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Adapting Hospital Records to the Needs of Transgender People

Posted on July 13, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

More than just about any other institution, hospitals and clinics have to deal with the unexpected. People with the most unusual characteristics and problems drop in all the time. But electronic records, being formal documentation, like regularity. The clash between diversity and computerization explodes into view when a transgender or gender-queer person walks in.

I have written about the strains that transgender people put on an EHR earlier as part of my family’s encounter with the medical system. Recently I got a chance to talk with a leader who has taken some of the necessary steps to fix systems: Scott MacDonald, MD, working in the health system of University of California at Davis.

A thrust to improve UC Davis’s handling of LGBT clients preceded Dr. MacDonald’s arrival. A group of staff and clinicians interested in providing better care to LGBT people decided to take steps to address the needs in that area. The institution made an ethical commitment to reducing disparities in care. The group recognized that the information in the record was deficient–they often didn’t even know who identified as LGBT.

The first step in this information gathering is training health providers to ask for the information in ways that are sensitive to patients’ feelings, and to become comfortable with it. The next step is deciding what to do with that information, and the third is figuring out how to store it in a structured way.

As MacDonald says, “The first priority is to train providers to understand why this data collection is important, explaining that they cannot care for a person whose life situation they don’t understand. Research (especially for reducing disparities) is a close second priority. An electronic means to capture the data needs to be created along with these efforts. Once the data is available in a formal, structured way, we can encourage and train clinicians to ask the pertinent questions and respond to the information sensitively.”

When MacDonald joined the organization, he brought technical expertise with working on disparities in ethnicity and language. He started two surveys: one of the patient population and another of the staff.

The patient survey showed that for the most part, patients were glad to be asked about their sexual orientation (which is different from sexual behavior, although related). They were particularly open to the question if their primary care provider was the one holding the discussion. Naturally, some expressed privacy concerns and wondered who might have access to the information once it was recorded.

Health care providers also showed a willingness to learn more about LGBT issues and be listed in the UC Davis registry as an LGBT-welcoming provider. Over time, without an explicit mandate from leadership, the information collected on the sexual preference of patients increased. UC Davis also provided resources for training in LGBT issues via a web site.

Before starting, UC Davis interviewed the clients of a local clinic specializing in gender issues, in order to flesh out their understanding of patient needs and sensitivities.

Now we get to the heart of the IT issues. Any record system used by a health care institution needs at least the following to handle transgender and gender non-conforming patients:

  • A way to list their preferred name and gender, along with the name and gender that appear on their insurance cards and other official documents. Transitions can take years, and patients often have insurance with the old name and gender long after they have made the determination to be known in a new way. Gender can also be a fluid and evolving concept for some patients.

  • Ways to record the factors that affect gender, such as what surgeries they have had for gender dysphoria and what hormones they are taking. Someone who identifies as male may still need to have a regular Pap smear. A male-to-female transgender person may have a very different normal range on a blood test from someone born female (cis-female).

UC Davis had licensed an Epic EHR, but at that time Epic had only a few suggestions to offer. For instance, they suggested adding a special flag for transgender patients, but this would be too limited a way of handling the range of gender issues encountered, and would not provide adequate clinical information. UC Davis thus launched into a series of customizations, which Epic in turn compiled into an implementation guide that has been used by other customers.

The goal at UC Davis was to make it easy for patients to enter data at in the privacy of their homes through Epic’s patient portal. The interviews at the partner clinic had showed that many were comfortable providing information this way that many were comfortable providing information this way. Besides asking for assigned sex at birth, click-buttons in the portal’s web page offer common choices for current gender identity and sexual orientation. The patient could also enter free-text comments if the predefined choices didn’t capture their identity.

The same information could be entered by clinicians as well. People viewing the record could not tell whether the gender information was entered by a clinician or directly by the patient (although on the back-end, the system preserves the provenance of the information). MacDonald said that the source of the information was ultimately the patient, so it doesn’t really matter who entered it.

What’s important is that the gender-related information, formerly stuffed into free-form text somewhere in the record, was now stored in a structured format. This allows UC Davis to fulfill its mandate to track how it is addressing disparities in care. In the future, such information may also feed into clinical decision support tools.

The gender information is not displayed prominently, but is available to all staff who have access to patient records and seek it our for purposes of patientcare. It is protected by the usual information security measures in place at UC Davis. The information is of greatest use to the primary care provider, but is also used by in-patient nurses and special departments dealing with transgender issues.

The patient’s preferred name was easier to handle. Epic already allows records to distinguish between the official name–used for legal and insurance purposes–and the preferred name. The record offers several descriptors that explain what the preferred name is, such as a nickname or alias. To this list of descriptors, UC Davis added an option applicable to transgender patients.

The remaining missing information is the status of a patient during and after transition. A record can’t yet record birth sex in a separate field from gender identity. It can capture sex as cis-male or trans-man, but that doesn’t gracefully account for the combinatorics of birth sex, gender identity, legal sex, and so on. Transition-specific surgeries and hormone therapy can be captured as a part of surgical history and the medication list, but there is no standard way to record organ inventory. Those things are still listed in free-form text.

However, Epic is looking at ways to adapt its software at the deep level to show this diversity of status. This is something all vendors need to do, because more and more people of all ages are identifying as transgender or non-conformaing as the public gets used to the idea that this kind of identity is within the range of normal. The needs of the population are complex and urgent, so the faster we fix the records, the better will be the care we provide.

Meaningful Use Stage 3 Should Address Care Disparities

Posted on September 13, 2013 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A consumer coalition of more than 50 consumer, patient and labor organizations has published a plan designed to thrust care disparities into center stage as part of Stage 3 of Meaningful Use, according to Healthcare IT News.

According to data from the Joint Center for Political and Economic Studies, the combined costs of premature death and health disparities in the U.S. were $1.24 trillion between 2003 and 2006. The group, the Consumer Partnership for eHealth, argues that these disparities will only increase as the country grows more diverse.

CPeH developed the plan after a year-long review of scientific literature, along with collaboration with experts on disparities in care and health IT. The plan focuses on data collection and use to identify disparities; language; literacy and communication and care coordination and planning, HIN reports.

The plan is designed to integrate disparities reduction with the other Stage 3 criteria to improve the identification and understanding of health disparities. The CPeH has submitted the plan to the Health IT Policy Committee, and has asked the committee to act on its recommendations.

Right now, the Meaningful Use program only requires basic identification of race, ethnicity and gender data collection. But the action plan would like to see Stage 3 include more stringent data collection standards designed by HHS, which would include disability status, sexual orientation and gender identity.

The group’s action plan includes recommendations that:

* EMRs have the ability to stratify patients’ specific conditions by demographic variables such as race, ethnicity, language, gender identity, sexual orientation and socio-economic status

* Providers make greater use of patient data collected and shared through mobile health applications

* Clinicians effectively communicate EMR information to patients, so patients can better make use of its benefits

While the goals outlined here are laudable, my sense is that even for doctors ready for Stage 3 Meaningful Use, requiring this level of data collection and analysis would be a difficult burden. I guess this one is a “wait and see” proposition.