Matching Known Patients to Health Records in Washington State Data
Not on the map. Why?
Abstract (arXiv)
The State of Washington sells patient-level health data for 50. This publicly available dataset has virtually all hospitalizations occurring in the State in a given year, including patient demographics, diagnoses, procedures, attending physician, hospital, a summary of charges, and how the bill was paid. It does not contain patient names or addresses (only ZIPs). Newspaper stories printed in the State for the same year that contain the word "hospitalized" often include a patient's name and residential information and explain why the person was hospitalized, such as vehicle accident or assault. News information uniquely and exactly matched medical records in the State database for 35 of the 81 cases (or 43 percent) found in 2011, thereby putting names to patient records. A news reporter verified matches by contacting patients. Employers, financial organizations and others know the same kind of information as reported in news stories making it just as easy for them to identify the medical records of employees, debtors, and others.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jul 2013.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2023Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Recordscited by 2
- 2023Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Educationcited by 1
- 2022The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analyticscited by 0
- 2024Generation and De-Identification of Indian Clinical Discharge Summaries using LLMscited by 0
- 2026Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Misscited by 0
- 2026Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss17 Aug 2026
- 2024Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs8 Jul 2024
- 2023Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education18 Jul 2023
- 2023Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records11 Feb 2023
- 2022The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analytics11 Apr 2022