Start Here: Who’s In The Room—and What Keeps Them Up at Night?

• Who will be our primary contact for denial outcomes and day-to-day pilot decisions? Options: Denial Management Director, Revenue Cycle VP, CFO, Billing Manager, Managed Care Director, IT Lead, Other
• Which executives or committees must sign off on pilot results and ongoing expansion? Options: CFO/Finance, Chief Medical Officer, Revenue Cycle Steering Committee, IT Security/Compliance, Provider Leadership, Board/Executive Team, Other
• What timeline pressure exists from leadership—are there target quarters or fiscal deadlines we need to hit? Options: Within 30 days, 30–90 days, By quarter end, Within 6 months, Within 12 months, No set deadline
• List the single KPI or financial metric leadership will use to judge success (be specific: $ amount, % reduction, overturn rate, etc.).
• How does your denial management director currently demonstrate progress to the CFO or board (reports, case stories, dashboards)? Options: Regular executive report, Ad-hoc case studies, Monthly KPIs, No formal reporting, Other

If This Wasn’t ‘Just How It Is’—What Would Change?

• If denials stopped being an endless inbox of rework, what would your team's week look like instead?
• Describe your current denial workflow from receipt to resolution—who touches a denial and what tools do they use? Options: Manual spreadsheets + email, Denial management platform (limited), Billing system only, Hybrid (spreadsheets + point tools), Other
• Walk me through a recent denied claim you escalated—how did it flow across systems and people, and where did it stall?
• Which systems are treated as the single sources of truth for claims, coding, and payer responses today? Options: EHR, Billing system (e.g., Epic/Cerner billing), Clearinghouse, 3rd-party denial tracker, Spreadsheets, Other
• Which failure mode do you see most often driving denials: documentation gaps, coding errors, payer-specific logic, front-end auth failures, or something else? Options: Documentation gaps, Coding errors, Payer rule logic, Authorization/PA failures, Charge mapping issues, Other
• On average, how long from denial receipt to appeal submission—and which step causes the longest delay?

Where the Money Actually Leaves: Stories of Lost Revenue

• Tell me about the last time a payer denial led to a write-off you couldn’t recover—what happened and why did it stick with you?
• Which payers and which service lines currently create the largest share of your write-offs? Options: Medicare, Medicaid, Commercial, Managed Medicare/Medicaid, ED/Urgent Care, Inpatient Surgery, Outpatient Imaging, Behavioral Health, Other
• Estimate the share of net patient revenue you believe is lost to denials today. Options: <1%, 1–2%, 3–5%, 5–10%, 10–15%, >15%, Unsure
• How often do contract changes or new prior authorization rules cause sudden denial spikes for you? Options: Very frequently, Often, Occasionally, Rarely, Never
• Give a concrete example of a payer rule, contract clause, or prior authorization requirement that consistently surprises or trips up your team.

When Analytics Let You Down: Trust & Data Doubts

• Tell me about a time analytics produced a confident-sounding insight that didn’t translate into measurable savings—what broke the promise?
• Which data quality issues undermine analytics for you most often? Options: Inconsistent coding/ICD/CPT, Missing clinical notes or attachments, Payer remittance mapping errors, Multiple unconnected source systems, Poor or missing timestamps, Other
• Do you have documented data mapping and normalization processes—who owns them and how long do mappings take to update?
• How confident are you in the labeling of historical denial reasons in your systems? Options: High confidence, Moderate confidence, Low confidence, Unknown / haven’t assessed
• What’s your biggest worry about running a predictive model on your claims (e.g., prep time, false positives, appeal quality, clinician pushback)? Options: Months of prep before insights, High false positive rate, Appeals lacking clinical specificity, Model misses payer nuance, Creates more work than it saves, Other

What Success Would Actually Feel Like (Not Just a KPI)

• If we hit your top KPI target, what would change about your team's priorities, recognition, and the way leadership talks about denials?
• Which outcome signals would make you confident to expand beyond a pilot? Options: X% reduction in write-offs, Y% reduction in denied-volume, Increase in appeal overturn rate, Faster time-to-appeal, Reduced manual workload, Positive CFO validation
• What pilot length and sample size would make results defensible for leadership (be specific about months, claim volumes, or service lines)? Options: 4–6 weeks, 6–8 weeks, 3 months, 6 months, Custom—please describe
• Which acceptance criteria are non‑negotiable for you to sign off on going enterprise-wide? Options: Root-cause accuracy threshold, Predictive precision threshold, Billing/EHR integration, No increase in appeals workload, Security/compliance sign-off, Other
• Who beyond denial management must see early wins to champion scaling—name roles and why their buy-in matters.

Who Needs to Change—and What Might Fight Back?

• Which department is most likely to resist upstream prevention, and what will they say when asked to change behavior?
• Where do you expect the stiffest cultural barriers to live (clinicians, registration, coding, billing, contracting)? Options: Clinicians, Front desk/registration, Coding team, Billing team, Managed Care/Contracting, Physician leadership, No major resistance
• Has a previous automation or workflow change failed because people felt threatened or blamed? Tell us what happened and who needed reassurance.
• What existing incentives or governance structures could we leverage to make prevention stick (e.g., KPIs tied to bonuses, executive reviews)? Options: No incentives, KPIs/Bonuses, Executive review meetings, Interdepartmental committees, Operational playbooks, Other
• What would make it psychologically and practically safe for teams to adopt preventative work—training, clear ownership, executive mandate, or something else?

If We Ran a Pilot Tomorrow—What Would We Need to See?

• What specific demonstration would convince you our model is not just clever, but reliably actionable on your claims?
• Which datasets can you realistically share for a pilot (select all that apply)? Options: Six months historical denied claims, Claim-level EHR notes/attachments, Charge master/service-line mapping, Payer remittance files/EOBs, Authorization/prior-authorization logs, Other
• Which integrations are required during the pilot to show value (billing system, EHR, appeals tool, etc.)? Options: Billing system, EHR, Clearinghouse, Appeals management tool, No integrations required for pilot, Other
• Who on your side will own data extracts, mapping, and validation for the pilot—please name roles and backups.
• What turnaround time for automated appeal generation would feel like a clear win for your team? Options: <4 hours, 4–8 hours, Next business day, 2+ business days
• What single deliverable from the pilot would most directly drive your 'go' decision to scale?

Next Steps That Feel Realistic and Human

• If we agreed to a small pilot, what realistic commitments could you make in the next 30 days?
• Which of these start-up tasks can your team commit to in 30 days? Options: Provide initial data extract, Assign primary contact and backups, Schedule kickoff meeting, Approve DPA/statement of work, Allocate IT resource time, None of the above
• Which leaders or stakeholders need a one-page executive briefing before we begin, and can we draft that for you? Options: CFO, CMO, Revenue Cycle VP, Denial Director, IT Security, Board/Executive Committee, Other
• How would you prefer we communicate pilot progress (pick all that apply)? Options: Weekly email summary, Bi-weekly review call, Shared Slack/Teams channel, Live dashboard access, Monthly executive memo, Other
• What unanswered concerns or unknowns should we address now to make your team comfortable moving forward?

Start Here: Share Your Denial Snapshot

• In one sentence, how would you summarize the state of denials and write-offs that brought you to this conversation?
• What is your current annualized denial write-off as a percentage of net revenue? Options: <1%, 1–2%, 3–5%, 5–10%, 10–20%, >20%
• How has denial volume or write-offs trended over the past 12 months? Options: Steeply rising (≥20% YoY), Rising (5–20% YoY), Stable, Slightly declining, Significantly declining
• Which event most closely triggered the need to investigate denials today? Options: Quarterly report spike, New payer contract/prior auth change, CFO mandate to reduce write-offs, Operational burnout in denials team, Regulatory/payer audit, Other
• Which leaders will need to sign off on a pilot and initial outcomes? Options: CFO/Finance, Revenue Cycle VP/Director, Denial Management Director, Clinical Leaders (CMO/CNO), Revenue Integrity, IT/Analytics, Managed Care Director, Other
• What's your target timeline for seeing a measurable improvement you can present to executives? Options: 30 days, 60 days, 90 days, 6 months, 12 months, Unsure

Where Are the Leaks That Keep Reappearing?

• If you had to point to the single recurring problem that 'eats' revenue most reliably, what would you name?
• Which denial root causes currently contribute the most volume or dollars (select top 3)? Options: Missing/insufficient documentation, Coding errors/DRG mismatches, Prior authorization failures, Eligibility/coverage issues, Bundling/charge capture errors, Timeliness/filing limits, Payer policy or medical necessity denials, Incorrect modifiers, Other
• How often do high-dollar denials (top 10% by value) arise from systemic process issues versus one-off clinical documentation mistakes? Options: Mostly systemic (≥75%), Mostly one-off (≥75%), About half/half, Unsure
• Tell us about a recent denial that surprised leadership—what happened and why did it stand out?
• Which payers or payer groups account for the largest share of your denials right now? Options: Medicare, Medicaid (state-specific), Largest commercial payer, Top 3 commercial payers, Multiple small payers, Self-pay/charity

How Trusted Is the Data You’d Rely On?

• How confident are you that your existing claims + denial data would support a reliable root‑cause analysis you could show the CFO? Options: Very confident, Somewhat confident, Marginally confident, Not confident at all
• Which systems contain the records we’d need to analyze denials and upstream preventable events? Options: EHR/Clinical notes, Practice management/billing system, Clearinghouse/EDI feeds, ERP/financials, Coding platform (encoder), Payer remittance/EOB data, Document repository, Other
• Where do you see the most frequent data quality issues (select all that apply)? Options: Inconsistent procedure coding (CPT/HCPCS), Inaccurate diagnosis coding (ICD), Missing clinical notes or scanned docs, Incorrect payer assignment, Mismatched patient identifiers, Incomplete prior auth fields, Timing/lags in exports
• On average, how long does it take to get a usable export of six months of claims+denials from your systems? Options: Less than 1 week, 1–2 weeks, 2–4 weeks, More than a month, We don’t know
• Who in your organization is responsible for preparing data extracts and ensuring their quality? Options: IT/BI team, Revenue Cycle Analytics, Denials Team, Third‑party vendor, Finance, Unsure
• Give one concrete example of a data mismatch or coding inconsistency you’ve found that changed the root‑cause story.

Who Owns Decisions When a Claim Breaks?

• Which teams make the most impactful decisions that lead to preventable denials—often before denial management sees the claim? Options: Front desk/registration, Case management/utilization, Clinical documentation specialists, Coders/Coding audit, Billing specialists, Charge capture/clinicians, Managed care/contracting, Other
• Where do handoffs typically fail—registration to clinical, clinical to coding, coding to billing, or billing to appeals? Options: Registration → Clinical, Clinical → Coding, Coding → Billing, Billing → Appeals, Multiple weak handoffs, Handoffs are relatively clean
• When an upstream team changes a workflow (e.g., new admission triage), how are those changes communicated to denial/billing teams? Options: Formal governance meetings, Email/bulletins, Ad hoc conversations, Rarely communicated, We don’t have a process
• Describe the typical escalation path for a high-dollar denial—who gets looped in, and how quickly?
• How would you describe frontline staff’s morale and bandwidth for manual rework? Options: Burned out and overworked, Stretched but coping, Adequate bandwidth, Underutilized

If Appeals Were a Machine, Where Are the Gears Missing?

• What percent of denials are appealed today, and how often do appeals succeed? Options: <10%, 10–25%, 25–50%, 50–75%, >75%
• How long does the typical appeals bundle take to prepare—from identifying supporting docs to submission? Options: Under 4 hours, Same day, 24–48 hours, 2–5 days, More than a week
• What tools or templates do you use for appeals, and where do they fall short? Options: In-house templates, EMR-generated letters, Third‑party software, Manual assembly in shared drives, No templates
• Do automated appeals exist in your process today? If so, how is clinical specificity ensured? Options: Yes—fully automated with clinical logic, Yes—partially automated, manual clinical review, No—manual only, Planning automation but not live
• Share an example where an automated or template appeal failed because it lacked clinical context—what was missing?

Which Metrics Move the Needle for Your Team?

• What are the 3–4 KPIs you report to executives about denials and revenue recovery today? Options: Write-off %, Denial rate per claim, Appeal success rate, Days in denial, Value recovered from appeals, Pre-bill prevention rate, Other
• Which single KPI, if improved, would most convince leadership this solution delivered real value in a pilot? Options: Reduction in write-offs, Increase in appeal overturn rate, Reduction in preventable denials pre-bill, Faster appeal turnaround, Improved coding accuracy
• How do you currently attribute recovered revenue back to specific root-cause fixes or upstream changes? Options: Attribution model in analytics, Manual reconciliation, Not attributed/just reported as recovered, We don’t track this
• What level of false positives in predictive flags would be acceptable to leadership during a pilot? Options: Very low (<5%), Low (5–10%), Moderate (10–20%), High (>20%), Unsure
• How quickly do you need to translate pilot results into an executive-ready slide or brief? Options: 30 days, 60 days, 90 days, 6 months

The Things People Don’t Say Out Loud (Politics & Practical Risks)

• What political, cultural, or ownership issues would quietly sink this project if they aren’t surfaced and handled?
• Which past initiatives related to revenue cycle or denials failed or stalled—and what was the primary reason? Options: Data quality/availability, Lack of stakeholder buy-in, Underestimated IT effort, Poor change management, Vendor mismatch, Other
• How aligned are your clinical leaders with denial prevention efforts that require documentation or workflow changes? Options: Fully aligned, Generally supportive, Neutral, Resistant
• If we need executive sponsorship, who is the most likely internal champion and why?
• What resource constraints (FTEs, IT time, budget, SMEs) would limit the scope of an initial pilot? Options: Denials FTEs, Coding SMEs, IT/Integration time, Analytics/BI support, Budget for tools, None significant
• What mitigation strategies have you considered for the highest‑risk barriers?

Quick Wins, Non‑Starters, and What We Must Deliver

• If the pilot must produce one tangible 'win' for the CFO in 90 days, what is the non‑negotiable deliverable?
• Which outcomes are highest priority for you to see in a pilot (choose up to 3)? Options: % reduction in write-offs, Increase in appeal overturn rate, Number of preventable denials intercepted pre-bill, Time-to-appeal reduced, Root causes identified and ranked, Integration proof-of-concept
• What would be a deal-breaker or non‑starter for you regarding a vendor or pilot approach? Options: Cannot access live claims/clinical data, Requires heavy customization before results, No payer‑specific logic, Poor security/compliance posture, No executive-level reporting
• Which integrations are absolutely required to run a meaningful pilot? Options: EHR/clinical notes, Billing/PM system, Clearinghouse/837/835 feeds, Document repository (scans/PDFs), Coding system/encoder, None—analytics only
• Are there regulatory or contract constraints (state Medicaid rules, payer contracts, BAA limits) that would affect data sharing or pilot scope? Options: Yes—state Medicaid constraints, Yes—commercial contract restrictions, Yes—BAA/privacy limitations, No major constraints, Unsure

Practicalities: Samples, Access, and a Realistic Start

• Could you deliver six months of anonymized claims + denial records and supporting clinical documents for a pilot? If so, how quickly? Options: Yes—under 1 week, Yes—1–2 weeks, Yes—2–4 weeks, Yes—>4 weeks, No
• What format are your typical exports in (select all that apply)? Options: CSV/flat files, HL7/FHIR extracts, EDIFACT/EDI 835, API access, PDF/document bundles, Other
• Are there PHI or contract restrictions we should know about up front that affect how data can be shared or stored? Options: Yes—PHI restrictions, Yes—contractual restrictions, Yes—state-specific rules, No significant restrictions, Unsure
• Who would be our day‑to‑day point of contact for data access and validation during a pilot (name & role)?
• Realistically, how many hours per week can your key SMEs (coding, denials, IT) allocate to a pilot? Options: <4 hours, 4–8 hours, 8–16 hours, 16–32 hours, >32 hours
• What would make you say 'we're ready to start' on a pilot—list the top 3 prerequisites?

Quick Snapshot: Who’s in the Room?

• What is your primary role in denial management or revenue cycle? Options: VP Revenue Cycle, Director of Denial Management, CFO/Finance Leader, Managed Care Director, Billing Manager, Other
• Which describes your organization? Options: Single hospital, Small health system (2–5 sites), Medium health system (6–25 sites), Large health system (26+ sites), Specialty clinic/ambulatory network, Other
• Which service lines are highest priority for denials right now? Options: ED/Observation, Inpatient, Surgery/OR, Oncology, Behavioral Health, Radiology/Imaging, Outpatient/Clinic, Other
• What triggered this conversation today—what specific report or event moved this to the top of your list? Options: Quarterly write-off increase, New payer contract/prior auth requirements, CFO reduction directive, Audit findings, Operational overload in billing, Other

If Write-Offs Could Speak, What Would They Tell You?

• How large is the problem right now—what percent of net revenue do you estimate is lost to denials/write-offs? Options: <1%, 1–3%, 3–5%, 5–10%, 10%+
• How has denial-related write-off trended in the last 12 months? Options: Decreased, Flat, Increased 1–10%, Increased 11–25%, Increased 25%+
• Which payers or payer types are driving the most write-offs or denials for you? Options: Medicaid, Medicare, Large commercial 1, Large commercial 2, Regional commercial, Medicare Advantage, Workers’ comp, Self-pay/charity
• Tell us about one recent denial example that felt emblematic of a bigger issue—what happened and why did it stick with you?

Where the Day-to-Day Feels Broken

• If someone asked why denials keep returning to your team, what blunt answer would you give?
• Which parts of your current denial workflow create the most manual rework? Options: Identification/categorization, Appeal letter creation, Gathering clinical documentation, Follow-up with payers, Root-cause analysis, Reporting/analytics
• How long does it typically take your team to generate an appeal with supporting documentation once a denial is assigned? Options: <4 hours, 4–24 hours, 1–2 days, 3–7 days, More than a week
• Which upstream teams (e.g., clinicians, coding, scheduling) resist or are neutral about prevention changes, and why? Options: Physicians/clinicians, Coding team, Pre-cert/prior auth, Front desk/scheduling, Clinical documentation team, None resist/very collaborative
• When denials reveal systemic problems, which of those problems fall outside your team’s authority to fix? Options: Clinical documentation, Physician ordering patterns, Insurance verification, Coding practices, Contract/payer negotiations, IT/EHR configuration

Data: The Elephant We Ignore (Until It Bites)

• How would you rate the overall cleanliness and consistency of your historical denial data? Options: High quality, Mostly usable, Patchy — some usable segments, Poor — inconsistent coding/fields, Unusable without heavy cleansing
• Which systems hold the denial, billing, and clinical data we’d need to analyze? Options: Epic, Cerner/Oracle, Meditech, Allscripts, Multiple EHRs, Third‑party billing system (e.g., RCM vendor), Homegrown/other
• Are consistent denial reason codes and payer denial mappings available in your exports today? Options: Yes, consistent, Mostly consistent with exceptions, Inconsistent across sites, No, we map manually
• Do you have the ability to produce sample exports (6 months) with claim, adjustment, remittance, and clinical document links? Options: Yes — ready now, Yes — with a week lead time, Yes — needs IT support and longer, No — not available
• What specific fields do you worry will be missing or inconsistent (examples: modifier, place of service, provider NPI, admission/discharge dates)?

Lessons from the Trenches: What Past Pilots Taught You

• Have you run pilots with denial analytics or automation before, and what was the outcome? Options: Yes — met goals, Yes — mixed results, Yes — failed, No, this would be our first pilot
• When pilots didn’t meet expectations, what was the most common reason? Options: Poor data quality, Lack of clinical specificity in appeals, Integration delays, Poor stakeholder engagement, Unrealistic scope/goals, Other
• What acceptance criteria would make a pilot an unambiguous success for you?
• How long are you willing to let a pilot run before you expect measurable results? Options: 4 weeks, 6–8 weeks, Quarter (3 months), Two quarters (6 months), Longer — depends on scope
• If we could only prove one thing in the pilot to unlock funding and scale, what single outcome would you pick? Options: % write-off reduction, Appeal overturn rate improvement, Predictive flag precision, Time-to-appeal reduction, Root-cause concentration identified

Decision Pressure: Who’s Pushing for Change and What’s the Timeline?

• Is there an executive directive or financial target driving urgency (e.g., reduce write-offs X% in 12 months)? Options: Yes — specific target, Yes — general pressure, Not yet, but under review, No directive currently
• Who are the decision-makers that must sign off to start a pilot and then scale? Options: CFO/Finance, VP Revenue Cycle, Denial Management Director, CMO/Clinical leadership, IT/Infrastructure, Legal/Compliance
• What fiscal or budget window do we need to align with to secure funding? Options: Immediate (current month), This quarter, Next quarter, Next fiscal year, Unsure
• How will you evaluate vendor claims (e.g., 70% predictive capture) — do you require replicated results from your own data? Options: Yes—must validate on our data, Partially—accept vendor benchmarks + some validation, Rely on vendor benchmarks, Undecided
• What are the non-negotiable deal-breakers for you (examples: no PHI access, unacceptable integration effort, vendor lacks payer-specific logic)?

What Would Count as a Win (and What Keeps You Up at Night)?

• Which KPI would you show the board to prove this project worked? Options: Reduction in write-offs (%), Increase in appeal overturn rate (%), Time saved per appeal (hrs), Reduction in denied claims volume, Dollar value recovered
• What minimum threshold would you require on that KPI to call the pilot successful? Options: Small improvement (1–3%), Meaningful (3–7%), Transformational (7–15%), Specific $ target
• Which unknowns in a pilot would make you pause mid‑pilot? Options: Poor data readiness, Low predictive precision, Appeals failing payer-specific logic, Security/compliance concerns, Low stakeholder engagement
• How important is clinician-level specificity in automated appeals to your acceptance criteria? Options: Critical — must be clinician-specific, Important but not always required, Nice to have, Not important
• If early results look promising but some upstream departments resist scaling, what would you need to move past that barrier?

Getting People Onboard — Who Will Fight (or Block) for This?

• Who are the internal champions who will actively support running the pilot (names/roles)?
• Which groups will need training or role changes to sustain prevention workflows? Options: Denial team, Billing/coding, Pre-cert/prior auth, Clinical documentation specialists, Physicians, IT/Integration
• How receptive are frontline denials and billing staff to automation that changes daily tasks? Options: Very receptive, Cautiously open, Resistant without proof, Actively resistant
• What communications or governance forum do you use to resolve cross-department disputes (e.g., Revenue Integrity Committee)?
• If we surfaced clinician workflow issues during the pilot, who must sign off on changes to ordering or documentation practices? Options: CMO/Clinical leadership, Service line medical directors, Coding manager, Revenue cycle VP, Other

Data Access & Security: The Gates We Must Open

• What level of PHI access and export is your team comfortable providing for a pilot (e.g., de-identified, limited PHI, full PHI under BAA)? Options: De-identified only, Limited PHI, Full PHI with BAA, Unsure — legal must advise
• What internal approvals are required before data can be shared (e.g., legal, privacy officer, information security)? Options: Legal, Privacy Officer, InfoSec, Executive sponsor, None/documented
• Which transfer or integration methods are acceptable for you (choose all that apply)? Options: SFTP, API (FHIR/HL7), Direct DB query, Secure file share, Vendor-hosted connector
• Are there payer contracts or carve-outs that restrict our ability to analyze or appeal certain claim types? Options: Yes — multiple restrictions, Yes — isolated payers, No known restrictions, Unsure
• What timeline should we expect for security/BAA review and sign-off? Options: <2 weeks, 2–4 weeks, 1–2 months, 2+ months, Unsure

If We Piloted Together, What Does a Realistic First 90 Days Look Like?

• Which scope would you prefer for an initial pilot (pick the best fit)? Options: Single service line, single payer, Single service line, multiple payers, Multiple service lines, single payer, System-wide limited sample
• Who will own day-to-day pilot milestones on your side (name/role)?
• What internal resource commitment is reasonable from your team during the pilot (hours/week per role)? Options: <5 hrs/week, 5–10 hrs/week, 10–20 hrs/week, 20+ hrs/week
• Which integrations must be completed before meaningful analytics can run (billing system, EHR, document store)? Options: Billing/RCM system, EHR, Document repository (CDM/PACS), Payer remittance feed, None — sample exports suffice
• What cadence of check-ins and deliverables would you prefer for the pilot (weekly, bi-weekly, milestone-based)? Options: Weekly, Bi-weekly, Monthly, Milestone-based only
• What would make you comfortable moving from pilot to a phased roll‑out?

Quick Win — Tell Us Who You Are

• What is your role and the primary team you represent? Options: Denial Management Director, Revenue Cycle VP/Director, CFO/Finance Leader, Managed Care Director, Revenue Integrity/Compliance, Other
• What single metric has leadership asked you to improve first? Options: % of net revenue lost to denials, Write-off dollars, Denial rate (per 100 claims), Days in Accounts Receivable, Other
• Briefly describe the event, report, or directive that triggered this conversation (what changed or got worse recently?).
• Which stakeholders or committees must be involved in approving a denial-focused pilot? Options: Denial Management/Appeals Team, Revenue Cycle Leadership, Finance/CFO office, Clinical Leadership (Med/Surg), IT/EHR Team, Legal/Compliance, Other
• What timeline has leadership given you to demonstrate measurable impact? Options: 30 days, 60 days, Quarter (90 days), 6 months, 12 months

If We Don't Fix This, What Breaks Next?

• How much longer can your organization absorb a 5–10% revenue leakage before priorities shift and resources are pulled away? Options: We can't absorb it — action now, One quarter, Two quarters, A year, Unsure
• What are the operational consequences you already see when denials spike (e.g., staffing pressure, cash shortfalls, executive interventions)?
• Who feels the most personal pressure when denial-related revenue drops (role or person), and how does that influence decision-making?
• How often do denial surprises appear in your leadership reporting—monthly, weekly, or only when finance escalates? Options: Weekly, Monthly, Quarterly, Only when escalated, Other
• If denials continue unchanged for the next two quarters, what is the most likely executive action? Options: Budget cuts, Headcount changes, Contract renegotiation with payers, Escalation to board/CFO, No action/low visibility

Where Money Is Actually Falling Through the Cracks

• Which denial reason codes or categories do you suspect account for the largest dollar impact today? Options: Medical Necessity/Authorization, Coding errors/DRG issues, Registration/Eligibility, Bundling/Modifier issues, Timely filing, Other
• Which payers drive the majority of your denial volume and write-offs?
• Which service lines or specialties show the highest denial rates and why do you think that is? Options: ED/Observation, Surgery/OR, Inpatient Medicine, Behavioral Health, Imaging/Radiology, Outpatient Clinics, Other
• Can you share a recent example of a denied claim that feels representative of a larger pattern? What happened and what was missed?
• How do you currently quantify the dollar impact of each denial root cause (estimates, sampling, system reports)? Options: Automated reporting, Manual sampling spreadsheets, Finance estimates, We don't consistently quantify, Other

What You Already Tried (And Why It Stayed Broken)

• When you look at the tools and playbooks you're using today, what's the one thing missing that keeps you trapped in rework?
• Which of these processes or tools do you currently rely on to manage denials? Options: Spreadsheets/manual queues, EHR denial module, Third-party recovery vendor, In-house automation/scripts, Business intelligence dashboards, Other
• Have you run past pilots or vendor evaluations for prevention/prediction? What specifically failed or under-delivered?
• How consistent is your historical denial coding and metadata (diagnosis codes, reason codes, payer responses)? Options: Consistent and reliable, Mostly OK with some gaps, Inconsistent across systems, Poor/very inconsistent
• Where do upstream teams usually push back when asked to change workflows to prevent denials? Options: Clinical teams, Scheduling/Registration, Physician documentation, Case management, Billing/Coding, None resist

Imagine a Different Quarter

• If next quarter showed a measurable drop in write-offs, what would that free up for you or your organization?
• Which measurable targets would make you call a pilot a success (pick primary and acceptable stretch)? Options: 5% write-off reduction, 10% write-off reduction, 20% write-off reduction, 70% preventable denial catch rate, Other
• What acceptance criteria would leadership require to expand prevention upstream (e.g., precision thresholds, dollars recovered, stakeholder sign-off)?
• How fast would you expect to see early signal improvements once a pilot starts (days, weeks)? Options: Within days, 2–4 weeks, 1–2 months, 3+ months
• What resources would you commit to scale prevention (people, integration support, change-management budget)?

What Would Convince You To Trust Automation?

• What would an automated appeal need to include for you to let it send without manual rewrite?
• Which elements are non-negotiable in an appeal letter or submission package? Options: Clinical note excerpts, Correct coding rationale, Payer-specific policy citations, Prior authorization history, Signed provider attestations, Other
• What level of predictive flag precision would you require before using automated prevention to block claims? Options: >90% precision, 80–90%, 70–80%, Willing to pilot lower with human review
• How many false positives per 100 flagged claims is tolerable for your team before it creates unacceptable rework? Options: <5, 5–10, 10–20, >20
• Describe a past automated workflow that felt trustworthy — what specifically made it feel reliable?

What the Data Tells Us (and What It Doesn't)

• If your historical denial dataset could only answer three questions for us, what would you make those be?
• Which of these data fields are available and consistently populated in your exports? Options: Claim ID/Line ID, Payer response text, Reason code, DOS/service line, Diagnosis/procedure codes, Provider NPI, Prior auth numbers, Other
• Which billing and EHR systems would we need to integrate with for a pilot?
• Do you have six months of historical denial data readily exportable for sampling? If not, what is available? Options: 6+ months ready, 3–6 months, Less than 3 months, Not exportable without IT help
• What are the most common data quality issues we should expect (missing fields, inconsistent codes, payer text variability)?

Who Will Own Change (and How We Keep Momentum)

• When prevention moves upstream, who will own the day-to-day coordination and change management? Options: Denial Management, Revenue Cycle/Operations, Clinical Leadership, IT/EHR Team, A cross-functional governance team, Other
• Who needs to be comfortable with automated appeals or blocked claims before we scale prevention (roles or committees)?
• How do you prefer to govern risk and exceptions during a pilot (weekly scorecard, steering committee, ad-hoc reviews)? Options: Weekly scorecard + ops review, Biweekly steering committee, Monthly executive update, Ad-hoc as issues arise
• What escalation path should we follow if an automated decision causes an unexpected negative outcome?
• Who is authorized to sign off to move from pilot to broader rollout?

Pilot Logistics — Realities, Risks, and Commitments

• What single risk would make you pause a pilot today? Options: Data quality too poor, Clinical leadership resistance, Integration timeline too long, Budget constraints, Regulatory/compliance concerns, Other
• Which mitigations would make that risk acceptable (data cleansing plan, manual review gate, sandboxed scope, indemnity)?
• What pilot scope feels manageable to your team (volume/sample size, service lines, payers)? Options: One service line + top 3 payers, Multiple service lines organization-wide, High-dollar claims only, Random sample across lines
• What internal commitments can you make now to support a pilot (data access, SME time, decision-maker availability)?
• How would you like pilot results presented to feel confident in a go/no-go decision (dashboard, narrative case studies, executive summary)? Options: Interactive dashboard + drilldowns, Narrative with representative cases, Executive one-pager with KPIs, All of the above

Next Steps That Feel Doable

• What next-step would make it easy for your team to say yes to exploring a pilot? Options: Data discovery workshop, Executive alignment session, Short feasibility analysis (2–4 weeks), Reference calls with peers, Other
• Who needs to attend the first data discovery call for it to be productive? Options: Denial manager, Revenue cycle lead, Finance/CFO rep, IT/EHR analyst, Clinical SME, Other
• Realistically, when could you make the data available for an initial analysis? Options: Within 1 week, 1–2 weeks, 2–4 weeks, 1+ month, Need to check
• What would a low-effort pilot kickoff look like for your team (time commitment, format, deliverables)?
• Is there anything else we should know before building a tailored pilot plan for your team?