Beyond Clinical Trials: Connecting Clinical Research with Continuous Patient Care

From study-specific data collection and fragmented systems to a research platform supporting clinical trials, routine care and longitudinal research.

1. The traditional clinical research model

Clinical research has traditionally been organised around a defined study and a specific research question. A medicinal product or treatment is investigated according to a protocol, with specified eligibility criteria, patient populations, endpoints, assessments and observation periods.

Depending on the research question and development stage, this may involve Phase I, II and III clinical trials, randomisation, control groups and double-blind designs. Patients are recruited, data are collected, results are analysed and conclusions are drawn. Once the study is completed, its formal research activities come to an end.

This model has been fundamental to the development and approval of medicines. It provides a controlled framework for assessing safety and efficacy. However, it also creates a separation between clinical research and the longer-term treatment journey of the patient.

A patient does not stop needing medical care when a clinical trial ends. Treatment may continue, change or be discontinued. Further effects may appear, the disease may progress, and new medical questions may arise. Some effects can only be understood over a longer period or through observations involving larger and more diverse patient populations.

Furthermore, modern therapies and treatment pathways are becoming increasingly complex. A protocol cannot anticipate every clinical situation or every possible treatment decision. Even within a clinical trial, physicians may need to modify treatment, adjust doses, interrupt treatment or introduce additional interventions. Such decisions are part of clinical practice and need to be appropriately documented and considered in the interpretation of research results.

This raises a broader question: should clinical research always be organised as a separate, time-limited activity, or could it become more closely connected with routine clinical care?

2. From Standard of Care to Research-Oriented Care

For the purpose of this discussion, I suggest using two terms to distinguish between routine medical treatment and treatment connected to a structured research process.

Standard of Care (SoC) refers to medical treatment provided according to applicable clinical practice, guidelines and an individual physician-defined treatment plan. The plan may include diagnosis, medication, dosage, monitoring, visits, assessments and criteria for changing or discontinuing treatment. The physician remains responsible for clinical decisions, and relevant changes and their medical reasoning should be documented.

Research-Oriented Care (ROC) describes a possible extension of this approach. The patient receives treatment through a clinical care pathway, but the treatment and its outcomes are also connected to a defined research question, research protocol and appropriate research data infrastructure.

The important distinction is not necessarily the treatment itself. A patient receiving ROC does not automatically receive a different treatment from a patient receiving SoC. Treatment may change in both situations. The difference is the additional research purpose: what we want to learn, which data are required, how outcomes are assessed, and under which governance and regulatory conditions the information may be used.

These expressions are proposed working terminology for explaining the model. They are not intended to replace established concepts such as real-world evidence (RWE), pragmatic clinical trials, research embedded in care or learning health systems.

Aspect	Standard of Care (SoC)	Research-Oriented Care (ROC)
Primary purpose	Diagnosis, treatment and care of the individual patient	Patient care combined with a defined research objective
Treatment	Individual physician-defined treatment plan	Treatment through a clinical care pathway, with research requirements defined separately
Documentation	Routine clinical records and relevant treatment decisions	Clinical records plus structured research documentation and protocol-specific data
Data use	Primarily supports clinical care	Supports defined research questions and analysis, subject to appropriate permissions and governance
Outcomes	Assessed as part of individual patient care	Assessed against research objectives and defined endpoints where applicable
Oversight	Clinical and healthcare governance	Clinical governance plus applicable research, ethics and regulatory oversight

A patient receiving routine care may also contribute selected clinical data to research without necessarily participating in an interventional clinical trial. For example, a patient could agree to the use of data required for a particular research question, or could agree to broader use of relevant longitudinal clinical records for research into their condition.

These are different possible participation models. The scope of data use, consent requirements and other lawful bases must be defined according to the applicable jurisdiction and research purpose. Routine clinical care should not automatically be treated as research participation.

3. From individual clinical trials to continuous research

The proposed approach does not mean that conventional clinical trials become unnecessary. Controlled trials remain essential, particularly when establishing the safety and efficacy of new treatments.

However, clinical trials could become one component of a wider research environment rather than the only structure through which clinical evidence is generated.

Consider a patient with a chronic or complex condition. The patient receives treatment according to an individual clinical care pathway. During this period, the patient may participate in a specific clinical trial. After the trial has ended, treatment and medical monitoring continue. Additional clinical observations may become relevant to other research questions concerning the same condition.

The original trial may have investigated one medicinal product, dose or endpoint. Subsequent research might investigate longer-term outcomes, treatment sequences, combinations of therapies, disease progression, safety signals or differences in response between patient groups.

The same patient's clinical history could therefore contribute to more than one research question over time, provided that the relevant data use is permitted and scientifically appropriate.

This also opens possibilities for research beyond the original clinical trial. For example, researchers could investigate outcomes among patients receiving different standard treatments, compare treatment pathways, identify patterns associated with particular complications or examine longer-term effects after a product has been commercialised.

The research question would determine which data are required. It would not be necessary to collect every possible clinical record for every research project.

4. A clinical research platform: connecting care, protocols and data

Such an approach requires more than an electronic data capture system. It requires a clinical research platform that connects clinical information, research protocols, study operations and the documentation needed to demonstrate how research was conducted.

Today, clinical research commonly relies on several separate systems. Hospitals and healthcare providers use electronic health records (EHRs), laboratory systems, imaging systems, pharmacy systems and other clinical applications. Sponsors and CROs use electronic data capture (EDC), clinical trial management systems (CTMS), electronic trial master files (eTMF), interactive response technology (IRT), safety systems and financial applications.

These systems perform different functions, but the separation between them can result in repeated data entry, manual reconciliation, multiple records of the same information and additional administrative work. Data generated during routine care may need to be transferred or re-entered into a study-specific EDC system. Study operations, essential documents and financial information may be managed elsewhere.

The proposed clinical research platform would provide a common environment for these activities. Its central component would be a research data warehouse or structured data repository, connected to the clinical data sources and supported by dedicated operational and documentation functions.

The platform would not simply copy all hospital records into a single database. Instead, it would connect to relevant data sources and make information available according to the requirements of a particular research protocol or approved research purpose.

The main components of the platform

1. Clinical data integration

The platform would connect to existing healthcare information systems, including EHRs, laboratory and imaging systems, pharmacy records, registries and, where relevant, patient-generated data.

Data integration standards and interfaces, such as HL7 FHIR, together with appropriate clinical terminology and research data standards, could support the exchange and mapping of information.

The original source, date, units, method of measurement and relevant clinical context would need to be preserved. Data quality, completeness, provenance and traceability would be essential.

2. Research data warehouse

The central data warehouse would hold or provide controlled access to structured clinical and research data over time. It would support longitudinal patient records, while maintaining the separation between identifiable clinical information and research datasets where required.

Rather than creating a completely separate data environment for every study, the platform could make relevant data available to different research projects according to their protocols, permissions and access rights.

The warehouse would also need to support additional data types and sources as research requirements evolve.

3. Protocol and research specification layer

The research protocol would become more than a document describing study procedures. It would also serve as a structured specification of the evidence required.

For example, the protocol would define the research question, target population, eligibility criteria, endpoints, assessments, required variables, data sources, observation periods and analysis requirements.

The platform could then identify which information is already available in the clinical data environment, which information needs to be collected specifically for the study, and which assessments or procedures need to be performed prospectively.

This distinction is important. Not every clinical variable needs to be entered again into an EDC system if reliable information is already available from an appropriate source. At the same time, study-specific assessments, patient-reported outcomes or other information not routinely collected would still need dedicated data capture.

4. Study operations and management

The platform would also need to cover the operational functions traditionally supported by CTMS.

These include study and site management, patient and visit tracking where applicable, milestone monitoring, study progress, resource planning, issue management, monitoring activities and oversight.

For conventional clinical trials, these functions remain necessary. In a broader research environment, they could also support observational studies, registries, prospective research embedded in care and other research activities.

5. Essential documentation and quality management

The platform would need to provide the documentation and traceability functions associated with eTMF and clinical quality management.

This includes essential study documents, approvals, investigator and site documentation, monitoring records, training records, deviations, CAPAs, audit trails and other evidence required to demonstrate appropriate study conduct.

The objective would not be to remove these requirements, but to integrate them with the research process and relevant data.

6. Additional research and analytical capabilities

Once the infrastructure is established, it could be extended to support further research using data collected during routine care, clinical trials and post-commercialisation follow-up.

Depending on the research question, this could include real-world evidence studies, disease registries, longitudinal outcome research, safety surveillance, treatment pathway comparisons, external control groups and other observational or interventional research designs.

Access to additional data would remain controlled by the relevant permissions, governance and research requirements. The platform should not automatically make all patient data available to every research project.

5. What happens to EDC, CTMS and eTMF?

The proposal is not that EDC, CTMS and eTMF should simply disappear. Their functions remain important, but they could become integrated capabilities within a wider clinical research platform rather than separate, study-specific environments.

Existing system	Main function	Possible role within the clinical research platform
EDC	Collection and management of study data	Protocol-specific data capture, validation and review, including information not available from routine care
CTMS	Study, site, patient and operational management	Integrated study and operational management
eTMF	Essential study documentation	Integrated document management, quality oversight and audit-ready evidence
IRT	Randomisation and investigational product supply management	Integrated or connected randomisation and supply functions where required
Safety systems	Collection and management of safety information	Connected safety reporting and signal management, with appropriate regulatory interfaces
Financial systems	Budget, costs, payments and financial reporting	Connected study budgeting, resource and cost management

The distinction is between a collection of separate applications and a common research environment in which the different functions are connected.

For a conventional clinical trial, the platform would still need to provide the complete set of capabilities required to plan, conduct, document, monitor and close the trial. For research embedded in routine care, it could use relevant clinical information already available in the healthcare environment. For subsequent research, the same infrastructure could support additional protocols and analyses without rebuilding the entire data collection environment each time.

In this sense, the platform would be extendable: it would support the complete operational and documentation requirements of a clinical trial while also allowing additional data sources, research questions and study types to be connected over time.

6. After commercialisation: research does not necessarily end

Marketing authorisation is an important milestone in medicine development, but it does not mean that every relevant scientific question has been answered.

After commercialisation, medicines are used in broader and often more heterogeneous patient populations than those included in pre-approval trials. Patients may have several conditions, receive multiple treatments and follow different treatment pathways. Long-term outcomes and less frequent safety events may become apparent only through wider and longer observation.

Regulatory pharmacovigilance already addresses important parts of this area. In the United States, for example, the FDA uses adverse event reporting and systems such as the Sentinel Initiative to monitor the safety of marketed medical products using real-world data.

However, safety surveillance is only one part of the potential research environment. Further questions may concern comparative effectiveness, disease progression, treatment sequences, long-term outcomes, patient subgroups and the impact of different clinical pathways.

A clinical research platform could provide an infrastructure for investigating these questions using appropriately governed longitudinal clinical data. New research protocols could be developed for a particular condition, with relevant data drawn from multiple sources and patient populations.

For example, a research project initially investigating one treatment for a particular disease could later be extended to examine long-term outcomes, compare different standard treatments or investigate complications and coexisting conditions. The original trial data would remain part of the evidence, but additional data from routine clinical care and other research projects could contribute to new analyses.

This would not mean that all post-commercialisation research could be performed without additional studies or regulatory oversight. The research design, data suitability, patient permissions and applicable regulatory requirements would still determine what conclusions could be drawn.

7. A different economic and organisational model for research

The proposed approach could also have implications for how clinical research is organised and financed.

Traditional clinical development requires substantial infrastructure. Sponsors and CROs establish study teams, select and initiate sites, configure study systems, train personnel, collect and clean data, monitor study conduct and maintain essential documentation.

Some of these activities are indispensable. Others may be repeated because systems and processes are organised around individual studies rather than a shared research infrastructure.

For large pharmaceutical companies, investment in dedicated trial infrastructure is part of the established development model. However, universities, independent developers and smaller biotechnology companies may not have the same resources or organisational structure.

For some research questions, these organisations could potentially work with healthcare providers and existing clinical data infrastructure to organise distributed research. Rather than building every operational and data collection process from the beginning, they could use an established platform to connect an approved research protocol with relevant clinical data and healthcare partners.

This would not remove the need for sponsors, investigators, ethics review, informed consent where required, safety oversight or regulatory authorisation. An investigational medicinal product still requires the applicable regulatory pathway. Nor would the use of real-world data automatically make an observational comparison sufficient to demonstrate efficacy.

However, a shared infrastructure could reduce unnecessary duplication and make certain types of research more accessible to organisations that cannot maintain the infrastructure of a large pharmaceutical sponsor.

8. Scientific and regulatory requirements remain essential

A broader research environment would also create important challenges.

Clinical data collected for patient care are not automatically suitable for every research question. Records may be incomplete, variables may be defined differently across healthcare providers, and treatment decisions may be influenced by factors that are not fully recorded.

Comparisons between patients receiving different treatments may be affected by selection bias, confounding, differences in disease severity and changes in clinical practice over time. External control groups and real-world evidence therefore require careful design, appropriate data quality assessment and pre-specified analytical methods.

The distinction between clinical care and research must also remain clear. Physicians retain responsibility for individual treatment decisions. Research protocols define the scientific objectives and requirements; they should not replace the physician's clinical judgement. Patients need to understand when information is being used for care and when it is being used for research.

The platform would therefore need appropriate data protection, access controls, consent and other lawful bases where applicable, audit trails, data provenance, quality management and regulatory oversight. Where the research involves an investigational treatment, the relevant clinical investigation requirements remain in place.

The objective is not to weaken the scientific or regulatory framework. It is to organise the infrastructure so that the framework can be applied more efficiently and consistently.

9. Towards a continuous research environment

The central idea is to connect three elements that are often managed separately:

• Clinical care: the patient receives treatment according to an individual clinical care pathway.

• Research protocol: the protocol defines what we want to learn, which evidence is required and how outcomes will be assessed.

• Clinical research platform: the platform connects relevant clinical data, study operations, documentation and analysis.

A patient may participate in a conventional clinical trial, receive routine care without participating in a trial, or contribute to research through an approved observational or other research arrangement. These activities can be connected through a common infrastructure without treating them as the same activity.

The longer-term possibility is a research environment in which a patient’s clinical history can contribute to different research questions over time, subject to appropriate permissions and governance. A clinical trial would remain a defined research project, but it would no longer have to be the only starting point for generating clinical evidence.

Perhaps the question is therefore not whether clinical trials should be replaced by routine care. It is whether we can build a better connection between clinical trials, routine care and longitudinal research.

The patient receives treatment according to a clinical care pathway. The research protocol defines what we want to learn. The clinical research platform connects the two.

P.S: This is not an entirely new direction for clinical research. Several initiatives already address parts of this concept. The FDA has explored the integration of randomised trials into routine clinical practice, while the EMA's DARWIN EU network uses data from healthcare databases across Europe to generate real-world evidence. The European Health Data Space also establishes a framework for the secondary use of health data for research. However, these initiatives address different aspects of the challenge. An additional question is how clinical trial operations, research protocols, data management and essential documentation could be brought together within a common, extendable clinical research platform.

References: 

1. Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-design-and-conduct-externally-controlled-trials-drug-and-biological-products

2. Electronic Systems, Electronic Records, and Electronic Signatures in Clinical Investigations: Questions and Answers https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electronic-systems-electronic-records-and-electronic-signatures-clinical-investigations-questions

3. FHIR to CDISC Joint Mapping Implementation Guide https://www.hl7.org/fhir/uv/cdisc-mapping/index.html

4. Integrating Randomized Controlled Trials for Drug and Biological Products Into Routine Clinical Practice https://www.fda.gov/regulatory-information/search-fda-guidance-documents/integrating-randomized-controlled-trials-drug-and-biological-products-routine-clinical-practice

5. Morain S, Largent E. Think Pragmatically: Investigators' Obligations to Patient-Subjects When Research is Embedded in Care. Am J Bioeth. 2023 Aug;23(8):10-21. doi: 10.1080/15265161.2022.2063435. Epub 2022 Apr 18. PMID: 35435790; PMCID: PMC9576818. https://pmc.ncbi.nlm.nih.gov/articles/PMC9576818/