Clinical Data Integration

What is clinical data integration?

Clinical data integration is the process of combining different types of patient health data from multiple sources into a single, organized format. These data sources can include electronic medical records (EMR) or electronic health records (EHR), laboratory test results, health insurance claims and billing information, images, disease registries, genomics databases, clinical trial data, and more.

For healthcare service providers, clinical trial sponsors, and policy-makers, clinical data integration is about gaining deep and structured insights into patient health profiles and patterns in order to make informed decisions about treatments and care.

Importance of clinical data integration in healthcare

Clinical data integration is essential for making the best use of healthcare data and improving healthcare services. By combining all relevant patient information into one cohesive view, researchers gain access to organized data from which they can extract actionable insights.

Further, the advanced analytics capabilities enabled by integrated data allow for enhanced methods and models in clinical trials, such as predictive modeling, real-time monitoring, and automated data consolidation/cleaning.

Objectives of clinical data integration

The primary objectives of clinical data integration are to improve the depth of insights into medical data and enhance research efforts, while reducing costs associated with manual processes such as paperwork and consolidating data across disparate systems. Effective data integration and management also supports streamlined reporting and higher-quality data and results.

Clinical data integration and clinical data management systems (CDMS)

The concept of clinical data integration in clinical trials is closely related to clinical data management systems (CDMS), a type of software application used to manage large volumes of structured and unstructured clinical research information, such as that collected during clinical trials. A CDMS offers sponsors and investigators an efficient way to collect, organize, store, and even clean and analyze diverse types of patient information from multiple sources. Many CDMS also support powerful analytics capabilities that allow researchers to extract meaningful insights from the clinical datasets, which can be used for real-time monitoring (during trial operations), data analysis and reporting (after database lock), and more.

Benefits of healthcare data integration in clinical research

Combining and organizing disparate data from multiple sources can help improve the accuracy, consistency, and quality of data, while also providing enhanced support for clinical decision-making. In addition to these benefits, healthcare data integration also enables real-time analytics and monitoring of patient health information, and generally streamlines data processing steps for sponsors when properly implemented. Let’s take a closer look at some of the benefits of data integration in clinical trials, and more broadly in healthcare in general.

Improved data accuracy, consistency, and quality

Data integration helps investigators ensure that all relevant data is captured accurately and consistently across different sources. For example, data pulled from EHRs may be in a very different format from that collected through informed consent forms (ICFs); if this data is to be analyzed together, it needs to be made coherent according to some predetermined framework. This framework will normally be defined by the clinical data manager as part of the data management plan (DMP) for a study. It could be based on a standardized format, like Fast Healthcare Interoperability Resources (FHIR), or on internal processes established by the sponsor.

Precise data integration also ensures that any errors or inconsistencies can be detected and corrected quickly. The use of data management software tools supports automated edit checks and validation, which can make the identification (and sometimes even correction) of certain errors automatic.

Having uniform data across all systems makes it easier for clinicians to compare results between patients enrolled in a study across different sites, facilitates data sharing, and reduces the chances for manual errors such as inputting incorrect codes during transcription or misplacing paper documents.

Enhanced support for clinical decision-making

By combining different kinds of healthcare information into a single repository or database, researchers can assess patients’ conditions more thoroughly and make informed decisions based on incoming evidence.

Further, the integration of external or supplementary data sources, such as real-world data/real-world evidence or data from EHRs, opens up possibilities for new research directions and research questions to be explored. With the massive amounts of health data currently available, as well as the increasingly powerful data processing capabilities of artificial intelligence (AI) and machine learning (ML) tools, there are deep insights waiting to be uncovered which have the potential to improve healthcare interventions and policy at a wide scale.

Varied sources of data can also be integrated to support predictive modeling, which has already seen various practical applications in clinical research, such as in identifying new targets to guide drug development or predicting early termination of proposed trial designs.[1]

Real-time analytics and monitoring

With incoming data integrated into a central system, researchers can monitor changes in the health status of trial participants in real-time or near-real time. The enhanced analytics functions of data management systems also provide deeper insights and a much clearer picture of potential trends emerging among certain demographics or subgroups within the studied populations. It may even enable earlier detection of adverse events or other issues and subsequent intervention to prevent their further development into bigger problems.

Technical considerations for clinical data integration solutions

Clinical data integration requires consideration of many technical aspects, especially when dealing with disparate data sources or records originating in different countries. This includes understanding the different formats and standards for data collection/storage/processing, software tools for data management, and also being familiar with and respecting applicable data privacy regulations.

Data formats and standards: HL7, FHIR

Two of the most important designations when it comes to healthcare-related data are Health Level Seven (HL7) and fast healthcare interoperability resources (FHIR). HL7 provides a framework for the processing of digital health information; more information on HL7.[2] FHIR was developed by HL7, and is a standard specifically designed to facilitate sharing of electronic health records; more information on FHIR.[3] There are nearly unlimited possible sources of data that may be drawn upon in clinical research; having a reliable data manager who is well versed in data formats and consolidation is a great asset for sponsors integrating clinical data from multiple sources.

Data collection and storage

Clinical data can originate from many sources, both from within a clinical trial (ePRO, eCOA, EDC tools, wearables, surveys, etc.) and external to it (EHRs, claims data, etc.). As such, it is important to prepare the data management plan with the data sources in mind, setting up software tools and procedures to streamline the entry and consolidation of data.

Data security and storage is a subsequent consideration that cannot be overlooked; unauthorized access must be prevented through strong data security practices in order to maintain patient data privacy and confidentiality, and original clinical data should be backed up continually.

Data cleaning and validation

Data integration will usually involve a significant degree of consolidation and cleaning before the data can be analyzed efficiently. The goal of cleaning is not only to identify and correct/remove errors, but also to ensure consistency across all datasets. This means making sure that all entries are valid, properly formatted, classified appropriately, etc.

Validation may also be required, which could involve checking the data against the source data, as in source data verification (although in general there are more potentially more-efficient methods of data validation, as discussed in the referenced article).

Healthcare data integration challenges

Data heterogeneity and complexity

As we’ve already touched upon, data heterogeneity refers to the range of different types/formats of information that might be gathered during a clinical trial or in other types of studies such as health economics and outcomes research (HEOR). Heterogeneous data increases the complexity of integrating the data into one unified system, but with a proper data management plan and appropriate tools and workflows in place, there are almost no limits to the data types that can be analyzed for health insights.

Interoperability issues

Interoperability describes the ability for two or more systems to exchange information in a relatively streamlined way, without sacrificing accuracy or requiring intensive manual involvement. Unfortunately, due to legacy systems often used (especially in larger organizations), a lack of standardized protocols, and numerous incompatible formats across different platforms, interconnectivity is often limited. This presents an obstacle when integrating data from disparate systems. For this reason, comprehensive eClinical software solutions are becoming increasingly popular amongst trial sponsors, as these systems integrate diverse operations into single sign-in, unified platforms. For example, a provider might offer a clinical trial management system (CTMS) that uses its own electronic data capture (EDC) module to populate electronic case report forms (eCRFs) automatically, which are then edit checked and relayed to the electronic trial master file (eTMF), which feeds data into a remote monitoring dashboard that allows a central monitor to oversee data incoming across all sites and monitor risk in real-time. Now imagine all of those functions being provided by disparate systems provided by different suppliers, which don’t integrate with one another, and it’s easy to see the genuine advantage of comprehensive single-provider solutions.

For now, particularly until data standardization becomes more internationally prevalent, it may often be necessary to perform some degree of manual integration and consolidation when joining distinct sources of clinical data.

Institutional change, user adoption, and readiness

Incorporating new processes, tools, or workflows into existing infrastructure not only requires getting staff up to speed, but also convincing stakeholders of the value of the proposed changes in order to overcome institutional inertia. Time constraints are another factor, as successful implementation may require some time. When it comes to data integration, proper training on software tools and data processing workflows is absolutely necessary in order to maintain compliance and uphold ethical and regulatory standards regarding data privacy and patient confidentiality (see next).

Regulatory and compliance considerations in clinical data integration

As clinical trial sponsors, pharma execs, investigators, and medical professionals are aware, there are a range of regulatory and ethical considerations to consider when conducting research that involves (or otherwise processing) protected health information (PHI). In general, the ICH Good Clinical Practice (GCP) guidelines are a good starting point. In the US, the Health Insurance Portability and Accountability Act (HIPAA) must be respected when integrating PHI into databases or processing it as part of a clinical trial. In Europe, the Clinical Trials Regulation (CTR) and the General Data Protection Regulation (GDPR) are the main applicable regulations. For a deeper dive into this topic, have a read through our article on patient data privacy.

Legal liability, compliance risks, and ethics

Careful attention must be paid to all applicable regulations when designing architecture for managing or processing PHI. Failure to do so could lead to serious legal implications not only from regulatory bodies but also from patients or other stakeholders involved in any potential data breaches or compliance issues. In clinical trials, the sponsor is ultimately responsible for all aspects of regulatory compliance, even if parts of the trial operations are outsourced to a CRO or if a breach/error is caused by software that the sponsor has chosen to use.

Safeguarding data privacy and preventing non-compliance may necessitate the allocation of additional resources to staff training on proper handling of PHI. Standard operating procedures should be developed for all data handling tasks, and internal policies should be established that clearly define the ethical boundaries related to the usage of patient records. This is important in order to maintain the trust of research participants and minimize potential legal consequences stemming from non-compliance with applicable laws.

If this all seems like simply too much to take on amidst your current responsibilities, consider hiring or contracting a data manager, regulatory compliance officer, or other similarly experienced professional who is well-versed in regulatory affairs and patient privacy.

Conclusion

Clinical data integration has become an increasingly relevant concept in clinical research and healthcare. Simultaneously, it is becoming more complex as the amount of data collected is increasing almost exponentially with new technologies. However, the same technological advancements are also opening up new possibilities for analyzing these massive amounts of data and gaining new, high-resolution insights into health patterns.

Through clinical data integration, researchers gain access to more comprehensive insights that can be used to inform trial design, future treatments, and improve patient outcomes. Electronic solutions and tools are vital for streamlining and even automating aspects of data integration, as they significantly reduce the amount of time required as well as the risk of errors arising from manual tasks. Nonetheless, the adoption of new data sources, tools, and processes, staff training, and the navigation of regulatory landscapes in light of these new workflows are extremely important topics for researchers and healthcare professionals to keep in mind while transitioning to and exploring the potential of these new data analytics capabilities.