Date of Award

8-7-2018

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Information Systems

First Advisor

Abhay N. Mishra

Second Advisor

Mark Keil

Third Advisor

Jeong-ha (Cath) Oh

Fourth Advisor

Katherine E. Masyn

Abstract

Health Information Technology (HIT) has an important and widely acknowledged role in enhancing healthcare performance in the healthcare industry today. A great amount of literature has focused on the impact of HIT implementation, yet the studies provide mixed and inconclusive results on whether HIT implementation actually helps healthcare providers enhance healthcare performance. Here, we identify three possible research gaps that lead to these mixed and inclusive results. First, prior IS research has exclusively examined HIT complementarity simultaneously, but ignored the temporal perspective. Second, extant HIT research has primarily examined the relationship between HIT implementation and healthcare performance in a static framework, which may neglect the dynamic relationship between HIT and healthcare performance. Third, prior HIT value studies have typically examined HIT’s impact on hospital-level outcomes, but no extant studies consider HIT impact on transition-level outcomes as disease progresses over time.

This dissertation addresses these gaps in three essays that draw upon three different lenses to study HIT implementation’s impact on healthcare performance using three analytics methods. The first essay applies econometrics to study how various types of HIT complementarities simultaneously and sequentially impact diverse healthcare outcomes. In so doing, we find evidence of simultaneous and sequential complementarity wherein HIT applications are synergistic—not only within the same time period, but also across periods. The second essay uses advanced latent growth modeling to explore the dynamic, longitudinal relationship between HIT and healthcare outcomes after incorporating the nonlinear trajectory change of different HIT functions and the various dimensions of hospital performance. The third essay applies multi-state and hidden Markov models to examine how HIT functions’ implementation levels impact a finer, more-granular-level healthcare outcome. This approach includes the dynamics of the transitions, including observable transitions (chronic to acute, acute to chronic, chronic to death, and acute to death) and underlying and unobservable transitions (minor to major disease and major disease to death). This essay examines how different types of HIT can improve different transitions types as diseases progress over time.

DOI

https://doi.org/10.57709/12492103

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