What is the difference between Data Quality and Data Health, if any?
The healthcare sector relies heavily on patient data, and because of its consequences, it is crucial to find the balance between healthy and high-quality data. Data quality (DQ) measures the data’s overall condition, assessing factors such as accuracy, completeness, consistency, reliability, and validity.
Essentially, this means that any errors, inconsistencies, or inaccuracies within those factors can impact data quality. Moreover, DQ reflects the real-world entities as closely as possible. Therefore, it also should be complete and contain all the necessary information for its intended use. It must not miss any critical elements, such as allergies or medicine schemes. Data quality control is thereby essential in the healthcare sector for facilitating risk management, as well as quick and accurate billing.
High data quality is deliberately coherent, meaning that it does not contain conflicting or contradictory information. Consistency of patient data is significant because it can impact the medical care they will receive. The hospital discharge cannot say that a patient has left-sided lumbar scoliosis when the image description from another clinic states it is the thoracolumbar type. It could impact the remedial exercises advised by a physiotherapist. Data consistency is not valid within one entity but should be universal across all networks and applications it concerns.
High-quality data can also be consistently relied upon to make informed decisions or draw meaningful insights. It makes them trustworthy and dependable, which are the most crucial traits in the healthcare sector. Its validity should meet the standards of rules and parameters set by the network so that it would be accessible in the correct format and fall within the faultless range.
The last important characteristic of high-quality data is its relevance for the intended purpose. Therefore, the data must always be up to date, e.g., it is possible that when having a bone marrow transplant or certain kinds of leukaemia, the patient’s blood type changes. This information must be logged in their file accessible in all ER data systems this patient would arrive to. Furthermore, data uniqueness secures its accessibility. Uniqueness means that there are no duplications or overlapping of values across all data sets.
Health Data: something our system craves
On the other hand, we have health data. Data can be considered healthy when it is clean, complete and aligns with legal and regulatory requirements. This means that data is accessible to everyone in the organisation, interested or involved, e.g., a GP, an orthopaedist, and a patient. Data is healthy when it is easily discoverable, understandable, and relevant. To check if the data that you store is healthy, you may check their validity, completeness, and sufficient quality to produce analytics that decision-makers (both specialists and patients) can rely on.
Doesn’t it sound similar to data quality? Yes, it does! To understand the difference better, please imagine this scenario: you go into a forest, and on one side of the path, you see a strong and high tree covered with green leaves. You would easily identify it as healthy. However, on the other side of the road, you can see a tree that is convolute, growing more to the left than straight. Its crown does have fewer leaves, and you can notice a hole in it. Even though both trees grow and can propagate their seeds, one only survives when the other one is healthy. One of them did not adapt well to the changes in their environment.
It is the same with data systems. It is impossible to have only complete and correct data in your system. However, to ensure that the patient data is of the highest quality possible, the entity can collect it healthily. The strategy sounds simple and is called adaptation. To achieve immunity to poor-quality data, the dataset should be exposed to ‘real-life’ data, and the system should be constantly improved. Hence, health data means the flexibility of data to adapt and balance itself around a certain equilibrium.
To keep the harmony, the database has to contain clean data and ensure that it is collected, structured and stored without errors. This is what guarantees data quality. When it is put on a data platform like Smart Data Fabric, the platform ensures that the aggregated data is enabled for usage in different analytical environments. It can be encoded in any system. It is flexible, like clay and can be formed into any puzzle to fit any system and format while keeping its properties.
The ultimate solution – Unified Care Record
The question that may cross one’s mind now is how can we achieve having these healthy data? How can we make it accessible, detailed and relevant? Let us (shortly) introduce Unified Care Record, which gives patients access to all medical files digitally. It ensures continued treatment within and beyond the hospital network. It reduces the frustration of chronic patients of never-ending data filling. Moreover, it reduces the risk of prescribing the wrong medication or taking duplicate tests and allows the specialist to transfer patient data to different entities safely. In Unified Care Record, no data is missing, but it is clean and healthy like everything around our healthcare should be.
We know that the line between healthy and high-quality data seems thin, and it can be hard to understand for many. However, we hope that with this blog, we resolve your doubts, and now we all know that data can be high-quality and healthy, but no data can be healthy without having quality first. Health Data is simply a broader term for what data quality stands for. Nevertheless, we need both to ensure the best possible healthcare for everyone, no matter which entity you come to, which can happen once the Unified Care Record is implemented.