Business model and strategy analysis for radiologists to use electronic health records (EHR)

Abstract

Radiology is a medical specialty that employs imaging to diagnose and treat disease. It has long been an advance user of technology to capture, store, share, and use images electronically. In 2009, President Obama signed into law a measure, the HITECH Act (part of the stimulus package), that incentivizes healthcare providers to use electronic health records (EHR) in care delivery to improve quality, efficiency, safety, and reduce cost. The meaningful use (MU) program's Stage 1 requirements (part of HITECH Act) did not include imaging requirements, leading to confusion among radiologists and other specialties with regard to what MU offers to and requires of them. This thesis attempts to clarify the contribution radiology can make to MU by understanding radiology as a system, including its surrounding issues and its drivers, using Stage 1 MU requirements, data from qualitative research, and results from analysis. It answers the following question: Should Radiologists be considered part of the care team, leveraging EHR for meaningful use and hence eligible for incentive payments? It does so via the following methods: a) Discussing in detail current issues surrounding radiology systems from quality, safety, efficiency, and cost perspectives; b) Discussing MU in the context of radiology and reviewing what is missing in it for radiologists; c) Providing deeper systems analysis of current behaviors and why they have this form at this time; and d) Explaining how MU objectives can help to overcome many current issues and ultimately help to improve health outcomes. Specific changes to MU criteria to achieve these benefits are recommended. This thesis employs systems concepts and tools including system architecture and system dynamics for research and analysis to understand the system and derive hypotheses. A system dynamics model is used to analyze current drivers in imaging and to clarify the impact MU can have on these drivers. Thesis conclusions are supported by the analysis performed using the model as well as information gathered through industry interviews, online articles, academic and industry journals, and blogs.