Brant Oliver is a healthcare improvement and implementation scientist, educator, and board-certified family and psychiatric nurse practitioner (FNP-BC, PMHNP-BC). Dr. Oliver's work focuses on applied healthcare improvement science research with a focus on "3C" (complex, costly, and chronic) conditions including MS, IBD, CF, RA and others, coproduction, learning health systems, and shared decision making (SDM). He has been in clinical practice since 2003, working primarily as a certified MS specialist (MSCN) and a MS neurobehavioral nurse practitioner.
Dr. Oliver holds faculty appointments at Dartmouth, VA Quality Scholars (VAQS), and the MGH Institute of Health Professions School of Nursing. He is a Lean Six Sigma Black Belt and experienced in Clinical Microsystems and IHI Model for Improvement approaches, including IHI Breakthrough Series (BTS) collaboratives. He teaches graduate students, residents and post-doctoral fellows in improvement science, methodology, measurement, and analytics.
Alexander C, Rovinski-Wagner C, Wagner S, Oliver BJ
J Nurs Care Qual|2020 Sep 14
Newland P, Lorenz R, Oliver BJ
J Health Psychol|2020 Aug 23
Horstman MJ, Miltner RS, Wallhagen MI, Patrician PA, Oliver BJ, Roumie CL, Dolansky MA, Perez F, Naik AD, Godwin KM
Acad Med|2020 Aug 4
Hakim H, Newland P, Oliver BJ
J Neurosci Nurs|2020 Aug
Newland P, Oliver B, Newland JM, Thomas FP
J Neurosci Nurs|2019 Dec
Statistical Measurement and Analysis (elective)
This course explores the history and theory of statistical process control and its application to health care. Specific topics covered include: development of measures; data collection; graphical display of data; the theory and construction of control charts for means, proportions, counts and rare events; statistical testing with control charts; analysis of means. Benchmarking and an organizational approach to measurement and improvement are discussed. Different study designs for improvement work are explored. The course emphasizes application of theories and principles through the use of case studies, small group exercises and interactive discussions with guest presenters. Lab exercises, a group project and a take-home final exam are required elements of the course.