ASF Standards · Laboratory · Standard 8
Standard 8 — Post-Examination: Reporting & Critical Values
Criteria in this standard
8.1 — Result Authorization Before Release
8.2 — Report Content Meets a Defined Minimum
8.3 — Report Delivered to the Correct Recipient
8.4 — Turnaround Time Monitored Against a Target
8.5 — Supplementary and Referred Test Results Tracked to Closure
8.6 — Cumulative/Historical Results Available at Review
8.7 — Interpretive Comments Added Where Clinically Needed
8.2 — Report Content Meets a Defined Minimum
8.3 — Report Delivered to the Correct Recipient
8.4 — Turnaround Time Monitored Against a Target
8.5 — Supplementary and Referred Test Results Tracked to Closure
8.6 — Cumulative/Historical Results Available at Review
8.7 — Interpretive Comments Added Where Clinically Needed
8.1
Result Authorization Before Release
Non-Negotiable
Every result is reviewed and authorized by qualified staff before release to the requesting clinician, with the authorization step distinct from the generation of the raw instrument value and traceable to a named individual.
In plain terms: A real, qualified person actually reviews and approves every result before it goes out — that approval is a genuine, separate step, not just the instrument spitting out a number.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
An instrument generating a numeric output is not the same as a qualified authorization decision — the review step is what catches an unflagged specimen quality issue, an implausible value given the patient’s clinical context, or a pattern suggesting instrument malfunction that the raw number alone wouldn’t reveal. Auto-verification rules can legitimately handle routine, predictable results, but only when the rules themselves were deliberately validated and are periodically revisited, not simply left running indefinitely since initial configuration.
What good looks like
- Authorization is a distinct, attributable step separate from instrument output.
- Auto-verification rules, where used, are validated and periodically reviewed.
- Any result’s authorizer and timing is retrievable on request.
Common failure modes
- Results pass through with no real human review step.
- Auto-verification rules were configured once and never revisited.
- No clear record exists of who authorized a specific result.
Worked example
In practice
A chemistry section with auto-verification enabled for routine panels.
BeforeAuto-verification rules had been configured three years earlier when the analyzer was first installed and had never been reviewed since, despite the laboratory’s patient population and test menu having changed meaningfully over that period.
ActionThe quality manager scheduled an annual review of auto-verification rule performance, comparing auto-released results against a manual review sample to confirm the rules were still appropriately calibrated.
AfterThe Monitor reviewed the auto-verification rule set and found a documented annual review history with the most recent review completed within the past year. Criterion verified.
If you are starting from zero — do this first
- Check when your auto-verification rules, if used, were last reviewed.
- Confirm you can trace any specific result to its named authorizer.
- Schedule a periodic review if none currently exists.
The most common mistake: Configuring auto-verification rules once at system setup and treating them as permanently correct, without revisiting them as the laboratory’s actual patient population or test menu evolves.
Self-assessment questions
1. Is there a documented authorization step separate from instrument output, attributable to a specific named person? — Auto-verification rules, where used, still need a documented, validated basis — not simply every result passing through untouched.
Evidence: Authorization record
Evidence: Authorization record
2. Where auto-verification rules release results without manual review, are the rules themselves validated and periodically reviewed? — An auto-verification rule set that was configured once and never revisited is a specific, checkable gap.
Evidence: Auto-verification rule review history
Evidence: Auto-verification rule review history
3. Can the laboratory show, for any result selected at random, who authorized it and when? — Retrievable on request, not reconstructed from memory.
Evidence: Live record retrieval
Evidence: Live record retrieval
Common reasons for a PARTIAL answer
- Auto-verification rules exist but haven’t been reviewed since initial setup.
- Manual authorization happens but isn’t clearly attributed to a named individual.
Implementation plan
| When | What |
|---|---|
| Week 1 | Audit current authorization practice and any auto-verification rules. |
| Week 2 | Schedule a review of auto-verification rules if overdue. |
| Week 3 | Confirm clear attribution exists for every authorization. |
| Ongoing | Review auto-verification rules annually or whenever test menu changes. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Authorization record check | Selects a result at random and verifies authorizer attribution and timing. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 7.4.1. Geneva: ISO; 2022.
Clinical and Laboratory Standards Institute. AUTO10-A: Autoverification of Clinical Laboratory Test Results. Wayne (PA): CLSI; 2006.
8.2
Report Content Meets a Defined Minimum
Non-Negotiable
Every report includes, at minimum, patient identification, specimen type and collection date/time, test name, result with units, reference interval, and any flag or interpretive comment needed for safe clinical use.
In plain terms: Every report has everything a clinician actually needs to correctly understand a result — not just the number by itself.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
A number without units, a reference interval, or context about specimen quality is not genuinely interpretable, regardless of how accurate the underlying measurement was — report completeness is what converts a correct analytical result into something a clinician can safely act on. Gaps here are often subtle, a reference interval occasionally missing from certain report templates, rather than a complete absence of structure.
What good looks like
- Every required element is present, not just the result value.
- Out-of-range values are clearly flagged, not reported as falsely precise.
- Specimen quality issues affecting interpretation are noted when relevant.
Common failure modes
- A reference interval is missing from a specific report template.
- A value beyond reportable range is shown as an exact number with no flag.
- Specimen quality issues go unnoted even when they could affect interpretation.
Worked example
In practice
A laboratory auditing its various report templates across test categories.
BeforeMost report templates included all required elements, but a recently added point-of-care test’s report template, built quickly during service launch, omitted the reference interval entirely.
ActionThe quality manager built a standard report-template checklist covering all required elements, applied it retroactively to every existing template, and made the checklist a mandatory step for any future new test launch.
AfterThe Monitor selected reports at random across several test categories and found every required element present consistently. Criterion verified.
If you are starting from zero — do this first
- Pull sample reports from every test category you offer.
- Check each against the full required element list.
- Fix any template gaps found, especially newer or less-used templates.
The most common mistake: A newer test category’s report template, built quickly during service launch, misses an element that older, more established templates include by default.
Self-assessment questions
1. Pick a report at random — are all required elements present, not just the result value itself? — A result with no reference interval or units attached is not safely interpretable by the receiving clinician.
Evidence: Sample report review
Evidence: Sample report review
2. Where a result falls outside the reportable range of the method, is this clearly flagged rather than reported as an exact number? — Reporting a value beyond the validated range as if it were precise is misleading.
Evidence: Out-of-range result example
Evidence: Out-of-range result example
3. Is specimen quality that may affect interpretation — hemolysis, lipemia — noted on the report when relevant? — A clinician interpreting a result without this context may draw the wrong conclusion.
Evidence: Specimen quality flag example
Evidence: Specimen quality flag example
Common reasons for a PARTIAL answer
- A newer or less common test category’s template is missing an element.
- Out-of-range flagging isn’t consistently applied.
Implementation plan
| When | What |
|---|---|
| Week 1 | Audit all report templates against the required element checklist. |
| Week 2 | Fix any templates found missing elements. |
| Week 3 | Build the checklist into any future new-test launch process. |
| Ongoing | Re-audit templates periodically, especially after system updates. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Report content review | Reviews sample reports across test categories for required element completeness. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 7.4.1. Geneva: ISO; 2022.
8.3
Report Delivered to the Correct Recipient
Non-Negotiable
A written procedure confirms the result report reaches the correct requesting clinician or facility, with a check in place to catch and correct misdirected reports before they reach the wrong recipient.
In plain terms: There’s a real safety net catching reports that might go to the wrong place, not just hope that the system always routes correctly.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
A correct result delivered to the wrong clinician is, from the patient’s perspective, functionally equivalent to no result at all — the clinician who actually needs to act on it never sees it, while the recipient who received it by mistake has no reason to act on information that isn’t theirs to interpret. A misdirection-catching safeguard that has never actually caught anything is less reassuring than one with a genuine, documented catch demonstrating it functions in practice.
What good looks like
- A real, documented instance exists of a misdirected report being caught and fixed.
- Electronic delivery includes actual confirmation of receipt, not just transmission.
- A defined process exists for pending results following a transferred patient.
Common failure modes
- A theoretical safeguard exists but has never actually been tested.
- Electronic delivery is treated as successful once sent, with no receipt confirmation.
- Results pending at patient transfer silently fail to follow the patient.
Worked example
In practice
A referring clinic network with multiple similarly named practitioners.
BeforeTwo physicians with similar surnames occasionally had results confused in the electronic routing system. The laboratory was unaware this had ever happened until a physician called asking why she’d received a result for a patient she didn’t recognize.
ActionThe laboratory built a secondary verification step specifically for similarly named recipients, cross-checking against the ordering clinician’s unique provider identifier rather than name alone, and logged the earlier catch as a formal nonconformance with corrective action.
AfterThe Monitor reviewed the nonconformance record and the resulting corrective action, confirming the verification step had since prevented any further similar incidents. Criterion verified.
If you are starting from zero — do this first
- Check whether your delivery process confirms actual receipt, not just transmission.
- Identify any recipient name-collision risks in your routing system.
- Build a specific process for pending results following a patient transfer.
The most common mistake: Assuming electronic transmission success is equivalent to delivery confirmation, when a sent message can fail silently without the sender ever knowing.
Self-assessment questions
1. Is there a documented instance of a misdirected report being caught and corrected, demonstrating the check actually functions? — A theoretical safeguard that has never actually been tested in practice is less reassuring than one with a real catch on record.
Evidence: Nonconformance record
Evidence: Nonconformance record
2. For electronic delivery, is there confirmation the receiving system actually accepted the transmission, not just that it was sent? — A sent message with no delivery confirmation can silently fail.
Evidence: Delivery confirmation log
Evidence: Delivery confirmation log
3. Where a patient has been transferred between facilities, is there a defined process for ensuring pending results follow them? — Results pending at the time of a transfer are a well-documented point where results get lost.
Evidence: Transfer process documentation
Evidence: Transfer process documentation
Common reasons for a PARTIAL answer
- No verification process exists for similarly named recipients.
- Pending results at patient transfer have no defined follow-through process.
Implementation plan
| When | What |
|---|---|
| Week 1 | Audit delivery confirmation practice for electronic routing. |
| Week 2 | Identify and address any recipient name-collision risks. |
| Week 3 | Build a pending-results-at-transfer process. |
| Ongoing | Log any misdirection catch as a formal nonconformance. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Delivery and nonconformance review | Checks for delivery confirmation records and any documented misdirection catch. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 7.4.3. Geneva: ISO; 2022.
8.4
Turnaround Time Monitored Against a Target
Core
Turnaround time from specimen receipt to result release is tracked against a defined target for each major test category, reviewed periodically, with documented action when targets are consistently missed.
In plain terms: The lab actually tracks how long each type of test takes and reviews whether it’s meeting real targets — and does something about it when it isn’t.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
A single blanket turnaround target applied across wildly different test complexities obscures genuine performance — a basic chemistry panel and a complex send-out test have fundamentally different realistic timelines, and measuring both against one figure makes neither number meaningful. Data collected but never actually reviewed provides no operational value at all; the review step is where turnaround problems actually get identified and addressed.
What good looks like
- Targets are defined per test category, reflecting genuine complexity differences.
- Turnaround data is actually reviewed on a schedule.
- Consistent target misses trigger a documented response, not just observation.
Common failure modes
- One blanket target is applied regardless of test complexity.
- Data is collected but sits unreviewed.
- The same shortfall is noted repeatedly with no action taken.
Worked example
In practice
A laboratory tracking turnaround across a mixed test menu.
BeforeA single sixty-minute turnaround target was applied to all tests, from simple basic chemistry to complex send-out panels, making the metric essentially meaningless since complex tests routinely and unavoidably exceeded it while simple tests had no real stretch target at all.
ActionThe quality manager built category-specific targets reflecting genuine achievable timelines, with monthly review meetings specifically examining any category consistently missing its target.
AfterThe Monitor reviewed the past six months of category-specific turnaround data and found one category with a documented, implemented response to a consistent shortfall. Criterion verified.
If you are starting from zero — do this first
- Check whether your current targets are category-specific or one blanket figure.
- Build realistic, test-specific targets if needed.
- Schedule a genuine periodic review, not just data collection.
The most common mistake: Applying one convenient, round-number turnaround target across the entire test menu, which makes the metric meaningless for both the fastest and slowest test categories.
Self-assessment questions
1. Are turnaround targets defined per test category, not one blanket figure applied regardless of complexity? — A single target applied to both a basic chemistry panel and a complex send-out test obscures real performance.
Evidence: Category-specific target documentation
Evidence: Category-specific target documentation
2. Is turnaround time data actually reviewed on a schedule, not just collected and left unexamined? — Data collection without review provides no operational value.
Evidence: Review meeting records
Evidence: Review meeting records
3. Where targets are consistently missed for a category, is there a documented response, not just repeated observation? — Noting the same shortfall month after month without action is not functioning monitoring.
Evidence: Response action record
Evidence: Response action record
Common reasons for a PARTIAL answer
- Targets exist but aren’t genuinely category-specific.
- Reviews happen but produce no documented action.
Implementation plan
| When | What |
|---|---|
| Week 1 | Build category-specific turnaround targets. |
| Week 2 | Set up tracking by category if not already in place. |
| Week 3 | Schedule the first formal review meeting. |
| Ongoing | Document action whenever a category consistently misses target. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Turnaround data and review record check | Reviews category-specific turnaround targets and any response to consistent misses. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 7.4.1. Geneva: ISO; 2022.
8.5
Supplementary and Referred Test Results Tracked to Closure
Core
Tests referred to another laboratory are tracked from referral through to receipt and reporting of the result, with a defined escalation point if the result does not return within an expected timeframe.
In plain terms: A test sent out to another lab doesn’t just disappear from view — it’s actively tracked until the result actually comes back and gets reported.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
A referred test exists in a genuine accountability gap — it has physically left the laboratory’s own systems, but the requesting clinician still expects the result to come from this laboratory. Without an active tracking log, a referred test can be forgotten entirely, with nobody specifically responsible for noticing it never came back, since it isn’t visible in the laboratory’s own routine workflow the way in-house tests are.
What good looks like
- A log of all currently outstanding referred tests exists with expected return dates.
- Overdue referred results are actively followed up, not passively awaited.
- Referred results go through the same authorization and critical-value process on arrival.
Common failure modes
- No tracked log exists; referred tests are simply awaited informally.
- An overdue result is noticed only when the clinician calls asking about it.
- Referred results bypass normal authorization because they arrive differently.
Worked example
In practice
A referred specialized hormone panel sent to an external reference laboratory.
BeforeReferred tests were tracked informally by whichever staff member had sent the specimen, with no central log. A referred test was forgotten for three weeks until the requesting physician called asking why no result had come back.
ActionThe laboratory built a central referred-test log with expected return dates based on the reference laboratory’s stated turnaround, with a weekly review flagging anything overdue for active follow-up.
AfterThe Monitor reviewed the central log and found a recent instance of an overdue result being actively followed up within two days of its expected return date, before any clinician inquiry prompted it. Criterion verified.
If you are starting from zero — do this first
- Build a single central log of all currently outstanding referred tests.
- Set expected return dates based on reference laboratory turnaround data.
- Schedule a regular review to flag anything overdue.
The most common mistake: Relying on individual staff memory to track referred tests informally, rather than a central log that doesn’t depend on any one person remembering.
Self-assessment questions
1. Does a log exist of all currently outstanding referred tests, with expected return dates? — Without a tracked log, a referred test can be forgotten entirely.
Evidence: Referred test log
Evidence: Referred test log
2. Is there a documented instance of an overdue referred result being actively followed up, not just passively awaited? — Evidence the escalation actually happens, not just exists as policy.
Evidence: Follow-up record
Evidence: Follow-up record
3. When a referred result finally arrives, does it go through the same authorization and critical-value process as an in-house result? — Referred results sometimes bypass normal review simply because they arrive through a different channel.
Evidence: Referred result authorization record
Evidence: Referred result authorization record
Common reasons for a PARTIAL answer
- Tracking relies on informal staff memory rather than a central log.
- Referred results bypass the normal authorization workflow.
Implementation plan
| When | What |
|---|---|
| Week 1 | Build a central log of all currently outstanding referred tests. |
| Week 2 | Set expected return dates and a review schedule. |
| Week 3 | Confirm referred results route through standard authorization. |
| Ongoing | Review the log weekly for overdue items. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Referred test log review | Reviews the central log for completeness and checks for proactive overdue follow-up. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 6.6.3. Geneva: ISO; 2022.
8.6
Cumulative/Historical Results Available at Review
Core
Prior results for the same patient and analyte are available alongside a new result at the point of review, allowing delta-check comparison, so a clinically significant change from a patient’s own baseline is not missed.
In plain terms: Whoever reviews a result can easily see the patient’s own past results right alongside it — so a meaningful change from their personal baseline doesn’t get missed.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
A result that falls entirely within the general population reference range can still represent a dangerous change for a specific patient — a significant drop in hemoglobin, for instance, might still land within the broad normal range while representing genuine, active bleeding for that particular person. Delta checking against the patient’s own history catches exactly this category of risk, but only if prior results are genuinely visible without requiring extra deliberate effort that gets skipped under time pressure.
What good looks like
- Prior history is visible automatically at review, no separate lookup required.
- The system flags a significant delta from the patient’s own baseline.
- A documented instance exists where a delta check caught a genuine issue.
Common failure modes
- History requires a separate manual lookup, easily skipped under pressure.
- Only absolute reference-range flags exist, with no patient-specific delta check.
- No example exists of the delta check ever actually catching something.
Worked example
In practice
A hematology result reviewed during a routine authorization workflow.
BeforePrior patient results were accessible but required navigating to a separate screen, a step reviewing technologists frequently skipped during busy periods since the current result fell within the general reference range and appeared unremarkable on its own.
ActionThe laboratory reconfigured the system to display the three most recent prior results automatically alongside any new result at the review screen, with an automatic flag for any analyte showing a significant percentage change from the immediately prior value.
AfterThe Monitor reviewed a recent case where the delta flag had caught a significant hemoglobin drop that remained within the general reference range, triggering a clinician call that led to timely identification of active bleeding. Criterion verified.
If you are starting from zero — do this first
- Check whether prior results require a separate lookup or display automatically.
- If display requires extra effort, work with your system to make it automatic.
- Build a delta-flag mechanism if one doesn’t currently exist.
The most common mistake: Having prior-result history technically accessible but requiring an extra deliberate step to view it, which busy staff predictably skip for results that otherwise appear unremarkable.
Self-assessment questions
1. Is prior-result history actually visible to the person authorizing a new result, not requiring a separate manual lookup they may skip? — If accessing history takes extra deliberate effort, it will often be skipped under time pressure.
Evidence: Review screen configuration
Evidence: Review screen configuration
2. Does the system flag a significant delta from the patient’s own prior value, not just an absolute reference-range flag? — A result that stays within the general reference range can still represent a dangerous change for a specific patient.
Evidence: Delta-flag configuration
Evidence: Delta-flag configuration
3. Is there a documented instance where a delta check caught a potential specimen mix-up or clinically significant change? — Evidence the check functions as a real safety mechanism, not just a theoretical feature.
Evidence: Delta-check catch record
Evidence: Delta-check catch record
Common reasons for a PARTIAL answer
- History is technically available but requires an extra, skippable lookup step.
- No real delta-flag mechanism exists beyond absolute reference ranges.
Implementation plan
| When | What |
|---|---|
| Week 1 | Check current review-screen history visibility. |
| Week 2 | Work with your system provider to automate history display if needed. |
| Week 3 | Build or confirm a delta-flag mechanism. |
| Ongoing | Document any instance where the delta check catches a genuine issue. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| OBSERVE | Review workflow observation | Observes the actual review screen to confirm prior-result visibility without extra steps. |
Evidence base
Clinical and Laboratory Standards Institute. EP33: Use of Delta Checks in the Medical Laboratory. Wayne (PA): CLSI; 2016.
8.7
Interpretive Comments Added Where Clinically Needed
Standard
Where a result pattern requires clinical interpretation beyond the raw numbers — an unusual combination, a known interference, a testing limitation — qualified staff add an interpretive comment to the report rather than releasing the number alone.
In plain terms: When a result genuinely needs extra context to be understood correctly, a qualified person adds that context to the report — not just leaving the clinician with a bare number.
| Facility category | Standalone lab | Hospital lab | Clinic lab |
|---|---|---|---|
| Applicability | Full | Full | Full |
Why this matters
Certain result patterns carry meaning that isn’t obvious from the number alone — a known assay interference, an unusual combination suggesting a specific condition, a testing limitation relevant to the specific clinical question. A laboratory that never adds interpretive comments, treating every result as self-explanatory, may be under-using a genuinely valuable tool for improving the clinical usefulness of its reports, particularly for less common or more complex findings.
What good looks like
- Examples exist of interpretive comments genuinely added where needed.
- A defined list identifies which situations trigger a required comment.
- Comments are written by staff qualified to make the specific clinical judgment.
Common failure modes
- No interpretive comments are ever added, even where clearly warranted.
- Whether a comment gets added depends entirely on individual staff initiative.
- Comments are added by staff without the relevant clinical qualification.
Worked example
In practice
A laboratory reviewing its use of interpretive commenting over the past year.
BeforeInterpretive comments were occasionally added by individual technologists when they personally noticed something warranting explanation, with no defined list of triggering situations and significant variation in practice between staff members.
ActionThe laboratory built a defined list of specific result patterns requiring a mandatory interpretive comment, with comments restricted to staff holding the appropriate qualification to make that specific clinical judgment.
AfterThe Monitor reviewed recent reports matching the defined trigger list and found consistent, appropriately qualified interpretive commenting applied. Criterion verified.
If you are starting from zero — do this first
- Check your recent reports for any history of interpretive comments at all.
- Build a defined list of situations that should trigger a comment.
- Confirm who is actually qualified to write them.
The most common mistake: Leaving interpretive commenting entirely to individual staff initiative with no defined trigger list, resulting in significant inconsistency in when and whether comments actually get added.
Self-assessment questions
1. Can the laboratory show examples of interpretive comments added to reports where genuinely needed? — Not every result needs commentary, but a laboratory with zero examples ever may be under-using this practice.
Evidence: Sample report with interpretive comment
Evidence: Sample report with interpretive comment
2. Is there a defined list of result patterns or situations that trigger a required interpretive comment? — Without defined triggers, whether a comment gets added depends entirely on individual initiative.
Evidence: Defined trigger list
Evidence: Defined trigger list
3. Are interpretive comments written by staff qualified to make that clinical judgment, not added by whoever happens to be reviewing the result? — Interpretation requires a level of expertise beyond routine result authorization.
Evidence: Comment authorization record
Evidence: Comment authorization record
Common reasons for a PARTIAL answer
- No defined trigger list exists; practice depends on individual initiative.
- Comments are occasionally added by staff without the relevant qualification.
Implementation plan
| When | What |
|---|---|
| Week 1 | Review recent reports for interpretive comment practice. |
| Week 2 | Build a defined trigger list for required comments. |
| Week 3 | Confirm and restrict comment-writing to appropriately qualified staff. |
| Ongoing | Review comment consistency periodically. |
How the Monitor verifies this
| Method | What | Detail |
|---|---|---|
| DOCUMENT | Interpretive comment review | Reviews sample reports against the trigger list for consistent, appropriately qualified commenting. |
Evidence base
International Organization for Standardization. ISO 15189:2022, Medical laboratories — Requirements for quality and competence, Clause 7.4.1. Geneva: ISO; 2022.