Doctor of Healthcare Administration (D.H.A.), Franklin University, 2025, Health Programs

This study examined the impact of electronic prescription generation and transmission, patient access, and secure electronic messaging on health information exchange (HIE) utilization during healthcare transitions. Leveraging longitudinal data from the 2018 CMS EHR Incentive Program, this research tested hypotheses concerning the influence of these variables on HIE utilization using a quantitative method. Findings from logistic regression analyses indicated that electronic prescription practices (B = 2.265, OR = 9.628, p < .001) and patient electronic access capabilities (B = 1.108, OR = 3.027, p < .001) significantly increased HIE usage, aligning with previous studies that underscored the importance of digital prescription systems and patient empowerment in HIE enhancement. Additionally, secure electronic messaging showed a significant association with HIE utilization (χ²(1) = 126.982, p < .001), further reinforcing the role of secure communication in effective healthcare information exchange. A combined predictive model revealed that electronic prescriptions and patient electronic access drastically improved the likelihood of HIE adoption (B = 4.546, OR = 94.284, p < .001), highlighting a synergistic effect. These findings underscored the need for integrated technological frameworks within healthcare systems to optimize communication and care coordination, ultimately improving patient outcomes. The study advocated continued investment in digital health tools to strengthen HIE systems and enhance healthcare delivery.

Committee: Crissie Jameson (Committee Chair); Sunddip Aguilar (Committee Member); Alexander Akulli (Committee Member) Subjects: Health Care Management

Doctor of Healthcare Administration (D.H.A.), Franklin University, 2022, Health Programs

An Accountable Care Organization (ACO) was first created during the enactment of the Affordable Care Act (ACA) in 2010. An ACO is a group of physicians, hospitals, and other providers that voluntarily form together to coordinate a value-based care approach that handled the best quality of care for patients and delivers the right care at the right time. The appropriate means of communication between these various groups are through referral management and processing. This study used an exploratory qualitative approach to understand the perceived barriers around components impacting the timeliness of referrals between Primary Care providers and Specialists. In-depth interviews with 21 participants that represented departments of primary care, specialty care, and operations were conducted via Zoom or Microsoft Teams in gathering their understanding on the efficiencies, barriers, and root cause analysis as it pertains to the referral process. The interviews were transcribed verbatim, coded, and analyzed for major themes. ATLAS.ti Cloud software was used for coding analysis of the collected data. The three major components that were discussed during the interviews were regarding network management, operational excellence, and technology enablement. Six major themes and 16 sub- themes resulted from the interviews. Recommendations for the perceived barriers were included for future healthcare administrators operating ACOs.

Committee: David Meckstroth (Committee Chair); Jesse Florang (Committee Member); Scott McDoniel (Committee Member) Subjects: Finance; Health; Health Care; Health Care Management

PHD, Kent State University, 2021, College of Communication and Information

The coronavirus disease 2019 (COVID-19) pandemic has revealed challenges and opportunities for data analytics, semantic interoperability, and decision making. The sharing of COVID-19 data has become crucial for leveraging research, testing drug effectiveness and therapeutic strategies, and developing policies for control, intervention, and potential eradication of this disease. Translating healthcare data between various clinical coding schemes is critical to their functioning, and semantic mappings must be established to ensure interoperability. Using design science research methodology as a guide, this work explains 1) how an ETL (Extract Transform Load) workflow tool could support the task of clinical coding scheme mapping, 2) how the mapping output from such a tool could support or affect annotation of clinical trials, particularly those used in COVID-19 research and 3) whether aspects of the socio-technical model could be leveraged to explain and assess mapping to achieve semantic interoperability in clinical coding schemes. Research outcomes include a reproducible and shareable artifact, that can be utilized beyond the domain of biomedicine in addition to observations and recommendations from the knowledge gained during the design and evaluation process of the artifact development.

Committee: Marcia Zeng (Advisor); Athena Salaba (Committee Member); Mary Anthony (Committee Member); Yi Hong (Committee Member); Rebecca Meehan (Committee Member) Subjects: Bioinformatics; Information Science

PHD, Kent State University, 2015, College of Arts and Sciences / Department of Computer Science

Interoperability in healthcare is stated as the ability of health information systems to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities. The current healthcare information technology environment breeds incredibly complex data ecosystems. In many cases pertinent patient records are collected in multiple systems, often supplied by competing manufacturers with diverse data formats. This causes inefficiencies in data interoperability, as different formats of data create barriers in exchanging health information. This dissertation presents a semantic framework for healthcare information interoperability. We propose a system for translation of healthcare instance data, based on structured mapping definitions and using RDF as a common information representation to achieve semantic interoperability between different data models. Moreover, we introduce an entity similarity metric that utilizes the Jaccard index with the common relations of the data entities and common string literal words referenced by the data entities and augmented with data entity neighbors similarity. The precision of the similarity metric is enhanced by incorporating the auto-generated importance weights of the entity descriptors in the RDF representation of the dataset. Furthermore, we provide an automatic classification method, which we call summary graph generation, based on the pairwise entity similarities, and we propose that the summary graph can further be utilized for interoperability purposes. Finally, we present a suggestion based semi-automatic instance matching system and we test it on the RDF representation of a healthcare dataset. The system utilizes the entity similarity metric, and it presents similar node pairs to the user for possible instance matching. Based on the user feedback, it merges the matched nodes and suggests more matching pairs depending on the common relations and neigh (open full item for complete abstract)

Committee: Austin Melton (Advisor); Angela Guercio (Committee Member); Ye Zhao (Committee Member); Alan Brandyberry (Committee Member); Helen Piontkivska (Committee Member); Javed I. Khan (Committee Chair); James L. Blank (Other) Subjects: Computer Science; Health Care; Health Sciences; Information Systems; Information Technology; Medicine