A huge share of prescriptions never turn into actual medication for the patient, costing the industry an estimated $637 billion a year. Drop-off happens at several points:

• About 30% of prescriptions never even reach the pharmacy
• Another 20% are abandoned when patients see a high copay
• More are lost while waiting for prior authorization approval.

‍

Analytics that track where patients drop off (doctor’s office, pharmacy counter, or PA queue) let you target fixes like copay support, faster authorization help, or clearer coverage communication at the exact failure point.

You can track patient persistence and identify early warning signs of discontinuation by monitoring refill timing, days on therapy, and adherence metrics such as percentage of days covered and amount of medication supplied over a time period (PDC and MPR). This helps you spot patterns of declining engagement.

‍

Common early indicators include:

• delayed or missed refills
• shorter days of supply
• switching pharmacies
• increased prior authorization activity
• rising out-of-pocket costs

‍

By comparing each patient’s behavior to expected refill schedules and typical adherence patterns, you can detect risk signals early. You can then intervene with support programs that help keep patients on therapy longer.

You can identify where patients drop off between prescription and first fill by tracking each step of the medication start process and measuring where progress stops. You do this by analyzing data from:

• prescribing
• benefits verification
• prior authorization
• pharmacy processing
• claims activity

‍

This shows which stage has the highest abandonment, such as:

• delays in approval
• high out-of-pocket costs
• lack of patient follow-up

‍

This step-by-step visibility makes it easier to pinpoint the exact barrier preventing patients from moving from prescription to first fill.

AI analytics identifies root causes by automatically scanning patient journey data to detect unusual patterns. It also pinpoints the factors most strongly associated with the drop-off. The system compares recent behavior to historical baselines. It analyzes variables such as:

• payer changes
• prior authorization delays
• pharmacy rejections
• rising copays
• inventory gaps
• patient disengagement

‍

AI highlights which factors changed at the same time as the abandonment spike. It also ranks their impact. This provides fast, data-driven explanations that help teams understand what happened and respond quickly.

AI agents can proactively alert you by continuously monitoring patient journey metrics. They automatically detect unusual changes within specific regions, payer groups, or physician segments.

‍

When the system identifies a deviation from normal patterns (such as a sudden rise in abandonment for a particular payer or geographic area), it triggers an alert that:

• highlights the affected segment
• explains the likely drivers behind the change

‍

This real-time, automated monitoring helps teams respond quickly to emerging access issues, payer behavior shifts, or provider-level barriers.

When abandonment jumps suddenly, teams usually lose days doing manual triage. A hub leader sees abandonment rise from 18% to 53% in the Southeast and asks what changed. The team then pulls specialty pharmacy rejection data, checks payer PA approvals, reviews benefits verification logs, looks at copay support usage, breaks results by prescriber, and searches for recent formulary updates. This work can take 2–3 days, and patients keep dropping off during the delay.

‍

Agentic patient journey analytics automates this investigation. When abandonment rises beyond expected levels, the system triggers an investigation automatically.

‍

• Automatic anomaly detection: The system monitors abandonment by region, payer, specialty pharmacy, prescriber group, and patient segment. It accounts for normal weekly variation and seasonality, and then flags true “breaks” in the pattern.
  
• Parallel root-cause checks: AI agents investigate common drivers at the same time:
  
  • Payer policy, access shifts and whether the effective dates match the spike.
  • Specialty pharmacy issues using rejection codes and dispense timelines.
  • Hub workflow bottlenecks
  • Copay assistance gaps
  • Prescriber concentration
    
• Quantified attribution with confidence: The output is not a list of guesses. It estimates how much each factor contributed and includes confidence based on timing and strength of evidence.
  
• Impact and action plan: It shows which payers, pharmacies, or practices drove the spike, estimates the NBRx risk, and recommends actions in priority order (for example, “PA documentation support for these practices” or “proactive BV for this payer segment”).
  
• Learning over time: The system remembers which patterns led to which causes and starts with the most likely paths first, so investigations get faster.

‍

Teams can move from “we noticed a spike” to “here is what changed, who is affected, and what to do next” in minutes instead of days, which reduces lost starts during access disruptions.

Yes. This is the practical way to monitor thousands of payer–region–pharmacy–therapy combinations without missing issues. Leaders cannot realistically scan hundreds of dashboards and catch early problems. Many issues are discovered only in weekly or monthly reviews, after patients have already been lost.

‍

Agentic monitoring continuously watches abandonment, time-to-therapy, adherence signals, PA delays, specialty pharmacy rejection patterns, and hub workflow bottlenecks across payers, regions, pharmacies, indications, and provider segments.

‍

• The system uses anomaly detection to separate real signals from normal noise by learning what “normal” looks like for each cohort, including seasonality and expected variation.
  
• When something meaningful changes, agents do more than alert. They automatically run a root-cause investigation, such as checking payer policy changes, PA approval shifts, pharmacy operational issues, copay friction, and provider-level clustering.
  
• Alerts are then prioritized, so leaders see only the few issues that matter most, ranked by business impact, confidence, urgency, and actionability.

‍

Each alert should include a plain-English summary of what changed, where it happened, what likely caused it, how big the risk is, and what actions are recommended. Over time, the system should learn which patterns usually lead to real losses and which interventions tend to work, so alerts and recommendations improve.

Early warning signs of drop-off include:

• Taking a long time to fill the first prescription
• Filling only part of what was prescribed
• Increasing gaps between refills

‍

Machine learning models can use patient characteristics, drug type, and these early fill patterns to flag patients who are likely to stop treatment. These models can often do this with around 75% accuracy.

This gives you a 90-day window to intervene before patients disappear. Typical interventions include: nurse outreach, payment help, or adherence programs.

Yes. Early signs in the first 30–60 days often indicate whether a patient will reach treatment goals. These early markers can include side effects, early lab results, and how regularly a patient takes the drug.

‍

Machine learning models can learn patterns from past patients and use them to flag new patients who look unlikely to do well if nothing changes. This allows care teams to step in early with dose changes, extra support, and therapy switches

‍

The key advantage is timing. You act early instead of waiting months to confirm that the regimen is not working.

Where a patient lives (their ZIP code) often predicts whether they will stay on medication better than many clinical factors. Social factors like income, education level, access to transportation can explain 30–40% of the differences in adherence. Yet many programs focus mainly on disease education or reminders.

‍

Analytics can help by combining:

• claims data
• outside data such as census information, maps of areas with few pharmacies, and local socioeconomic indicators.

‍

This can flag patients who are more likely to struggle because of their life situation. Then you can add the right kind of support, such as transportation help, financial support, or local resources, instead of relying only on clinical coaching.

Patient journey analytics integrates these sources by consolidating them into a unified data model that tracks each step of a patient’s access and adherence journey.

‍

Each source contributes different signals:

• Claims data shows fills and discontinuation.
• Specialty pharmacy data adds dispense details and rejection insights.
• Hub services data provides visibility into benefits verification and prior authorization steps.
• CRM systems contribute patient outreach and support activity.

‍

By linking these datasets at the patient or case level, platforms create a more complete view. This helps teams understand drivers of access, speed-to-therapy, and persistence, using a connected record instead of separate spreadsheets and dashboards.

This is hard because when patients switch specialty pharmacies due to insurance changes or service issues, their data becomes scattered across systems that often do not share a common patient ID. On top of that, each pharmacy may define and calculate adherence differently, which makes comparisons inconsistent.

‍

Tellius can address this using identity resolution, which matches patients across systems based on patterns in their data. This helps stitch together one continuous journey so you can see adherence and access even when patients move between pharmacies, without relying on manual stitching.

Most pharma companies do not have direct access to EMR (Electronic Medical Record - a full clinical picture) data, so they rely on claims data. Claims show billing and fills, but it does not capture clinical details like lab results or symptom changes.

‍

Some real-world evidence platforms solve this by aggregating de-identified (removing or masking information that can identify a person) EMR data and selling access, but those datasets can be expensive.

‍

Tellius can sit on top of the real-world data you already have such as the following, and analyze them together.

• claims,
• limited EMR feeds (if available),
• patient services data

‍

This gives you the richest view possible from your available data while staying within privacy and budget limits.

Working with patient-level data requires HIPAA compliance, which includes:

• De-identifying (removing or masking information that can identify a person like name, address etc.) data where possible,
• Using the minimum necessary information,
• Having proper Business Associate Agreements (BAAs) in place.

‍

Many generic analytics tools were not designed with this in mind.

‍

Life sciences-focused platforms are built to support HIPAA-compliant setups. For example, Tellius has SOC 2 Type II certification and can be deployed with:

• strong access controls and role-based permissions,
• encryption of data at rest and in transit,
• detailed audit logs showing who accessed what and when.

‍

The technology can support HIPAA, but you still need appropriate governance and operational controls on top of the platform.

Deployment timelines typically range from a few weeks to a few months, depending on the complexity of your data sources and integration needs.

‍

Most platforms can connect to claims, specialty pharmacy, hub, and CRM systems quickly. However, timelines vary based on:

• data availability,
• data quality,
• amount of required customization.
  
  

Many modern AI-driven solutions offer prebuilt connectors and templates that accelerate onboarding. This helps teams start exploring patient insights and abandonment patterns faster than traditional implementations.

Patient journey analytics should answer the questions reps care about in plain language, such as:

• “Where are patients in my territory dropping off?”
• “Which doctors’ patients wait the longest to start therapy?”

‍

The most effective approach is to push these insights into tools field teams already use, such as call prep context and alerts. By this way, reps are not running reports. They get clear signals on who to see, what to discuss, and which barriers to address in each account this week.

Building in-house usually means:

• buying large claims datasets,
• setting up systems to match patient records across sources,
• and putting strong privacy controls in place.

‍

This can cost millions up front, plus ongoing support.

‍

Partnering with an external vendor is faster, but it gives you less control and can expose some of your strategy and data to a third party.

‍

Tellius offers a middle option, where you run patient journey analytics on your own data, and plug in outside data only where needed, so you keep more control while still moving quickly.

Most patient journey programs still depend on claims that arrive 30–90 days late. If a patient stops filling in June, you might not see it clearly until August, after the patient has been off therapy for weeks. This delay hurts every workflow: abandonment reporting, adherence outreach, hub staffing, and program ROI measurement.

‍

Why the lag happens

• Claims adjudication takes time. Reversals, resubmissions, and coordination of benefits mean “final” claims often settle weeks later.
  
• Vendor and delivery cycles are slow. Aggregators standardize and project data on weekly or monthly schedules.
  
• Integration is batch-based. Files move through exports, FTP, ETL, and validation steps that add more days.
  
  

How modern platforms fix it without pretending claims will be instant

• A multi-speed data approach: Use faster operational signals for decisions, and keep adjudicated claims for final measurement.
  
• Proxy signals for early risk: The system watches leading indicators that appear weeks before claims confirm a stop, such as PA abandonment, repeated refill reminders with no response, copay card rejections, BV stalls, or repeated failed outreach.
  
• Streaming and API integrations: Instead of monthly file drops, the platform connects to hub and SP systems so “case moved to closed-no-response” is visible quickly.
  
• Data blending: The platform combines claims, SP, hub, and assistance data to create a more current journey view than claims alone.

‍

Traditional setups see discontinuation 60-90 days late and intervene when success rates are low. Modern setups see risk signals in 7-14 days, which gives teams a much better chance to re-engage patients while they still remember the therapy and the process.

Specialty pharmacies can look similar on paper but behave very differently in the real world. Some fill prescriptions quickly, support patients well, and drive strong adherence. Others are slower or offer weaker support.

‍

You can compare pharmacies using patient-journey and support signals such as:

• Time from prescription to first fill
• Refill patterns
• Use of support services, such as nursing or reminders

‍

These comparisons show:

• which pharmacy partners provide the best patient experience
• where you should steer more volume or push for performance-based contracts

Many patients drop out during prior authorization because the process takes too long or paperwork keeps bouncing back. Analytics helps by showing where the process breaks down most often, segmented by:

• payer,
• drug, or
• patient type.

‍

Tellius can map each step of the prior auth journey (from request to approval or denial). and spot where cases pile up or stall. This helps teams redesign workflows and hub support to move cases faster and cut down the 30% drop-off.

To measure real impact, you need to link program participation and longitudinal adherence behavior using patient-level data integration across program platforms and claims databases.

Analytics with proper attribution windows matters because education effects can fade over 6–12 months. With the right setup, you can quantify education ROI instead of guessing.

‍

Alongside prescription data, Tellius can analyze:

• the educational content delivered (PDFs, videos stored in Google Drive)
• nurse educator call recordings
• patient feedback forms

‍

This helps identify which specific messages and delivery methods are linked to sustained adherence (i.e., the patient keeps taking the medication consistently over time, not just for the first few weeks) improvement. It moves measurement beyond “who participated” to content effectiveness.

Patient support programs (nurse calls, injection training, reminder texts) often sit in a separate system from prescription data. As a result, you see activity (calls made, emails sent), but not whether patients actually stayed on therapy. This makes ROI hard to prove.

‍

The fix is to connect program enrollment and interaction data to long-term prescription data, so you can see which patients stayed on treatment and for how long.

T

ogether with claims data, Tellius can pull in:

• nurse call notes from Google Drive
• recorded support calls
• enrollment spreadsheets

‍

Then it can show which specific touchpoints and education content are linked to better adherence, not just which programs generated the most activity.

Different teams measure adherence differently using metrics like:

• PDC (Proportion of Days Covered) - percent of days a patient is covered by medication in a period
• MPR (Medication Possession Ratio) - total days’ supply dispensed divided by days in the period, based on fills
• persistence (how long a patient stays on therapy)
  
  

Often, each team uses its own formulas. When specialty pharmacies, payers, and internal teams all define adherence differently, it becomes almost impossible to compare results or have a clear discussion.

‍

What you need is:

• one standard way to calculate each metric
• one “source of truth” that everyone uses
• controlled flexibility for special cases (for example, oncology vs chronic primary care drugs).

‍

A single source of truth for adherence math brings clinical, access, and commercial teams onto the same page.

Patient services teams constantly face triage. You might identify 5,000 patients at risk, but only have capacity to proactively contact 800 per month. Simple rules like “call the highest risk first” waste effort because some patients are hard to reach or have barriers the hub cannot solve. AI optimization makes the tradeoff explicit and improves outcomes with the same capacity.

‍

• Impact scoring, not just risk scoring: The system estimates expected benefit for each patient by combining:
  
  • likelihood of discontinuation,
  • likelihood the intervention will work (based on similar past cases),
  • barrier type (PA vs copay vs operational delay),
  • effort required (time and complexity),
  • expected value of keeping the patient on therapy (duration and margin assumptions).
    
    
• Constraint-aware allocation: It optimizes within real limits like call capacity, nurse specialization, and the need to protect reactive support levels.
  
  
• Continuous re-prioritization: The list updates when new signals arrive. A patient can jump up the list when a PA stalls, a copay card gets rejected, or a refill date passes.
  
  
• Best intervention recommendation: It also suggests the best route, such as contacting the prescriber’s office for missing PA documentation instead of calling the patient when the patient cannot solve the issue.
  
  
• Scenario planning: Leaders can test “what if we add 2 FTE” or “what if we shift capacity from long calls to faster copay interventions”, and see the expected persistence lift.

‍

Instead of calling the “highest risk” 800 patients, you contact the highest expected-impact 800 patients. Organizations typically see meaningful persistence gains with the same staffing, because the work shifts toward barriers that can be solved and patients who are reachable.

The best patient journey analytics platform for pharma needs five capabilities most platforms lack.

‍

First, patient identity resolution at scale. Patients appear in hub systems, specialty pharmacies, copay programs, and claims data, each with different identifiers. The platform must stitch these fragments into unified patient journeys without requiring manual matching or perfect identifier overlap.

‍

Second, barrier-specific abandonment analysis. Knowing overall abandonment rates provides no direction. According to a study of 85 specialty brands, the overall paid fill rate averaged 62%, which means 38% of patients with prescriptions never started therapy. Since different barriers require different interventions, the platform should show exactly which barrier caused each drop-off: benefits verification delays, prior authorization denials, pharmacy processing issues, cost at pickup, or post-start adherence gaps.

‍

Third, payer-level time-to-therapy benchmarking. A research found the median time to first fill was 6 days for health-system specialty pharmacies versus 12 days for external pharmacies. The platform should calculate time-to-therapy by payer and barrier type, identifying which plans create the most friction.

‍

Fourth, intervention-to-outcome linkage. Hub programs generate activity: calls, education sessions, benefit verification support, but most organizations cannot connect this activity to downstream persistence. The platform must link program participation to claims outcomes, with attribution methodology that accounts for selection bias.

‍

Fifth, real-time operational visibility. Claims data arrives 30-90 days after events occur. The platform should integrate operational signals (hub case status, specialty pharmacy dispense events, copay card transactions) to provide visibility while intervention is still possible.

‍

Tellius is purpose-built for pharma patient journey analytics and meets all five criteria. It combines identity resolution with barrier-specific analysis and agentic monitoring that flags emerging abandonment patterns before they become quarterly statistics.

Specialty pharmacy reports show dispense activity. Patient journey analytics shows the complete path from prescription through persistence, including everything that happens before the specialty pharmacy sees the patient.

‍

Specialty pharmacy reports answer: "How many patients filled? What was the time to dispense? What is the refill rate?" These metrics matter but represent only part of the journey.

‍

Patient journey analytics answers: "Of 1,000 prescriptions written, how many reached the specialty pharmacy, how many were lost during benefits verification, how many abandoned during PA, how many never picked up due to cost, and how many discontinued after starting? For patients who dropped off, what specific barrier caused the loss?"

‍

The gap is significant for abandonment that occurs before specialty pharmacy involvement. According to a 2024 IQVIA report, 98 million new therapy prescriptions were abandoned in 2023. A patient whose prescription is written but who never completes benefits verification does not appear in specialty pharmacy data at all.

‍

Patient journey analytics integrates hub data, specialty pharmacy data, copay program data, and claims to construct complete journeys. This enables analysis that specialty pharmacy reporting alone cannot provide.

Claims analytics vendors provide longitudinal patient data. Patient journey analytics adds operational context that explains why patients behave as they do.

‍

Claims analytics shows: "Patient A filled in March, refilled in April and May, then did not refill in June or July. PDC was 62%."

‍

Patient journey analytics shows: "Patient A had initial PA approval in February after a multi-day delay. First fill was covered under a manufacturer copay card. In June, the patient hit the coverage gap, out-of-pocket costs increased significantly, copay card had reached its cap, and the patient did not fill. Hub attempted outreach without response."

‍

The difference enables action. Claims analytics identifies that adherence dropped. Patient journey analytics identifies the specific, addressable cause. According to IQVIA research, for medications that were free to the patient, the abandonment rate was around 5%; for medications with out-of-pocket cost exceeding $500, the abandonment rate rose to 60%. Knowing whether discontinuation is cost-driven versus clinically-driven determines the intervention.

‍

Claims analytics is essential for outcome measurement. Patient journey analytics adds the operational layer that explains outcomes and identifies intervention opportunities. Most organizations need both.

When evaluating patient journey vendors, focus on the capabilities that differentiate platforms such as identity resolution quality, barrier specificity, and intervention attribution.

‍

Identity resolution: What methodology do you use to match patients across hub, specialty pharmacy, copay program, and claims data? What match rates do customers typically achieve? How do you handle patients who appear in some systems but not others?

‍

Abandonment granularity: Can you show drop-off rates by specific journey stage? According to the 2020 Medication Access Report, 7% of all prescription claims get rejected due to prior authorization requirements, and 37% of those end up abandoned. Can your platform break down abandonment drivers within each stage? For example, within PA, documentation issues versus clinical criteria versus processing delays?

‍

Time-to-therapy precision: Research from Mayo Clinic documented median time to first fill of 6 days at health-system specialty pharmacies versus 12 days at external pharmacies. Can you calculate days from prescription to first fill by payer, barrier type, and patient segment? Can you track improvement over time as you target specific bottlenecks?

‍

Intervention attribution: How do you connect hub program activity to downstream persistence outcomes? What methodology do you use to account for selection bias? Can you quantify the persistence lift attributable to specific intervention types?

‍

Data freshness: How current is your patient journey view? Research from Surescripts indicates 62% of prescribers and 65% of specialty pharmacists say it takes three to four weeks to start a patient on new specialty therapy. Can you see hub case status and specialty pharmacy signals in near-real-time, or only when claims arrive months later?

Deployment follows four phases, with initial abandonment visibility typically available within two months.

‍

Phase one: data integration and identity resolution (weeks 1-6). Connect hub services, specialty pharmacy feeds, copay program data, and claims sources. Configure identity resolution rules and validate match rates. Identify gaps in source coverage and determine workarounds.

‍

Phase two: journey mapping and barrier analysis (weeks 5-9). Define journey stages and transition logic specific to your therapy and channel. Configure time-to-therapy calculations. Build barrier-specific abandonment analysis by payer and patient segment.

‍

Phase three: monitoring and alerting (weeks 8-12). Configure alerts for emerging abandonment patterns. Payers where drop-off is increasing, barriers that are spiking, segments showing unusual behavior. Establish thresholds that balance signal detection with alert fatigue.

‍

Phase four: intervention attribution (weeks 10-15). Connect hub program activity to patient outcomes. Build attribution models that account for selection effects. Validate findings against operational knowledge and refine methodology.

‍

Initial value of understanding where patients drop off and why, with payer-level specificity typically arrives within two months. Full deployment with intervention attribution takes three to four months.

ROI materializes across four categories: recovered patients, improved persistence, hub efficiency, and program optimization.

‍

Abandonment reduction. According to a study, the overall paid fill rate across specialty brands averaged 62%. For non-life-threatening therapies, 20% of brands had conversion rates under 40%. Even modest improvement in conversion represents meaningful patient volume. Research indicates that an integrated specialty pharmacy model reduced time to medication approval from 67 days to 15 days and time to medication initiation from 82 days to 26 days. This demonstrates that operational improvements can meaningfully impact patient access.

‍

Time-to-therapy compression. Research shows patients who wait longer to start are more likely to never fill and show lower early adherence. A pharma journal documented that average time to treatment initiation was 6 days shorter for patients using health-system specialty pharmacies compared to external transfers. Reducing delays improves start rates.

‍

Hub resource optimization. Analytics that identify which at-risk patients to prioritize (based on intervention success likelihood, not just risk level) improve outcomes with existing hub capacity. Better targeting means more effective use of existing resources.

‍

Program ROI demonstration. Connecting hub program activity to persistence outcomes enables evidence-based program decisions. Organizations can identify which program components generate meaningful persistence impact versus those that can be redirected.

‍

Typical payback period depends on therapy value and abandonment rates, but organizations generally see returns within the first year from recovered patients and hub efficiency gains.