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.

Clinical Focus

  • Medical Informatics
  • Internal Medicine

Academic Appointments

Administrative Appointments

  • 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)

Professional Education

  • 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)

All Publications

  • Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center. Journal of palliative medicine Seevaratnam, B., Wang, S., Fong, R., Hui, F., Callahan, A., Chobot, S., Gensheimer, M. F., Li, R. C., Nguyen, D., Ramchandran, K., Shah, N. H., Shieh, L., Zeng, J. G., Teuteberg, W. 2023


    Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.

    View details for DOI 10.1089/jpm.2023.0254

    View details for PubMedID 37935036

  • Implementing an Interdisciplinary Team-Based Serious Illness Care Program (SICP) in Stanford Healthcare Giannitrapani, K., Garcia, R., Teuteberg, W., Brown-Johnson, C. ELSEVIER SCIENCE INC. 2023: E627-E628
  • The Team-based Serious Illness Care Program, a qualitative evaluation of implementation and teaming. Journal of pain and symptom management Garcia, R., Brown-Johnson, C., Teuteberg, W., Seevaratham, B., Giannitrapani, K. 2023


    CONTEXT: Earlier and more frequent serious illness conversations with patients allow clinical teams to better align care with patients' goals and values. Non-physician clinicians often have unique perspectives and understanding of patients' wishes and are thus well-positioned to support conversations with seriously ill patients. The Team-based Serious Illness Care Program (SICP) at Stanford aimed to involve all care team members to support and conduct serious illness conversations with patients and their caregivers and families.OBJECTIVES: We conducted interviews with clinicians to understand how care teams implement team-based approaches to conduct serious illness conversations and navigate resulting team complexity.METHODS: We used a rapid qualitative approach to analyze semi-structured interviews of clinician and administrative stakeholders in two Team-based SICP implementation groups (i.e., inpatient oncology and hospital medicine) (n=25). Analysis was informed by frameworks/theory: cross-disciplinary role agreement, team formation and functioning, and organizational theory.RESULTS: Implementing Team-based SICP was feasible. Theme 1 centered on how teams formed and managed to come to agreement: teams with rapidly changing staffing/responsibilities prioritized communication, whereas teams with consistent staffing/responsibilities primarily relied on protocols. Theme 2 demonstrated that leaders and managers at multiple levels could support implementation. Theme 3 explored strengths and opportunities. Positively, Team-based SICP distributed work burden, timed conversations in alignment with patient needs, and added unique value from non-physician team members. Role ambiguity and conflict were attributed to miscommunication and ethical conflicts.CONCLUSION: Team-based serious illness communication is viable and valuable, with a range of successful workflow and leadership approaches.

    View details for DOI 10.1016/j.jpainsymman.2023.01.024

    View details for PubMedID 36764413

  • Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer. JCO oncology practice Gensheimer, M. F., Gupta, D., Patel, M. I., Fardeen, T., Hildebrand, R., Teuteberg, W., Seevaratnam, B., Asuncion, M. K., Alves, N., Rogers, B., Hansen, J., DeNofrio, J., Shah, N. H., Parikh, D., Neal, J., Fan, A. C., Moore, K., Ruiz, S., Li, C., Khaki, A. R., Pagtama, J., Chien, J., Brown, T., Tisch, A. H., Das, M., Srinivas, S., Roy, M., Wakelee, H., Myall, N. J., Huang, J., Shah, S., Lee, H., Ramchandran, K. 2022: OP2200128


    Patients with metastatic cancer benefit from advance care planning (ACP) conversations. We aimed to improve ACP using a computer model to select high-risk patients, with shorter predicted survival, for conversations with providers and lay care coaches. Outcomes included ACP documentation frequency and end-of-life quality measures.In this study of a quality improvement initiative, providers in four medical oncology clinics received Serious Illness Care Program training. Two clinics (thoracic/genitourinary) participated in an intervention, and two (cutaneous/sarcoma) served as controls. ACP conversations were documented in a centralized form in the electronic medical record. In the intervention, providers and care coaches received weekly e-mails highlighting upcoming clinic patients with < 2 year computer-predicted survival and no prior prognosis documentation. Care coaches contacted these patients for an ACP conversation (excluding prognosis). Providers were asked to discuss and document prognosis.In the four clinics, 4,968 clinic visits by 1,251 patients met inclusion criteria (metastatic cancer with no prognosis previously documented). In their first visit, 28% of patients were high-risk (< 2 year predicted survival). Preintervention, 3% of both intervention and control clinic patients had ACP documentation during a visit. By intervention end (February 2021), 35% of intervention clinic patients had ACP documentation compared with 3% of control clinic patients. Providers' prognosis documentation rate also increased in intervention clinics after the intervention (2%-27% in intervention clinics, P < .0001; 0%-1% in control clinics). End-of-life care intensity was similar in intervention versus control clinics, but patients with ≥ 1 provider ACP edit met fewer high-intensity care measures (P = .04).Combining a computer prognosis model with care coaches increased ACP documentation.

    View details for DOI 10.1200/OP.22.00128

    View details for PubMedID 36395436

  • Embedded Specialist Palliative Care in Cystic Fibrosis: Results of a Randomized Feasibility Clinical Trial. Journal of palliative medicine Kavalieratos, D., Lowers, J., Moreines, L. T., Hoydich, Z. P., Arnold, R. M., Yabes, J. G., Richless, C., Ikejiani, D. Z., Teuteberg, W., Pilewski, J. M. 2022


    Background: Cystic fibrosis (CF) is a progressive genetic disease characterized by multisystem symptom burden. Specialist palliative care (PC), as a model of care, has been shown to be effective in improving quality of life and reducing symptom burden in other conditions, but has not been tested in CF. Objectives: To develop and test the feasibility and acceptability of a specialist PC intervention embedded within an outpatient CF clinic. Design: Single-site, equal-allocation randomized pilot study comparing usual care with addition of four protocolized quarterly visits with a PC nurse practitioner. Participants: Adults with CF age ≥18 years with any of the following: FEV1% predicted ≤50, ≥2 CF-related hospitalizations in the past 12 months, supplemental oxygen use, or noninvasive mechanical ventilation use, and moderate-or-greater severity of any symptoms on the Edmonton Symptom Assessment Scale. Measurements: Randomization rate, intervention visit completion, data completements, participant ratings of intervention acceptability and benefit, and intervention delivery fidelity. Results: We randomized 50 adults with CF of 65 approached (77% randomization rate) to intervention (n=25) or usual care (n=25), mean age 38, baseline mean FEV1% predicted 41.8 (usual care), and 41.2 (intervention). No participants withdrew, five were lost to follow-up, and two died (88% retention). In the intervention group, 23 of 25 completed all study visits; 94% stated the intervention was not burdensome, and 97.6% would recommend the intervention to others with CF. More than 90% of study visits addressed topics prescribed by intervention manual. Conclusions: Adding specialist PC to standard clinic visits for adults with CF is feasible and acceptable.

    View details for DOI 10.1089/jpm.2022.0349

    View details for PubMedID 36350712

  • Considerations in the reliability and fairness audits of predictive models for advance care planning Frontiers in Digital Health Lu, J., Sattler, A., Wang, S., Khaki, A. R., Callahan, A., Fleming, S., Fong, R., Ehlert, B., Li, R., Shieh, L., Ramchandran, K., Gensheimer, M., Chobot, S., Pfohl, S., Li, S., Shum, K., Parikh, N., Desai, P., Seevaratnam, B., Hanson, M., Smith, M., Xu, Y., Gokhale, A., Lin, S., Shah, N. 2022: 943768
  • Coaches Activating Reaching and Engaging Patients (CAREPlan): A randomized controlled trial combining two evidence-based interventions to improve goals of care documentation Parikh, D., Asuncion, M., Hansen, J., Seevaratnam, B., Khateeb, S., Rosenthal, E., Teuteberg, W., Patel, M. I. LIPPINCOTT WILLIAMS & WILKINS. 2021
  • Earlier identification of seriously ill patients: an implementation case series. BMJ supportive & palliative care Lakin, J. R., Desai, M. n., Engelman, K. n., O'Connor, N. n., Teuteberg, W. G., Coackley, A. n., Kilpatrick, L. B., Gawande, A. n., Fromme, E. K. 2019


    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