US-based women’s healthcare clinic Tia has partnered with Nabla, a France-based medical software company providing ambient AI assistant for clinicians, to enhance patient care.
Following a successful two-month pilot, Tia has implemented Nabla’s AI assistant across its clinical team, advancing its technology-driven, patient-centred care model.
The collaboration aims to mitigate clinical burnout and reduce the time clinicians spend on medical documentation during Tia appointments, in-person or virtual.
Tia’s Primary Care ‘Plus’ model integrates primary care, reproductive health, mental health, and wellness services, prioritising active listening and shared decision-making.
Tia co-founder and CEO Felicity Yost said: “We want our providers to feel inspired by their work and deeply connected to their patients and not buried in administrative tasks.
“Nabla has given them back that time, allowing us to stay true to our values of gender equity and patient-centred care.”
During the pilot, 30 Tia providers from seven specialities reported that Nabla halved the time needed for clinical note submission.
The specialities include acupuncture, family medicine, gynaecology, primary care, preventative medicine, and psychiatry.
Tia expanded Nabla’s use across all its clinics, integrating it into both telehealth and in-person appointments.
Currently, more than 90 Tia providers use Nabla, generating more than 50,000 clinical notes.
With Tia, Nabla is now deployed in over 80 provider groups, including Catalight, Children’s Hospital of Los Angeles, Denver Health, and University of Iowa Health Care.
Nabla’s AI assistant reduces practitioner stress and enhances patient care by producing AI-generated clinical notes in seconds, powered by unique LLMs tuned for the medical field.
Nabla co-founder and COO Delphine Groll said: “From the start, Nabla has ensured that the training datasets for our speech-to-text model include a great diversity of genders, accents, and languages to mitigate bias in clinical documentation.
“Moreover, our clinical documentation evaluation process includes checks to carefully assess whether facts are wrongly inferred, particularly due to gender biases.”
