Our Process

Learn more about how real world experiences shape the direction of Virtual Mentor

Maternity care nurses in peri-urban Nairobi were the first group of health workers we approached for input. They prioritized postpartum bleeding as a common and deadly scenario for which they needed decision support. Together, we test multiple iterations of the solution through low-cost, high-fidelity emergency simulations. 

Through countless team-based content revisions, we prioritize efficiency and effectiveness of treatments according to patient status, word choice and statement structure that reduce cognitive burden for the target user, and reassurance for both the health worker and the patient.

As we move to expand the use cases and contexts for Virtual Mentor, we expect to repeat this established research and development cycle to build a library of algorithms, and variations of algorithms most appropriate to the level and location, and language of the facility in which the health workers serve. At the request of the health workers who test Virtual Mentor, the next stage of product development will be to add maternal high blood pressure, infection during or after birth, and neonatal resuscitation to the content library.
To date, we have not tested Virtual Mentor in actual patient care. We currently use Virtual Mentor in simulated emergency scenarios, as a tool that helps nurses and midwives practice the correct actions that will save a life. Our next steps are to study the effectiveness of Virtual Mentor to impact health worker behaviors in both the training and actual patient care environments.

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