by Jens Declerck
i~HD Data Quality Manager
As the lockdown was imposed, the Welsh Government compiled a list of 75,000 people living in Wales who were classified as high risk to the Covid virus. These were mainly older people or those with existing health conditions that would make them at risk should they come in contact with the virus. Letters were sent out advising these patients to stay at home for several weeks. It was reported that 13,000 letters (17%) were sent to the wrong addresses, with the outcome that 17% vulnerable people were not advised to shield and others who were not in the vulnerable category told to stay at home. This had enormous implications; high risk people may become severely ill or worse as a result. And this also brought damage to the trust in the government, at a time when combating Covid relied heavily on the population complying with its instructions.
The government assembled this list from several health data sources across Wales and it emerged that this data had many underlying data quality problems.
These problems included;
- Incomplete and missing data
- Duplicate data records
- Out of date addresses and contact numbers
- Lack of data standards and format inconsistencies, which made it difficult to merge sources effectively into a definitive list.
This is just one example that shows us the importance of data quality in healthcare. Our healthcare setting is changing to a data-driven environment, where healthcare data can come from diverse sources and can be of any type. This makes it easier for healthcare providers to access patients medical data and histories, improve patients care outcomes and reduce medical errors. But what if this data is of insufficient quality, for example if the data is incorrect, incomplete or not updated. This will have a direct impact on patient safety and quality of care.
What is data quality?
Data quality is about having confidence in the quality of the data that you record and the data you use. For this data to be utilizable, they have to meet some fundamental requirements. For example in order to assure that the data is of a high quality, the data has to be complete, correct, up-to-date, consistent, reliable,… These requirements are also called data quality dimensions and we will need to define these different dimensions before concluding if the data is of a high quality.
Because data quality is defined within the context of different user requirements that often change, data quality should be considered to be an emergent construct. As such, we can not expect that a sufficient level of data quality will last forever. Therefore, the quality of clinical data should be regularly assessed and reassessed in an iterative process to ensure that appropriate levels of quality are sustained in an acceptable and transparent manner.
Why is health data quality important?
In any healthcare organization it is critical that the importance of data quality be properly explained. From management perspective to the healthcare providers, a sensible and understandable rationale must be given in order to obtain active participation in the data quality effort. Using high quality data will provide many healthcare quality and research benefits;
- Reduce medical errors
- Better informed decision making
- Better patient-physician relationship
- Efficient patient service and improve patient care outcomes
- Advanced risk and disease management
- Higher profitability
- Improve clinical research (the re-use of the data)
- Planning for the future
- …
Health Data Quality Program
Data management, the process of collecting and maintaining the data, is not the only process that contributes to data quality. This distinction is often overlooked in care providers and healthcare management. Achieving high quality data is also the result of clear communication among team members, well-documented study objectives and adequate training of care providers who collect the data. All stakeholders within this process (data collectors, healthcare managers, patients,…) have a role in improving and maintaining data quality.
Although data quality is amongst the most important challenges in healthcare, there is no powerful and coordinated effort to promote awareness of what good data quality means. To provide an answer on this question, i~HD has created a front runner educational program for professionals in creating and using high-quality health data; Health Data Quality Program.
The content of the program helps to understand the value of that data to the organization and to patients, for safe and effective continuity of care and for clinical research.
Conclusion
Data without quality can neither contribute value nor serve any useful purpose. High-quality data is not a “nice-to-have” requirement but a “must-have” requirement. While measurement is an integral part of the data quality journey, data quality management involves much more than measurement. It also involves the management of people, processes, policies, technology, standards and data within a hospital or GP clinic.
About Jens Declerck
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
