HI253 Unit 9 Coding Accuracy Review Example

Reviewed by Chester Goodwin, MBA Medical Coding I Purdue University Global Free custom sample in 24 to 48h

This HI253 Unit 9 coding accuracy review tests fifteen composite outpatient encounters against their visit notes. Purdue Global HI 253 closes its applied units in the BS in Health Information Management by asking students to audit codes rather than assign them. The encounters arrive with 40 diagnosis codes, the review adds one documented condition no one coded, and 34 of the 41 hold. The seven errors are sorted into four types: two cases of lost specificity, three symptoms coded beside the diagnosis that explains them, one omission and one missed convention. Code-level accuracy works out to 82.9 percent and record-level accuracy to 53.3 percent, because the errors are spread across seven of the fifteen visits. The review closes with one recommendation aimed at the most frequent habit and a note on its own limits.

CourseHI253 Medical Coding I
UnitUnit 9
Paper typeCoding accuracy review
LengthAbout 880 words, 6 pages double-spaced
FormatAPA 7 student paper
SchoolPurdue University Global
ProgramBS in Health Information Management
UpdatedOctober 2026

Free sample paper for HI253 Unit 9

1

Thirty-Four of Forty-One Codes Hold: A Unit 9 Coding Accuracy Review Testing Fifteen Composite Outpatient Encounters Against Their Notes and Sorting Seven Errors Into Lost Specificity, Unsupported Codes, an Omission and a Missed Convention

[Student Name]

Purdue University Global

HI253: Medical Coding I

Unit 9 Coding Accuracy Review

[Instructor Name]

[Date]

The encounters, notes and original coding are composite; the code entries, guideline rules and sources cited are real and follow the FY 2026 ICD-10-CM.

What this part is doingThe error key comes before the grid so that every verdict in it points back to a defined category instead of the reviewer's impression.
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Scope and Method

The review covered fifteen consecutive outpatient visits coded by one coder in the family medicine clinic during one week. Each assigned code was compared with the visit note and the FY 2026 classification. A code was marked agree when the note supported it at the level of detail assigned and no guideline or tabular note changed it. Where the note documented a condition that affected the visit but no code reported it, the missing code was added to the review as a disagreement. Diagnosis codes only were reviewed; procedure and visit-level codes were outside the scope.

Error Key

1. Specificity lost: the note held detail that the assigned code dropped.

2. Unsupported: nothing in the documentation stood behind the code, including complaints that are simply part of how a confirmed diagnosis presents, which the guidelines tell coders to leave uncoded (National Center for Health Statistics, 2025).

3. Omitted: a documented condition that affected the visit went uncoded.

4. Convention missed: a tabular note or guideline changed the correct answer and was not applied.

Review Grid

1. Encounter 1: J02.0, Z88.0. Agree, agree.

2. Encounter 2: E11.9, I10, Z79.84, E78.5. E11.9 disagree, specificity lost. Note: "type 2 diabetes with diabetic polyneuropathy, monofilament reduced both feet." Corrected: E11.42. Others agree.

3. Encounter 3: S93.409A, W01.0XXA. S93.409A disagree, specificity lost. Note: "sprain of left ankle after slipping on a wet floor." Corrected: S93.402A. W01.0XXA agree.

4. Encounter 4: J45.31, Z79.51. Agree, agree.

5. Encounter 5: J02.0, R50.9. R50.9 disagree, unsupported. Note: "rapid strep positive, fever 101.4." The fever is part of the confirmed streptococcal pharyngitis. Corrected: delete R50.9.

6. Encounter 6: M17.12, I10, Z79.1. Agree on all three.

7. Encounter 7: N39.0, B96.20, Z87.440. Agree on all three.

8. Encounter 8: J20.9, R05.9, Z87.891. R05.9 disagree, unsupported. Note: "acute bronchitis, productive cough five days." Cough is integral to the diagnosis. Corrected: delete R05.9.

9. Encounter 9: F41.1, G47.00. Agree, agree.

10. Encounter 10: G43.909, R51.9. R51.9 disagree, unsupported. Note: "migraine, typical pattern, headache since this morning." Corrected: delete R51.9.

11. Encounter 11: K21.9, E78.5, Z79.899. Agree on all three.

12. Encounter 12: I10, E78.5, Z87.891, Z79.899. All four agree. Omitted: J44.9. Note: "COPD, more short of breath on stairs, adding a long-acting inhaler." The condition was assessed and treated at this visit.

13. Encounter 13: I10, N18.4, D63.1. I10 disagree, convention missed. Hypertension with chronic kidney disease is presumed linked under the chapter guideline. Corrected: I12.9, N18.4, D63.1, in that order.

14. Encounter 14: L03.116, E11.9, Z79.4. Agree on all three.

15. Encounter 15: H66.001, Z77.22. Agree, agree.

Three of the seven errors are the same habit: coding what the patient felt next to the diagnosis that already explains it.

What this part is doingEach disagreement quotes the phrase from the note that decides it, which lets a grader check the verdict without the full record.
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Summary

Codes in the review: 41, of which 40 were assigned and 1 was omitted. Codes that hold: 34. Code-level accuracy: 34 divided by 41, or 82.9 percent. Encounters with every code correct: 8 of 15. Record-level accuracy: 53.3 percent. Errors by type: specificity lost 2; unsupported 3; omitted 1; convention missed 1. The gap between the two accuracy figures shows how errors spread. Seven errors across forty-one codes looks modest, but they fell in seven different encounters, so nearly half of the claims carried at least one wrong or missing code. Accuracy studies have long warned that a single summary figure can hide this pattern, and that the way accuracy is counted, by code, by record or by field, changes the answer substantially (O'Malley et al., 2005).

What this part is doingReporting both rates and explaining why they differ shows the grader that the reviewer understands what each figure measures.
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Findings and Recommendation

The unsupported symptom codes are the clearest pattern and the easiest to fix. The coder appears to be coding from the chief complaint line as well as the assessment. A short refresher on the guideline for signs and symptoms integral to a diagnosis, with these three encounters as examples, should come first. The specificity errors suggest the coder stops at the first acceptable code rather than reading the full assessment. The convention error is a single case but a costly kind, since it misstates the relationship between two chronic conditions. A follow-up review of fifteen encounters in a month will show whether the refresher worked. Published reviews of coded data find that accuracy varies widely between settings and shifts over time as processes and incentives change, which supports a repeat sample rather than a one-time audit (Burns et al., 2012).

Limits of the Review

Fifteen encounters from one coder in one week are a small sample, and the rates above describe this week's work rather than the coder's typical accuracy. The review also judged codes only against the notes as written; where a note was thin, the reviewer did not guess at what the clinician meant. No borderline cases arose in this set, but had one appeared, the right verdict would have been a disagreement resolved by a query rather than a forced correction.

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References

Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2012). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. https://doi.org/10.1093/pubmed/fdr054

National Center for Health Statistics. (2025). ICD-10-CM official guidelines for coding and reporting FY 2026. Centers for Disease Control and Prevention.

O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring diagnoses: ICD code accuracy. Health Services Research, 40(5p2), 1620-1639. https://doi.org/10.1111/j.1475-6773.2005.00444.x

HI253 Unit 9 instructions, in plain terms

The ninth unit usually provides a set of coded encounters, often ten to twenty, with their documentation, and asks you to review each code for accuracy. You are expected to mark every code as correct or incorrect, give the corrected code where one is needed, and classify the error by type. Many prompts supply an error key or ask you to build one. Most also want accuracy calculated at the code level and the record level, with the formulas shown. A short findings section and one or two recommendations aimed at the patterns you find are standard. Some instructors limit the review to diagnosis codes, while others include procedure or visit-level codes. A reference to the current guidelines, and to published accuracy research if you compare your rates, is normally expected.

Inside the HI253 Unit 9 example

The review opens with its scope and method: fifteen consecutive visits from one family medicine coder, diagnosis codes only, each compared with the note and the current classification. An error key defines the four categories used. The review grid follows as a numbered list, one encounter per line, with each code marked agree or disagree, the phrase from the note that decided it and the corrected code. A highlighted line points out that three of the seven errors share one habit. The summary gives the counts and both accuracy rates with their arithmetic. Findings and a single recommendation follow, aimed at symptom codes reported with their explaining diagnosis. A closing section names the limits of a fifteen-visit sample from one coder and one week.

Where the points sit in the HI253 Unit 9 rubric

Accuracy reviews are commonly graded on three things: correct judgments on each code, correct classification of each error and correct calculation of the summary rates. The first block is the largest, and a missed omission tends to cost as much as a wrong correction, since finding what is absent is part of the skill. Error categories must match the key, and loose labels like coding mistake earn little. Both accuracy rates need their formulas shown; the gap between them is often where analysis points are given. Recommendations score when they follow from the pattern found rather than from general advice. A short limits section, APA references and orderly presentation of the grid take the remaining marks in most sections.

HI253 Unit 9 help from the desk

Students most often skip the search for omitted codes and review only what was assigned, which hides a whole error type. A second problem is calling a code wrong because of personal preference when the note supports it. Some calculate a single accuracy rate and miss the record-level figure, which tells a different story. Error types also get mixed, with lost specificity filed as an unsupported code. Recommendations that ask coders to be more careful, without naming what to check, rarely earn credit. We can review the encounters in your assignment against the documentation provided, build the grid and calculate both rates. Send the coded encounters, the error key if one was given and the code set edition, and say whether procedure codes are in scope.

Get an HI253 Unit 9 example written to your instructions

The encounters and assigned codes from your Unit 9 assignment are the material to send, together with the review template if one is provided and the rubric. The grid, both accuracy rates and the recommendation are built from that set, with delivery in 24-48h. A first custom sample is not billed. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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HI253 Unit 9 questions, answered

How do code-level and record-level accuracy differ?

Code-level accuracy divides the codes that were correct by all codes reviewed. Record-level accuracy counts an encounter as correct only if every code on it was right. A coder can score well on the first and poorly on the second when errors are spread thinly across many records, which is why the sample reports both and explains the gap between them.

Should the review include financial impact?

Only if the instructions ask for it. Many HI253 reviews stay with accuracy against documentation and guidelines, because payment effects depend on payer contracts the scenario rarely supplies. Where a section wants impact noted, a column marking whether each correction would change the claim is usually enough, without computing amounts that the case gives no basis for.

Where can I find a free HI253 Unit 9 sample paper?

This page holds a complete Unit 9 coding accuracy review: fifteen composite visits, 41 codes, seven errors in four types and accuracy of 82.9 percent by code and 53.3 percent by record.

Why is record-level accuracy lower than code-level accuracy?

A record counts as correct only if every code on it is right. Seven errors spread over seven different visits cut the record rate to 53.3 percent while most individual codes still hold.

What error types belong in a coding accuracy review?

The sample uses four: lost specificity, a code the note does not support, a documented condition left uncoded and a missed convention. Your instructor may supply a different key.