Modalities & Learnings of Direct Data Capture (DDC) in Clinical research through the Integration of Electronic Health Records (EHR) with Electronic Data Capture (EDC)

Given the surge of electronic health records (EHRs) implementation across healthcare organizations throughout the early 21st century, the pharmaceutical industry and greater clinical trial community have a lot of interest in better understanding what information and data can be directly captured from EHR platforms and how to best integrate that with electronic data capture (EDC) systems to help enhance current clinical trial processes. AbbVie has conducted four pilot studies to assess different modalities of EHR-EDC integration with the objective of obtaining a further understanding of market trends, integration methods, and their considerations. The report will provide an overview of the different methods available for direct data capture (DDC) in clinical research and the characteristics of those methods.

EDCs are used to collect, clean, and process the incoming data from a clinical trial, which allows for a more streamlined method of data collection from the clinical sites with stronger data integrity [1]. Data entry in an EDC system is manually done by site users onsite who extract patient data from the site’s source documents and medical records. An electronic medical record (EMR) is a patient’s digital chart that contains and tracks their medical history from one single office or practice over time. However, the patient’s health information in an EMR is not easily accessible for providers outside of that one practice, which is where EHRs prove to be most useful. An EHR does everything an EMR can do with the key difference being that EHRs allow for the sharing of patient information between authorized providers from all the healthcare organizations involved in the patient’s care [2]. While EHRs and EDCs are both an advancement over the use of paper records, the lack of integration between the systems creates an abundance of rework and risks inaccurate or incomplete medical information in clinical trials. To help further investigate how to best combat this gap, AbbVie explored several different modalities of EHR-EDC integration in clinical research: direct integration from EHR to sponsor EDC, direct integration from EHR to sponsor clinical data repository (CDR), and indirect integration from EHR to EDC via a syndicated data provider. This paper aims to describe the developmental process and approach behind each method of integration and the key considerations, as well as AbbVie’s recommendations for further implementation for future clinical trials. There are many entities (academic centers, providers, and system integrators) involved in these four pilots. AbbVie will not share the names of these entities within this paper due to legal obligations and potential conflicts of interest.

Significance and Benefits of EHR-EDC integration in Clinical Research
Adoption rates of EHR and EDC systems have dramatically increased in recent years. As of 2017, nearly 9 out of 10 office-based physicians had implemented an EHR platform, more than doubling the 42% rate from ten years prior [3]. During that same time, the rate of EDC usage in clinical trials extensively grew even more remarkably. In 2008, only 2% of investigative sites had reported being using EDCs as their sole method of collecting trial data. A decade later that same rate expanded to nearly 78% [4].
The advantages of integrating EHRs with EDCs range from process improvements to reduced costs, to increased data integrity and site satisfaction. The manual process of inputting trial data for site-based clinical research into an EDC system is often redundant to what clinicians and other healthcare providers enter on a patient’s digital chart during a clinical visit or hospitalization. In addition to reducing cumbersome data re-entry, abstracting existing data from EHRs to EDCs would not only allow for faster data availability, but also establish a single point of source data, preserving data integrity and providing improved data traceability, all while reducing the need for data queries, data cleaning, and source data verification [5].
Industry Trends and Experiments of DDC in Clinical Research
Direct use of EHR data in clinical trials has long been a goal for researchers, and so across the pharmaceutical industry, both software and pharmaceutical companies have been developing novel technologies and software solutions aimed at modernizing direct data capture from clinical sites and provider EHR data to sponsor EDC systems. Starting in 2020, Takeda Pharmaceutical invested in Seqster to accelerate their technology for clinical trials and improve patient engagement and outcomes by providing real-time EHR, wearable, and DNA sequencing data [6]. In another pilot study conducted by Duke University, eSource was compared to traditional manual data capture methods by observing time spent completing registry forms and data quality. The study found a significant reduction of 151s (about 2 and a half minutes) per case in the average data capture time, as well as a reduction in data field transcription errors from 9% without eSource to 0% with eSource [7]. Another example of the recent push to integrate EHR and EDC is Clinical Pipe, which allows for the transfer and mapping of structured, discrete data points in clinical trials from the EHR to the EDC [8]. Clinical Pipe was designed to take EHR data and transform it to an internal, consistent format, which then allows for transforming the data into formats that EDC sponsor systems can understand. The program was used in a leukemia trial, Beat Acute Myeloid Leukemia (AML) Master Trial, where Clinical Pipe was shown to decrease time spent by staff performing manual data entry and reduce data errors [9].

To improve clinical trial EHR-EDC integration processes, AbbVie’s Clinical Data Strategy & Operations (CDSO) group collaborated with several vendors to conduct four pilot projects that investigated different methods of integration. These projects helped the AbbVie CDSO team to focus on two primary modalities for direct data capture from EHRs to EDCs: direct site integration and indirect site integration via syndicated data provider. Potential exists for other methods of EHR-EDC integration, but AbbVie has decided to proceed with concentrating on these two methods.
Direct Site Integration
As mentioned previously, the majority of clinics and hospitals across the country are now using an EHR platform. With direct site integration, sponsors would directly build a bridge with the site’s EHR vendor to automatically pull the data required for the given study from the information already in the patient’s chart into their EDC or CDR system. To evaluate this method more effectively, AbbVie partnered with a leading university and a leading oncology provider to conduct two separate pilot studies, which will now be referred to as University A and Provider A, respectively. The University A was leveraged as a System Integrator (SI) to build the integration with Provider A. The project was done in two phases.
In Phase, I of the project integration was built between Provider A EMR system and University A validated repository. This integration involves the transfer of EMR data to University A’s validated repository through proprietary technology and then to AbbVie’s EDC system. Phase 2 will bypass the EDC transfer and instead, robust datasets on study participants will be packaged and put into AbbVie’s CDW as flat files instead of AbbVie’s EDC system
AbbVie’s pilot project supported the transfer of subject data for three trials that were actively recruiting/accruing subjects at Provider A site. This pilot tested the ability to allow clinical site staff to review and electronically transfer trial patient data from an EMR system, such as oncology specific applications Varian Aria, Epic Beacon, McKesson iKnowMed, and Elekta Mosaiq, to the EDC or CDR system used by the trial sponsor, such as Medidata Rave. In three separate AbbVie studies, Abbvie retrieved subject data from Provider A’s Production EMR platform and transferred that data to the appropriate study trial in AbbVie’s development EDC environment. This occurred alongside normal data entry procedures which acted as a control to compare results from the direct data capture. Study data such as demographics, labs, vital signs, performance status (ECOG), physical exam, and tumor markers were transferred. Operational data was recorded for the resource(s) responsible for data transfer, the length of time required to complete data transfer, the number of queries and time to resolve generated by the EDC, the number of transcription errors identified and the length of time to resolve.
Syndicated Data Provider
The syndicated data provider modality involves using a third-party vendor who can build an integration with the EHR platform. The sponsor then contracts with the third-party vendor to access the already deanonymized and de-identified data, rather than contracting with the EHR vendors separately. Given the broad spectrum of diverse ways vendors can integrate with EHRs, AbbVie conducted pilot studies with two different third-party vendors to research this method more thoroughly: Syndicated Data Vendor (SDV) A and SDV B.
SDV A, a Software as a Service (SaaS) healthcare solution, provides instant interoperability to retrieve and harmonize EHR, genetic, and continuous monitoring data from distinct sources. SDV A will collect the volunteer's historical longitudinal health data (EMR, Devices, Nutritional, Fitness, DNA), as well as data from one routine clinical visit. The de-identified data will then be transferred into AbbVie’s clinical data repository (CDR) system for evaluation. In one study conducted by AbbVie, up to 50 volunteers were recruited for a non-interventional study to access their longitudinal health records, half of which were chosen to go through SDV A process flows. SDV A accumulates data by linking patient portal data from all the different systems that the patient has been to, obtaining the health data instantaneously when the data is available in the subject’s personal patient portal.
SDV B aims to solve the immense burden put on patients to manage their own medical records by improving medical record management. In the same study that highlighted SDV A’s patient portal integration technique, SDV B was utilized by the other half of the subjects to integrate site EHR and sponsor EDC programs. SDV B collects patient data by acting as an agent on behalf of the patient and reaching out to all the different networks identified by the patient to compile the subject’s healthcare data and enter the required information where a sponsor can easily access it. With this method, SDV B has some additional data extraction capabilities, so their process also has the potential to gather data that goes beyond what is captured in an EHR/EMR system.

Direct Site Integration
The direct site integration modality found that data points like demographics and medications were able to be successfully mapped from the EHR system into the EDC. However, using the data points from these studies as a predictive framework, it can be presumed that the more complex the data elements, the more difficult the mapping. The results of the direct site integration pilot studies aligned with current industry trends regarding EHR-EDC direct data capture objectives by demonstrating the ability to significantly reduce data entry time, error rates, and operational costs by integrating EHR and EDC systems, as opposed to using the traditional manual data transfer method. The studies also discovered a common major pain point with this modality, and in clinical trials in general, with the continued use of paper documentation. This contributed to still having redundant data entry and made automation more difficult to implement.
Syndicated Data Provider
The syndicated data provider studies verified the public’s current concern regarding their data privacy. Certain populations had difficulty committing to sign up for a vendor’s platform if they had a long, confusing consent form. Even after jumping through the legal and compliance hurdles that this method presented, these pilots demonstrated the challenge of reconciling copious amounts of external health data from various organizations into one internal system, with much of the incoming data being unstructured or formatted differently across systems. Despite the lengthy implementation, the syndicated data provider method proved to reduce site data entry time, reduce the risk of inaccurate data entries, and reduce overhead costs.

Regardless of the specific modality used, there are significant considerations and barriers that need to be acknowledged when attempting direct EHR-EDC integration. Failure to thoroughly contemplate these potential implications could result in an ineffective or unsuccessful integration. AbbVie has broken down these noteworthy considerations into three main classifications: legal and compliance, data quality and integrity, and technical and operational.
Legal and Compliance
Both EHRs and clinical trials are heavily regulated operations and so it is only natural that integrating the two presents various legal and compliance considerations. Contracting between the EDC vendor, site, and sponsor takes a considerable amount of time to complete. One of the major challenges that add to the length of time in this process is patient privacy. Patient privacy has been a frontline issue in electronic health information exchange (eHIE) conversations, and EHR-EDC integration is no exception, as it involves transferring protected health information (PHI) and personal identifiable information (PII) between multiple systems.10 The Health Insurance Portability and Accountability Act’s (HIPAA) Privacy Rule intentionally has a broad definition of PHI/PII to include information such as name, address, or SSN; as well as indirect identifiers, such as zip codes or date of birth, when attached to any health information. Without authorizations or approval from an Institutional Review Board (IRB), covered entities, such as clinicians onsite, must de-identify health information before disclosing it to the sponsor for research purposes. However, the Privacy Rule also gives the research subjects in clinical trials the right to authorize use and disclose his/her PHI/PII for research purposes [11]. With EHR-EDC integration, it will be imperative to receive consent not only for trial data, but also for the rest of health data that would be in the patient’s EHR/EMR, such as their medical history, lab results, and imaging. With all the risk and legal considerations brought about with EHR-EDC integration, indemnification clauses will inevitably take a considerable amount of time when negotiating a clinical trial agreement (CTA). Indemnification is a contractual agreement between the parties whereby one party, the indemnifying party, agrees to protect the other party, the indemnified party, against harms or losses brought by a third party that the indemnified party may incur [12]. With a third-party vendor being involved, both the site and the sponsor will look to request indemnification rights for third-party losses or claims that either one incurs due to the conduct of the other. There are considerable legal obstacles when attempting direct EHR-EDC integration within the United States, however, it is important to note that any global or outside of the United States studies will have even more considerations to acknowledge given different international laws and regulations. AbbVie’s pilot studies were only conducted in the United States, so international considerations will not be probed for the purposes of this report.
Data Quality and Integrity
Data quality and integrity factors must also be considered when integrating EHR and EDC systems. It is the sponsor’s responsibility to meet quality requirements and ensure they are documented appropriately through contractual agreements and audits. If the EHR is not certified by the US Office of the National Coordinator for Health Information Technology (ONC), the sponsor will have to determine if the site has validated the EHR vendor properly and has quality control measures in place, such as audit trails, security, and data protection [13]. Clinical Data Management (CDM) is an essential aspect of clinical research that leads to the creation of high- quality, reliable, and statistically sound data from clinical trials. Discrepancy management, or query resolution, is the most critical element in the CDM process, as it requires absolute full attention while cleaning up the data [14]. EHR platforms tend to lack certain quality control measures when entering patient data. In many cases, clinicians are not prompted to complete all fields or may have the option to replace discrete data fields with free text, all of which can lead to discrepancies or errors when attempting to pull data directly from EHRs to EDCs. Because of this, additional processes will likely need to be put in place for when investigators require additional clarifications from clinicians in regards to discrepancies from EHR data [5]. Even with a high percentage of automatically filed data into the eCRFs from EHR systems, there is almost never 100% of data needed and so a separate evaluation will need to be done in order to determine the best route to capture the remaining data.
Technical and Operational
In addition to legal and quality considerations, there are still technical restrictions that must also be thoroughly vetted for direct EHR-EDC integration to be successful. Data mappings and translations are major components of executing an effective data migration since the benefits of the integration are contingent upon the accurate mapping of the clinical data elements in the source EHR platform, so that no critical information is neglected from the new target EDC system. This process involves extracting data from the source system, transforming, or converting it as needed, and loading it into the target system. Incomplete or incorrect mappings could lead to severe implications, and so the need for additional help and verification in the mapping process is critical. Physicians and other clinical providers should be a part of the governance that determines how to correctly translate the data elements to be mapped [15]. The Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard for exchanging healthcare information electronically defines the application programming interface (API) for transferring data between healthcare enterprises, specifically EHRs [16]. The Clinical Data Interchange Standards Consortium (CDISC) defines the Study Data Tabulation Model (SDTM) standard for organizing and formatting data for submission to the FDA and to streamline processes in the collection, management, analysis, and reporting [17]. FHIR and CDISC standards organize information differently, CDISC data is grouped into a series of domains, such as demography or vital signs, while FHIR data is grouped into resources, such as prescriptions or procedures [16]. With EHR-EDC integration, it will be imperative to harmonize these data groupings from the FHIR standard to the CDISC SDTM standard.
With all these factors being considered, it may be easy to forget the EHR platform itself must be heavily considered when attempting to integrate with EDCs. According to HIMSS Analytics’ Logic, a database that looks at more than 571,000 providers affiliated with more than 4,000 hospitals, there are sixteen distinct EHR platforms being utilized at healthcare organizations, with most using multiple EHRs throughout their organization.18 This amount of variability in EHR vendor use in healthcare enables an immeasurable number of potential customizations and individualizations at each site, which presents difficulty when aiming to standardize EHR-EDC integration across the industry. Thoroughly reviewing each consideration for EHR-EDC integration requires having the proper personnel available to conduct the comprehensive research required, all of which takes time and costs money, something not yet extensively analyzed publicly.

With the increasing adoption rates of EHR usage in healthcare and EDC advancements in clinical trials, it is pivotal that EHR-EDC integration continues to be researched and implemented until it becomes a standard across the industry. As industry research and these pilot studies suggest, switching to electronic forms and the utilization of direct data capture can have significant benefits. Without redundant data entry, there will be data error rate, time, and money savings. There will be initial implementation costs and trial and error of testing different vendors and systems, but the benefits outweigh these initial challenges.
After investigating several modalities for EHR-EDC integration, AbbVie believes that each method has pros and cons, and the method used should be determined on a study-by-study basis. No singular method of integration can be expected to be the perfect fit for each study given the number of potential variables; site location, EHR vendor used, and patient population are just a few that would have a tremendous impact on which method might be deemed most suitable. Therefore, AbbVie suggests following the “Fit for Purpose” approach when implementing EHR- EDC integration. Following this course of action ensures that the EHR-EDC integration methodology used will be most successful for providing the desired benefits.
Data Quality Index (DQI) will also play a crucial factor in helping determine which method is most appropriate for a study. AbbVie will share the DQI from the four pilot studies conducted in future research publications.

Funding

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Conflict of interest

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