The four key requirements of patient registry software

3 minute read
Lifebit

Lifebit

December 2023

Author: Hannah Gaimster, PhD
Contributors: Hadley E. Sheppard, PhD and Amanda White 

 

Introduction to patient registry software

Patient registries are databases that collect and store information about individuals with a particular illness or condition. Examples include databases of patients with rare diseases such as Cystic Fibrosis or Chordoma. For patients, researchers, and healthcare professionals, they are important resources. 

However, effectively managing and securely accessing patient data held in patient registries can be challenging. Patient database software plays a crucial role in the success of patient registries. These software solutions are designed to safely collect, store, connect and analyze patient data securely and efficiently. 

 

 

What are the crucial requirements of patient registry software?


Patient registry software should have the capability to rapidly and securely gather, store, and evaluate patient data.

 

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The ideal software solution for a patient registry prioritizes interoperability, modularity, flexibility, and security to enhance research and innovation at the same time as earning patient trust. 

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Graphic_Four key requirements to enabling federated data analysis



There are four key features of ideal patient registry software, which are discussed below.

 

1. Best-in-Class security features

Patient data is highly sensitive so software to access and analyze it requires a security by design approach. Data federation addresses the issue of accessing data while ensuring its security. This is a software process that allows linkage of multiple databases to safely collaborate as a single unit. This technology helps keep patient health data safe by preventing data sharing and/or downloading and keeping it within the right jurisdictional boundaries.  




To protect patient privacy and build trust in using health data, it is important to follow further strict security measures for patient data protection. These measures should include 

  • Implementation of strict standards for data encryption. It is necessary to encrypt data at every stage, encompassing both when it is at rest, in transit and during analysis.

  • Data pseudonymization, or 'de-identification', removes personal identifiers from a dataset to protect a participant's privacy.

  • The implementation of role-based access control to data is essential where only authorized employees can decrypt the data. The security network also restricts specific users' access, viewing, or editing of encrypted files.

  • Careful considerations on how to safely export results. An example of this would be via an ‘airlock’ process as used by Genomics England.

 

 

 

 

2. In-house data standardization to ensure interoperability

Researchers will be limited in the novel insights they can gain if the data contained within the patient registry cannot be effectively combined with other data sources to enhance its statistical power. Common Data Models (CDMs) are crucial to ensuring data is interoperable, with several growing in popularity in the health sciences sector recently including Observational Medical Outcomes Partnership (OMOP) in the case of clinical-genomic data.




Harmonizing health data to a CDM such OMOP provides structure according to common international standards which ensures it is fully interoperable with other clinical datasets from other labs or clinics. This fully enables the integration and analysis of datasets across distributed sources and platforms.
 
Data sharing platforms with in-built extraction, transformation, loading pipelines (ETL) pipelines that can automate this work to process and convert raw data to analysis-ready data help further simplify this process for researchers. Furthermore, in-house data standardization, compared to outsourcing to a third-party company, limits security risks and further costs to patient databases.

 

 

 

 

3. Leverages emerging technologies

The ultimate goal for patient databases is to move to a ubiquitous data collection environment that fully utilizes novel technologies. 

When dealing with large volumes of patient data, it is crucial to have access to sufficient computational resources. Furthermore, a reliable database infrastructure and a scalable platform is necessary to effectively process and analyze the data. 

As a result, there has been a growing trend in healthcare data analysis towards utilizing commercial cloud infrastructure, which offers unparalleled flexibility. Cloud computing allows researchers to only pay for the specific resources they require, due to its elastic nature.
 


4. Employs end-to-end solutions and no/low code interfaces

Even if researchers can securely access standardised, disparate datasets in patient registries, these may not be provided on an easy to use, low code platform. In particular, researchers and clinicians without a data science background may be at a disadvantage to using analytical tools that require coding. 

The software industry is currently shifting towards “no/low-code” tools to support a wider range of end users with and without a data science background, thus enabling full democratization of access to patient data and the insights derived. 

Patient registry software that offers advanced features such as end-to-end data visualization and reporting, can make it easier for researchers and healthcare providers to gain novel insights from the data. 

 


 

Summary

 

Patient registries are essential for healthcare providers, researchers, and patients. These databases play a crucial role in improving patient care, advancing medical research, and identifying gaps in care. With the help of secure patient registry software, these databases can continue to provide valuable insights, improve the healthcare industry and enhance patient outcomes.


Author: Hannah Gaimster, PhD
Contributors: Hadley E. Sheppard, PhD and Amanda White


About Lifebit


Lifebit provides health data solutions for clients, including Genomics England, Boehringer Ingelheim, Flatiron Health and more, to help researchers transform data into discoveries. 

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