Healthcare
19 columns
100 rows
8 formats

Medical Coding and Audit Results

This dataset provides a comprehensive view of professional medical coding assignments, audit results, coding accuracy, DRG and HCC validation, and compliance with official guidelines. It is designed for healthcare quality assurance, reimbursement optimization, and regulatory compliance analytics, supporting both operational and strategic decision-making.

last updated 2 Nov 2025
by GoMask

The brief that made it

Medical coding quality assurance and audit tracking

Sample rows

preview · 8 of 100 rows · all 19 columns

assignment_idstring	drg_validation_statusstring	coding_accuracy_scorefloat	guideline_complianceboolean	secondary_diagnosis_codesstring	patient_idstring	encounter_datedate	coder_idstring	coding_statusstring	primary_diagnosis_codestring	procedure_codesstring	drg_codestring	hcc_codesstring	audit_idstring	audit_datedate	auditor_idstring	reimbursement_amountfloat	audit_findingsstring	coding_notesstring
CA10001	valid	0.98	true	I10	P20001	2023-07-11	CODER05	completed	E11.9	99213	291	HCC19,HCC85	blank	blank	blank	150.67	blank	Standard outpatient visit for diabetes follow-up.
CA10002	blank	blank	false	blank	P20002	2023-07-12	CODER07	pending	J45.909	blank	blank	blank	blank	blank	blank	0	blank	Awaiting physician documentation for asthma severity.
CA10003	corrected	0.85	true	E78.5,I10	P20003	2023-07-13	CODER02	audited	I21.3	92928,93010	246	HCC85	AUD30001	2023-07-18	AUDITOR02	8700.5	DRG corrected due to additional comorbidity code.	Acute MI with stent placement.
CA10004	blank	0.92	true	blank	P20004	2023-07-14	CODER03	completed	N39.0	87086	blank	blank	blank	blank	blank	70	blank	UTI confirmed by urine culture.
CA10005	valid	0.95	true	E11.9	P20005	2023-07-15	CODER01	audited	K21.9	43239	377	HCC19	AUD30002	2023-07-19	AUDITOR03	1200	Procedure code validated, no discrepancies.	Endoscopy for GERD, diabetic patient.
CA10006	blank	0.11	false	blank	P20006	2023-07-16	CODER05	rejected	R07.9	blank	blank	blank	blank	blank	blank	0	blank	Insufficient documentation for chest pain coding.
CA10007	blank	0.96	true	blank	P20007	2023-07-17	CODER06	completed	M54.5	97110,97112	blank	blank	blank	blank	blank	230.89	blank	Physical therapy for low back pain.
CA10008	blank	blank	false	blank	P20008	2023-07-18	CODER08	pending	F41.9	blank	blank	blank	blank	blank	blank	0	blank	Awaiting psychologist documentation for anxiety disorder.
CA10009	blank	0.91	true	R50.9	P20009	2023-07-19	CODER09	completed	J06.9	99214	blank	blank	blank	blank	blank	135	blank	Acute upper respiratory infection with fever.
CA10010	invalid	0.72	true	I10	P20010	2023-07-20	CODER03	audited	E66.9	99401	640	HCC22	AUD30003	2023-07-22	AUDITOR01	350	DRG invalid; comorbidity missing.	Counseling for obesity and hypertension.
CA10011	valid	0.99	true	Z87.81	P20011	2023-07-21	CODER01	completed	S72.001A	27236	480	HCC85	blank	blank	blank	6200	blank	Hip fracture repair in patient with history of osteoporosis.
CA10012	blank	blank	false	blank	P20012	2023-07-22	CODER02	pending	G43.909	blank	blank	blank	blank	blank	blank	0	blank	Migraine coding pending neurologist notes.
CA10013	valid	0.97	true	E11.9,I10	P20013	2023-07-23	CODER04	completed	I50.9	93306	291	HCC85,HCC19	blank	blank	blank	4800	blank	CHF with diabetes and hypertension, echo performed.
CA10014	corrected	0.82	true	J44.9	P20014	2023-07-24	CODER06	audited	C34.90	31622	177	HCC12	AUD30004	2023-07-26	AUDITOR02	11300	DRG updated after additional diagnosis code.	Lung cancer patient with COPD, biopsy performed.
CA10015	blank	0.94	true	blank	P20015	2023-07-25	CODER07	completed	B34.9	blank	blank	blank	blank	blank	blank	60	blank	Viral infection, no specific findings.
CA10016	blank	blank	false	blank	P20016	2023-07-26	CODER08	pending	M25.561	blank	blank	blank	blank	blank	blank	0	blank	Awaiting radiology report for joint pain.
CA10017	blank	0.93	true	blank	P20017	2023-07-27	CODER09	completed	R51	96372	blank	blank	blank	blank	blank	40	blank	Injection for headache relief.
CA10018	blank	0.18	false	blank	P20018	2023-07-28	CODER05	rejected	K52.9	blank	blank	blank	blank	blank	blank	0	blank	Documentation does not support diagnosis of noninfective gastroenteritis.
CA10019	blank	0.9	true	blank	P20019	2023-07-29	CODER06	completed	E78.5	blank	blank	blank	blank	blank	blank	68	blank	Hyperlipidemia with no active intervention.
CA10020	blank	blank	false	blank	P20020	2023-07-30	CODER01	pending	F32.9	blank	blank	blank	blank	blank	blank	0	blank	Depression diagnosis pending psychiatric evaluation.

What the 100 rows show

from the 100-row sample

Valid (drg validation status) stands out: mean coding_accuracy_score is 0.95, against 0.79 for the rest.

68%guideline_compliance = true
0.94median coding_accuracy_score
4coding statuses
13hcc codeses
22coders
182.6median reimbursement_amount

Mean coding_accuracy_score by drg_validation_status41 rows

coding_accuracy_score84 rows, in bands of 0.1

Median 0.94, from 0.0 to 0.99.

secondary_diagnosis_codes37 rows with a value · 63 left blank

I1013
E11.9,I104
E78.5,I102
E11.92
R50.92
I12.9,E11.222
Z87.811
J44.91
F41.91
F32.91

19 columns by typefrom the column list below

string 14
float 2
date 2
boolean 1

Columns

19 columns in four groups

blueprint · 19 columns
column	type	description	example
Text 14 columns
`assignment_id`	string	Unique identifier for each coding assignment or case.unique	CA10001
`patient_id`	string	Unique identifier for the patient associated with the coding assignment.	P20001
`coder_id`	string	Unique identifier for the professional medical coder.	CODER05
`coding_status`	string	Status of the coding assignment (e.g., completed, pending, audited).completed · pending · audited · rejected	completed
`primary_diagnosis_code`	string	Primary ICD or other diagnosis code assigned for the encounter.	E11.9
`secondary_diagnosis_codes`	string	Comma-separated list of secondary diagnosis codes assigned.optional	I10
`procedure_codes`	string	Comma-separated list of procedure codes assigned for the encounter.optional	99213
`drg_code`	string	Diagnosis Related Group (DRG) code assigned for reimbursement and validation.optional	291
`drg_validation_status`	string	Status of DRG validation (e.g., valid, invalid, corrected).valid · invalid · corrected · not_applicable · optional	valid
`hcc_codes`	string	Comma-separated list of Hierarchical Condition Category (HCC) codes assigned.optional	HCC19,HCC85
`audit_id`	string	Unique identifier for the audit performed on the coding assignment.optional	AUD30001
`auditor_id`	string	Unique identifier for the auditor who performed the audit.optional	AUDITOR02
`audit_findings`	string	Detailed findings from the audit, including discrepancies and recommendations.optional	DRG corrected due to addi…
`coding_notes`	string	Additional notes or comments from the coder regarding the assignment.optional	Insomnia, no procedures.
Numbers 2 columns
`coding_accuracy_score`	float	Accuracy score for the coding assignment, typically between 0 and 1.0 to 1 · optional	0.98
`reimbursement_amount`	float	Amount reimbursed for the coded encounter.0 or more · optional	150.67
Dates and times 2 columns
`encounter_date`	date	Date of the medical encounter being coded.	2023-07-11
`audit_date`	date	Date when the coding audit was performed.optional	2023-07-18
True or false 1 column
`guideline_compliance`	boolean	Indicates if the coding assignment complies with official coding guidelines.	true

Use it for

A healthcare dashboard
The guideline_compliance rate, coding_accuracy_score by drg_validation_status and a breakdown of secondary_diagnosis_codes. Excel, Power BI or Tableau.
Why do the 28 valid rows have a mean coding_accuracy_score of 0.95?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Assignments100
CA100010.98valid
CA10002—
CA100030.85corrected
A software demo
Believable assignments with patient_id, encounter_date and coder_id to fill a screen in front of a buyer.

This dataset100 rows19 columns

Yours10,000 rows19 columns

blueprint · medical-coding-and-audit-results

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 19 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Coded by certified coder (CPC, CCS, RHIA, RHIT)
ICD-10-CM diagnosis codes to highest specificity
ICD-10-PCS procedure codes for inpatient (7-character)
CPT and HCPCS codes with correct modifiers
Principal diagnosis selection for inpatient
DRG assignment and validation
HCC (Hierarchical Condition Category) for RAF score
Coding accuracy rate from audits

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: medical-coding-and-audit-results

What should your data show?

No signup. No card.

Sample rows

What the 100 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?