Data is an indispensable enterprise asset particularly in the field of healthcare. Healthcare advancements and discoveries are all founded on well-researched data. Without data, medical treatments may still be stuck in the past, resulting in poor patient prognosis rates. This is only part and parcel of the reason why those working in clinical research have a huge role to play in society. They can make the difference between good medical treatments and poor and defective ones.
Fortunately, technology has also paved the way for clinical data quality to consistently be improved. This means that research is more thorough and efficient while ensuring the best results consistently. Clinical data today have less errors and are more conclusive, bringing in great effects to the local society where such data can be applied.
With that, it’s good to learn and unlearn certain techniques, all in the pursuit of improving data quality. These include:
- Get To Know The Latest In Designing CRFs
CRFs or case report forms are the basis of clinical research. It’s through the information gathered from CRFs that a collation and analysis can be made. This important function creates the need to ensure that CRFs are well-designed by applying the latest techniques and standards in the industry.
Two of the methods used in CRFs are ODM and CDASH. The former refers to operational data model, and the latter pertains to clinical data acquisition standards harmonization. Expert clinical researchers will know which ones to apply to their CRFs; as they say, the choice is usually on a case-to-case basis. The most important thing is that whichever mode is used, all necessary standards should be complied with.
- Create A Well-Balanced Team
Even if your clinic or laboratory has all sorts of powerful, automated clinical equipment and machinery, do take note that those aren’t going to function by themselves. There’s still the need for human participation, and that’s the team working behind the scenes to make all those data collection software and apps work.
This is the reason why it’s a must to hire and create a well-balanced team. Make the hiring process as stringent as possible, so you can be certain that only those who are truly qualified will make it. Filter through all your candidates thoroughly so you don’t make any room for incompetent members on your team.
With the importance of clinical data, there really is no bargaining on this. If your team is inexperienced, then, unfortunately, the quality of your clinical data will always be poor.
- Select Appropriate Data Management Systems And Providers
If your team is still stuck with a manual system of data filing, collection, and analysis, then the chances of human error will consistently be high. Technology is penetrating so many industries today, with the healthcare industry not falling far behind. It’s up to you to use that to your advantage by moving from a manual to automated systems.
When you automate, the likelihood of human error is reduced, and the efficiency is also affected positively, such that now deadlines can be met with less difficulty. Moreover, when data management is more efficient, the integrity of data is preserved such that data loss may be reduced as well.
- Practice Data Standardization
There won’t be a sense of unity and collective effort in your team if data standardization isn’t practiced. This is needed to enhance both quality and efficiency, the reason being that with standardization, there’s a uniform set of metrics your team has to observe for them. There’s only minimal confusion when team members don’t have to second-guess what data or information collected means.
When data is standardized, the moving of information from one department to the next will also be more seamless. It’s consistent across multiple data releases, making the study also more coherent and comprehensible not just to the participants but even to the general public who may be interested to go through such information.
- Address Data Quality Issues Right From The Source
The best way to address any problem with data is to tackle it straight from the source. With this, the pursuit of improving data quality should also include analyzing where you get your research data from. If the source is false or questionable, then chances are the results will also be the same.
For instance, a scientist in your team has discovered errors and empty fields on a particular data sheet. They work on that problem, but such changes haven’t unanimously been sent to other sources now in the hands of other team members. When data are then moved forward to the next department, there’ll be multiple copies, each with its own variations. It’ll be confusing, to say the least.
Whenever changes have to be made, be sure that each source is corrected. Make it a part of your laboratory culture to prevent the propagation of bad data so that every clinical study result being released and all information that can be obtained from it is consistently accurate.
Conclusion
The healthcare sector is still one of those that suffers significantly if data quality is hampered and not preserved. After all, healthcare deals with the lives of people—there should be no taking any chances or making any mistake. The goof thing is that there’s always room to learn and improve, hence, the need to apply the tips discussed above. Discuss such pointers with your team so that you, too, can make that change by becoming a clinical research team that consistently delivers topnotch results.
