Every business needs to have a data policy. Most companies focus on data protection and security when they are building theirs, but in environments where having the correct information is essential and mission critical having a data quality management model is vital. One such example is the healthcare industry.
Leaders in healthcare are faced with a number of daily challenges, especially with regards to reporting and documentation requirements, and ensuring the sustainability of information exchanges. As a result of these challenges, many healthcare leading companies are setting up teams to focus on data quality governance, information management and stewardship.
What is a Data Quality Management Model?
Data Quality Management refers to the processes that are used to ensure the integrity of your organization and their data collection processes, as well as how the data is aggregated, warehoused, and analyzed.
The process includes things like collecting and measuring information and coming up with ways to quantify the things that are going on in your business. It includes things like quality measurements, positive health outcomes, and more.
For best results, it is important that data is collected on an ongoing basis and checked via data management software. Ideally it should be drawn on as raw material for your research and to compare data providers and institutions.
In the healthcare world there is a new rule called ICD-10 CM/PCS, which impacts anyone who is using diagnosis and inpatient procedure codes – things that are incredibly common in medicine – both for reporting, epidemiology, reimbursement systems and more.
The goal of healthcare data quality management model is to improve the service that is provided to patients. There is an increasing movement towards putting patients in charge of their care, and improving the way that families and patients are treated, and the power that they feel they have over their treatment. Privacy is important, but it is also important that people feel they have access to their own data and can make choices about their care. Telemedicine, mobile devices and greater flexibility for outpatient care are all important.
The goal of a DQM assessment should be to make sure that the healthcare data being collected is applicable, fit for purpose and used only for the intended purpose. If it is shared or re-purposed then this should be in an anonymized form, and only the data that is absolutely required for whatever the new purpose is should be shared. Again, there may be local legislation which puts even more restrictions on what can be done in this regard.
All staff must be trained on data collection and data quality management tools, and standards should be agreed upon for things like metric vs imperial units, the use of military time, and what to do if some data is unclear. Regular refresh sessions should be provided so that everyone knows what they are doing, and how to accurately communicate with other departments for managing data quality properly. A mix-up over doses or timings could quite literally be a fatal mistake, which is why precision and clarity are so important.