patients are likely to pay out of pocket. These details change often, and payer documents are usually hard to read and don't always show every restriction clearly. In real life, teams pull information from three places: public formulary files, what account and field teams hear in payer and HCP meetings, and win-loss notes from contracting. The real goal is to see your relative position: being on tier 2 can be an advantage if competitors are on tier 3, but a weakness if they are on tier 1. Tellius can bring all of this data into one view and build simple scorecards by payer and region showing comparative positioning that reveals where you have access advantages to exploit and where you face competitive disadvantages requiring strategic response.

Question 14

How do we measure field force impact on payer pull-through rates?

Accepted Answer

Winning better access is only half the job; you also need the field team to turn that access into prescriptions. The hard part is separating growth driven by access changes like better formulary position from growth driven by field promotion and execution. One useful approach is difference-in-differences analysis: compare prescription trends before versus after an access change across territories with different levels of field activity including call frequency, reach, and programs. If territories with high field intensity grow much faster than similar territories with low field intensity after the same access win, that extra lift is likely pull-through driven by the field. Tellius Kaiya's causal analysis can formalize this separation, helping you separate access-driven growth from promotion-driven growth and understand the mix, which guides how you allocate budget and headcount between market access investments and field force resources.

Question 15

How can we deliver market access insights to field and brand teams so they can coordinate effective pull-through?

Accepted Answer

Market access insights can be shared with field and brand teams through integrated dashboards, CRM alerts, and simplified payer profiles that translate complex access data into clear, actionable pull-through guidance. By embedding insights about formulary changes, PA trends, rejection hotspots, and payer opportunities directly into field workflows, teams can tailor their messaging and resources for each account based on current access conditions rather than outdated assumptions. When insights are pushed in real time and aligned across brand, access, and field stakeholders, organizations can execute coordinated pull-through strategies that improve prescribing and patient access outcomes. This integration ensures that favorable formulary changes and resolved access barriers translate into actual prescription growth rather than remaining unrealized opportunities.

Question 16

Can we track competitor access positions without expensive third-party data?

Accepted Answer

Yes, you don't have to spend $500K+ per year on competitive formulary data if you use the information you already have access to. A lot of competitor access intelligence is available from public payer documents including online formulary listings, medical policies, and PDFs, and field intelligence from reps hearing about competitor coverage in calls. A cost-effective approach is to use web scraping to pull formulary data from payer sites, capture field feedback in CRM or simple forms, and apply natural language processing to parse and structure unstructured text. Tellius can aggregate competitive access intelligence from multiple unstructured sources such as field emails in Google Drive, Gong call transcripts where customers mention competitor coverage, and payer formulary PDFs alongside your structured access data. Using NLP, it can extract and organize restrictions, tiers, and policy language into a unified view so you can track competitor positioning without relying entirely on expensive syndicated subscriptions.

Question 17

When PA approval rates suddenly drop, can AI automatically investigate which payers, criteria, and HCP segments drove it?

Accepted Answer

When PA approval drops fast, dashboards usually show the decline but not the cause. Leaders immediately ask which payers drove it, what changed, which criteria are failing, and which HCP segments are impacted. Traditional investigation can take days because analysts have to pull hub data, denial codes, and formulary updates, then slice everything by payer, geography, and specialty. Agentic investigation workflows automate this by monitoring access KPIs including PA approval, denials, time-to-therapy, and abandonment. When a meaningful change happens, the system automatically launches an investigation that checks drivers in parallel: payer policy changes like new criteria or tighter rules, denial reason shifts showing more missing documentation or step therapy issues, segment concentration revealing whether the problem is focused in certain specialties or regions, and external context like competitive events. It quantifies contribution by estimating which drivers explain most of the drop, and recommends targeted actions like updating PA templates or focusing education on practices with high incomplete submissions.

Question 18

What analytics can help forecast the portfolio impact of IRA Medicare price negotiations?

Accepted Answer

You can forecast the portfolio impact of IRA Medicare price negotiations using scenario modeling on top of claims-based forecasting and patient-flow analytics. The goal is to estimate how negotiated Medicare prices could change revenue, volume, and access across products. These models usually account for eligibility timing showing when a product becomes negotiation-eligible, historical Medicare mix and utilization showing how dependent the brand is on Medicare volume, competitive dynamics showing what competitors may do in response, therapy switching risk showing patients moving to alternatives, and patient cost and behavior effects showing out-of-pocket changes that affect adherence and persistence. By running multiple price-and-volume scenarios, teams can quantify financial exposure across the portfolio, identify which assets are most vulnerable, and prioritize strategic responses including contracting, access strategy, and portfolio timing at the portfolio level to minimize overall impact.

Question 19

How can analytics help us anticipate formulary changes before they affect market share?

Accepted Answer

You can anticipate formulary changes before they hit market share by using predictive analytics to spot early signals in payer behavior. The model looks at historical payer behavior showing how each plan has changed policies in the past, competitive access trends showing where competitors are gaining preferred status, contract events including renewals and rebate changes, and utilization management shifts like rising PA rates and new step edits. Predictive models can then flag payers that are likely to tighten restrictions, add step therapy, or change preferred agents after competitor pricing moves or guideline updates. By combining formulary intelligence with claims data and other market signals, teams can often see risk weeks to months earlier, giving enough time to prepare proactive contracting and pull-through plans instead of reacting after share drops when recovery is much harder and more expensive.

Question 20

How can AI predict the impact of upcoming payer or formulary changes on our brand's performance?

Accepted Answer

AI can forecast the impact of upcoming access changes by learning from past payer policy updates and how they affected performance. It looks at signals like historical policy changes and timing, denial and rejection patterns, competitive moves, and utilization management trends including PA, step therapy, and criteria tightening. Then it models how similar changes affected TRx, NBRx, access, and share in the past and simulates likely payer responses based on these historical patterns. This gives teams an early view of risk so they can plan contracting, messaging, and pull-through before performance drops, rather than reacting after volume has already been lost. The predictive capability is particularly valuable for new launches and competitive entry scenarios where historical analogies from similar situations can guide strategic response before market dynamics fully play out.

Question 21

How can AI agents continuously monitor payer behavior and flag early access risks?

Accepted Answer

AI agents can monitor payer behavior by continuously scanning formulary updates and policy language changes, PA trends including approval and denial rates, rejection patterns, and claims-based utilization shifts. By comparing current activity to historical norms and expected patterns, the agent can flag early warning signs such as denial spikes, new step edits, shifting preferred products, or sudden policy changes before they show up in quarterly reports. This gives market access and brand teams visibility weeks before the impact shows up in TRx and NBRx, allowing proactive response while problems are still manageable. The continuous monitoring aspect is critical because payer behavior can shift rapidly, and waiting for scheduled monthly or quarterly reviews means missing the window for effective intervention when recovery is still possible without major strategic shifts or investments.

What changed	Invoice	Contract	Governing	Status
Unit of measure Helidrive Systems · line 3	$126.00 per CASE	$12.60 per EACH	1 CASE = 10 EACH material master	Released no variance
Contract master Norwell Industrial · line 1	$14.20 per EACH	$13.60 stale	Amendment A-491 effective 1 Mar	Approve master update prepared
Above the governing term Merrow Components · line 7	$41,200 over term	§4.1 band 3	Tiered price effective 1 Jan	Held claim packet written
No amendment on file Castara Logistics · line 4	$14.72 per KG	$13.75 base, all year	No index clause 4th line this quarter	Dispute debit note transmitted
Units not comparable Arden Polymers · line 9	$9.85 per EACH	$480.00 per PALLET	No conversion exists 47 lines blocked	Stopped it will not guess

Cause	Lines	Held	Ball with	Prepared
Contract master stale	106	$1.24M	your buyer	master update staged
Supplier overcharge	38	$612K	the supplier	debit note transmitted
Unauthorised PO change	19	$148K	the buyer’s manager	PO correction staged
Index miscalculated	11	$67K	the supplier	partial dispute sent
No governing contract	9	$41K	sourcing	routed as maverick spend
Evidence insufficient	4	$38K	you	blocked on data, not on judgment

Everything you negotiated,
actually realised.

The contract changed. The price master did not. Every line matched, and the price was still wrong.

The invoice passed every control and was still overbilled.

The invoice is right. Your master is wrong.

Five hundred cases at $126 is not a variance.

What were we owed and never asked for?

A recovery firm bills a share of what you already lost.

Two years

Tellius decides which record is wrong.
Your team approves what moves.

Finds the term that actually governs the line.

Clears the exceptions that were never real.

Names which record is wrong. The invoice, the contract, or neither.

Hands it to the person who can fix it.

Two jobs. One before you pay, one after.

2,847 exception lines.
Only 4 needed a person.

Reads every source you already run.

Your rules decide what matters.

Twelve things can be wrong.

How much of last year's queue

Everything you negotiated,actually realised.