Doctor explaining the correct use of a wrist blood pressure monitor to patient; data management challenges concept

Overcoming Data Management Challenges in Self-Reporting Clinical Trials

Self-reporting clinical trials offer significant opportunities to collect real-world, real-time patient data. However, data management challenges in such trials are significant. Patient data, often reported qualitatively, contains individual idiosyncrasies that can be challenging to integrate into a cohesive dataset. Inconsistencies in data collection and reporting further complicate the process, and ethical and security considerations loom large.

By proactively addressing these challenges and employing effective digital tools, clinical trial teams can benefit from the collection of self-reported patient data while also managing that data effectively. This approach allows for the optimization of data collection processes while ensuring robust data management.

Distinguishing Self-Reported Data from Logged Data

Self-reported data originates directly from patients and encompasses “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else,” as highlighted by Lara Philipps and fellow researchers in a 2022 study examining the use of patient-reported data in oncology-related clinical trials.

In contrast, logged data is sourced from external entities, such as information collected during medical visits or collected by medical devices used by patients during a clinical trial. Common examples include blood glucose meters and blood pressure cuffs. Devices like blood glucose meters and blood pressure cuffs involve a level of patient participation, providing quantitative readings.

Many methods of data collection incorporate quantitative readings from various devices and patient reporting. For example, patients testing their blood glucose daily as part of a clinical trial may manually log readings on a paper chart or a digital interface, storing the information for later retrieval by a clinician or digitally transmitting it if the device is internet-enabled.

While some distinctions between self-reported and logged data are clear-cut, many clinical trial scenarios involve an overlap of these two spheres. For example, patients self-report qualitative symptoms, such as pain levels. The results of an imaging test or a biopsy are logged — patients do not perform these tests on themselves or report on the test results.

Some efforts to collect and analyze self-reported medical data extend beyond the clinical trial space. These include a 2020 study that used machine learning to analyze public tweets from March 2020. Tim Mackey and fellow researchers sought to understand the public’s early experiences with COVID-related symptoms, testing, and recovery periods. Results, however, were limited and inconclusive — indicating that medical knowledge generated from public social media data is still more speculative than useful.

With so many potential sources of patient data, a clear and consistent approach to data management from self-reporting clinical trials is imperative. A comprehensive understanding of the challenges associated with collecting self-reported data empowers clinical trial teams to navigate these obstacles and capitalize on related opportunities.

Man Testing Glucose Level With A Continuous Glucose Monitor On His Arm; data management challenges concept 

Data Management Challenges in Self-Reporting Clinical Trials

Self-reported and self-logged patient data offer a firsthand view of a patient’s experiences and progress — a view that can be expressed qualitatively and quantitatively. However, challenges emerge in the collection and maintenance of data quality.

One constant concern about patient self-reported or self-logged data is the privacy and security constraints of personal medical devices. In a study of self-reporting cardiovascular implantable medical devices, Antonia Molloy and fellow authors wrote that “smart” medical devices pose ethical and security concerns, including the need to gather informed consent from patients who use these devices and an increasing demand for cybersecurity protections — particularly with implanted devices.

Security issues extend to other medical devices too, especially those connected to the internet. Even patients’ self-reported information carries privacy risks: notes may be lost, or accessing a data collection website from a public terminal could expose data to opportunistic hackers.

Maintaining data quality also poses challenges. “Today, more than ever, high-quality data sets are essential to enable robust analyses and insights,” write Chris Anagnostopoulos and fellow authors in McKinsey’s Life Sciences practice. High-quality data sets are particularly important when using artificial intelligence or machine learning tools for analysis. The opaque nature of these tools makes it challenging to discern their response to errors or artifacts in the data.

Given that patient self-reported data is inherently qualitative and originates directly from patients, addressing the idiosyncrasies in reported information is crucial. Streamlining the collection of self-reported information is necessary to ensure data quality.

Doctor teaches female patient to use mobile healthcare app; data management challenges concept 

Tools to Improve Management of Self-Reported Patient Data

In many clinical trial settings, self-reported patient data holds immense value. However, the methods of collecting, protecting, and harmonizing this data greatly affect its utility in the clinical trial context. 

To enhance the management of self-reported and self-logged patient data, consider the following:

  1. Build Ethical and Security Protections From the Start
  • In a study of patient self-reporting of COVID-19 symptoms, Niccolo Tempini recommends several best practices in data management when collecting self-reported patient data. These include using data trusts and data cooperatives, with their robust tools for privacy and security, and creating “proactive research ethics processes and committees” to address security, privacy, and other ethics concerns. 
  • “Flexible and sustained ethical oversight is key,” writes Tempini. Integrating oversight measures into the tools used to collect patient self-reported data can help create and maintain an environment that protects patients’ data and privacy rights. 
  1. Simplify Patient Tasks
  • A simple interface focused on a few key questions can help patients report data more effectively. In a study of patient self-reporting of toxicity symptoms during chemotherapy, Ethan Basch and fellow authors found that “patients are capable of reporting symptoms experienced during chemotherapy using a Web-based interface.”
  • One example of such a user-friendly, web-based interface for patients is Anju’s TrialMaster EDC system, which has a built-in ePRO (Patient-Reported Outcomes) function. Patients can use any device (desktop, laptop, tablet or smartphone) to enter their data directly into the EDC system, which is translated to their preferred language, providing a simple, unified environment and immediate data access for the sponsor and site personnel.
  • Online interfaces also help clinical trial teams harmonize data for interoperability. A web page or platform patients use to submit data can automatically store that data in forms usable by other clinical trial team software, such as an eTMF
  1. Utilize Advanced Digital Tools
  • In a 2014 study of methods for collating and using patient-generated trial data, Sandeep K. Gupta and Roopa P. Nayak advocated for the use of then-cutting edge data management tools, including clinical data management systems and electronic data collection (EDC), also known as remote data capture. 
  • Today, both clinical data management systems and remote data capture tools have advanced significantly. These tools can harmonize and protect data more effectively and also capture and transmit more accurate, nuanced information. Tools that have developed in the past decade, such as eTMF systems, can also bolster efforts to manage self-reported patient data effectively. 

Patient self-reported data is an essential element in real-world datasets, which provide deeper insights in many clinical trial settings. By employing appropriate digital tools and prioritizing ethical and security considerations, clinical trial teams can control data management challenges, enhancing the overall administration, quality, and confidentiality of self-reporting clinical trials.


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