ISA 530 · Healthcare

Sample Size Calculator for Healthcare

Pre-configured sampling guidance for healthcare audits. Covers patient revenue testing, grant compliance sampling, and pharmaceutical inventory verification with ISA 530 methodology.

ISA 530 · LIVEv2026.04MUS

Sample size, defended.
Not just computed.

Session
0x12AA
Fiscal Year
FY 2026
Confidence
95%
inputs.conf
methodology.conf
README.md
01// engagement— ISA 530.4
02entity_name=
03fiscal_year_end=
04currency=
05public_interest=
06first_year=
09// method— ISA 530.A4–A14
10sampling.method=
11method.rationale=
Method rationale · ISA 530.A4 documentation
14// parameters— ISA 530.7–8
15confidence_level=
16population=
17tolerable_misstmt=€ · TM ≤ PM
18expected_misstmt=€ · EM · optional
19confidence.rationale=
20tm.rationale=
21em.rationale=
Parameter rationales · ISA 530.9 documentation
24// finite_population_correction— optional
25population_item_count=items · FPC applied if >10%
30// objective— ISA 530.6 · what you're testing + what counts as error
31assertion_tested=
32misstatement_definition=
Objective · ISA 530.6 what you test + error definition
35// population_completeness— ISA 530.5(f) · population must be complete
36population.definition=
37completeness_test=
38sampling_unit=
Population · definition + completeness test
40// stratification— ISA 530.A8 · optional; reduces variability
No strata defined. Stratification is optional — add strata if population has very different sub-groups (by $ size, risk, or nature).
48stratification.rationale=
Stratification · ISA 530.A8 (optional)
50// selection_method— ISA 530.8 · how items are selected
51selection.method=
52selection.rationale=
Selection method · ISA 530.8
55// evaluation— ISA 530.12–14 · MLE / tainting
56
Evaluation · ISA 530.12–14 MLE / tainting
65// sensitivity— EM ±20% impact on n
Enter population and tolerable misstatement to see sensitivity.
Sensitivity · EM ±20% impact on n
70// method_comparison— MUS vs Classical
Enter inputs to compare methods.
Method comparison · MUS vs Classical
75// risk_flags— 15-rule engine · regulator deficiency patterns
Enter inputs to run risk analysis.
Risk flags · regulator deficiency intelligence
80// conclusion— ISA 530.15 · narrative evaluation
81conclusion.narrative=
82qualitative_factors=
Conclusion · ISA 530.15 narrative + qualitative
awaiting input·3/8 core fieldsEUR·MUS
previewwp-mus-2026.pdf
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Sample size
Awaiting input
PRIMARY
Sampling interval
Population ÷ n
Top stratum
Items ≥ interval
Max errors
Before exceeding TM
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Professional working paper with engagement header and sign-off fields
Full ISA 530 methodology with paragraph citations
Step-by-step sample size calculation workings
TM and EM sensitivity analysis tables
Method comparison (MUS vs Classical) with rationale
Risk intelligence flags with ISA references
Sample evaluation section with Stringer bound (MUS)
Population selection documentation and checklist
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Sampling Working Paper
ISA 530 (Revised) · ISA 500
1Sampling ParametersISA 530.7–9
2Sample Size CalculationISA 530.A10–A11
3Method RationaleISA 530.A4–A5
4Sensitivity AnalysisTM & EM ±
5Risk IntelligenceISA 530.A2–A8
6Selection MethodISA 530.A12–A14
7Evaluation CriteriaISA 530.14–15
8Documentation ChecklistISA 530.9
Prepared by ________Reviewed by ________Date ________
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Sampling Considerations for Healthcare

Healthcare audits combine high-volume patient billing transactions with complex funding arrangements (government grants, insurance reimbursements, self-pay). Revenue recognition requires testing across multiple payer types, each with different contractual terms, approval processes, and timing considerations.

Sampling focus: Healthcare

Patient revenue sampling should be stratified by payer type — government-funded, insurance, and self-pay streams each carry different risk profiles. For publicly funded healthcare entities, grant compliance testing uses attribute sampling to assess adherence to funding conditions. Pharmaceutical and medical equipment inventories require both existence and valuation testing.

Key sampling considerations

Stratify revenue populations by payer category (government, insurance, self-pay) because each has different recognition timing, approval workflows, and collection risk.

Insurance claim adjustments and denials are common — sample claims to test whether revenue is being recognised net of expected adjustments.

Grant-funded expenditure requires attribute sampling to test compliance with specific conditions — non-compliance can trigger clawback provisions.

Pharmaceutical inventory has expiry date risk — sample items to verify that provisions for expired or near-expiry stock are adequate.

Capital equipment often involves donated assets or government-funded purchases — sample additions to verify correct recognition, valuation, and any restrictions on use.

Frequently asked questions

What are the key sampling considerations for healthcare audits?
Stratify revenue populations by payer category (government, insurance, self-pay) because each has different recognition timing, approval workflows, and collection risk. Insurance claim adjustments and denials are common — sample claims to test whether revenue is being recognised net of expected adjustments. Grant-funded expenditure requires attribute sampling to test compliance with specific conditions — non-compliance can trigger clawback provisions. Pharmaceutical inventory has expiry date risk — sample items to verify that provisions for expired or near-expiry stock are adequate. Capital equipment often involves donated assets or government-funded purchases — sample additions to verify correct recognition, valuation, and any restrictions on use.
What is the sampling focus for healthcare?
Patient revenue sampling should be stratified by payer type — government-funded, insurance, and self-pay streams each carry different risk profiles. For publicly funded healthcare entities, grant compliance testing uses attribute sampling to assess adherence to funding conditions. Pharmaceutical and medical equipment inventories require both existence and valuation testing.
How does the ISA 530 MUS formula work?
The standard MUS formula is: n = (Population x Confidence Factor) / (Tolerable Misstatement - Expected Misstatement x Expansion Factor). The confidence factor reflects the acceptable risk of incorrect acceptance. Expected misstatement increases the required sample size because the auditor must leave headroom above the expected level before reaching the tolerable threshold.

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