One of the great challenges in modern healthcare is how to improve access to health services and medications in remote and underserved communities. Major barriers include financial viability, availability of personnel and transportation.

In particular, capital investment in permanent facilities and equipment can be infeasible due to the cost per person. It can be difficult to find medical professionals to provide full-time care to these communities. And bringing healthcare to remote/rural communities often requires covering large geographic areas that have less-than-desirable infrastructure.

At the latest event in our Distinguished Lecture Series, Jonathan Helm from Indiana University discussed these challenges and potential solutions that lie at the intersection of supply chain management, operations management and analytics. His focus was on mobile healthcare resources.

These resources can provide services that rotate between multiple underserved communities and deliver medications in a more cost-effective manner. Mobile resources reduce the financial and logistical burden while providing much higher access to care for residents of these regions that have historically been lacking.

Helm also highlighted some of the data challenges in developing analytics in rural, remote and underserved regions. He drew upon several research projects in the U.S. and Africa to highlight specific challenges.

Examples included projects to address the critical nursing shortage in the U.S., deliver malaria medication in Malawi and prevent and treat addiction in the Democratic Republic of the Congo.

About the speaker:

Jonathan Helm is a professor in the Kelley School of Business at Indiana University and W.W. Grainger Inc. Faculty Fellow. His research has been broadly implemented in the healthcare industry and published in top-ranked business, medical, analytics and healthcare engineering journals.

He has won upwards of 20 international research awards. In 2024, he won first place in the Innovative Application of Analytics Awards for a project that combines human expertise and machine learning to battle the nursing- shortage crisis.