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Decentralized Clinical trials (DCT)

Follow your patients directly with no middle man, ensure full access to your patients' medical records and labs for as long as you need to achieve the best possible results.

   

  Our data aggregation platform can be integrated into the workflow of decentralized clinical trials in several ways:


  1. Data collection: Our data aggregation platforms can be used to collect data from remote trial sites, including electronic medical records, patient-reported outcomes, and sensor data, and securely transmit it to a central repository.
  2. Data management: It can be used to manage the data collected during a decentralized trial, including data cleaning, validation, and normalization.
  3. Data analysis: Data aggregation platforms can be used to analyze the data collected during a decentralized trial, including statistical analysis, data visualization, and machine learning.
  4. Study coordination: It can be used to coordinate the activities of the various trial sites, including study design, protocol adherence, and patient recruitment.
  5. Real-time monitoring: It can be used to monitor the progress of a decentralized trial in real-time, including patient enrollment, data quality, and safety events.
  6. Interoperability: It can help to facilitate interoperability between different trial sites, electronic health records (EHR) systems and other data sources, which can improve the continuity of care for patients and make it easier to conduct decentralized clinical trials.
  7. Quality control: It can be used to ensure data quality and compliance with regulatory requirements, and to perform audits, as needed.


How can iFiiT help?


  1. Data Integration: iFiiT can provide a centralized platform for collecting and aggregating data from multiple sources, such as electronic medical records, wearable devices, and lab results. This can help to ensure that all relevant data is captured, enabling more effective analysis and decision making.
  2. Improved Data Management: iFiiT can provide tools for managing, organizing and analyzing large amounts of healthcare data. This can help to improve the efficiency and accuracy of data management in DCTs, reducing the risk of data loss or errors.
  3. Improved Patient Recruitment: iFiiT can provide a platform for identifying eligible patients for DCTs and for tracking patient engagement, helping to increase patient enrollment and participation.
  4. Improved Data Sharing: iFiiT can enable secure data sharing between different stakeholders in DCTs, including trial sponsors, investigators, and patients, ensuring that everyone has access to the most up-to-date information.
  5. Improved Compliance: iFiiT can help DCTs to comply with regulatory requirements, such as reporting requirements, by providing a centralized platform for collecting, managing and analyzing data.
  6. Better Coordination: iFiiT can help to improve the coordination between different sites and stakeholders involved in DCTs, by providing a centralized platform for sharing data, tracking progress and communicating information.
  7. Streamlined Data Analysis: iFiiT can provide tools for data analysis and visualization, enabling trial teams to identify trends and patterns in the data, supporting more data-driven decision making.

   

By leveraging iFiiT, DCTs can achieve greater efficiency and effectiveness, leading to better health outcomes and reduced costs.


Why do you need an EMR data platform?


Electronic Medical Record (EMR) data has significant value to decentralized clinical trials (DCTs) because it provides a centralized, comprehensive, and organized source of information about patients' medical history, treatment history, and current health status. This information can be used to identify eligible patients for clinical trials, monitor patient progress and response to treatment, and improve the overall efficiency and accuracy of clinical trial data. 

Additionally, the use of EMR data in DCTs can improve patient engagement, reduce data collection time and cost, and ensure the quality of trial data by providing access to real-world patient data.

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