A Core Reference Hierarchical Primitive Ontology for Electronic Medical Records Semantics Interoperability

Currently, electronic medical records (EMR) cannot be exchanged among hospitals, clinics, laboratories, pharmacies, and insurance providers or made available to patients outside of local networks. Hospital, laboratory, pharmacy, and insurance provider legacy databases can share medical data within a respective network and limited data with patients. The lack of interoperability has its roots in the historical development of electronic medical records. Two issues contribute to interoperability failure. The first is that legacy medical record databases and expert systems were designed with semantics that support only internal information exchange. The second is ontological commitment to the semantics of a particular knowledge representation language formalism. This research seeks to address these interoperability failures through demonstration of the capability of a core reference, hierarchical primitive ontological architecture with concept primitive attributes definitions to integrate and resolve non-interoperable semantics among and extend coverage across existing clinical, drug, and hospital ontologies and terminologies

Doctor of Philosophy

dissertationControlled clinical terminologies are essential to realizing the benefits of electronic health record systems. However, implementing consistent and sustainable use of terminology has proven to be both intellectually and practically challenging. First, this project derives a conceptual understanding of the scope and intricacies of the challenge by applying informatics principles, practical experience, and real-world requirements. Equipped with this understanding, various approaches are explored and from this analysis a unique solution is defined. Finally, a working environment that meets the requirements for creating, maintaining, and distributing terminologies was created and evaluated

Automated clinical coding:What, why, and where we are?

Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html . Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. 1 , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html. Publisher Copyright: © 2022, The Author(s).Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable processof a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Peer reviewe

Architecture and usability of OntoKeeper, an ontology evaluation tool

Abstract Background The existing community-wide bodies of biomedical ontologies are known to contain quality and content problems. Past research has revealed various errors related to their semantics and logical structure. Automated tools may help to ease the ontology construction, maintenance, assessment and quality assurance processes. However, there are relatively few tools that exist that can provide this support to knowledge engineers. Method We introduce OntoKeeper as a web-based tool that can automate quality scoring for ontology developers. We enlisted 5 experienced ontologists to test the tool and then administered the System Usability Scale to measure their assessment. Results In this paper, we present usability results from 5 ontologists revealing high system usability of OntoKeeper, and use-cases that demonstrate its capabilities in previous published biomedical ontology research. Conclusion To the best of our knowledge, OntoKeeper is the first of a few ontology evaluation tools that can help provide ontology evaluation functionality for knowledge engineers with good usability.https://deepblue.lib.umich.edu/bitstream/2027.42/152214/1/12911_2019_Article_859.pd

A framework for development of android mobile electronic prescription transfer applications in compliance with security requirements mandated by the Australian healthcare industry

This thesis investigates mobile electronic transfer of prescription (ETP) in compliance with the security requirements mandated by the Australian healthcare industry and proposes a framework for the development of an Android mobile electronic prescription transfer application. Furthermore, and based upon the findings and knowledge from constructing this framework, another framework is also derived for assessing Android mobile ETP applications for their security compliance. The centralised exchange model-based ETP solution currently used in the Australian healthcare industry is an expensive solution for on-going use. With challenges such as an aging population and the rising burden of chronic disease, the cost of the current ETP solution’s operational infrastructure is certain to rise in the future. In an environment where it is increasingly beneficial for patients to engage in and manage their own information and subsequent care, this current solution fails to offer the patient direct access to their electronic prescription information. The current system also fails to incorporate certain features that would dramatically improve the quality of the patient’s care and safety, i.e. alerts for the patient’s drug allergies, harmful dosage and script expiration. Over a decade old, the current ETP solution was essentially designed and built to meet legislation and regulatory requirements, with change-averting its highest priority. With little, if any, provision for future growth and innovation, it was not designed to cater to the needs of the ETP process. This research identifies the gap within the current ETP implementation (i.e. dependency on infrastructure, significant on-going cost and limited availability of the patient’s medication history) and proposes a framework for building a secure mobile ETP solution on the Android mobile operating system platform which will address the identified gap. The literature review part of this thesis examined the significance of ETP for the nation’s larger initiative to provide an improved and better maintainable healthcare system. The literature review also revealed the stance of each jurisdiction, from legislative and regulatory perspectives, in transitioning to the use of a fully electronic ETP solution. It identified the regulatory mandates of each jurisdiction for ETP as well as the security standards by which the current ETP implementation is iii governed so as to conform to those regulatory mandates. The literature review part of the thesis essentially identified and established how the Australian healthcare industry’s various prescription-related legislations and regulations are constructed, and the complexity of this construction for eTP. The jurisdictional regulatory mandates identified in the literature review translate into a set of security requirements. These requirements establish the basis of the guiding framework for the development of a security-compliant Android mobile ETP application. A number of experimentations were conducted focusing on the native security features of the Android operating system, as well as wireless communication technologies such as NFC and Bluetooth, in order to propose an alternative mobile ETP solution with security assurance comparable to the current ETP implementation. The employment of a proof-of-concept prototype such as this alongside / coupled with a series of iterative experimentations strengthens the validity and practicality of the proposed framework. The first experiment successfully proved that the Android operating system has sufficient encryption capabilities, in compliance with the security mandates, to secure the electronic prescription information from the data at rest perspective. The second experiment indicated that the use of NFC technology to implement the alternative transfer mechanism for exchanging electronic prescription information between ETP participating devices is not practical. The next iteration of the experimentation using Bluetooth technology proved that it can be utilised as an alternative electronic prescription transfer mechanism to the current approach using the Internet. These experiment outcomes concluded the partial but sufficient proofof- concept prototype for this research. Extensive document analysis and iterative experimentations showed that the framework constructed by this research can guide the development of an alternative mobile ETP solution with both comparable security assurance to and better access to the patient’s medication history than the current solution. This alternative solution would present no operational dependence upon infrastructure and its associated, ongoing cost to the nation’s healthcare expenditure. In addition, use of this mobile ETP alternative has the potential to change the public’s perception (i.e. acceptance from regulatory and security perspectives) of mobile healthcare solutions, thereby paving the way for further innovation and future enhancements in eHealth