Improving person-centered care by developing inclusive standards

In an area like healthcare, where precision can save lives, I often think of how unclear we were about “sex” and “gender”. One of many anecdotes is that the prefix “administrative” is sometimes added in order to inexplicably narrow the meaning in a way that is seldom universally understood.

While this may seem like a whole lot of commotion, the disambiguation (I play SCRABBLE®) of the meaning, context, and expected use of gender and gender identity can have a significant impact on a person’s quality and safety. This ranges from a lack of preventive examinations to inaccurate reference ranges in laboratory tests to rejected claims because the patient’s “administrative gender” on the clinical side did not match his “gender” on the health insurance side.

All of these experiences are why it is so important to get this right as we look to a future where decision support rules will drive clinical workflow, artificial intelligence / machine learning algorithms recognize new patterns, and more automation will be introduced in healthcare . At the beginning of October my renowned co-authors and I published one Article in the Journal of the American Medical Informatics Association (JAMIA) describing the Logical Model of Gender Harmony from Health Level Seven International (HL7®) as a potential approach to improving the “accurate representation of clinical gender and gender identity in interoperable clinical systems”.

While the approach outlined in the Gender Harmony Logical Model anticipates the need for future changes to the healthcare IT system and work flow, it is also carefully designed to ensure that continued use of legacy data, referred to as “recorded gender,” context to existing transactions is made possible, while more precise data is recorded and exchanged over time. Importantly, our article identifies the need for a new “gender for clinical use” which is a “sex classification element based on one or more clinical observations such as organ examination, hormone levels, and chromosome analysis”. The model also explains how to represent “gender identity,” “name to use,” and “pronouns”. Future interoperable transactions that incorporate this information will provide the specificity and precision that is sorely lacking today.

Yes, it will take time for these new concepts to take shape in the healthcare IT ecosystem. But as we strive to promote a more inclusive health system, it pays to do hard work. I encourage you to join this HL7 community and read the paper that we made open to all.