Probabilistic linkage without personal information successfully linked national clinical datasets
| Autor: | Katie Harron, Helen A. Blake, Linda D. Sharples, Kate Walker, Jan van der Meulen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Epidemiology Computer science media_common.quotation_subject Datasets as Topic Data security Audit State Medicine Record linkage 03 medical and health sciences 0302 clinical medicine Intestinal Neoplasms medicine Humans Electronic health records Quality (business) 030212 general & internal medicine Data Management media_common Linkage (software) Models Statistical Public health Probabilistic logic Reproducibility of Results Patient identifiers medicine.disease United Kingdom Probabilistic linkage National clinical datasets Original Article Medical Record Linkage Personal information Medical emergency Personally identifiable information 030217 neurology & neurosurgery |
| Zdroj: | Journal of Clinical Epidemiology |
| ISSN: | 0895-4356 |
| DOI: | 10.1016/j.jclinepi.2021.04.015 |
| Popis: | BACKGROUND: Probabilistic linkage can link patients from different clinical databases without the need for personal information. If accurate linkage can be achieved, it would accelerate the use of linked datasets to address important clinical and public health questions. OBJECTIVE: We developed a step-by-step process for probabilistic linkage of national clinical and administrative datasets without personal information, and validated it against deterministic linkage using patient identifiers. STUDY DESIGN AND SETTING: We used electronic health records from the National Bowel Cancer Audit and Hospital Episode Statistics databases for 10,566 bowel cancer patients undergoing emergency surgery in the English National Health Service. RESULTS: Probabilistic linkage linked 81.4% of National Bowel Cancer Audit records to Hospital Episode Statistics, vs. 82.8% using deterministic linkage. No systematic differences were seen between patients that were and were not linked, and regression models for mortality and length of hospital stay according to patient and tumour characteristics were not sensitive to the linkage approach. CONCLUSION: Probabilistic linkage was successful in linking national clinical and administrative datasets for patients undergoing a major surgical procedure. It allows analysts outside highly secure data environments to undertake linkage while minimizing costs and delays, protecting data security, and maintaining linkage quality. |
| Databáze: | OpenAIRE |
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