In any other area, poor data quality usually leads to financial losses. In the healthcare sector, poor data quality, as well as late delivery of information, can cost lives. To deliver innovative healthcare services, the qualifications of doctors, and the quality of patient data collected are equally important. In this article, we will discuss how to improve data quality in healthcare.
Getting Rid of the Legacy Systems
Legacy systems are valuable, and often the only sources of data. In healthcare, such legacy systems are either paper files or patient management systems built using outdated technology on outdated platforms.
Therefore, the first step to improving data quality is choosing the right strategy for working with your legacy system, as well as a competent approach to data migration. However, even after your legacy system and all of the stored data has been merged by the new system, data quality will not improve overnight. You need to continue strategically improving the quality of the information stored and received.
Profiling your data reveals the most significant defects. Data profiling is necessary to analyze and structure your data array, including data extracted from legacy systems, as well as new information obtained through patient wearables, for example. What is more, competent data profiling can provide you with insights to further data management and quality improvement.
Since your data is collected and sourced from different places, the next step to improve its quality is normalization. For example, different sources may use different spellings of dates, names, and titles. For example, the USA instead of the US, dd-mm-year instead of mm-dd-year, or contain other human-made errors.
However, these mistakes can be quite expensive – especially if it is a mistake in the patient’s age or the presence/absence of allergic reactions to certain drugs. At this stage, data normalization is needed in order to initially bring the data to a single form, plus eliminate significant errors and inaccuracies.
Come up With Data Quality Standards
What constitutes good quality data for your particular hospital? The answer to this question will be extremely individual and will depend on the technologies already implemented. For example, if you use Patients Health Records only, then your data can be considered qualitative if they fully describe the patient’s medical history, features of their health, vaccinations, and so on.
However, if new data is constantly coming from wearable devices of patients, then your data quality standards should be uniform for the information received from all sources, without exception.
Teach Your Doctors to Work With Data and Explain Its Importance
Instant access to quality information about remote patients and those treated in the hospital greatly facilitates the work of doctors. However, in order for the work with data to be effective, and for the data daily received to be of really high quality, your doctors and other responsible staff members must be able to correctly handle this information.
Wearable devices greatly facilitate this task as they automatically transfer the required data set for each patient. However, in those situations where doctors are forced to enter information into the system manually, they should understand that even a minor inaccuracy can affect the quality and reliability of information about the patient as a whole.
High-quality and accurate data collected and processed by advanced technologies has become as important as the work of doctors themselves. What is more, high-quality patients’ data storage and processing is the first step towards providing innovative and low-risk healthcare services, inspiring trust between a patient and a medical institution, managing available resources wisely, and cutting costs.