What are the most effective ways to use HIT for clinical research?

Health information technology (HIT) is the use of electronic systems and tools to collect, store, analyze, and share health data. HIT can improve the quality, efficiency, and safety of healthcare delivery, as well as enable new discoveries and innovations through clinical research. In this article, you will learn about some of the most effective ways to use HIT for clinical research, and how they can benefit patients, providers, and researchers.

Find expert answers in this collaborative article

Selected by the community from 2 contributions. Learn more

1 Data integration

One of the key challenges of clinical research is to access and combine data from multiple sources, such as electronic health records (EHRs), registries, biobanks, surveys, and wearable devices. Data integration can help overcome this challenge by creating a unified and standardized data platform that supports data sharing and interoperability. Data integration can also facilitate data quality checks, data governance, and data security. By using data integration, clinical researchers can access more comprehensive and diverse data sets, and conduct more complex and sophisticated analyses.

Add your perspective

2 Data mining

Another way to use HIT for clinical research is to apply data mining techniques to extract useful and actionable insights from large and complex data sets. Data mining can help identify patterns, trends, associations, and anomalies in the data, and generate hypotheses and predictions. Data mining can also support decision making, risk assessment, and outcome evaluation. Some examples of data mining methods are classification, clustering, regression, association rule mining, and text mining. By using data mining, clinical researchers can discover new knowledge and evidence, and optimize their research design and execution.

Add your perspective

3 Artificial intelligence

Artificial intelligence (AI) is the branch of computer science that aims to create machines and systems that can perform tasks that normally require human intelligence, such as reasoning, learning, and problem-solving. AI can enhance the capabilities of HIT for clinical research by enabling more advanced and efficient data processing, analysis, and interpretation. AI can also automate and augment various aspects of clinical research, such as recruitment, consent, monitoring, intervention, and reporting. Some examples of AI applications are natural language processing, image recognition, machine learning, and deep learning. By using AI, clinical researchers can improve the quality, speed, and scalability of their research, and achieve better outcomes and impacts.

Add your perspective

Assaf Landschaft

Medical Informatics Scientist and hands-on Chief Technologist | M.Sc, NLP, ML
Report contribution
AI interpretability is pivotal for the success of clinical AI projects. Medical professionals are adept at leveraging advanced technology. Yet, the opacity of many deep AI models can impede their full integration in medical practice. Integrating AI well in doctors and researchers workflows is tough. To make doctors believe in AI, we need to make sure AI tools are easy and intuitive for them to use. To achieve this, we need not only well-designed interfaces that fit their existing workflows but also good communication and teamwork between AI experts and healthcare professionals.

Like

4 Data visualization

Data visualization is the process of presenting data in graphical or pictorial forms, such as charts, graphs, maps, and dashboards. Data visualization can help communicate and disseminate the results and findings of clinical research in a clear and engaging way. Data visualization can also help explore and understand the data, and identify patterns, trends, gaps, and outliers. Data visualization can also support collaboration and feedback among different stakeholders, such as patients, providers, researchers, and policymakers. By using data visualization, clinical researchers can increase the visibility and accessibility of their research, and foster data-driven decision making and action.

Add your perspective

5 Data ethics

Data ethics is the branch of ethics that deals with the moral and social implications of collecting, storing, analyzing, and sharing data, especially personal and sensitive data. Data ethics can help ensure that the use of HIT for clinical research is respectful, responsible, and beneficial to the individuals and communities involved. Data ethics can also help address the challenges and risks of data privacy, security, consent, ownership, and governance. Data ethics can also promote the values and principles of transparency, accountability, fairness, and trust. By using data ethics, clinical researchers can uphold the highest standards of professional and scientific integrity, and protect the rights and interests of their data subjects and partners.

Add your perspective

Assaf Landschaft

Medical Informatics Scientist and hands-on Chief Technologist | M.Sc, NLP, ML
Report contribution
Health Information Technology (HIT) plays a pivotal role in upholding data ethics within the realm of medical research and administration. By harnessing the power of HIT, medical researchers and administrators gain the ability to query a wide array of medical notes (millions+), ensuring a rich diversity among patients and medical contexts. The integration of HIT enables the secure handling and controlled access to large volumes of sensitive medical information, reinforcing the equality based ethical foundation upon which the field of medicine is built, ultimately advancing patient-centric care while minimizing bias. Therefore medical research is increasingly interconnected to big-data-driven healthcare.

Like

6 Data literacy

Data literacy is the ability to understand, interpret, and use data effectively and appropriately. Data literacy can help enhance the skills and competencies of clinical researchers and other stakeholders who use HIT for clinical research. Data literacy can also help foster a culture of data awareness and appreciation, and encourage data sharing and collaboration. Data literacy can also help prevent and avoid data errors, biases, and misinterpretations. By using data literacy, clinical researchers can improve their data quality and reliability, and increase their data impact and value.

Add your perspective

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

What are the most effective ways to use HIT for clinical research?

1

2

3

4

5

6

7

1 Data integration

2 Data mining

3 Artificial intelligence

4 Data visualization

5 Data ethics

6 Data literacy

7 Here’s what else to consider

Healthcare

Rate this article

Thanks for your feedback

More articles on Healthcare

More relevant reading

What are the most effective ways to use HIT for clinical research?

1

2

3

4

5

6

7

1 Data integration

2 Data mining

3 Artificial intelligence

4 Data visualization

5 Data ethics

6 Data literacy

7 Here’s what else to consider

Healthcare

Rate this article

Thanks for your feedback

Explore Other Skills