Conducting clinical trials has become a complex and time-consuming affair. Stakeholders, investigators, and pharmaceutical companies are working along with researchers and medical professionals, aiming for effective recruiting, practical achievements, and quick trial turnarounds.

Experts and global authorities have realized that the key to achieving any given study objective is establishing a reliable and secure data management plan. Electronic case report forms have become a major factor of good source documentation practices.Each clinical trial involves numerous procedures, constant monitoring, and thousands of participants. To be more precise, a clinical trial will normally involve the generation of multi-site heterogeneous data with complex input-formats and forms (Mathura, 2007). Data may vary from personal information, group assignments, adverse-event logs, questionnaires to lab results.

Therefore, having reliable, readable, transparent, accessible, and safe data is crucial for each study. What’s more, all data collection procedures should be done in accordance with global ethical regulations and standard practices.Although in research ‘one model can’t fit them all,’ usually, data collection methods can be divided into pen-and-paper and electronic formats. Traditionally, data collection can be done by summarizing medical reports, lab results, and charts on paper case report forms (pCRFs). However, this method shows to be time-consuming, tiring, and prone to errors. Readability issues may arise, which in addition may become an obstacle for audits. What’s more, when it comes to data collection and documentation, training of personnel is needed, which is a significant financial burden on the research budget. In fact, studies show that staffing reaches almost half of the study budget; to be more precise – 40-45% of research allocations (“A paradigm shift in patient recruitment for clinical trials,” 2017).

Since the 1980s, researchers have started to implement electronic case report forms (eCRFs) in clinical trials and research. As mentioned above, eCRFs have become a leading aspect of good source documentation practices as they are linked to easier monitoring and data collection, improved data quality and completeness, and audit facilitation and safety. With the inclusion of technical features, such as alarms, automatic completions, and reminders, eCRFs permit speedier database processing and shorter study periods. Moreover, studies show that the use of eCRFs results in reduced losses, errors, costs, and transport logistics. Last but not the least, eCRFs are especially beneficial for multi-center trials (Le Jeannic, 2014). Establishing connections between research is crucial for practice and patients’ well-being.

All the advantages eCRFs have over pCRFs have led to a crucial shift in research preferences. A survey among investigators, clinical research associates (CRAs), and data managers (DMs) reveals a positive attitude towards electronic forms. Note that younger and tech-savvy individuals show even higher preferences to eCRFs.As mentioned above, paper forms are slowly being replaced by digital formats. For instance, Electronic Data Capture (EDC) and Clinical Trial Management System (CTMS) are widely used in research (Schweitzer, 2016), with a focus on medical data registries. Usually, eCRFs facilitate data collection and are highly beneficial in pragmatic trials, without interfering with drug administration and constant monitoring.

In a nutshell, eCRFs are an electronic case report forms used to collect detailed data and facilitate coordination between investigators and sponsors; eCRFs can help researchers answer study objectives or test hypotheses. Depending on the size of the study, data collection can be a flexible process: data can be captured over a period of weeks, months or years. In fact, it may also include data from check-up visits, even months after patients’ treatment has ceased.

Note that traditionally, the research institution conducting the clinical study is responsible for the specific design of any eCRF. Specific eCRFs help researchers achieve an integrated solution for the needs of their research body (of any size). Nevertheless, perhaps the most beneficial aspect of eCRFs (relevant to our global society), is the fact that eCRFs enable the remote sharing of clinical data and multi-center analysis, allowing productive audits. At the same time, research standards and confidentiality are the main objectives of any eCRF design, and in fact, software solutions like Qolty guarantee 100% safety.

Goals and Benefits of eCRFs

Data Capture: eCRFs allows detailed data collection at distinct times (Meinecke, 2017). Having data in real time guarantees accurate observations and reliable findings.

Data Management & Flexibility: eCRFs facilitate the rapid capturing of vital clinical data, including transfer from and to external devices. Better results often lead to practical improvements, effective drug development, and high levels of patients’ satisfaction.

Work-flow Process: As eCRFs implement automated alerting processes, medical staff and patients benefit from research. Automated features eliminate the need for additional training and at the same time, they boast people’s motivation. Pop-up windows can be helpful attributes.

Data Integration: eCRFs enable the standardized storage of data, which is focused on both research and clinical care, and eliminate interference with treatment. In other words, electronic forms benefit research theory and medical practice.

Transparency & Interoperability: eCRFs give access to transparent data at all times. At the same time, eCRFs support the sharing of data with all the different stakeholders involved in each study. These semantic and syntactic interoperability features are especially beneficial in multi-center trials and international research.

Standards: eCRFs follow standards (described below) to provide interoperability for data exchange, which may facilitate research. The Standards are created in accordance with ethical principles and safety procedures.

Thus, the levels of satisfaction regarding eCRFs data collection (among investigators, CRAs, and DMs), are high:Research standards are needed to eliminate errors, increase interoperability, and improve multi-center approach (Schweitzer, 2016). Researchers have conducted a detailed review, which systematizes the following standards used in eCRFs:

            The ISO/IEC 11179 standard

The ISO standard outlines the main scheme for describing metadata in eCRFs. This standard helps researchers establish a common conceptual data model. As a result, data models can be used as information types, facilitating interoperability for data exchange. According to the ISO standard, data set needs to identify, describe, designate, classify, explain, and locate an information resource.

Therefore, the core element researchers need to focus on is the data element. When it comes to the ISO standard, there’s a Data Description Package, with its conceptual level (e.g., “person’s smoking status”) and a representational level (e.g., “every-day smoker”). Also, experts need to consider the Concepts Package, which defines concepts through controlled vocabularies and networks (e.g., “smoking status” is a part of “tobacco addiction,” which on the other hand, is a part of “substance abuse and addiction”). Note, the codes are usually better than words as they eliminate any language barrier which may occur.


            The Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard is an XML-based standard for metadata models. It’s another standard that can help researchers manage data generated in studies and clinical trials. At the same time, the CDISC ODM standard enables the exchange of clinical protocols and data sets.

          Open EHR archetypes

          The open EHR foundation normally sets out the standards for maintaining electronic health records (EHRs). It’s based on the so-called dual approach. Note that the EHR dual model approach is used to describe clinical data: with an information model (generic) and a conceptual model of archetypes (domain-specific). The dual model approach of the open EHR archetypes can be used to model eCRFs and any related data elements.

Use Cases

             This is another vital standard. The Use Cases standard is focused on building a new eCRF from evidence-based templates. The templates are made of items, groups, and sections, which are linked to annotations of protocols, literature documents, patient-related medical cases, etc. When designing new eCRFs, the team conducting the study must define the data elements required to answer the intended research question. Any data to be gathered should be detailed, unambiguous, and void of unwanted details and redundancies. Note that often, published scientific literature may contain existing designs of eCRFs that provide suitable templates to build upon. This study approach is referred to as Evidence-based Design of Electronic Case Report Forms (eDiFy).Now let’s explore the practical side of research and clinical trials with eCRFs. Before any clinical study is officially initiated, a proper data management plan must be established. The plan sets out the scope, templates, operating procedures, standards, and responsibilities for managing data collection.

            The structure and data validation plan: The structure of the eCRF will be guided by the aims and the protocol of a given study. The aim of the study defines the names and characteristics of all the items that should be included in the eCRF. The proposed structure plan is then reviewed and approved by all the stakeholders involved in the study. Once the structure is approved, the criteria for online and offline plausibility checks are set out.

            Specifications and reports: After validation, study specific status reports are then programmed with a view to supporting the ongoing study (e.g., patient status by country).

            Work-flow definition: The work-flow document defines the roles, rights, and workflows of individuals within the study. This procedure also defines the staff allowed to read, enter, and modify data, or raise/answer queries regarding the eCRFs.

System testing: Before being implemented, the system is tested for functionality and usability. All components of the system are tested, preferably by people who are not familiar with the eCRF idea. Their findings and recommendations are then reviewed and implemented if applicable.

            The Launch of the system: Once approved by all the stakeholders involved, the system can then be transferred to the production environment.

            End-user training: All users must be trained in handling the system in correspondence with their roles. The training manuals are handed out to the different users after review and approval by all the interested parties.

            The Inclusion of external electronic data: At times, eCRFs may have to include electronic data from external data providers, e.g., lab results. Such data must be described in terms of content, structure, and frequency in the data transfer specifications (DTS). The DTS is created, tested, and validated with the participation of the concerned external provider long before the first planned data transfer occurs.

Eliminate Interference: Perhaps the most crucial aspect is to avoid interference. With systems like Qolty, researchers do not see data collection and report forms as a burden. Quite the opposite, the user-friendly interface motivates participants, data is easily accessible and safe at the same time, and system implementation can be performed by not tech-savvy people.To sum up, there are many challenges researchers, sponsors, and patients face. However, one thing is for sure: Good documentation practices guarantee success. Simply because even the most valuable study, creative team, and advanced technology might fail without good documentation. The documented records, procedures, forms, videos, and other research techniques/tools need to be the main aim of research and clinical trials.

So far, the ICH E6 practice and the ALCOA-C checklist have been established as the Gold Standard for International Good Clinical Practice. Thus, health experts need to implement these principles in research. Professionals need to aim for Attributable, Legible, Contemporaneous, Original, Accurate, and Complete Data, which is Consistent, Enduring, Available, Credible, and Corroborated at the same time. Most of all, results and data need to be transparent in order to facilitate audits, collaboration, and research in general.

Only by validating, questioning and replicating findings, clinical trials can lead to success. In the end, good documentation can help not only research institutions and sponsors but future medical studies. All that with the sole purpose of helping patients!

To sum up, the success of any clinical trial depends on data.

Data collection faces many challenges. For instance, often, a clinical trial spans across several locations with the monitoring and integration of data being done from a single central location. At the same time, each trial normally comprises of dozens of different pieces of data to be compiled and processed for statistical analysis. Let’s add all the ethical regulations that come with medical research and trials. Thus, constant reporting may become a burden, and data may lack accuracy and confidentiality.

Nevertheless, eCRFs present a flexible and versatile integrated solution for managing and capturing data during clinical trials.

What’s more, in our tech-driven society, electronic case report forms play a crucial role in the digitization and inclusion of other technologies into clinical research. Sophisticated software tools, such as Qolty, can greatly improve integration of data and monitoring of the trial sites, which consequently can lead to rapid, practical results and benefits for patients.Le Jeannic, A., Quelen, C., Alberti, C., & Durand-Zaleski, I. (2014). Comparison of two data collection processes in clinical studies: Electronic and paper case report forms. BMC Medical Research Methodology; 14:7.

Mathura, V., Rangareddy, M., Gupta1, P., & Mullan, M. (2007). CliniProteus: A flexible clinical trials information management system. Bioinformation; 2(4):163-5.

Meinecke, A., Welsing, P., Kafatos, G., Burke, D., Trelle, S., Kubin, M., Nachbaur, G., Egger, M. and Zuidgeest, M. (2017). Data collection in pragmatic trials. Journal of Clinical Epidemiology. pii: S0895-4356(17)30776-X

Schweitzer, M., & Oberbichler, S. (2016). Requirements for Evidence-Based Templates in Electronic Case Report Forms.  Stud Health Technol Inform; 223:142-9.

A paradigm shift in patient recruitment for clinical trials. (2017, January). Retrieved from;.aspx;/