Data quality is of central importance to enterprises that rely on data for maintaining their operations. To give you an example, eye care practices need to make sure that accurate invoices are sent in the correct format to medical aids, administrators and customers to receive payment. To make the most of customer data and to boost the value of the practice, businesses need to focus on data quality.
What is data cleaning?
Data cleaning is the process of ensuring data is correct, consistent and usable. You can clean data by identifying errors or corruptions, correcting or deleting them, or manually processing data as needed to prevent the same errors from occurring.
The latest data management features within Ocumail have been designed to minimise data errors saving significant admin time, but a portion of it must be done manually. Although this can make data cleaning an overwhelming task, it is an essential part of managing your practice-patient data.
Data cleaning in three steps
The first step before starting a data cleaning project is to first look at the big picture. Ask yourself: What are your goals and expectations? For example do you want to increase patient bookings? Or are you wanting to decrease time between visits? Looking for a higher conversion rate with your marketing efforts?
Whatever your high level goals may be, it all starts with great patient data including and not limited to valid contact details and updating clinical and personal records at each visit.
Three easy to implement steps to assist you achieve your goals and manage your data cleanup strategy:
1. Validate patients' email addresses
While the Ocumail integration supports Optimax, Eminance, Quickbooks and other third party data integrations, the quality of email addresses imported is limited to the email addresses captured, may have changed or may longer be in use. One of the new Ocumail data management features give you a full breakdown of your patient email health, where the problems exist and enables your team to fix and validate email addresses in real time as part of your data strategy.
Tip: Every Patient Report and Ocumarketing campaign is tracked for you to better understand effectiveness and delivery success. All communication is updated so that you know exactly when messages are sent, delivered, read, clicked or bounced. A full summary of email address health for all communication sent to date can be found under the Data Management screen and within Ocumarketing campaigns which include patient interactions and tracking metrics.
2. Update records at each visit
As the average patient only returns to their eye care practitioner in excess of 36 months, patient’s changes in personal information may be long forgotten at the point of return. This is understandable, especially when their contact data may remain unchanged for over two years. Spending a few moments checking existing records and recording all new clinical findings on the day of examination saves significant time in the long run. Ensuring all data is updated then and there rather than updating records at a stage later as part of bulky admin task means things never slips through the cracks – make data maintenance a protocol as part of every patient visit.
3. Eliminate duplicate patients
As patient’s preferred contact details, surname or payment details can change over time, it’s common to see duplicate profiles being created for the same patient. Eliminating data inconsistencies through the merging of duplicate records for the same patient means clinical history is maintained further strengthening patient loyalty. Additionally, saving staff time and reducing unnecessary expense incurred managing duplicate patient records makes sense and goes a long way to reduce marketing spend and improve marketing uptake and ROI.
Try avoid communicating to old duplicate profiles as this often results in email bounces, which over time negatively effects your practice’s online reputation and can have serious consequences with third party email carriers. Ocumail works hard to protect online reputation and the the real-time machine learning algorithm checks all new data patient records against potential existing records. If a potential match is found, the user is presented with the possible match to merge, thus reducing manual workload and protecting your business and patients data integrity in a digital world.
Tip: The new Data Management Screen also gives a complete summary and analysis of potential duplicate patients to merge: