The amount of data being store in the healthcare industry is exploding. IDC predicts that the healthcare datasphere will grow faster than all other industry’s given the advancements in analytics and the huge amount of data being captured. The increasing frequency and resolution of MRI’s along with other image and video related data are example of todays advanced modes of medical care – and they result in a lot of data. Furthermore, greater intelligence is being built into patient equipment and devices, allowing for the collection of more patient data.
With all this data a healthcare migration can be a daunting task. On top of this the importance of electronic health records (EHRs) and abiding by regulatory data protection rules means it’s vital that data is stored safely, with enough storage capacity, and in a usable format. To deliver to these requirements a migration into a cloud or from a legacy to a modern system may be required.
Healthcare data is particularly sensitive, containing personal patient health and contact information. This puts a lot of pressure on to get it right. But before you dive into the migration it’s important that a full Data Migration Strategy is developed so that the end result is data that is migrated accurately, securely and fully reconciled. There’s a lot to consider, so the following covers some challenges that organisations face during a healthcare migration that you can be aware of along with some solutions to these.
Low Quality Data
A healthcare migration generally includes a significant portion of unstructured data. Unstructured data is any data that is not organised in a predefined manner, such as text documents or images. Structured data, on the other hand, is data that is organised in a predefined manner, such as in a database. One of the challenges with unstructured data is that it makes assigning the right document to the right patient difficult. This is critical to get right as it ensures that patients receive the correct care and treatment.
For legacy systems, it is important that the document format is supported by the target system. This means that the data can be read and understood by the new system – and that users can easily access the information they need.
Determining the most efficient mechanism for migrating large volumes of unstructured data can also be a challenge, and often time constraints are a barrier. Ideally, unstructured data should be converted to structured data prior to the migration, and there are various ways to do this – but all take expertise and time. The Extract, Transform, Load process incorporates Data Cleansing, which can be done in the source system, during the transformation process, or in the target system. Organisations need to carefully consider these options in line with their Data Migration Strategy to ensure that the migration is completed on time and within budget.
Data Governance Oversight
During healthcare migration the appropriate data governance must be applied to safeguard patient privacy. Patient data needs be anonymised during the different phases of the migration to ensure privacy is maintained so that both the patient and the healthcare provider are protected. Data anonymisation involves removing all personally identifiable information from the data, such as names, addresses, and treatment details. This also helps to make sure that the data cannot be traced back to an individual patient.
Visibility of Business Rules and Transformations
Another important factor in healthcare migration is ensuring visibility of the business rules and transformations that have been applied to the data set during the migration. This confirms that the data is accurate and can be easily understood by healthcare professionals. One example of this is how to differentiate an Inpatient versus an Outpatient event. Healthcare organisations need to ensure that the correct event is identified to provide the correct treatment to the patient. If a patient were to return for the same medical issue, the data needs to accurately show the patients history.
Dirty data issues or missing data can be a real challenge in many migrations. Dirty data is data that is faulty in some way – it may be duplicated, outdated or incomplete. It can lead to a loss in productivity, misinformed decision making, along with ineffective activity that has been designed off incorrect data. As with unstructured data, organisations need to determine the most efficient way to fix these issues to ensure that the data is accurate and complete.
Finally, merging duplicated patient records where the unique patient identifier is different can also be a challenge. To identify potential duplicates, specific details like the patients first and last name and their data of birth need to have a 100% match. In New Zealand patients also have an NHI number which can assist in verification. This verification step is key to making sure the patient record is complete and accurate, delivering a single unified record for the new system.
Don’t be intimidated by your healthcare migration
The sheer amount of data and the importance of patient privacy makes a healthcare migration a complex process that requires careful consideration and planning.
Anticipating the challenges and taking the time to plan and work through each step can help, as can contacting a specialist who’s done multiple healthcare migrations before. Our deep expertise in healthcare migration allows us to take your pain away and ensure your migration is on time, on budget and fully reconciled – guaranteed. Get in touch and let’s plan the move together. We’ll address these challenges so that your healthcare professionals have access to accurate patient health information, providing the best possible care to patients.