The short answer
AI-assisted documentation is the broader category: software uses encounter-specific material to prepare documentation for review. Ambient documentation is one input workflow within that category; it captures audio or other signals in the background during an encounter instead of relying only on material entered afterward. Whether a product records audio, creates a transcript, retains either artifact, or requires a particular notice or consent process depends on the product, configuration, and setting. Generated text should remain visibly unreviewed until an authorized user completes the organization’s review and sign-off process. Buyers should verify the complete artifact path and test whether the workflow actually improves accuracy, editing burden, and encounter-to-signature time.
What are AI progress notes?
An AI progress note describes an output, while ambient documentation describes an input method. A clinician-directed workflow may draft from dictation, shorthand, template fields, or other entered material without being ambient. An ambient workflow may capture encounter audio or other signals while the session is underway and may create recordings, transcripts, drafts, or other intermediate artifacts.
These approaches can coexist, but they create different operational questions. For each supported workflow, determine what is captured, what derived artifacts are created, where each artifact appears before and after review, who can access it, how it is labeled, and when it is retained or deleted. Do not infer those answers from the terms “AI” or “ambient.”
How the mechanics actually work
A documentation workflow can include the following stages; the exact sequence and artifacts must be confirmed for the product and configuration being evaluated:
- Input or ambient capture. The source may be clinician-entered fields, shorthand, dictation, encounter audio, or another supported input. Verify when capture begins and ends, how notice and consent are handled where applicable, and what happens if capture fails.
- Processing and intermediate artifacts. The system may transcribe audio, extract details, or structure entered material. Identify whether it creates a recording, transcript, prompt, temporary file, or support log and where each artifact is processed and stored.
- Draft generation. The system prepares text from the available input and configured note structure. Treat the result as unreviewed: it may omit facts, confuse speakers, introduce unsupported details, or use language that does not fit the encounter.
- Human validation. An authorized reviewer compares the draft with the encounter and permitted source material, corrects errors and attribution, and applies the organization’s clinical-documentation standards.
- Configured finalization. The organization’s workflow records approval or signature and controls downstream use. A signature event alone does not determine the legal, retention, discoverability, correction, or record status of earlier drafts, recordings, transcripts, or other artifacts; those classifications depend on the applicable rules, policy, and system design.
As an operational practice, organizations should consider controls that keep unreviewed output from being relied upon as finalized documentation. That safeguard belongs alongside controls for inappropriate capture, access, retention, attribution, downstream transmission, outages, and incident handling.
What the workflow is designed to do — and what to validate
These tools are designed to turn encounter-specific input into a structured draft. Whether a particular workflow produces useful drafts quickly enough to reduce work is an empirical question: test it by note type, input method, setting, and reviewer. For a documentation-only use case, generated text should support rather than substitute for clinical judgment, and the product’s full feature scope should be verified rather than assumed.
| Candidate function to test | Human validation question |
|---|---|
| Organizing input into a configured note structure | Does each statement accurately reflect the encounter and belong in the selected note type? |
| Summarizing or drafting narrative language | Are material facts missing, speakers misattributed, or unsupported details introduced? |
| Populating configured fields or sections | Does the output follow the organization’s documentation rules without masking missing information? |
| Suggesting language related to diagnosis, risk, treatment, or medical necessity | Has an appropriately authorized person independently evaluated and approved the clinical content? |
| Routing a draft toward approval | Is the draft visibly unreviewed, and do permissions prevent unintended reliance or release? |
The useful boundary is procedural: generated text remains a draft until the organization’s authorized review process is complete. The use policy should identify who may review each note type, what source material may be consulted, which errors require escalation, and what happens when the input or generation step fails.
How to test whether the workflow saves time
Measure efficiency rather than assuming it. Establish a baseline before the pilot, use the same definitions during the pilot, and separate draft-generation speed from total documentation work.
- Track total time from encounter end to approved note, active editing time, and after-hours documentation time.
- Stratify results by note type, input method, setting, clinician, and session complexity.
- Record omissions, speaker-attribution errors, unsupported details, critical corrections, and abandoned drafts.
- Count time spent obtaining or documenting consent, correcting failed capture, switching to a fallback workflow, and resolving support issues.
- Set acceptance thresholds for time, quality, and failure rates before deciding whether to expand the pilot.
As one product-specific benchmark, Sunwave reports that JourneyPure measured 44 minutes saved per BPS assessment. Treat that as a vendor-reported result to investigate—not a forecast for another organization—and ask for the baseline, sample, workflow, editing burden, and error measures behind it. See Sunwave’s SIA product page.
Privacy, security, and record-governance questions
Inputs and intermediate artifacts may contain identifiable health information, but their legal classification and required handling depend on the data, organization, use, and jurisdiction. Map the actual workflow first, then assess each artifact rather than assuming that a recording, transcript, prompt, draft, and signed note all have identical status or controls:
- What data and artifacts exist? Document every input, recording, transcript, prompt, draft, temporary file, signed note, export, and support log, including where each is processed and stored.
- Who receives or can access them? Review workforce roles, vendor access, model providers, subprocessors, integrations, support channels, exports, and downstream systems for each artifact.
- What security controls apply? Assess access enforcement, encryption boundaries and key management, authentication, logging, monitoring, backup behavior, and incident response in the actual deployment. The presence of any single control does not establish that the workflow is safe or compliant.
- How is data used? Check contracts and technical behavior for service delivery, model training, product improvement, human review, de-identification, opt-out choices, and deletion obligations.
- What is the lifecycle? Define creation, labeling, retention, deletion, correction, legal-hold, export, and downstream-use rules separately for unreviewed and finalized artifacts.
- Does 42 CFR Part 2 apply? HHS describes Part 2 as protecting specified records concerning identity, diagnosis, prognosis, or treatment maintained in connection with federally assisted substance-use-disorder programs or activities; its final-rule fact sheet also states that segregating or segmenting Part 2 records is not required. Those statements do not determine whether a particular organization or artifact is covered. Have privacy counsel and health-information-management leaders evaluate the actual use case. See the HHS Part 2 final-rule fact sheet.
- What happens when the workflow fails? Test interrupted capture, missing or low-quality input, incorrect speaker attribution, generation failure, outages, unauthorized access, deletion failure, and the fallback documentation process.
A pilot and adoption checklist
| Step | Decision evidence |
|---|---|
| 1. Define the use case | Supported note types, intended users, accepted inputs, prohibited uses, reviewer qualifications, and the fallback workflow |
| 2. Map the artifact path | Capture, processing, storage, access, labeling, transfer, retention, deletion, and downstream use for every source and derived artifact |
| 3. Test draft quality | Error and editing burden by note type and input method, including omissions, attribution mistakes, unsupported details, and clinically significant corrections |
| 4. Verify review controls | Where unreviewed output appears, who can act on it, how status is displayed, what changes require renewed review, and how approval is recorded |
| 5. Complete privacy and security review | Applicable obligations, data flows, subprocessors, contracts, access controls, encryption boundaries, logs, incident procedures, training use, and deletion behavior |
| 6. Measure operational effect | Baseline and pilot results for total encounter-to-approval time, editing time, after-hours work, quality measures, failures, and support effort |
| 7. Exercise failure scenarios | Expected behavior during failed capture, poor input, outages, generation errors, unauthorized access, and recovery or correction |
| 8. Make a bounded decision | Whether predefined quality, time, security, and reliability thresholds were met for the tested workflows—not for untested uses |
How to evaluate EHR integration
Sunwave’s SIA product page describes SIA as embedded in the Sunwave platform and says the documentation workflows it lists are reviewed by the clinician before signing. Those are vendor-described capabilities, not evidence that every configuration has the same inputs, artifacts, permissions, or outcomes.
In a demonstration, follow one note from input through finalization. Ask the team to show capture indicators, recording and transcript behavior, draft labeling, edits and attribution, reviewer permissions, signature controls, audit events, integrations, exports, subprocessors, retention and deletion settings, training or product-improvement use, outage behavior, and the boundary of the proposed feature set. Confirm which steps occur inside the EHR and which involve another service.
