Clinical Diagnostic Error Analysis

This dataset provides detailed records of clinical diagnostic errors, including patient demographics, error types, severity, contributing factors, and outcomes. It enables healthcare organizations to analyze patterns, identify root causes, and implement corrective actions to improve diagnostic accuracy and patient safety. The dataset is ideal for quality improvement initiatives, risk management, and clinical research.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Analyzing diagnostic error patterns for quality improvement

Sample rows

preview · 8 of 201 rows · all 21 columns
error_idstringclinical_settingstringpatient_ageintegercorrective_action_takenbooleanorganization_idstringpatient_idstringpatient_genderstringdiagnosis_datedateerror_detected_datedateinitial_diagnosisstringcorrect_diagnosisstringerror_typestringerror_severitystringprovider_idstringprovider_rolestringoutcome_impactstringreporter_idstringreport_datedatecontributing_factorsstringerror_descriptionstringcorrective_action_descriptionstring
ERR-10001outpatient67trueORG001PAT-90001female2020-01-122020-01-30Acute bronchitisPneumoniamissedhighPRV-001physiciantemporary harmREP-10012020-02-01Symptoms overlapped, chest x-ray not performed initially.Initial diagnosis failed to identify pneumonia due to overlapping symptoms with bronchitis and lack of imaging.Provider educated on use of imaging for similar presentations.
ERR-10002emergency34trueORG002PAT-90002male2020-03-052020-03-15MigraineSubarachnoid hemorrhagemissedcriticalPRV-002residentpermanent harmREP-10022020-03-16History not reviewed, no imaging performed.Patient presented with severe headache, incorrectly diagnosed as migraine, underlying hemorrhage missed.Mandatory checklist for headache workup implemented.
ERR-10003outpatient22trueORG001PAT-90003female2020-06-112020-06-13Urinary tract infectionKidney stoneincorrectmoderatePRV-003nursetemporary harmREP-10032020-06-15Incomplete history, limited physical exam.Pain and hematuria attributed to infection, kidney stone missed until imaging performed.Refresher training for nurses on differential diagnosis.
ERR-10004inpatient50trueORG003PAT-90004male2020-08-212020-09-05Stable anginaAcute myocardial infarctiondelayedcriticalPRV-002physicianpermanent harmREP-10042020-09-06ECG findings not interpreted correctly.Patient admitted for chest pain, MI diagnosis delayed due to misinterpretation of ECG.Cardiology consultation required for chest pain admissions.
ERR-10005outpatient41falseORG002PAT-90005female2020-10-022020-10-04Allergic rhinitisSinusitisincorrectlowPRV-004residentno harmREP-10052020-10-05Symptoms overlapping, physical exam not detailed.Patient with nasal congestion and headache diagnosed with allergies, sinusitis missed.blank
ERR-10006inpatient71trueORG003PAT-90006male2020-11-122020-11-22Congestive heart failurePulmonary embolismmissedhighPRV-005specialistdeathREP-10062020-11-23Risk factors for PE not considered.Dyspnea and edema attributed to CHF, PE missed until deterioration.PE risk assessment protocol introduced.
ERR-10007emergency29trueORG002PAT-90007male2021-02-142021-02-19Tension headacheMeningitismissedcriticalPRV-001physicianpermanent harmREP-10072021-02-20No lumbar puncture performed.Patient with fever and headache diagnosed with tension headache, meningitis not ruled out.Protocol for headache and fever revised.
ERR-10008emergency47trueORG001PAT-90008female2021-03-082021-03-10GastroenteritisAppendicitisincorrectmoderatePRV-003nursetemporary harmREP-10082021-03-11Abdominal pain attributed to viral infection.Abdominal pain and vomiting misdiagnosed as gastroenteritis, appendicitis identified after worsening.Training on abdominal pain triage for nursing staff.

What the 201 rows show

from the 201-row sample

Inpatient (clinical setting) stands out: mean patient_age is 63.7, against 40.1 for the rest.

  • 75%corrective_action_taken = true
  • 47median patient_age
  • 4patient genders
  • 4error types
  • 4error severities
  • 4provider roles
Mean patient_age by clinical_setting201 rows
0408040.1emergency52 rows40.1outpatient87 rows63.7inpatient62 rows
patient_age201 rows, in bands of 10
0204026283637363417505090patient_age →

Median 47, from 5 to 83.

organization_id201 rows · top 10 of 13 values
  1. ORG00230
  2. ORG00329
  3. ORG30219
  4. ORG31719
  5. ORG00118
  6. ORG00514
  7. ORG86112
  8. ORG85711
  9. ORG86010
  10. ORG85810
21 columns by typefrom the column list below
  • string 16
  • integer 1
  • date 3
  • boolean 1

Columns

21 columns in four groups
blueprint · 21 columns
columntypedescriptionexample
Text 16 columns
error_idstringUnique identifier for each diagnostic error recorduniqueERR-10001
patient_idstringUnique identifier for the patient involved in the diagnostic errorPAT-90001
patient_genderstringGender of the patientmale · female · other · unknownfemale
initial_diagnosisstringThe original diagnosis given to the patientAcute bronchitis
correct_diagnosisstringThe correct diagnosis after error was identifiedPneumonia
error_typestringType/category of diagnostic error (e.g., missed diagnosis, delayed diagnosis, incorrect diagnosis)missed · delayed · incorrect · othermissed
error_severitystringSeverity of the diagnostic errorlow · moderate · high · criticalhigh
contributing_factorsstringSummary of key factors that contributed to the error (e.g., communication breakdown, incomplete history)optionalLab tests not ordered.
clinical_settingstringClinical environment where the error occurred (e.g., emergency, outpatient, inpatient)emergency · outpatient · inpatient · otheroutpatient
provider_idstringUnique identifier for the healthcare provider involvedPRV-001
provider_rolestringRole of the provider (e.g., physician, nurse, resident)physician · nurse · resident · specialist · otherphysician
organization_idstringUnique identifier for the healthcare organizationORG001
error_descriptionstringDetailed narrative describing the diagnostic erroroptionalInitial diagnosis failed …
outcome_impactstringImpact of the error on patient outcome (e.g., no harm, temporary harm, permanent harm, death)no harm · temporary harm · permanent harm · death · unknowntemporary harm
corrective_action_descriptionstringDescription of the corrective action taken (if any)optionalProvider educated on use …
reporter_idstringUnique identifier for the person reporting the errorREP-1001
Numbers 1 column
patient_ageintegerAge of the patient at the time of the diagnostic error0 to 12067
Dates and times 3 columns
diagnosis_datedateDate when the initial diagnosis was made2020-01-12
error_detected_datedateDate when the diagnostic error was detected2020-01-30
report_datedateDate when the error was reported2020-02-01
True or false 1 column
corrective_action_takenbooleanIndicates if corrective action was taken following the errortrue

Use it for

  • corrective act…75%151 of 201 rowsmean patient age by c…40.1emerge…40.1outpat…63.7inpati…

    A healthcare dashboard

    The corrective_action_taken rate, patient_age by clinical_setting and a breakdown of organization_id. Excel, Power BI or Tableau.

  • Why do the 62 inpatient rows have a mean patient_age of 63.7?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable errors with patient_id, patient_age and patient_gender to fill a screen in front of a buyer.

Not quite right?

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This dataset201 rows21 columns
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blueprint · clinical-diagnostic-error-analysis

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 21 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
  • Record diagnosis and outcome
  • Flag errors and misdiagnoses
  • Include provider experience level
  • Exclude secondary diagnoses
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
clinical-diagnostic-error-analysis

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