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A Novel Framework for Standardizing and Digitizing Clinical Pathways in Healthcare Information Systems

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A Novel Framework for Standardizing and Digitizing Clinical Pathways in Healthcare Information Systems Lakehead University Knowledge Commons,http://knowledgecommons.lakeheadu.ca Electronic Theses and Dissertations Electronic Theses and Dissertations from 2009 2020 A novel framework for standardizing and digitizing clinical pathways in healthcare information systems Alahmar, Ayman http://knowledgecommons.lakeheadu.ca/handle/2453/4701 Downloaded from Lakehead University, KnowledgeCommons A Novel Framework for Standardizing and Digitizing Clinical Pathways in Healthcare Information Systems by Ayman Alahmar A thesis presented to Lakehead University in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Electrical and Computer Engineering Thunder Bay, Ontario, Canada, 2020 © Ayman Alahmar 2020 Examining Committee Membership The following served on the Examining Committee for this thesis. The decision of the Examining Committee is by majority vote. External Examiner: Dr. Carson Kai-Sang Leung Professor, Dept. of Computer Science, University of Manitoba Supervisor: Dr. Rachid Benlamri Professor, Dept. of Software Engineering, Lakehead University Internal Member: Dr. Abdulsalam Yassine Assistant Professor, Dept. of Software Engineering, Lakehead University Internal-External Member: Dr. Salama Ikki Associate Professor, Dept. of Electrical Engineering, Lakehead University ii I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. iii Abstract Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are medical management plans designed to standardize medical activities, reduce cost, optimize resource usage, and improve quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HISs). More CP automation research is therefore required to fully benefit from the practical potentials of CPs. The common theme of current research in this field is to connect CPs with Electronic Medical Record (EMR) systems. Such view positions EMRs at the centre of HISs. A major long-term objective of this research is the placement of CP systems at the centre of HISs, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors. An important contribution to the realization of this objective is to develop an international CP-specific digital coding system, and to fully standardize and digitize CPs based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) medical terminology system. This makes CPs digitally visible and machine-readable. In addition, to achieve semantic interoperability of CPs, we propose a CP knowledge representation using ontology engi- neering and HL7 standard. Our proposed framework makes CP systems smoothly linkable across various HISs. To show the feasibility and potential of the proposed framework, we developed a prototype Clinical Pathway Management System (CPMS) based on CPs currently in use at hospitals. The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent healthcare system, performing novel CP-related functions including useful decision-support tasks. Furthermore, CP data were captured without loss, which contributes to reducing missing patient data and improving the results of data mining algorithms in healthcare. Standardized CPs can also be easily compared for auditing and quality management. The proposed framework is promising, and contributes toward solving major challenges related to CP standardization, digitization, independence, and proper inclusion in today's modern computerized hospitals. iv Acknowledgements I would like to express my gratitude and special thanks to Dr. Rachid Benlamri for his supervision, guidance, and follow-up. I also would like to thank the Graduate Coordinator, Dr. Abdelhamid Tayebi, as well as Ph.D. committee members, Dr. Abdulsalam Yassine and Dr. Salama Ikki, for their valuable comments and suggestions throughout the research. Sincere thanks are also extended to the Dean, to all staff members of the Faculty of Engineering, and to all my friends at Lakehead University (LU) for their constant support. Thanks to Matteo Crupi, who helped in the programming of the prototype system. My sincere appreciation goes to the Dean and all staff members of the Faculty of Graduate Studies, as well as to staff members of the LU library (the Chancellor Paterson Library) for their help throughout my Ph.D. study years. I also want to extend my profound gratitude to the physicians and nurses at the Regional Stroke Unit, who provided expertise and shared data that greatly assisted the research and improved its quality. Last but not least, a heartfelt thank you to my wife for her support, and for being the best mother to our children, always busy with them to let me find the time to succeed in the Ph.D. work. v Dedication To my parents, my brothers and sisters, my wife, and my children. vi Table of Contents List of Tables viii List of Figures ix 1 Introduction 1 1.1 Automation and Data Research Challenges in Healthcare .......... 3 1.2 Motivation and Thesis Contribution ...................... 6 1.3 Thesis Organization ............................... 7 1.4 List of Publications ............................... 8 2 Background and Related Work 9 2.1 Clinical Pathways ................................ 9 2.1.1 History, Evolution and Definitions of CPs .............. 9 2.1.2 Benefits of CP Applications in Healthcare . 10 2.1.3 Development of Clinical Pathways ................... 11 2.2 The Need for CP Computerization and Automation . 14 2.3 Literature Review ................................ 15 2.3.1 Semantic Based Methods ........................ 16 2.3.2 Non-Semantic Based Methods ..................... 22 vii 2.3.3 SNOMED CT in Healthcare Systems . 24 2.3.4 Critical Analysis of Literature Review . 25 2.4 Conclusion .................................... 27 3 Proposed Framework 29 3.1 Overview of the Proposed Framework ..................... 29 3.2 CP Automation ................................. 30 3.2.1 Medical Classification and Terminology Systems . 31 3.2.2 Systematized Nomenclature of Medicine-Clinical Terms . 31 3.2.3 Clinical Pathways Compliance with Terminology Systems . 34 3.2.4 Standardization of CP Terminology . 36 3.2.5 Development of a Coding System for Clinical Pathways . 41 3.2.6 Coding System of International Data . 41 3.2.7 Standardizing and Coding System of Local Data . 45 3.2.8 Ontology-Based Modeling and Description Logic . 47 3.3 CPMS Integration with HISs .......................... 52 3.3.1 Electronic Health/Medical Record Systems (EHR/EMR): . 52 3.3.2 Laboratory Information Systems (LIS): . 52 3.3.3 Radiology Information Systems (RIS): . 53 3.3.4 Pharmacy Information Systems (PIS): . 53 3.3.5 Clinical Pathway Management Systems . 53 3.4 Independence of CP Management Systems . 54 3.5 Conclusion .................................... 57 viii 4 Prototype Design and Architecture of the Proposed Framework 58 4.1 Knowledge Base ................................. 59 4.1.1 Meta Clinical Pathway Ontology ................... 59 4.1.2 SNOMED CT Ontology ........................ 67 4.1.3 Time Ontology ............................. 68 4.1.4 Disease-Specific Ontologies ....................... 72 4.1.5 SWRL Rules .............................. 72 4.2 Inference and Data Analytics ......................... 74 4.3 Clinical Pathways Management Tools ..................... 74 4.4 Prototype Validation .............................. 78 4.5 Conclusion .................................... 80 5 Data Analytics and Decision Support Scenarios 81 5.1 CP Variance Analysis and Action Plan .................... 81 5.2 Cost Management and Control ......................... 82 5.3 Managing Patient CP Traces .......................... 85 5.4 Hospital Resource Management (HRM) .................... 91 5.4.1 HRM through communicating best CP practices among hospitals . 94 5.4.2 HRM through blood test/intervention cost and count analytics . 96 5.4.3 HRM through CP intervention time analytics . 99 5.5 Hospital Length of Stay Prediction . 102 5.5.1 Results and Discussion . 104 5.6 Conclusion .................................... 108 6 Conclusions and Future Work 110 6.1 Conclusions ................................... 110 6.2 Limitation and Future Work . 112 ix References 114 APPENDICES 131 A Sample Interview Questions 132 x List of Tables 2.1 Definitions of CP in the literature. ...................... 10 3.1 SNOMED CT Structure. ............................ 33 3.2 Analyzing terms from Stroke CP ....................... 36 3.3 SNOMED CT partition identifiers ....................... 43 3.4 Description of medical data coded using the developed framework. 44 3.5 SNOMED CT local partition identifiers. ................... 45 3.6 Example SNOMED CT namespace identifiers. 46 4.1 Ischemic stroke CP Patients' data (CPID 422504039) . 78 5.1 Variance analysis for population of patients (e.g., for 4000 patients). 83 5.2 Categories of stroke patients. .......................... 89 5.3 Intervention/Cost management through communicating CP data digitally from different hospitals. ............................ 94 5.4 Sample of a hospital's CP-related blood tests encoded using the hyphenated coding system. ................................. 98 5.5 Blood tests costs scenario. ..........................
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