How to Have Full Access to a Centralized Bank of Patients’ Electronic Health Records While Preserving Patient Privacy?

Feb 18, 2021, 08:00 AM by Sophia Kapchinsky, PhD.

Modern-day research centers and early-adopters of digital innovations are reaping the benefits of using big data for clinical research. Using big data accelerates the research pipeline by facilitating study cohort identification and collaborations between research institutions, as well as increasing publication output and improving the ROI.

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Leverage bioinformatics tools

Logibec NOAH integrates transparent open-source platforms that have been developed by the NIH and tested by hundreds of research institutions. It is free from commercial bias and is backed by a reliable implementation process and professional support. This includes on-site data extraction, deidentification of all EHRs, and adjusting the data, including structured and unstructured EHR information, to a clinical and research ontology.

Logibec NOAH’s reliable automation of a research institution's governance and access policies tighten privacy compliance at a lower cost than if it was performed by internal resources.

Below is an example of the ontology classifications used by the Shared Health Research Information Network.

Data Type  Terminology
Diagnoses
ICD-10 and IMO
Procedures
CPT
Medications
Medications are classified based on the Medispan hierarchy used in the CCHMC Epic build. RxNorm    
Laboratory Results Laboratory tests are identified by a mixture of LOINC codes and internal Cerner numbers. The tests are listed under the same hierarchy used in Epic. 
  

EHRs are de-identified, codified, and collected into a master database that includes patient data from all the collaborating research institutions and clinical centers. This has been successful for over 200 research centers, resulting in hundreds of collaborations, robust grant applications, and high-impact publications.

Logibec NOAH supports research centers by organizing the data and making it searchable via Informatics for Integrating Biology and the Bedside (i2b2), a tool developed to identify patient cohorts and facilitate translational research. All EHRs within the database are automatically encrypted and access to the data is configured to different groups of users depending on their clearance level. The varying levels of authorization are built into the system and do not impact the original data.

The following table provides an example of how sensitive information, such as the date of birth (DOB) of a patient, would be displayed to users with different accessibility levels. The levels include: the original resolution, mask to year, age range, tokenize or no access depending on the user status. The dataset is also anonymized using k-anonymity, which provides an extra layer of protection when there is a potential risk for re-identification due to queries resulting in low number of patients. Further, any patient data can be automatically removed if a specific patient chooses to withdraw their consent.

 Role  Access Level  Patient 1 Patient 2   Patient 3
Data Curator or Physician Original resolution June 3, 1972 Dec 12, 1985
March 25, 1992
Researcher Mask to year *** ** 1972
*** ** 1985
*** ** 1992
Data Scientist Age range 40-49 30-39  20-29
Administrator Token x067A4F01...
x032Z1FE4..
x011b92FC..
Other Unauthorized n/a n/a  n/a

Table 1: Example of how sensitive data, such as DOB, is displayed to various users of the Logibec NOAH depending on their access level. While the users see and work with variations of the data, all original information is preserved within the dataset and all data queries are registered for ease of use.

Most importantly, regardless of the clearance levels, all users are restricted from accessing patient identities. To re-identify the anonymized patient data, the interested institution or center must follow the Institutional Review Board processes mandated by the host organization’s ethics committee. With Logibec NOAH, the process of obtaining re-identified data is automated which brings efficiency, cost savings, and peace of mind to research centers and attending physicians.

Logibec NOAH follows all compliance regulations, such as PIPEDA, and the tightly controlled built-in data governance and accessibility processes make it easy for researchers to focus on the important things like conducting research, securing grants, and improving health outcomes for their patients.

The EHR database is easy to use. The interface is equipped with a drag-and-drop dashboard that allows users to make an unlimited number of queries and search iterations in order to identify patient cohorts that match specific sets of criteria. Unlike many IT solutions in the clinical field, it doesn’t require experience in bioinformatics or changes to the internal IT infrastructure.

 

Conclusion

Logibec NOAH provides reliable automation for data governance and institutional access policies that ensures privacy compliance at a much lower cost than performed by internal resources. This solution gives clinical researchers access to the information they need, when they need it, in order to do cutting-edge research in today’s increasingly digital and competitive world.

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