Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM)

doi:10.21203/rs.3.rs-3479039/v1

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Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM)

https://doi.org/10.21203/rs.3.rs-3479039/v1

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Combined with pseudonymisation and standardised data quality assessments, the OMOP-CDM provides a powerful model to support ethical real-world ‘big’ data research. The continued adoption of OMOP-CDM, ongoing development efforts, and the emphasis on sound governance practices all contribute to the realisation of OMOP’s utility in unlocking valuable EMR data. These factors collectively support a wide range of applications, from health service operational reporting to diverse clinical, epidemiological, and translational research projects.

Medical Informatics

Governance

Electronic Health Records

Observation Medical Outcomes Partnership (OMOP)

Ethics

Medical Informatics

Secondary Heathcare Data

The Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM)

Adoption of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) internationally and in Australia has enabled the conversion of vast amounts of complex, and heterogeneous electronic medical record (EMR) data into a standardised structured data model. This conversion of data has the potential to provide hospitals, health departments, auditors, regulators, and universities with 'institution-specific' insights for operational and research purposes, as long as secure 'secondary use' of an institution's EMR clinical and administrative data, involving any application of data beyond its initial collection, is effectively harnessed.

Such data can be transformative, especially if used to monitor, evaluate, and audit health care to improve clinical practice, reduce inefficiencies, contribute to the evidence base, and develop a ‘learning healthcare system’ for improved patient care (1, 2, 3, 4). However, this potential is often not realised due to the inherent complexity of EMR databases – that comprise thousands of data elements across thousands of proprietary tables – where vast amount of data needs to be transformed, cleaned, and restructured to make it ‘fit’ for ‘secondary use’ (5). For highly powered collaborative research, where large volumes of EMR data are combined, use is further constrained by the heterogeneity of each institution’s EMR schema (6); concern over data sharing and privacy breaches; and lack of clarity over governance and consent (7).

The Observational Health Data Sciences and Informatics (OHDSI) consortium (8) is addressing these challenges through the transformation of each EMR database into the open-source Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), where EMR data elements are translated into the OMOP-CDM using standardised terminologies (i.e., SNOMED-CT (9), LOINC (10), or RxNORM (11)). Importantly, these transformed data are also able to be securely stored within their dedicated environment, complete with the necessary validation, analysis, and reporting tools (12).

The Utility and Adoption of the OMOP-CDM

An increasing number of Australian and international organisations are transforming their EMR data into the OMOP-CDM as these converted databases provide health services and researchers a valuable data source to monitoring health service utilisation, contribute to the evidence base through research, and develop clinical decision support systems to improve quality of care. Furthermore, it enables researchers to ‘scale-up’ and ‘de-risk’ collaborative research, by securely sharing de-identified and aggregated data and executing analyses across multiple OMOP-converted databases, ensuring that patient-level data remains securely firewalled within its respective local site (12).

The adoption of OMOP-CDM has been on the rise globally, with the conversion of approximately 12% of EMRs worldwide by 2022, which encompasses data from 453 databases, that accounts for more than 928 million unique patient records across 41 countries (12). This substantial adoption demonstrates the recognition of OMOP-CDM's utility in leveraging EMR data for various purposes.

An Australian OHDSI Chapter has been established to support the use of OMOP and develop collaborations between database stakeholders. OMOP members include clinicians and researchers from the University of Melbourne, University of South Australia, University of Queensland, University of New South Wales, and Western Australia (13). The Australian databases that have undergone OMOP-CDM conversion include those that contain data from large tertiary hospitals in major cities, specialised hospitals that hold data for children’s and cancer care services, joint replacement registries, practice-based research networks (AU-ePBRN), local health district (LHD) databases, primary care databases (PATRON), pharmaceutical registries and the Australian Department of Veterans Affairs (12).

Aim

In this overview, we describe the OMOP-CDM, the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to EMR data, by health service providers and researchers. Governance, privacy, consent, and ethics vary by country or jurisdiction. In this paper, we have applied an Australian context, however the general nature of the guidance here is applicable internationally.

The OMOP-CDM – Structure and Process

The OMOP-CDM can be implemented using many of the existing database management systems. The OMOP-CDM extraction, transformation, and loading (ETL) process converts complex clinical and administrative EMR data into a simplified standard format consisting of 16 data tables and other derived tables (8). Through this process it is important to note the source EMR data is not changed or lost, OMOP conversion just provides a new representation of existing EMR data. We recommend that the OMOP instance is located within the institution’s existing information system infrastructure (Fig. 1), allowing the OMOP-CDM instance to be maintained under the existing repository data access and governance mechanisms established by each data custodian.

Figure 1 OMOP-CDM

Adapted from Standardised Data: The OMOP Common Data Model (12)

OMOP, Data Quality, and the Principles of FAIR, CARE, and FIVE SAFES

The use of OMOP-CDM aligns well with the need for systematic data evaluation and adherence to data quality standards and the Principles of FAIR (Findable, Findable, Accessible, Interoperable, and Reusable), CARE (Collective benefit, Authority, Researcher, and Ethics), and the FIVE SAFES. Before use, OMOP-CDM data undergoes a rigorous data quality assessment process, which includes checks for completeness, concordance, plausibility, and currency when compared to the source EMR data (14). These quality checks are predefined and configured to run on datasets conforming to OMOP standards, and they can be executed using tools like Achilles, which is accessible via the OHDSI Data Quality Dashboard (15). In addition, the OMOP-CDM enables researchers to work within a secure and firewalled environment while conducting advanced analytics and prediction techniques. This aligns with the principles of making data ‘FARE’ Findable, Accessible, Interoperable, and Reusable, ensuring that data is available for a wide range of research applications (16, 17). Data accessed through an OMOP-CDM also adheres to the CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) as it operates within the governance framework established by the custodians of each local data repository. This framework ensures that data is used responsibly and ethically, benefiting both researchers and the broader community (16, 17)

OMOP data adheres to the ‘Five Safes’ guiding principles by providing a structured and secure framework for managing and sharing healthcare data while ensuring that privacy and security are maintained. In doing so it delivers:

‘Safe Projects’ by allowing for the creation of secure, firewalled environments for data analysis. Researchers can undertake advanced analytic and prediction techniques within these controlled environments without direct access to raw patient-level data, ensuring that projects are conducted safely and in compliance with privacy regulations.

‘Safe People’ by tightly controling and restricting access to OMOP data to authorised individuals only, such as researchers, who have undergone appropriate training and have the necessary permissions. This safeguards against unauthorised data access.

‘Safe Data’ is maintained by rigorous data quality checks and transformations to improve data accuracy and quality. This is supported by the use of a standardised format and terminologies used in OMOP-CDM enhance data consistency and quality.

‘Safe Settings’ the OMOP-CDM can be configured to adhere to specific governance and privacy frameworks established by the custodians of local data repositories. This ensures that data is used in accordance with the governing policies and procedures set by the data custodians.

‘Safe Outputs’ when researchers work with OMOP data safe results and outputs are genratred as they are aggregated and anonymised to prevent the identification of individual patients. This protects patient privacy while allowing valuable research insights to be shared (Table 1)

Table 1

Guiding Principles of FAIR, CARE, and the FIVE SAFES
The FAIR Guiding Principles
F	Findability	Metadata and data should be easily found by both humans and computers through the assigment of globally unique and permanent identifier to enable the automatic discovery of datasets and services via machine learning (16).
A	Accessability	Metadata and data should easily retrieved by authorised and authenticated users via a standard communication protocol (16) .
I	Interoperabilty	Data from one data source can be integrated with data from other sources so that it can be aggregated into a single, unified view, and refers to the intergration and exchange of applications, analysis, storage, and workflow processing across different data sources (16).
R	Reusability	Metadata and data characteristics are specified in detail to enable replication and/or linkage in different settings. Reusability includes the release of data usage licenses, provenance details, and disclosure around community standards relevant to the domain (16).
The CARE Principles
C	Collective Benefit	Collective benefit including where the wellbeing of Indigenous Peoples’ rights is of primary concern (17)
A	Authority	Indigenous Peoples’ rights and interests about their peoples, communities, cultures, and territories with regards to data is recognised and clearly articulated (17)
R	Researcher	Researchers have a responsibility to develop and nurture respectful relationship with Indigenous Peoples’ from whom the data originate (17)
E	Ethics	Minimise harm and maximise benfit for Indigenous Peoples’, for justice and future use (17)
The Five Safes Framework
People	Safe People Is the researcher appropriately trained and authorised to access and use the data? (18)
Projects	Safe Projects Is data used for an appropriate purpose that is valid and of public benefit? (18)
Settings	Safe Settings Does IT access and physical environment prevent unauthorised use? (18)
Data	Safe Data Has appropriate and sufficient protection been applied to the data to avoid risk of disclosure? (18)
Outputs	Safe Outputs Are the statistical results non-disclosive? (18)

OMOP-CDM and Governance

By virtue of its design and objectives, the OMOP-CDM enhances the governance of secondary health data, by ensuring data utilisation in both research and healthcare decision-making is ethical, transparent, and effective.

With the transformation of EMR data into a standardised structure, the OMOP-CDM ensures there is a uniform representation of these data regardless of the data’s original source. This uniformity streamlines data governance and, importantly, eases the complexities associated with conducting single site studies that contain raw EMR data and multi-site studies that involve integrating data from various disparate sources (19). In addition, the common data model emphasises data quality, allowing for consistent checks and ensuring that research data meets the highest standards (20, 21). The standardised model also ensures that security and privacy protocols are uniformly applied, safeguarding secondary health data from data breaches to maintain patient privacy. Given the structured approach of the OMOP-CDM, an institution can easily implement access controls, thereby ensuring that only authorised parties can access or interact with the data. As a result, the OMOP-CDM acts as a cornerstone for the conduction of rigorous and ethically sound research as it builds trust among stakeholders, mitigates information disparities, and encourages the production of high-quality medical evidence for rigorous and ethical research (22)

Operational Use and Quality Assurance Activities in a Hospital or Healthcare Setting

For operational use quality assurance activities where the ‘primary purpose is to monitor or improve the quality of service delivered by an individual or an organisation’ (23) data governance and principles for ethical use apply. However, within healthcare institutions, ethics approval is not mandated for the establishment of the OMOP database or data use, provided:

data being collected and analysed is coincidental to standard operating procedures with standard equipment and/or protocols;
data is being collected and analysed expressly for the purpose of maintaining standards or identifying areas for improvement in the environment from which the data was obtained;
data being collected and analysed is not linked to individuals;
none of the triggers for consideration of ethical review are present (23).

Research in a University Setting

For research use, the data custodian is usually the agency or organisation that commissioned the research and paid for the data collected by the owner (i.e., hospital/general practice). Existing local governance principles already developed by custodians can be applied to OMOP standardised data including: (i) data only being made accessible to named researchers on relevant ethics applications approved by the relevant institution, (ii) appropriate secure data management strategies for transfer and management of data using password protected computers or servers with multifactor authentication (iii) data restrictions that align with project scope and objectives, and (iv) storage of data outputs extracted from the OMOP-CDM as approved by the HREC. For OMOP converted data that contains linked data e.g., Australian Electronic practice-based research network (AU-ePBRN) where primary care data is linked with hospital admissions data (24), governance, and liability procedures would need to be explicitly developed to ensure the governance interests of all institutions are considered.

Consent

In any research, regardless of whether it is conducted by an individual researcher, clinician, or collaborative research team, it is imperative to determine the nature of the consent obtained from a patient for the secondary use of their data. This assessment should consider the risks and the potential for psychological, social, economic, and legal harm that may arise from data collection, utilisation, or any potential breaches.

In Australia, a ‘waiver of consent’ as per NHMRC guidelines can be applied to secondary use of health data (23) (Box 1). Some ethics committees may request an ‘opt-out’ model, necessitating the consideration of options for patients who wish to decline to participate (23, 24). Through the de-identification methods employed by OMOP-CDM, the risks related to data breaches, such as the re-identification of individuals, are significantly reduced. This is achieved by exclusively using aggregated results from OMOP-CDM and by refraining from reporting small cell sizes. Re-identification is further minimised by ensuring only aggregated outputs from OMOP-CDM are used and that small cell sizes are not reported.

Box 1 Consent

If an ethics committee deems a research project to be of minimal risk to the individual, an exception to obtaining the legislated requirement for patient consent can be managed using a ‘waiver of consent.’ A waiver of consent can be applied based on a duty of ‘easy rescue,’ where the potential benefits of data access are considered significant, and the harm associated with the risk of a loss of privacy are considered minimal (25). It is also hypothesised a waiver of consent avoids the consequence of consent bias (25). There are many studies that have applied a waiver of consent for research which involves the secondary use of EMR data (26, 27, 28, 29). The research to date on patient and community acceptability of the use of data within their EMR for research indicates, for social licence to be assumed, a breadth of patient and public values, needs and interests should be incorporated in to governance frameworks (30).

Arguments against waiver of consent weigh the costs and potential patient harm against benefits of participation. Costs include privacy breaches per se and the use of data for nefarious purposes increasing risk of reduced trust (31). This includes a loss of informational privacy that may cause harm by revealing facts a patient may themselves have considered irrelevant (31). For such projects, reasons for asserting participation in health records research is based on informed consent, that include the assumption that research ethics committees cannot adequately protect individuals’ values and interests, and the process of informed consent fosters trust in research and researchers (31).

Notwithstanding ethics committee considerations for patient consent, there should also be considerable social engagement across a breadth of stakeholders on research that uses health data. This engagement provides options for the provision of ‘social permission’ and ‘social licence’ for consent, where the determination of consent is co-created by patients and therefore morally legitimised - beyond the limits of law and outside of what is acceptable by an ethics committee - to preserve societal trust (32).

Risk Mitigation

OMOP mitigates many of the risks of using EMR data for secondary purposes including: (i) replacement of all personal identifiers with a generic number that does not allow re-identification back to the original personal identifier (12); (ii) an option for data custodians to perform analyses on behalf of an individual researcher, (i.e. no data release); (iii) the use of a user interface tool such as ATLAS, where researcher access to data in all tables can be configured to protect privacy (8); (iv) collaborative analyses are always conducted within each institution’s firewalled network (8); (v) use of standardised terminology only removes potential identifiers in the source terminology and (vi) there is an option to obscure dates from view, such that temporal association can be calculated from a relative date (33) (Box 2)

Box 2 Risk Mitigation

An access control policy is crucial for ensuring the privacy, management, and security of data, especially when it is related to research. This ensures use of data is managed appropriately and underpinned by respect of the rights and expectations of the individuals it represents.

Access control measures include the application of: strong passwords that are complex and contain alphanumeric characters as well as symbols; multifactor authentication (MFA) where data users apply two or more evidence pieces (or factors) to verify their identity; safe connectivity where the standard practice for data access is via devices connected to secure and private networks rather than devices that are connected to public networks; the prompt reporting of data breaches to mitigate the impact of any cybersecurity attack and prevent further vulnerabilities; the verification of ethics approvals before granting access to ensure that the research is conducted in an ethically sound manner; and the permissions for data access limited to those researchers who are authorised and working within the confines of an institution's environment.

A review of data that is due to be transmitted to researchers provides another important safety check, as does maintenance of version control, where the most recent database is always held as back up. Additional risk management controls include the delivery of explicit instructions to researchers on the appropriate use of the dataset; the incorporation of additional hardware authentication such as the YubiKey, Titan, Thetis, and Kensington Verimark hardware keys; restricted access to identifiers in the underlying (Structured Query Language) SQL database; and continuous evaluation of anonymisation adequacy instructions on appropriate use for dataset.

OMOP-converted databases offer a secure and standardised approach to EMR data analysis within an open-source framework, which produce aggregated results which are free of patient identifiers. This eliminates the need for direct access to raw EMR data or external sources that have data sharing restrictions, it also sets it apart from the less structured EMR 'data lakes’ that contain vast amounts of raw and disparate data. These ‘data lakes’ that lack standardised schemas, makes data management and analysis more challenging. In contrast, OMOP-CDM benefits from OHDSI's open-source tools and standardised analytics, by enhancing transparency, reducing coding errors, and supporting validation processes.

As an extension of OMOP data conversion, the OHDSI consortium has developed the OHDSI Quality Dashboard, a tool to ensure the quality of data converted into the OMOP-CDM to improve transparency, reduce coding errors, and enable validation (34). The OHDSI Quality Dashboard is designed to assess and monitor the quality of data that has been converted into the OMOP-CDM. It provides a set of data quality checks and validation tools that help identify issues or anomalies in the converted data (14). In doing so, it identifies and addresses data quality issues that may arise during the conversion process by checking data for completeness, consistence, accuracy, and adherence to standard terminologies. This ensures that the data in the OMOP-CDM is reliable and suitable for research and analysis.

While sometimes considered not flexible enough for specialist areas such as oncology, the OHDSI community continues to develop OMOP-CDM extensions for some cancer types and pregnancy (8). It is worth noting given EMRs capture similar data, such as specific pathology indicators, across various healthcare sites, they may use different data elements due to local clinical practice nuances. These nuances might not always be fully captured in the OMOP mapping process. Nevertheless, the mapping process is a continued effort across all OHDSI communities.

Institutional governance and privacy frameworks have evolved independently alongside the adoption of secondary EMR use (7), achieving a consensus on governance practices across institutions is an ongoing endeavour. This underscores the importance of ongoing collaboration and standardisation in the healthcare data field to ensure that valuable health data can be leveraged effectively and ethically for research and healthcare improvement. Given all the opportunities the OMOP-CDM offers for integrated data governance, these opportunities are limited by lack of standalone funding required for the comprehensive mapping of data from local EMRs to the common format. Despite these challenges, the commitment of the global community to the OMOP-CDM signifies a promising future for standardised health data, which will pave the way for transformative healthcare research and innovation.

Adoption of the OMOP-CDM internationally and locally is well worth the investment, as it enables conversion of large amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data. Combined with pseudonymisation and common data quality assessments, the OMOP-CDM provides a powerful model to support ethical real-world ‘big’ data research. The continued adoption of OMOP-CDM, ongoing development efforts, and the emphasis on sound governance practices all contribute to the realisation of OMOP's utility in unlocking valuable EMR data. These factors collectively support a wide range of applications, from health service operational reporting to diverse clinical, epidemiological, and translational research projects.

This study did not involve human subjects, nor did it involve the use of non-human vertebrates. All listed authors declare they do not have any conflicts of interest.

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Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM)

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Version 1

Abstract

Figures

Introduction

The Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM)

The Utility and Adoption of the OMOP-CDM

Aim

THE OMOP-CDM

The OMOP-CDM – Structure and Process

OMOP, Data Quality, and the Principles of FAIR, CARE, and FIVE SAFES

OMOP, Data Governance, Ethical Review, and Consent

OMOP-CDM and Governance

Operational Use and Quality Assurance Activities in a Hospital or Healthcare Setting

Research in a University Setting

Consent

Box 1 Consent

Risk Mitigation

Box 2 Risk Mitigation

Benefits, Limitations, and Considerations of OMOP-CDMs

Conclusion

Declarations

References

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