Mark R. Waddle, M.D.

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SUMMARY

The research of Mark R. Waddle, M.D., focuses on health services and clinical informatics. Dr. Waddle aims to use advances in data science and technology to identify opportunities to improve treatment techniques, reduce complications from treatment and improve the patient experience. An additional area of investigation is the value and economics of oncology care. This research aims to define and improve the value of care delivery in the United States on an individual, local and national level and to advocate for a patient-centered approach to policy changes.

Focus areas

• Predictive modeling for oncology. Vast amounts of data are now available for people treated at Mayo Clinic prior to diagnosis, during treatment and at follow-up after successful treatment. Dr. Waddle's aim is to use this data to develop tools that make physicians more effective by providing insight into how their treatment choices might influence outcomes, thereby allowing for the most informed decisions.
• Quality of care and quality measures in oncology. Cancer care is improving at a rapid pace, but the outcomes that matter most to patients and physicians during or after treatment are not always well defined. Dr. Waddle's research aims to identify intermediate measures of outcomes such as toxicity, quality of life or recurrence that can be rapidly collected from the medical record and used to inform policy and treatment decisions.
• Value of care in oncology. Cancer costs continue to rise, and the value of lifesaving treatments such as radiation therapy are not always well defined. Further, proton therapy offers advantages over traditional radiation in some cases, but the value added is not always clear. Dr. Waddle's research uses real-world standardized Medicare costs and Markov modeling, among other techniques, to clarify the value of care in oncology.

Significance to patient care

Clinical data are often difficult to use, fragmented and disconnected from other important information. Dr. Waddle's research aims to bring together patient-level data in a cohesive manner so that meaningful insights can be identified to improve patient care. The underlying goal of this research is to use patient data to drive immediate and meaningful improvement in the quality and timeliness of care delivery by harnessing novel and existing predictive tools and developing new workflows that incorporate these predictive tools into routine patient care.

Professional highlights

• Chair, Radiation Oncology Outcomes Database, Mayo Clinic, 2022-present
• Health Policy Grant, Improving the Radiation Oncology Alternative Payment Model: A Joint Academic Center and Community Practice Initiative, Harvey L. Neiman Health Policy Institute, 2022
• American Society for Radiation Oncology
  • Member, Code Utilization and Application Subcommittee, 2020-present
  • Member, Health Policy Committee, 2020-present
• Chair, Radiation Oncology Morbidity and Mortality Committee, Mayo Clinic, 2020-present
• Radiation Oncology Innovation Fund grant award, Using Artificial Intelligence to Identify Cancer Recurrences from the Electronic Health Record, Mayo Clinic, 2021
• Mayo Brothers Distinguished Fellowship Award, Mayo Clinic School of Graduate Medical Education, Mayo Clinic College of Medicine and Science, 2019