How To Improve Data Quality In Clinical Trials

(Photo : Adobestock)

The data quality in clinical trials will directly determine the effectiveness, reliability, and speed within which it takes place. A clinical trial tests the efficacy of medical, behavioral, or surgical intervention in people. These trials typically happen in four phases, each subsequent phase engaging more test subjects. To that end, a clinical trial depends on quality data to succeed.

For an investigator in a clinical trial, knowing how adhering to standard data helps determine the data collection methodology is not enough. A good investigator must also focus on improving data quality in clinical trials if they are to see any success. They must know how to collect, process, and interpret high-quality data faster and more effectively.

Here's how to improve data quality in clinical trials:

1. Incorporate Technology In The Data Collection Process

You should incorporate technology in every stage, from data collection to analysis and reporting. When it comes to clinical trial, speed and effectiveness is essential. Technology has made it possible to collect and process data faster and more accurately. Data collection technology can help improve data quality in clinical trials by:

• Automating the data collection, analysis, and reporting process

• Correcting errors adduced during data entry

• Improving the accuracy and speed of data entry

You should consider using tools like Electronic Data Capture (EDC) to accelerate data collection, sharing, and customization. An EDC's capabilities include camera integration to collect image data, a checklist for data flow validation, data protection, data customization capabilities, and others.

Technology has made getting cleaner, quality data fast and cheap. It has also made data collection and storage more secure due to cybersecurity safeguards by third-party technology vendors. In most countries, cybersecurity measures to protect user data must be proven before a medical technology gets approved.

2. Conduct Regular Data Cleaning Exercises

Data cleaning is fixing or removing incorrect, duplicate, improperly formatted, and incomplete data within a dataset. It removes datasets that shouldn't be in your database.

One way in which data gets corrupted is when it comes from multiple sources and ends up compiled into one. Some of the issues fixed by data cleaning include the removal of irrelevant or duplicate observations, fixing structural errors like naming conventions, mislabeled categories, and typos, and filtering outliers that don't fit within the data you need to analyze.

Incorrect data will lead to unreliable outcomes, so constantly conduct data cleaning exercises to improve data quality in clinical trials.

3. Introduce Quality Control Into Your Data Collection Methods

(Photo : Adobestock)

Quality control is the application of methods that investigate whether data collected in clinical trials meet the quality goals, standards, and criteria set. Some things you may need to check out for during quality control include missing information, errors during data transfer from one source to the next, and duplicate items.

Quality control checks will also help identify and fix errors early on, thus stopping them from impacting the data analysis process. Proper quality control checks ensure that the data remains clean from the collection to the analysis stage, making it practical and reliable.

4. Develop A Data Management Plan

A fundamental way to improve data quality in clinical trials is to implement a data management plan. A data management plan (DMP) is a written document with a detailed description of the data you expect to acquire during a clinical trial process.

A data management plan includes information on how you'll manage, describe, analyze, store, and preserve the collected data. A data management plan will help reduce errors and ensure consistency in the data you collect from your clinical trial subjects. It'll also help you identify and correct errors, improving the accuracy and dependability of your data.

5. Train Your Staff On Data Handling Procedures

Data collection is a collaborative effort. To improve data quality in clinical trials, you must involve all the relevant stakeholders in setting up standard operating procedures to govern the data collection process. The apportioning of roles and responsibilities will enable a seamless flow in the data collection process, leading to quality data collection with fewer errors.

Staff training is also necessary to enable everyone to utilize technologies deployed to aid data collection. To that end, you should develop a comprehensive training plan to ensure everyone set to handle the data used in clinical trials is conversant with the technology.

Parting Shot

Data quality isn't negotiable when it comes to clinical trials. The quality of the collected data will directly impact the reliability of the results of the trial. The use of unreliable data may also have a profound impact on the health of the clinical trial subject. Hence, it's recommended that you get the data collection process right the first time if you are to execute a clinical trial successfully.