Dr. Teuteberg completed residency training in Internal Medicine at the University of Chicago and a Palliative Medicine Fellowship at Massachusetts General Hospital. She is board-certified in Hospice and Palliative Medicine, Internal Medicine and Clinical Informatics. She joined the faculty at Stanford in 2017 after working at University of Pittsburgh Medical Center for 13 years. She currently sees palliative care patients in the inpatient setting and also provides palliative care to patients with heart and lung disease in clinic.
In addition, she is the medical informatics director for the Division of Primary Care and Population Health.
- Hospice and Palliative Medicine
- Medical Informatics
Clinical Associate Professor, Medicine - Primary Care and Population Health
Board Certification: Clinical Informatics, American Board of Preventive Medicine (2018)
Medical Education:Loyola University Stritch School of Medicine (1998) IL
Board Certification, American Board of Preventive Medicine, Clinical Informatics (2018)
Board Certification: Hospice and Palliative Medicine, American Board of Internal Medicine (2010)
Fellowship:Massachusetts General Hospital Palliative Care Fellowship (2004) MA
Board Certification: Internal Medicine, American Board of Internal Medicine (2001)
Residency:University of Chicago Medical Center Internal Medicine Residency (2001) IL
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