ArticleFrontiers in public health2017
Ensuring Privacy When Integrating Patient-Based Datasets: New Methods and Developments in Record Linkage.
Article in Frontiers in public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Data privacy protection in public health frameworks via legal and policy integration.Frontiers in public health · 2026Article
- Using general practice data for chronic disease prevalence: the impact of record linkage on estimation accuracy.BMC medical informatics and decision making · 2025Article
- Adherence and persistence to tafamidis treatment among Medicare beneficiaries in the presence of a patient assistance program.Scientific reports · 2024Article
- Engaging Patients and Other Stakeholders in "Designing for Dissemination" of Record Linkage Methods and Tools.Applied clinical informatics · 2023Article
- Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnetBMC research notes · 2022Article
- Unlocking Potential within Health Systems Using Privacy-Preserving Record Linkage: Exploring Chronic Kidney Disease Outcomes through Linked Data Modelling.Applied clinical informatics · 2022Article
- Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing.Healthcare (Basel, Switzerland) · 2021Article
- Chronic pain patients' willingness to share personal identifiers on the web for the linkage of medico-administrative claims and patient-reported data: The chronic pain treatment cohort.Pharmacoepidemiology and drug safety · 2021Article
- Lumos: a statewide linkage programme in Australia integrating general practice data to guide system redesign.Integrated healthcare journal · 2021Article
- Population Data Centre Profiles: Centre for Data Linkage.International journal of population data science · 2020Article
- Using Security Questions to Link Participants in Longitudinal Data Collection.Prevention science : the official journal of the Society for Prevention Research · 2020Article
- A Profile of the SAIL Databank on the UK Secure Research Platform.International journal of population data science · 2019Article
- Evaluation of approximate comparison methods on Bloom filters for probabilistic linkage.International journal of population data science · 2019Article
- Estimating parameters for probabilistic linkage of privacy-preserved datasets.BMC medical research methodology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an era where the volume of structured and unstructured digital data has exploded, there has been an enormous growth in the creation of data about individuals that can be used for understanding and treating disease. Joining these records together at an individual level provides a complete picture of a patient's interaction with health services and allows better assessment of patient outcomes and effectiveness of treatment and services. Record linkage techniques provide an efficient and cost-effective method to bring individual records together as patient profiles. These linkage procedures bring their own challenges, especially relating to the protection of privacy. The development and implementation of record linkage systems that do not require the release of personal information can reduce the risks associated with record linkage and overcome legal barriers to data sharing. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges. Enhancing and operationalizing PPRL protocols can help address the dilemma faced by some custodians between using data to improve quality of life and dealing with the ethical, legal, and administrative issues associated with protecting an individual's privacy. These methods can reduce the risk to privacy, as they do not require personally identifying information to be shared. PPRL methods can improve the delivery of record linkage services to the health and broader research community.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.