by Jens Declerck

The healthcare sector is increasingly evolving into a data-driven healthcare system. A crucial stakeholder within this ecosystem is the MedTech industry. By collecting, processing and managing health data MedTech companies play a key role in every phase of the patient pathway (prevention, diagnosis, monitoring, treatment and care).

In primary use, these digital technologies contribute to innovative care pathways (e.g. enabling disease self-managment) and generate insights that lead to better patient outcomes, decision-making and lower healthcare costs. But these technologies can also foster innovation and research (artificial intelligence for example), by enabling the secondary use of health data.

In other words, MedTech companies can offer benefits to multiple stakeholders within the healthcare ecosystem. Especially when it comes to the use and reuse of health data. But to unlock all this potential, we need to ensure that the data is of high quality. Using low-quality, out of date, incomplete or incorrect data can have a negative impact, which can harm the safety of patients, either when products are being developed and validated, or during their healthcare use.  

Obtaining and using high-quality data seems evident and straightforward, but unfortunately, this doesn’t prove to be the case in practice. It is vital to have trustworthy, objective, evidence of the quality of the data that is being collected and used. Data has to meet fundamental quality requirements and specifications (be complete, up-to-date, consistent, with adequate data documentation,…).

Because data quality is relative to the context of different users and initiatives, which may evolve, data quality needs to be regularly assessed through an iterative process in order to be assured as being fit for (potentially changing) purposes. Because health data is generated and collected at every point of health and care interaction, all stakeholders, including the MedTech industry, should be actively involved and participate in the data quality effort. Data quality is among the most important RWD challenges and due to its character, we must consider data quality as a dynamic complexity.

How can we be sure that we are collecting, processing and managing high-quality data? i~HD can guide MedTech companies throughout the data quality journey. First, i~HD has the experience and knowledge to perform data quality assessments. These assessments go through different steps from analyzing the data environment to developing specific data quality rules. Based on the assessment results, i~HD can provide individual improvement strategies. In this way, MedTech companies can use and re-use their health data in a trustworthy way, to continuously improve care and accelerate research. Second, i~HD has created the i~HD Academy. Within this Academy, i~HD has developed a data quality curriculum, which explains the core topics regarding health data quality. Through the i~HD Academy and its data quality curriculum, MedTech companies can enhance their knowledge regarding data quality.

It is important to realise that high-quality data is not a “nice-to-have” requirement but a “must-have” requirement. Because data without quality can neither contribute value nor serve any useful purpose.  

About Jens Declerck
Data Quality Manager, i~HD

Aside from being i~HD’s resident data quality manager, Jens also works as a Physical Therapist and Teaching Assistant, mainly helping runners improve their performances and recover from injuries

Read his profile here