Improving the Timeliness, Accuracy, and Completeness of Mortality Reporting Using FHIR Apps and Machine Learning

There are approximately 56 million deaths per year world-wide, with millions happening in the United States. Accurate and timely mortality reporting is essential for gathering this important public health data in order to formulate emergency response to epidemics and new disease threats, to prevent communicable diseases such as flu, and to determine vital statistics such as life expectancy, mortality trends, etc. However, accurate collection and aggregation of high-quality mortality data remains an ongoing challenge due to issues such as the average low frequency with which physicians perform death certification, inconsistent training in determining the causes of death, complex data flow between the funeral home, the certifying physician and the registrar, and non-standard practices of data acquisition and transmission. We propose a smart application for medical providers at the point-of-care which will use \glsfirst{fhir} to integrate directly with the medical record, provide the practitioner with context for the death, and use machine learning techniques to enable the reporting of an accurate and complete causal chain of events leading to the death.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Biomedical Engineering (Joint GT/Emory Department)

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Hoffman, Ryan Alan

Advisor dc:contributor.advisor

• Wang, May Dongmei

Committee members dc:contributor.committeemember

• Mitchell, Cassie S
• Lam, Wilbur A
• Maher, Kevin O
• Chanani, Nikhil K

• Biomedical informatics
• Public health
• Machine learning
• FHIR

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/67146

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/67146