The volume of data generated by hospitals, health systems, medical staff, and patients is driving an urgent need for expertly trained analysts who can get the data right. Analytical skills are as essential in an epidemic as they are in everyday wellness care, as vital for patients as they are for providers. These data skills are critical for analysts anywhere on the healthcare spectrum – on the clinical, pharmaceutical, risk, or management side. You must know how to operationalize systems to collect, measure, aggregate, interpret, and share the data your healthcare company and its partners need. Optimizing your data science skills can lead to better therapeutic options, enhanced business results, and – most important – improved patient outcomes.
Consider the impact of big data on both business health and patient health:
As much as 25% of U.S. per capita spending on healthcare is wasteful. Much of this waste can be mitigated by sharing and analyzing data
Predictive analytics greatly improves oncologists’ diagnoses. Google’s AI algorithm may detect as many as 99% of metastatic breast cancers. Not surprising, 89% of healthcare executives plan to use predictive analytics in the next five years.
In this program, you will learn to:
Data Science in Healthcare is designed for technical professionals who have at least a moderate level of comfort with some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics. In this program, you will learn to:
Although these topics could be applied to a range of businesses, this program will be particularly useful for entry to mid-career professionals in roles similar to the following:
Analysts - Ideal for professionals working in analytics roles in healthcare or industries adjacent to healthcare, such as insurance, pharmaceuticals, or biotech.
Mid-Level Managers - Ideal for professionals on the executive track who have quantitative responsibilities and relevant experience in a healthcare field.
Entry-Level Professionals - Ideal for professionals just beginning their careers who are looking to develop a data foundation with applications in the healthcare industry.
Representative roles well suited to this program include:
CHRISTIAN DARABOS, PhD
At Dartmouth, Dr. Darabos manages and coordinates research projects across departments. His research focuses on biologically inspired heuristics applied to modeling complex dynamical systems. He designs and implements intelligent machine learning-based techniques and tools to large-scale biomedical datasets. His projects have included the study of complex genetic interactions in human diseases.
Eugene Demidenko, PHD
At Dartmouth, Dr. Demidenko teaches statistics to undergraduate students and graduate students in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. He is an expert in theoretical and applied statistics, such as epidemiology and biostatistics, statistical analysis of images, mixed models, nonlinear regression, mathematical tumor regrowth modeling, and partial differential equations with mixed boundary conditions.
Todd A. MacKenzie, PHD
At Dartmouth, Dr. MacKenzie teaches biostatistics in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. His research focuses on the use of data science to help clinical and health services researchers across a spectrum of disciplines and specialties. Many of his projects involve comparative effectiveness, clinical trials, association studies, and prediction modeling. He is particularly interested in survival analysis, causal inference, and the decline in American longevity.
James O’Malley, PHD
At Dartmouth, Dr. O’Malley teaches biostatistics at The Dartmouth Institute and the Biomedical Data Sciences program at the Geisel School of Medicine. His research focuses on applying statistical methods to health policy and health services to improve patient care. He is an expert in statistical inference for social networks, comparative effectiveness, Bayesian statistics, vascular surgery, cardiology, the design and evaluation of medical device clinical trials, and the relationship between health and social networks. He collaborates with physicians, sociologists, health economists, health services researchers, and epidemiologists.
RAMESH YAPALPARVI, PhD
At Dartmouth, Dr. Yapalparvi is the course director for the graduate program in healthcare data science, data mining, and data visualization in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. In addition to his academic work, he is senior manager of data science for Optum/UnitedHealth Group, where he manages a team of data scientists to identify and prevent fraud, waste, and abuse. He also has worked with orthopedic surgeons to develop tools for clinical interventions and predictive analytics tools for patient reported outcomes data.
Upon successful completion of the program, participants will receive a verified digital certificate of participation from Dartmouth QBS. Your digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Dartmouth.Download Brochure