Starting with Today's Reality

• Tell me about the last time your team missed critical clinical detail in documentation and the downstream effect that had on coding, billing, or quality reporting.
• Walk me through a typical clinician encounter in one specialty where note creation takes the most time, from room entry to chart signoff.
• Describe who on your team is accountable day to day for documentation quality and how they currently surface problems.
• When you review inpatient and outpatient charts, how often do you find missing diagnosis specificity, incomplete problem lists, or absent HPI details? Options: Almost always, Often, Sometimes, Rarely, Unknown
• How many coding queries or DRG downgrades does your revenue cycle team process per month, roughly? Options: 0-50, 51-200, 201-500, 501-1,000, 1,000+
• In the last 6 months, which specialties have shown the largest decline in documentation completeness at your organization? Options: Emergency Medicine, Hospital Medicine, Primary Care, Surgery, Cardiology, Orthopedics, Behavioral Health, Other

What's Actually Breaking Down

• If a payer audit uncovered systemic under-coding across three service lines, what immediate organizational risks would you be most concerned about?
• Could you point to the single workflow step where clinicians most often lose critical clinical detail — pre-charting, live patient encounter, post-encounter cleanup, or elsewhere? Options: Pre-charting, Live encounter, Post-encounter cleanup, At handoff between clinicians, Other
• Tell the story of a recent claim or appeal that turned on missing narrative or specificity, and what it cost in time or dollars.
• Which documentation error today creates the largest revenue, quality, or compliance exposure for your teams? Options: Missing diagnosis specificity, Incomplete problem list, Absent HPI or ROS, Insufficient procedure detail, Medication reconciliation gaps, Other
• What single technical or political hurdle would cause you to stop a documentation automation initiative before pilot?
• Who would be the first internal stakeholder to raise the alarm if automation began degrading clinician workflow or chart accuracy? Options: Chief Medical Officer, CMIO, VP Revenue Cycle, Chief Compliance Officer, Clinical Directors, Other

How Documentation Affects Money, Quality, and Teams

• Quantify the monthly revenue swing you estimate results from documentation variability across your system, even if you must give a best guess.
• Why has that variability persisted despite any prior documentation improvement efforts?
• Give an example of a case where added clinical detail changed the coding outcome, quality measure capture, or clinician workflow, and what changed.
• Estimate how much clinician time per day could be reclaimed if note creation required 30 percent fewer clicks or less typing. Options: <15 minutes saved per clinician/day, 15-30 minutes, 30-60 minutes, >60 minutes, Unsure
• If a pilot proved a measurable uplift in coding specificity across a test service line, what would stop you from expanding to other hospitals that same quarter?
• In your view, which single metric—coding accuracy, clinician time saved, or quality measure capture—most influences your executive willingness to invest? Options: Coding accuracy, Clinician time saved, Quality measure capture, Clinician satisfaction, Operational efficiency

The Other Options on Your Table

• Who are you actively evaluating or keeping on the shortlist as alternatives to an external documentation partner, including internal build options? Options: Incumbent external vendor, Internal documentation team, EHR vendor modules, Point solutions (speech only), No current vendor, manual process, Other
• Which part of your current approach would need to demonstrate sustained improvement for you to decide to keep it rather than switch partners? Options: Note completeness and specificity, Coding accuracy, Clinician adoption, EHR integration stability, Operational cost, Unknown
• Do you have an incumbent vendor or internal team that already covers pieces of this problem, and if so, which pieces (note capture, speech recognition, coding review, templates, training)? Options: Note capture, Speech recognition only, Coding review, Template management, Training and adoption, None
• Are there active internal proposals to solve documentation gaps without an outside partner, and who proposed them?
• What acceptance criteria would keep you with your existing approach instead of selecting a new partner?
• Rate how important vendor reference sites and clinician adoption metrics are in your final decision. Options: Not important, Somewhat important, Important, Very important, Critical

Readiness: Can This Ship Actually Sail?

• Point to the single integration dependency that concerns you most for a safe pilot, EHR API access, sandbox availability, network bandwidth, data governance, or something else. Options: EHR API access, Sandbox environment availability, Network bandwidth, Data governance / data sharing agreement, Third-party middleware, Other
• Name the team or role that owns EHR API access, sandbox provisioning, and integration approvals inside your organization. Options: EHR integration team, IT infrastructure, Clinical informatics, Security/compliance, Revenue cycle analyst, Other
• Where are your non-production EHR environments hosted and how many distinct sandboxes will we need to access for a pilot? Options: Local hospital data center, Cloud-hosted by EHR vendor, Hybrid environments, No non-production environment available, Unsure
• Do you have a dedicated technical lead and how many full-time equivalents can be assigned to deployment tasks during a pilot? Options: Yes, 1-2 FTEs, Yes, 3-5 FTEs, Yes, more than 5, No dedicated staff but can assign part-time, No resources available
• Identify any regulatory, legal, or contract approval whose absence would prevent a pilot from starting on your target timeline.
• Estimate whether your network and room hardware support continuous ambient audio capture: ready now, needs minor upgrades, requires major procurement, or unsure. Options: Yes, ready now, Minor upgrades required, Major procurement required, Unsure

Which Outcomes Will Make This Real?

• Suppose a pilot improved coding specificity by 10 percent, what operational or budget decisions would that trigger in the next 60 to 90 days?
• Could your billing and compliance teams validate coding accuracy within 30 days of a pilot, with access to sampled cases? Options: Yes, No, Need access to sampled cases, Unsure
• How often would you want adoption and quality dashboards to update during a pilot — daily, weekly, or monthly? Options: Daily, Weekly, Biweekly, Monthly, Ad hoc upon request
• Identify the person or role who can commit pilot-to-production budget if the pilot meets agreed metrics, and your ideal timeline for that commitment.
• In your decision-making, which three metrics must improve to justify an enterprise rollout? Options: Coding specificity, Query volume reduction, Average clinician note time, Quality measure capture rate, Clinician adoption rate, Denied claims reduction
• Are there seasonal windows, fiscal quarter constraints, or blackout periods that would accelerate or delay a full rollout? Options: Q1, Q2, Q3, Q4, No preference, Unsure

Signals That Move Us Toward Commit

• Assume the pilot meets the agreed KPIs, what approvals, committees, or procurement steps could still delay final signoff?
• Name the final decision-makers and approvers who must sign commercial and legal terms for a rollout. Options: CFO, CMO, CMIO, VP Revenue Cycle, Chief Legal Officer, Procurement Lead, Other
• Provide your target go-live window and any blackout dates we should avoid when planning rollout sequencing. Options: Next 1-3 months, 3-6 months, 6-12 months, 12+ months, Unsure
• Point to the pilot site you prefer and explain briefly why that location is the best testbed for documentation, coding, and clinician adoption.
• List the person or role who will sign the statement of work if the pilot meets acceptance criteria and outline the remaining procurement steps.
• On a scale of 1 to 5, how urgent is this project for your executive leadership team? Options: 1, 2, 3, 4, 5

• Solution Experience Session

• Confirm the current state and its cost
• You confirm the demonstrated workflows remove the specific rework that generates coding queries and DRG downgrades.
• Provide 2 to 3 representative patient encounters per priority specialty, including typical and complex cases, for the tailored scenario runs.
• You agree on measurable acceptance criteria for documentation completeness, coding specificity, and clinician time saved that will be used in a pilot.
• Agree on measurable success criteria
• Provide baseline metrics for documentation time per note, current coding query rates, and recent DRG downgrades to enable before/after measurement.
• Provide a list of current EHR templates and the integration contact or owner for each targeted environment.
• You validate that the EHR mapping shown meets your integration needs for the evaluated scenarios.
• Run Scenario 1: typical encounter end-to-end
• Prepare and deliver a tailored scenario proof run using the provided encounters, with recorded captures and a before/after documentation and coding specificity comparison.
• You agree on the next concrete evidence delivery, timing, and owner to progress toward a pilot decision.
• Run Scenario 2: complex or noisy environment
• Map outputs into your EHR workflow
• Schedule a technical pre-check with your integration owner and the seller's integration engineer before the pilot kickoff.
• Validate outcomes and next evidence needed

• Solution Experience Session
• Solution Experience Deck
• Solution Brief

• meeting
• slides
• document

• Install ambient audio capture devices
• Deploy edge speech-recognition engine
• Activate real-time dictation workflow
• Integrate structured note writeback to EHR
• Configure specialty-specific documentation templates
• Enable computer-assisted specificity suggestion engine
• Map clinical concepts to coding and quality tags
• Implement multi-speaker diarization and noise reduction
• Configure secure audio and data routing
• Deploy clinician training and workflow onboarding
• Launch adoption monitoring and usage reporting
• Provide ongoing model tuning and accuracy optimization
• Export structured clinical data for coding and quality

Install ambient audio capture devices

• Which clinical areas and room types require fixed ambient microphones (for example: outpatient exam rooms, ED bays, ORs)?
• How many clinician workspaces and portable devices need coverage for the initial rollout (give counts by ambulatory clinic, inpatient unit, and ED)?
• Where will devices be mounted relative to patient and clinician positions (ceiling, wall, countertop) for each room type? Options: Ceiling mount, Wall mount, Countertop/table, Portable clip-on
• What facility constraints exist for installed devices (e.g., sterile OR fields, negative-pressure rooms, MRI zones, or infection control policies)?
• Which on-site owner can approve physical installations and provide site floor plans for cabling and mounting?
• What target audio signal-to-noise ratio (SNR) or capture distance must be achieved for each room type based on typical ambient noise (for example: SNR >= 15 dB at clinician position)?
• Are Clinical Engineering or Facilities required to sign electrical or low-voltage permits before installation? Options: Yes, No

Deploy edge speech-recognition engine

• Which deployment model do you prefer for the speech engine: on-premise edge appliance, private cloud in your VPC, or hybrid edge with cloud model updates? Options: On-premise appliance, Private cloud (VPC), Hybrid edge + cloud updates
• How many concurrent recognition sessions must the engine support at peak times by site (estimate for ambulatory clinic hours and hospital peak hours)?
• What latency requirement for speech-to-text transcription is acceptable for real-time workflows (for example: <500 ms, <1 second, or batch post-encounter)? Options: <500 ms, <1 second, Near real-time (<30 seconds), Batch post-encounter
• Which on-prem network constraints or proxy requirements will affect the engine (for example outbound-only proxy, restricted ports, or no internet egress)?
• Which OS or virtualization platforms are available for an on-prem appliance (for example: VMware ESXi, Hyper-V, Linux distribution)? Options: VMware ESXi, Hyper-V, Linux (Ubuntu/CentOS), Physical appliance only, Other
• Who in your IT organization will provide access to host the engine and approve compute/network capacity?
• Are there language or specialty dialect models required (for example: Spanish primary, cardiology-specific lexicon, or heavy local accents)?

Activate real-time dictation workflow

• Which clinical note types should support real-time dictation at go-live (for example: progress note, H&P, discharge summary, ED triage note)? Options: Progress note, History & Physical (H&P), Discharge summary, ED triage note, Procedure note, Other
• How should the workflow begin in the EHR: voice-trigger from an in-chart button, ambient auto-capture based on encounter start, or clinician-initiated session outside the EHR? Options: In-chart button, Ambient auto-capture on encounter start, Clinician-initiated session
• What keystroke or click-to-finalize behavior do you require for clinician approval of a draft dictated note (for example: single-click accept, inline edits then sign, or separate review step)? Options: Single-click accept, Inline edit then sign, Separate review step before sign
• Who will be the clinical owner of the dictation workflow configuration and sign off on default templates and macros?
• Which clinician roles should be allowed to start and stop real-time dictation sessions (for example: attending MD, resident, NP/PA)? Options: Attending physician, Resident/Fellow, Nurse practitioner/PA, Scribe
• What escalation or fallback is required if real-time transcription quality degrades (for example: switch to manual dictation, store audio for later processing)? Options: Switch to manual dictation, Store audio for later processing, Fallback to basic speech-to-text only, Notify IT support

Integrate structured note writeback to EHR

• Which EHR integration method is supported for note writeback at your organization: direct API (FHIR R4 DocumentReference/Composition), HL7 v2 ORU, SMART on FHIR launch, or custom vendor API? Options: FHIR R4 (Composition/DocumentReference), HL7 v2 ORU, SMART on FHIR, Custom vendor API, Other
• For each target EHR instance, provide the environment names and API endpoints available for integration (for example: Epic PROD, Epic TEST, Cerner INT).
• Which discrete fields must be populated by the writeback (for example: chief complaint field, problem list ICD-10 fields, medication list, Vital Signs section)?
• What mapping tolerance do you require for structured field mapping (for example: 95% of mapped fields populated correctly during validation)? Options: 95%+, 90-95%, 80-90%, Custom threshold
• How will you verify successful writeback during UAT and who will provide signed evidence of acceptance (for example: UAT test plan, sample patient traces, or EHR audit logs)? Options: UAT test plan with sample traces, EHR audit logs review, Clinical validation sign-off, Other
• Are there existing EHR write-protection or record-locking windows that will affect when notes can be written back (for example: chart lock after patient discharge)? Options: Yes, No
• Identify any regulatory or compliance rules that affect writeback retention or audit trails (for example: state retention laws, internal auditing policies).

Configure specialty-specific documentation templates

• Which specialties should be configured in the initial template set (for example: primary care, cardiology, orthopedics, ED, radiology)? Options: Primary Care, Cardiology, Orthopedics, Emergency Department, Radiology, Other
• For each specialty, which encounter types require custom templates (for example: new patient H&P, follow-up visit, post-op visit, procedure note)?
• Which structured template elements are mandatory to capture for coding and quality reporting in each specialty (for example: modifiers, laterality, severity, time-based services)?
• Who will provide specialty SME (subject-matter expert) review of template drafts and final sign-off?
• What locale or terminology preferences must templates adhere to (for example: SNOMED CT preferred for problem list, ICD-10 for coding, or local phrasing standards)?
• Do you require versioning and rollback support for templates to revert to prior versions if clinical leadership requests it? Options: Yes, No
• Estimate the initial number of templates and mapped fields per specialty to scope configuration effort (for example: 3 templates with 25 mapped fields each).

Enable computer-assisted specificity suggestion engine

• Which specificity suggestion targets are highest priority: ICD-10 code specificity, CPT code capture, problem list detail, or quality measure extraction? Options: ICD-10 specificity, CPT capture, Problem list detail, Quality measure extraction
• What acceptance threshold will confirm suggestion engine performance (for example: PPV or precision >= X% for suggested specificity changes)? Options: Precision/PPV >= 90%, Precision/PPV >= 85%, Custom threshold
• Which clinician feedback loop should be used for suggestion acceptance (for example: inline accept/reject, post-encounter survey, or coded query integration)? Options: Inline accept/reject, Post-encounter feedback, Coded query integration
• How should the engine surface suggestions inside the EHR UI (for example: suggestion card in note sidebar, inline text suggestions, or pre-signature summary)? Options: Sidebar suggestion card, Inline text suggestion, Pre-signature summary
• Who will own clinical governance for suggestion rules and periodic review cadence (for example: coding team, CMIO, or specialty committee)?
• Which coding standards and reference sets must the suggestion engine use for mapping (for example: ICD-10-CM, CPT, CMS quality measure definitions)? Options: ICD-10-CM, CPT, CMS quality measures, SNOMED CT, Other
• How will you measure clinician trust and suggestion false-positive rates during pilot (for example: % suggestions accepted by specialty over 30 days)?

Map clinical concepts to coding and quality tags

• Which code systems and code versions must mappings target (for example: ICD-10-CM 2026 release, CPT 2026, LOINC for labs)? Options: ICD-10-CM, CPT, LOINC, SNOMED CT, Other
• What quality measures must be captured from notes at go-live (for example: HEDIS measures, CMS MIPS measures, or local quality metrics)?
• Which clinical concepts require automated tag mapping versus manual coder review (for example: common diagnoses vs. ambiguous findings)? Options: Auto-map common diagnoses, Manual review for ambiguous cases, Hybrid rules-based
• Provide an example list of high-priority concepts that must map correctly during validation (for example: heart failure with reduced EF, sepsis, laterality for fractures).
• What minimum mapping accuracy do you require for release to production (for example: 95% correct mapping for high-priority concepts)? Options: 95%+, 90-95%, 80-90%, Custom
• Which downstream systems consume the mapped tags (for example: CDI tools, coding queue, quality reporting warehouse) and what export format do they need?
• Who will provide authoritative mapping exceptions or local code overrides during initial tuning (for example: coding manager or specialty SME)?

Implement multi-speaker diarization and noise reduction

• Which scenarios require accurate speaker attribution (for example: multi-provider handoffs, family present, scribe + clinician)?
• What diarization accuracy is required at go-live (for example: speaker labeling correct >= X% over a 5-minute encounter)? Options: >= 95%, >= 90%, >= 85%, Custom
• Which noise sources are common at your sites and need targeted suppression (for example: monitor alarms, hallway noise, ventilators)?
• Where will voice-activity detection (VAD) thresholds need tuning because of low-volume speakers or masked clinicians?
• Who will own testing and sign-off of diarization and noise-reduction results in clinical pilot recordings?
• Do any clinical safety or legal policies restrict multi-party recording or require consent capture for family members or interpreters? Options: Yes, No
• Estimate the typical number of simultaneous speakers we must detect per encounter (for example: clinician + patient + family member + scribe). Options: 1-2, 3, 4+

Configure secure audio and data routing

• Which network zones will audio capture devices and edge engines connect to (for example: clinical VLAN, DMZ, or separate IoT network)?
• What encryption and transport requirements apply for audio and metadata in transit (for example: TLS 1.2+, mutual TLS, VPN)? Options: TLS 1.2+, Mutual TLS, IPsec VPN, Other
• Which data residency or cloud-region constraints must be observed for audio storage and processing?
• Which authentication model will your integration endpoint require for API calls (for example: OAuth2 client credentials, API key with IP allowlist)? Options: OAuth2 (client credentials), API key, Mutual TLS, Other
• Are there required retention and deletion policies for raw audio and derived transcripts (for example: retain raw audio for 90 days then purge)? Options: Retain 30 days, Retain 90 days, Custom retention policy, Purge immediately after processing

• Subscription Agreement / Order Form
• Master Services Agreement (MSA)
• Statement of Work (SOW)
• Service Level Agreement (SLA)
• Data Processing Agreement (DPA) / HIPAA Business Associate Addendum (BAA)
• Data Access Authorization
• Go-Live Acceptance Certificate
• Pricing and Payment Schedule
• Change Order Agreement
• Termination and Transition Addendum

• Go-live Health Check
• First Outcomes Measurement
• Acceptance Gate Review
• Quarterly Operational Review
• Annual Outcome Ratification

• Publish prioritized backlog items and expected delivery sprint for the top 3 enhancements.
• Capture the buyer's named signatory for the acceptance decision or a conditional acceptance plan with resolvable items and timelines.
• Define the verification method and date for any remediations required after this meeting.
• Publish the formal acceptance record with per-criterion pass/fail results and the buyer signatory details.
• Create a remediation tracker for any failed criteria with dates for verification.
• Schedule the next operational review cadence based on the acceptance outcome.
• Trend review for key metrics
• Confirm whether weekly active clinician users and coding query rate per 1,000 encounters are on track relative to targets recorded in the Solution Scope.
• Agree top 3 operational priorities for the next quarter and timebox their delivery.
• Ensure clinician feedback items are translated into specific training or configuration actions.
• Re-confirm acceptance criteria and owners
• Schedule clinician refresher trainings for identified specialties within 30 days.
• Update the risk register with mitigation owners and target completion dates.
• Annual outcomes presentation
• Ratify whether annual outcomes for reimbursement variance and clinician documentation time meet expectations recorded in the Solution Scope.
• Confirm closure or define final verification steps for multi-quarter remediations.
• Agree a set of multi-quarter optimization projects with clear outcomes and target dates.
• Publish the annual outcomes summary with supporting data and verification artifacts.
• Create project charters for agreed optimization workstreams with outcomes and milestones.
• Archive closed remediation evidence to the shared workspace for auditability.
• Confirm production integrations are live and data ingestion is occurring without critical errors.
• Identify and timebox all high-priority go-live issues with accountable owners and resolution dates.
• Ensure clinician access and initial user provisioning for the pilot specialties are complete.
• Validate integration logs and confirm API endpoint health during the next 24 hours.
• Publish the go-live issues tracker with owners and ETA for each ticket.
• Run a sample end-to-end test note for each specialty to confirm expected field mappings.
• Present first measurement data
• Determine whether documentation completeness rate by specialty and average clinician documentation time per encounter are moving toward targets recorded in the Solution Scope.
• Identify root causes for any KPI shortfalls and record a timebound remediation plan.
• Validate data sources and agree a single source of truth for ongoing measurement.
• Deliver a validated dataset and calculation workbook for the metrics discussed.
• Schedule targeted speech model tuning or template mapping fixes for identified specialties.
• Plan clinician-focused refresh training sessions addressing the top 2 workflow friction points.
• Restate acceptance criteria and numeric targets
• Produce a documented pass or fail outcome for each numeric acceptance criterion recorded in the Solution Scope.
• Deployment and integration validation
• Data quality and provenance check
• Quality and compliance impact review
• Present outcome data against each criterion
• Detailed review on persistent issue areas
• Document pass/fail per criterion and formal decision
• Outstanding remediation closure and verification
• Root-cause diagnosis for gaps
• Enhancement and defect backlog review
• Early adoption signals and usage patterns
• Agree remediation plan for any failed criteria
• Active issues and blockers
• Agree multi-quarter optimization projects
• Agree corrective actions and timeline to acceptance gate
• Clinician feedback and training needs
• Close the acceptance record
• Agree immediate remediation actions
• Escalations and risk register
• Open quality tickets and monitoring plan