Health Analytics for Decision Making in Healthcare Spatial Access

Harati, Pravara

Title:

Health Analytics for Decision Making in Healthcare Spatial Access

dc.contributor.advisor	Serban, Nicoleta
dc.contributor.author	Harati, Pravara
dc.contributor.committeeMember	Ayer, Turgay
dc.contributor.committeeMember	Keskinocak, Pinar
dc.contributor.committeeMember	Bullinger, Lindsey
dc.contributor.committeeMember	Gentili, Monica
dc.contributor.department	Industrial and Systems Engineering
dc.date.accessioned	2021-06-10T13:55:58Z
dc.date.available	2021-06-10T13:55:58Z
dc.date.created	2021-05
dc.date.issued	2021-01-14
dc.date.submitted	May 2021
dc.date.updated	2021-06-10T13:55:58Z
dc.description.abstract	Appropriate access to healthcare services is important for preventing the spread of disease, reducing hospitalizations and emergency department use, and increasing quality of life. However, within the United States healthcare system, there exist many disparities in access to care. In this dissertation, we aim to quantify and assess disparities in healthcare access, for informed decision making towards improving access. Compared to existing methods, our approach allows for local-level estimates, is data-rich, and is statistically rigorous. In Chapter 2 of this dissertation, we focus on access to pediatric primary care services in seven states. We design an optimization model to match primary care need with supply while taking into consideration system constraints such as health insurance acceptance and maximum travel distance. We perform statistical inference, both between and within states, to determine whether there are significant disparities in travel distance and congestion. In Chapter 3, we focus on primary care for non-elderly adults in Georgia and how it may be impacted by the Affordable Care Act. We additionally evaluate the impact of two other policies intended to improve access: increasing the number of residency positions in Georgia and implementing a parity program so that more providers accept Medicaid insurance. In Chapter 4, we begin analysis of psychosocial services for Medicaid-insured children. Using Medicaid claims data for 34 states, we identify which providers are likely to treat Medicaid-insured children and their practice settings, comparing across states, urbanicity, and provider specialties. Finally, in Chapter 5, we develop a modeling framework for one potential intervention to increase access to psychosocial services: collaboration between mental health providers and primary care providers. We create this framework by extending congestion games into a setting in which players have their own private cost function for each resource and resources have their own capacities and preferences over the players. We construct a polynomial-time algorithm to find a Nash equilibrium for singleton games with non-decreasing cost functions under this setting and demonstrate our model for services to Medicaid-insured children in New York.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/64627
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Health analytics
dc.subject	Spatial access
dc.subject	Medicaid
dc.subject	Psychosocial services
dc.subject	Congestion games
dc.title	Health Analytics for Decision Making in Healthcare Spatial Access
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Serban, Nicoleta
local.contributor.corporatename	H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	63115986-db70-4c06-87c4-dab394286f67
relation.isOrgUnitOfPublication	29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral