Challenge Overview
Welcome to the “PseudoVet - Create Randomizer + Aging Algorithm Proof of Concept Challenge”.
Overview
PseudoVet is an automated patient data fabrication engine which provides a set of active synthetic patients and clinical data that can be used for healthcare software development. Development against real patient data unnecessarily exposes patient health information (PHI) and personally identifiable information (PII) and cannot be used by developers outside of the VA network. However, fully functional, realistic data sets can be used safely in development, testing, training and other non-production environments in compliance with the Health Information Technology for Economic and Clinical Health Act (HITECH Act) and other regulations. Development against current fabricated data is not useful because the data sets are outdated, which requires development teams to spend time developing data sets to use in lieu of writing code or require licenses and cannot be shared.
Challenge Requirements
The first challenge in this series will focus on building a randomizer + aging algorithm proof of concept.
Algorithm Dataset Format (Input)
It is expected that the dataset follows the following model structure which is a CCDA template.
PATIENT SEED DATA:
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Names: Last, First, Middle, Suffixes (last names should be real)
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Phone
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Number
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Email
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Address
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Social Security Numbers (Must be valid but from deceased or otherwise assignable for development and testing purposes. Can use 000 or 666)
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Occupation and Incomes
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Gender
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Race
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Height
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Weight
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Religion
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Language
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Next of Kin
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Emergency Contact
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Smoking and Alcohol History
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ICN: Local and National
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Allergies / Drug Sensitivities
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Medications
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Vital Signs
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Military Service
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Service Connected and Non-Service Connected Disabilities:
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Locations (Address data)
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War Eras
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WWII, Korea, Vietnam, Gulf War
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ICD-10, SNOMED Diagnosis, DSM and Procedure Codes
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TIU Note Templates
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Lab Value Mapping
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Diagnosis Mapping
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Family History Data
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Immunizations
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Genetic Diagnosis Information
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Hospitalizations
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Insurance Data
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Consent
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Social History
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Problem
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Advance Directives
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Encounter Data (Outpatient/Inpatient)
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Appointments
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Procedures (Clinical, Surgical, Physical)
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Facility Name
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Facility Address
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Primary Care Provider Assignment
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Providers (Other) Assignment
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Clinics
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Consults
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Referrals
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Clinical Instructions
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Medications Administered
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Location of Admission and Discharge
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Health Plan Authorization Act
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Mapping between Lab Values and Diagnosis
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Mapping between Diagnosis and Procedure Codes
HEALTHCARE PROVIDER SEED DATA:
(i.e. Clerical, Nursing, Physician, Radiologist, Social Work, Laboratory, et al)
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Address
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Room Numbers
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Clinic Locations
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Clinical Hours
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Clinic Types (Initial)
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Internal Medicine
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Primary Care Outpatient
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Audiology
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Mental Health
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ENT
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Optometry
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Dental
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Cardiology
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Emergency Room
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The produced dataset MUST
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Incorporate ICD-10 Codes & Sub-classifications
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Incorporate database inputs from SNOMED, procedure and diagnostic codes, lab values & billing codes
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Be a data collection with medically relevant identified connection points
Algorithm Requirements
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Must use the CCDA template defined above and populate 1000 CCDA records with random but accurate data
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Once the 1000 CCDA records are created, the algo must be able to create versions of each record at 5 years, 10 years and 15 years
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The output of your algorithm must be populated CCDA records with random but accurate data, and then copies of those records at 5 years, 10 years, and 15 years
Final Submission Guidelines
Final Submission Guidelines
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Algorithm code with instructions on how to run the algorithm, input data and receive results
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Zipped dataset
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Demo video of how to configure and run your algo