Dr. Teuteberg completed residency training in Internal Medicine at the University of Chicago and a Palliative Medicine Fellowship at Massachusetts General Hospital. She joined the faculty at Stanford in 2017 and currently sees patients as a part of the inpatient palliative care consult team at Stanford Healthcare.
She has been the clinical director or Stanford Medicine's implementation of the Ariadne Labs' Serious Illness Care Program since its inception in 2018. Her interests include communication skills training, leveraging predictive algorithms to identify patients who would most benefit from serious illness conversations, how to empower non-physician clinical team members to participate in this work, and best practice for EHR builds related to advance care planning.
- Hospice and Palliative Medicine
- Medical Informatics
Clinical Professor, Medicine - Primary Care and Population Health
Clinical Director, Stanford Serious Illness Care Program, Stanford Department of Medcine (2018 - Present)
Medical Informatics Director, Stanford Division of Primary Care and Population Health (2017 - 2020)
Residency: University of Chicago Hospitals Internal Medicine Residency (2001) IL
Board Certification: American Board of Preventive Medicine, Clinical Informatics (2018)
Medical Education: Loyola University Stritch School of Medicine (1998) IL
Board Certification, American Board of Preventive Medicine, Clinical Informatics (2018)
Board Certification: American Board of Internal Medicine, Hospice and Palliative Medicine (2010)
Fellowship: Massachusetts General Hospital Palliative Care Fellowship (2004) MA
Board Certification: American Board of Internal Medicine, Internal Medicine (2001)
Considerations in the reliability and fairness audits of predictive models for advance care planning
Frontiers in Digital Health
View details for DOI 10.3389/fdgth.2022.943768
- Use of a computer model and care coaches to increase advance care planning conversations for patients with metastatic cancer LIPPINCOTT WILLIAMS & WILKINS. 2021
- Coaches Activating Reaching and Engaging Patients (CAREPlan): A randomized controlled trial combining two evidence-based interventions to improve goals of care documentation LIPPINCOTT WILLIAMS & WILKINS. 2021
Earlier identification of seriously ill patients: an implementation case series.
BMJ supportive & palliative care
To describe the strategies used by a collection of healthcare systems to apply different methods of identifying seriously ill patients for a targeted palliative care intervention to improve communication around goals and values.We present an implementation case series describing the experiences, challenges and best practices in applying patient selection strategies across multiple healthcare systems implementing the Serious Illness Care Program (SICP).Five sites across the USA and England described their individual experiences implementing patient selection as part of the SICP. They employed a combination of clinician screens (such as the 'Surprise Question'), disease-specific criteria, existing registries or algorithms as a starting point. Notably, each describes adaptation and evolution of their patient selection methodology over time, with several sites moving towards using more advanced machine learning-based analytical approaches.Involving clinical and programme staff to choose a simple initial method for patient identification is the ideal starting place for selecting patients for palliative care interventions. However, improving and refining methods over time is important and we need ongoing research into better patient selection methodologies that move beyond mortality prediction and instead focus on identifying seriously ill patients-those with poor quality of life, worsening functional status and medical care that is negatively impacting their families.
View details for DOI 10.1136/bmjspcare-2019-001789
View details for PubMedID 31253734
UTILIZATION OF ELECTRONIC HEALTH RECORD PREFERENCE LISTS TO IMPROVE EFFICIENCY, CONSISTENCY AND SATISFACTION AMONG PROVIDERS IN THE AMBULATORY CARE SETTING
SPRINGER. 2018: S837–S838
View details for Web of Science ID 000442641404248