As healthcare organizations seek to address the imbalance of care and outcomes with limited English speaking (LEP) members, the volume of content required to support this communication has grown significantly – patient education, Health Reimbursement Arrangements (HRAs), forms and interpretation are all requiring greater levels of support.
Raw MT (going to Google or Microsoft from a browser) can present two of the key risks identified above: it is very low security – risking patient integrity and generally does not support complex healthcare terms and conditions with any level of proficiency.
To address these challenges, but still benefit from MT solutions to address the rise in content demand, a secure MT deployment can be created by professional language solutions companies such as United Language Group. Within this environment, only your organization uses the specific MT usage and security can be controlled with the same type of protocol as your internal document management systems, allowing MT to exist within your security policies.
Additionally, the secure MT can also be customized for your specific voice tone and style – including key medical terms, phrases and usage that you focus on as an organization. MT can be trained to deal with different aspects of healthcare such as oncology, coronary conditions, etc. improving the ability of the MT to provide meaningful content within your LEP member engagement strategy.
Like most things, MT has many different flavors. It’s easy to see new technology as “holistic” – but the actual application of MT makes a tremendous difference in how it is set up, trained, and deployed, resulting in effect with an entirely different product.
As healthcare organizations rushed to use the technology, they often experienced raw MT – this is where an internal team member went to Google or Microsoft to cut and paste the content into a browser to get a translation. In many cases, this is surprisingly good. In others, it’s exceptionally bad. That’s a very high risk to release to members in a broad sense (as and as mentioned above, it’s a security risk, too!).
The key is to understand the intended purpose. For instance, to apply MT effectively to medical claims processing at a raw level will ultimately fail because of the variable output quality that could see as much as 60% of claims being rejected. However, if MT is paired with other technologies that allow intelligent filtering, the useful content can be filtered and rooted without any impact on the overall workflow. In a case study on this solution, MT went from being rejected to reducing the total turnaround time on claims processing by more than 30%. Read the case study here.
The other consideration is “training for purpose”. For a large payer, for example, you may consider they have a single Vietnamese MT. In reality, they have 6 different Vietnamese MT’s working in unison. Each is trained to deal with a specific aspect of content: One trained for marketing, one for enrollment, etc. This allows for far more reliable and accurate output.