Translational informatics in research: From inception to implementation

Pratiwi Dhein Hastania, BN, RN, Informatics Nurse
Hamad Medical Corporation, Doha, Qatar

Citation: Hastania, P. D. (2025). Translational informatics in research: From inception to implementation. Canadian Journal of Nursing Informatics, 20(2). https://cjni.net/journal/?p=14799

Abstract

In this article, the author examines the role of translational informatics within the translational research spectrum and its ability to close the gap between research findings and clinical practice, highlighting its importance in improving patient care and enhancing the development of new treatments. The translational research process is described in several stages, from basic research (T0) to community implementation (T4). However, there is little research on how health informaticians are to process their capabilities into the stages of translational research. Literature review findings identified key components of the translational informatics process, including stakeholder engagement, data collection, processing/integration, analysis, and dissemination. Further discussion was done on how the translational informatics approach can be implemented into translational research and of the informatics challenges, such as data confidentiality, interoperability, and the need for interdisciplinary collaboration. Recommendations for improving the translational informatics process are identified, including the establishment of standardized protocols and frameworks, investment in training for researchers and clinicians, and developing comprehensive knowledge management systems. The article ultimately emphasizes the prospect of the translational informatics process in transforming research discoveries into practical, patient-centered applications, thereby improving health outcomes.

Background

Translational research, often described as “bench to bedside,” aims to translate research discoveries into practical applications that improve human health (Cantor, 2012). This process involves multiple stages: from basic research, preclinical and clinical trials, to implementation in clinical practice (University of Arkansas for Medical Sciences [UAMS], n.d.). The goal of translational research is to bridge the gap between scientific research and clinical action, ensuring that new treatments and interventions are rapidly and effectively integrated into healthcare systems (American Medical Informatics Association [AMIA], n.d.; Shen et al., 2019).

In the early stages of translational research, basic research is used to understand the targeted health interest or disease (known as the “T0” stage), potentially identifying new treatments that then can be applied to general human subjects (“T1” stage) (UAMS, n.d.; Wichman et al., 2021). The next stage of the translational process, the “T2” stage, involves using the new treatments identified in the T0/T1 stage to the population associated with the targeted health issue or disease (Wichman et al., 2021). The “T3” stage is defined as translating the research findings into clinical practice and evaluating its effectiveness for health decision-making (Wichman et al., 2021). While in the “T4” stage, researchers translate the findings into communities, disseminating findings on a population level, and impacting policies or practices to benefit public health (UAMS, n.d.; Wichman et al., 2021). 

Health informatics plays a crucial role in bridging the gap between research findings and real-world healthcare applications. It involves the use of informatics tools to integrate and analyze complex data from various sources (such as, clinical trials, electronic health records, and genomic data) to improve patient care, guide clinical decision-making, and accelerate the development of new treatments (Javaid et al., 2024; Payne et al., 2016). This synergy enables researchers to identify and monitor patient outcomes, streamline clinical trials, and ensure that innovative treatments reach patients more efficiently (Javaid et al., 2024).

Translational informatics is a specialized branch of health informatics that uses advanced computational tools to accelerate the research outcomes into practical applications (Cantor, 2012; McDonough et al., 2020; Payne et al., 2016). By utilizing health information technologies, it enables the extraction of relevant population data, speeds up testing processes, and reduces the risk of failures in later stages of clinical trials (Javaid et al., 2024). This aspect of informatics is valuable when improving healthcare and disease treatment (Javaid et al., 2024; Sarkar, 2010). Additionally, translational informatics enables the creation of personalized treatment plans, enhancing patient outcomes and health management (AMIA, n.d.; Tenenbaum, 2016).

Despite the benefits of utilizing translational informatics in research, there is currently a gap in the literature about the detailed process of translational informatics. Henceforth, the purpose of this article is to evaluate the current evidence and analyze the content pertinent to the applications of the translational informatics approach. The author will then identify the common stages of the translational informatics approach and its practical implementations within translational research.

Discussion

Articles and studies were identified through searches of common research databases, and the content of the selected papers was evaluated. Key themes were extracted and organized based on their relevance to the translational informatics process. A notable gap in the literature is that few articles explicitly outlined the specific stages of translational informatics within translational research. Most papers provided only brief overviews of the informatics process, often covering similar content presented in different ways, or focusing on only a few aspects of the overall process. Among the sources reviewed, only the works of Payne et al. (2009) and Embi et al. (2009) offered detailed descriptions of the translational informatics framework, and these will be used as foundational references for this review. The common stages identified in the translational informatics approach are summarized as follows:

1. Stakeholder engagement
2. Data collection
3. Data processing and integration
4. Data analysis
5. Dissemination

Stakeholder Engagement

In the initial stage of the translational informatics approach, the reviewed articles identified key administrative factors that can enhance its effectiveness. These factors are grouped into three categories: people/stakeholders, organizational structures, and leadership (Payne et al., 2013).

Informatics specialists as individuals need to be skilled and knowledgeable in their field but also able to be engaging with relevant stakeholders and collaborative with researchers (Payne et al., 2013). The involvement of influential stakeholders can be advantageous in establishing interoperability standards, protocols, and best practices (Joshi, 2024). Once the key individuals are identified, they can assist the translational informatics team with the aim of specifying workflows, processes, and data sources involved in addressing research questions (Payne et al., 2009). Another important aspect of stakeholder engagement is to overcome financial constraints of the project, as they can be helpful in providing multiple ways of finding grants or funds to make the translational informatics project possible and sustainable (Joshi, 2024; Obeid et al., 2018).

Furthermore, the translational informatics approach relies on a robust health informatics infrastructure that aligns with an organization’s research needs and goals (Obeid et al., 2018; Sanchez-Pinto et al., 2017). Effectively integrating health informatics systems into organizational workflows and processes enhances the ability of informatics to support research activities (Payne et al., 2013). Consequently, informatics leaders are expected to play a strong advocacy role, directing efforts and resources toward the development and use of informatics to strengthen research capabilities (Payne et al., 2013; Sanchez-Pinto et al., 2017). Strategic investment in informatics infrastructure not only supports research functions but also generates outputs that ultimately benefit stakeholders across the organization (Cantor, 2012).

Data Collection

The next stage of the translational informatics approach relates to data collection. In this stage, informaticians will undergo the process of locating certain and relevant data sources and developing models that capture the representation of this data (Payne et al., 2009). These data sources can be in various forms, such as, electronic health records, data warehouses, and patient records (Embi et al., 2009; Sirintrapun et al., 2015). In this case, utilizing a data management package can be useful to store the data sensibly (Hulsen, 2019). There are challenges to this stage, primarily interoperability issues (Joshi, 2024). Data collection from one source will not be an issue, but when that expands to multiple sources with different healthcare information systems usage, it can obstruct the exchange and processing of data (Cantor, 2012; Joshi, 2024). As different systems have different annotations about the data, it will be difficult to ensure the data collected is complete and relevant. Ensuring the quality, extent, and complexity of data collected, increases the prospects for knowledge advancement and public health improvement (Bookman et al., 2021). During this stage the data is considered unstructured, and will have to undergo data processing to make it more relevant to a project.

Data Processing/Integration

In this stage, data integration within research projects facilitates comprehensive hypothesis discovery and testing (Embi et al., 2009). Relevant data is extracted from complex datasets through a scientific data integration process (Payne et al., 2009; Shen et al., 2023). Expertise in utilizing advanced computational tools to manage vast amounts of health data is essential for accelerating the translation of clinical findings into practical bedside applications (Sarkar, 2010; Zhang et al., 2018). The data is then processed to form a knowledge base and visual representations, using high-quality ontology integration (Shen et al., 2023). Since data is often sourced from various platforms and stored in different formats, integration requires careful attention to compatibility and consistency (Casotti et al., 2023; Sharma et al., 2016). This stage includes data cleaning, transformation, and resolving any discrepancies between sources. The goal is to refine the data, making it more actionable for subsequent analysis and decision-making in the translational informatics process.

Data Analysis

Following data integration, highly specialized data analysis platforms are used to validate the data (Payne et al., 2009). These platforms enable the extraction of valuable insights, helping to reveal complex relationships within the dataset and contributing to a more comprehensive understanding of the information collected (Zhang et al., 2018). This improved understanding allows for the identification of meaningful patterns that can be translated into practical applications. At this stage, project teams can choose from a variety of analysis platforms depending on the type of data. Where feasible, reusing the same platforms across studies is encouraged to enhance the reproducibility of results (Hulsen, 2019).

Dissemination

Once the data is validated, it is disseminated through various mechanisms. Informatics platforms play a critical role in sharing the outcomes of research projects and supporting the translation of findings into practice (AMIA, n.d.; Cantor, 2012). Representing complex relationships through structured data can enhance clinical decision-making for healthcare providers in real-world settings (Embi et al., 2009; Sharma et al., 2016; Zhang et al., 2018). However, resistance to adopting new technologies is a common challenge (Joshi, 2024). Therefore, multidisciplinary collaboration is often necessary to ensure that the resulting tools and systems are practical, user-friendly, and aligned with the needs of end-users (Tang et al., 2022).

Furthermore, disseminated data can inform stakeholders and stimulate their engagement in the early stages of the translational informatics approach, creating a continuous feedback loop within the process (Payne et al., 2009). Health informaticians can extend their impact by offering educational sessions to help research teams develop a foundational understanding of the informatics relevant to their studies (Mendonca et al., 2022). Strengthening data management practices through targeted informatics training contributes to the success of translational research by ensuring more effective use of data and technology (Mendonca et al., 2022).

Implementation into Translational Research

In the early stages of the translational research spectrum, specifically T0 and T1, large-scale data collection and analysis are essential (UAMS, n.d.). At this point, the expertise of health informatics professionals in using complex computational tools becomes critical to advancing the translational process (Sarkar, 2010; Zhang et al., 2018). Skilled application of health information systems and computational technologies supports the accumulation of high-quality sample data needed for basic research (Tenenbaum, 2016; Wichman et al., 2021). Furthermore, applying a translational informatics approach during these initial stages facilitates more efficient hypothesis testing by integrating early findings back into the research cycle, thereby minimizing delays and setbacks (Cantor, 2012; Casotti et al., 2023). This continuous feedback mechanism accelerates research progress and enhances the likelihood of success in later stages (Cantor, 2012).

In the “T2” stage of the translational spectrum, the literature highlights the informatician’s primary role in designing and maintaining computational systems that manage health data (Shen et al., 2019; Zhang et al., 2018). Health informatics professionals also contribute by analyzing data to identify relevant biological pathways and research targets, helping to determine which are most likely to succeed. This work supports the transition of treatments from preclinical research into clinical trials, playing a critical role in bridging basic science and clinical application (Cantor, 2012; Tenenbaum, 2016).

The “T3” and “T4” stages of the translational spectrum involve the application of new treatments in clinical practice (UAMS, n.d.). Data collected during these stages are used to enhance quality improvement efforts and support comparative effectiveness research (Tenenbaum, 2016). Implementing research findings into clinical settings requires strong multidisciplinary collaboration and robust data interoperability (Ragon et al., 2022; Rissanen, 2020). Facilitating the exchange of data and databases among researchers, both within and across institutions, can significantly accelerate progress, even at the national level (Becich, 2023; Cantor, 2012). With advances in secure data protection technologies, researchers and informaticians are encouraged to embrace open science principles, fostering collaboration and transparency to improve research outcomes (Ragon et al., 2022; Tenenbaum, 2016).

Key Challenges in Translational Informatics

Confidentiality and security of health data emerged as the most frequently cited informatics challenges, consistent with findings in the current literature (Sanchez-Pinto et al., 2017; Sarkar, 2010; Shen et al., 2019; Zhang et al., 2018). While these concerns remain significant, recent clinical studies have demonstrated that computational tools developed by informatics teams can effectively de-identify health data and restrict access to authorized users only (Amin et al., 2009; Becich, 2023; Hulsen, 2019; Sirintrapun et al., 2015). As technological advancements continue to strengthen data protection, researchers and informaticians are encouraged to embrace open science principles, fostering collaboration and data sharing to enhance research outcomes (Ragon et al., 2022).

Another major challenge is interoperability of systems when studies deal with large datasets from various sources (Amin et al., 2009; Shameer et al., 2016; UAMS, n.d.). Effective interoperability is important to enable data integration and sharing across different health information systems (Obeid et al., 2018; Sarkar, 2010). Data must be of consistent quality and reliability to ensure that findings are trustworthy and that translational applications are meaningful (McDonough et al., 2020; Sarkar, 2010). Variations in data format and quality can significantly affect the performance and effectiveness of informatics tools in research (Cantor, 2012).

Furthermore, for such complex technological tools to be utilized effectively in projects, there needs to be interdisciplinary collaboration within research teams and their organizations (Obeid et al., 2018; Shameer et al., 2016). A key challenge among team members is fostering engagement and collaboration across diverse perspectives, which can influence decision-making and hinder progress in translational research (Bookman et al., 2021; Rissanen, 2020). Research goals between multiple disciplines need to be aligned to address the complexities of bridging research findings into practice. Educating members of the research team to increase their skills and knowledge about health informatics and digital literacy would also benefit the progress of research (Mendonca et al., 2022).

Recommendations

To optimize the translational informatics process, several improvements are needed. First, additional research is required to clearly define the distinct stages of translational informatics and assess whether a standardized framework can be applied across different research contexts. Researchers should critically evaluate which stages are essential, which may need adaptation, and which could potentially be excluded. Additionally, informaticians should assess the practicality and usefulness of the approach based on their experiences in real-world settings. The development of clear guidelines would help formalize the role of health informatics and support research teams in streamlining data capture, analysis, and overall workflow.

The implementation of translational informatics in research also requires significant improvement. Developing standardized protocols and frameworks is essential to ensure seamless interoperability across institutions with varying health information systems. Such standardization supports more effective data analysis and informed decision-making throughout the translational research process. In parallel, robust protocols must be established to protect patient health data privacy. While existing regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) provide a foundation, additional guidelines are needed to address the ethical management of patient data and regulate access in the context of data sharing and interoperability.

For health datasets to be utilized proficiently, institutions need to invest in collaborative efforts between informaticians, researchers, and clinicians. By training researchers and clinicians in data science concepts and informatics tools, informaticians can empower their partners to enhance their technical skills in data analysis and interpretation. This will enable the multidisciplinary team to translate research findings more rapidly into practical applications.

The development of comprehensive knowledge management systems can provide a structured foundation for organizing complex datasets, enabling informaticians and stakeholders to efficiently access the critical information needed to support the translational process. Translational informaticians also play a key role in assisting clinicians by developing clinical decision support systems (CDSS), which help navigate complex data, individualize treatment plans, and ultimately improve patient outcomes. To enhance these efforts, continuous monitoring of the translational research process is essential. By evaluating the effectiveness of applied clinical interventions, informaticians can refine strategies and promote evidence-based practices that strengthen both research and clinical care.

At the local level, large organizations such as Hamad Medical Corporation (HMC) in Qatar—where the author is employed—are well-positioned to take the lead in standardizing the structure of the translational informatics approach. Within HMC’s Nursing Informatics department, efforts are already underway to apply research findings to enhance nursing practices and service delivery. These initiatives represent an important step toward integrating translational informatics into everyday clinical operations and promoting evidence-based improvements in patient care.

Translational informatics is a pivotal aspect of the translational research continuum, transforming scientific discoveries into practical, patient-centered applications. Through data integration, advanced analytics, and collaborative platforms, translational informatics can significantly accelerate the process of turning research into real-world health solutions. From the early stages of data acquisition to the final implementation and continuous evaluation of treatments, translational informatics ensures that the research journey is smooth, effective, and impactful in improving patient outcomes.

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