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The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review

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Abstract

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

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Data availability

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Abbreviations

AI:

Artificial Intelligence

BERT-BiLSTM-CRF:

Bidirectional Encoder Representation from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field

CDSS:

Clinical Decision Support Systems

CIG:

Computer-Interpretable clinical Guideline

CLIPS:

C Language Integrated Production System

CNN:

Convolutional Neural Network

COGENT:

Cognitive Objects within a Graphical Environment

CPG:

Clinical Practice Guidelines

DeGeL:

Digital Electronic Guideline Library

GASTINE:

GASTon INtentional Expressions

G-DEE:

Guideline Document Engineering Environment

GEM:

Guideline Elements Model

GESDOR:

Guideline Execution by Semantic Decomposition of Representation

GET:

Guide Enactment Tool

GLARE:

GuideLine Acquisition, Representation and Execution

GLEE:

GuideLine Execution Engine

GLIF:

GuideLine Interchange Format

GMT:

Guideline Markup Tool

LDA:

Latent Dirichlet Allocation

SAGE:

Standards-Based Sharable Active Guideline Environment

SVM:

Support Vector Machine

SWRL:

Semantic Web Rule Language

UMLS:

Unified Medical Language System

References

  1. Institute of Medicine Committee on Standards for Developing Trustworthy Clinical Practice Guidelines. In: Clinical Practice Guidelines We Can Trust. edn. Edited by Graham R, Mancher M, Miller Wolman D, Greenfield S, Steinberg E. Washington (DC): National Academies Press (US); 2011.

  2. Bierbaum M, Rapport F, Arnolda G, et al. Clinicians' attitudes and perceived barriers and facilitators to cancer treatment clinical practice guideline adherence: a systematic review of qualitative and quantitative literature. Implement Sci 2020; 15(1):39.

    PubMed  PubMed Central  Google Scholar 

  3. Shekelle PG: Clinical Practice Guidelines: What's Next? JAMA 2018, 320(8):757-758.

    PubMed  Google Scholar 

  4. Chen Y, Wang C, Shang H, Yang K, Norris SL: Clinical practice guidelines in China. BMJ 2018, 360:j5158.

    PubMed  PubMed Central  Google Scholar 

  5. Liu M, Zhang C, Zha Q,, et al. A national survey of Chinese medicine doctors and clinical practice guidelines in China. BMC Complement Altern Med 2017, 17(1):451.

    PubMed  PubMed Central  Google Scholar 

  6. Mickan S, Burls A, Glasziou P. Patterns of 'leakage' in the utilisation of clinical guidelines: a systematic review. Postgrad Med J 2011, 87(1032):670-679.

    PubMed  Google Scholar 

  7. Jin Y, Li Z, Han F, et al. Barriers and enablers for the implementation of clinical practice guidelines in China: a mixed-method study. BMJ open 2019, 9(9):e026328.

    PubMed  PubMed Central  Google Scholar 

  8. Oliveira T, Novais P, Neves J. Development and implementation of clinical guidelines: An artificial intelligence perspective. Artificial intelligence review 2014, 42(4):999-1027.

    Google Scholar 

  9. Peleg M, Tu S, Bury J, Ciccarese P, Fox J, Greenes RA, Hall R, Johnson PD, Jones N, Kumar A et al: Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc 2003, 10(1):52-68.

    PubMed  PubMed Central  Google Scholar 

  10. Cadario R, Longoni C, Morewedge CK. Understanding, explaining, and utilizing medical artificial intelligence. Nat Hum Behav 2021, 5(12):1636-1642.

    PubMed  Google Scholar 

  11. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019, 25(1):30-36.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial Intelligence and the Implementation Challenge. J Med Internet Res 2019, 21(7):e13659.

    PubMed  PubMed Central  Google Scholar 

  13. Yamada T, Yoneoka D, Hiraike Y, et al. Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm Validation Study. J Med Internet Res 2020, 22(12):e22422.

    PubMed  PubMed Central  Google Scholar 

  14. Schmidt L, Olorisade BK, McGuinness LA, Thomas J, Higgins JPT. Data extraction methods for systematic review (semi)automation: A living systematic review. F1000Research 2021, 10:401.

  15. Hussain M, Hussain J, Ali T, et al. Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach. Appl Sci-Basel 2021, 11(8):17.

    Google Scholar 

  16. Fazlic LB, Hallawa A, Schmeink A, Peine A, Martin L, Dartmann G. A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines. In: 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO): May 20–24 2019; Opatija, CROATIA: IEEE; 2019: 1025–1030.

  17. Boxwala AA, Rocha BH, Maviglia S, et al: A multi-layered framework for disseminating knowledge for computer-based decision support. J Am Med Inform Assoc 2011, 18 Suppl 1(Suppl 1):i132–139.

  18. Kaiser K, Miksch S. Versioning computer-interpretable guidelines: semi-automatic modeling of 'Living Guidelines' using an information extraction method. Artif Intell Med 2009, 46(1):55-66.

    PubMed  Google Scholar 

  19. Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022. http://www.training.cochrane.org/handbook (accessed 10 May 2022).

  20. Page MJ, McKenzie JE, Bossuyt PM, et al: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021, 372:n71.

    PubMed  PubMed Central  Google Scholar 

  21. Hui Yu. Research on event extraction of Chinese clinical guidelines based on deep learning. D. Peking Union Medical College; 2020.[Chinese]

  22. Baldwin T, Guo Y, Syeda-Mahmood T. Automatic Generation of Conditional Diagnostic Guidelines. AMIA Annu Symp Proc 2016; 2016:295-304.

    PubMed  Google Scholar 

  23. Becker M, Böckmann B. Semi-Automatic Mark-Up and UMLS Annotation of Clinical Guidelines. Stud Health Technol Inform 2017; 245:294-297.

    PubMed  Google Scholar 

  24. Serban R, ten Teije A, van Harmelen F, Marcos M, Polo-Conde C. Extraction and use of linguistic patterns for modelling medical guidelines. Artif Intell Med 2007, 39(2):137-149.

    PubMed  Google Scholar 

  25. Tsopra R, Lamy JB, Sedki K. Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy. Artif Intell Med 2018, 89:24-33.

    PubMed  Google Scholar 

  26. Zhu H, Ni Y, Cai P, Cao F. Automatic information extraction for computerized clinical guideline. Stud Health Technol Inform 2013; 192:1023.

    PubMed  Google Scholar 

  27. Galopin A, Bouaud J, Pereira S, Séroussi B. Comparison of clinical practice guidelines from a knowledge modelling perspective: a case study with the management of hypertension. Stud Health Technol Inform 2014; 197:21-25.

    PubMed  Google Scholar 

  28. Gindl S, Kaiser K, Miksch S. Syntactical negation detection in clinical practice guidelines. Stud Health Technol Inform 2008; 136:187-192.

    PubMed  PubMed Central  Google Scholar 

  29. Kaiser K, Akkaya C, Miksch S. How can information extraction ease formalizing treatment processes in clinical practice guidelines? A method and its evaluation. Artif Intell Med 2007; 39(2):151-163.

    PubMed  Google Scholar 

  30. Minard AL, Kaiser K. Supporting Computer-interpretable Guidelines' Modeling by Automatically Classifying Clinical Actions. In: 14th Artificial Intelligence in Medicine Conference (AIME) May 29-Jun 01 2013; Murcia, SPAIN: Springer-Verlag Berlin; 2013: 39–52.

  31. Amanda Bouffier, Poibeau T. Analyzing the Scope of Conditions in Texts: A Discourse-Based Approach. In: 11th Conference of the Pacific Association for Computational Linguistics. France; 2009.

  32. Song MH, Kim SH, Park DK, Lee YH. A multi-classifier based guideline sentence classification system. Healthc Inform Res 2011; 17(4):224-231.

    PubMed  PubMed Central  Google Scholar 

  33. Wenzina R, Kaiser K. Identifying Condition-Action Sentences Using a Heuristic-Based Information Extraction Method. Berlin: Springer; 2013.

    Google Scholar 

  34. Kaiser K, Seyfang A, Miksch S. Identifying Treatment Activities for Modelling Computer-Interpretable Clinical Practice Guidelines. Berlin: Springer 2011.

    Google Scholar 

  35. Hematialam H, Zadrozny W. Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features. arXiv 2017:1706.04206.

  36. Song MH, Lee YH, Kang UG. Comparison of machine learning algorithms for classification of the sentences in three clinical practice guidelines. Healthc Inform Res 2013; 19(1):16-24.

    PubMed  PubMed Central  Google Scholar 

  37. Gad El-Rab W, Zaïane OR, El-Hajj M. Formalizing clinical practice guideline for clinical decision support systems. Health Informatics J 2017; 23(2):146-156.

    PubMed  Google Scholar 

  38. Hagerty CG, Pickens DS, Chang J, Kulikowski CA, Sonnenberg FA. Prediction in annotation based guideline encoding. AMIA Annu Symp Proc 2006; 2006:314-318.

    PubMed  PubMed Central  Google Scholar 

  39. Georg G, Jaulent MC. An environment for document engineering of clinical guidelines. AMIA Annu Symp Proc 2005; 2005:276-280.

    PubMed  PubMed Central  Google Scholar 

  40. Bottrighi A, Terenziani P. META-GLARE: A meta-system for defining your own computer interpretable guideline system—Architecture and acquisition. Artif Intell Med 2016; 72:22-41.

    PubMed  Google Scholar 

  41. Codish S, Shiffman RN. A model of ambiguity and vagueness in clinical practice guideline recommendations. AMIA Annu Symp Proc 2005; 2005:146-150.

    PubMed  PubMed Central  Google Scholar 

  42. de Clercq PA, Hasman A, Blom JA, Korsten HH. Design and implementation of a framework to support the development of clinical guidelines. Int J Med Inform 2001; 64(2-3):285-318.

    PubMed  Google Scholar 

  43. Farkash A, Timm JT, Waks Z. A model-driven approach to clinical practice guidelines representation and evaluation using standards. Stud Health Technol Inform 2013; 192:200-204.

    PubMed  Google Scholar 

  44. Terenziani P, Molino G, Torchio M. A modular approach for representing and executing clinical guidelines. Artif Intell Med 2001; 23(3):249-276.

    CAS  PubMed  Google Scholar 

  45. Wenzina R, Kaiser K. Using TimeML to support the modeling of computerized clinical guidelines. Stud Health Technol Inform 2014; 205:8-12.

    PubMed  Google Scholar 

  46. Giordano L, Terenziani P, Bottrighi A, Montani S, Donzella L. Model checking for clinical guidelines: an agent-based approach. AMIA Annu Symp Proc 2006; 2006:289-293.

    PubMed  PubMed Central  Google Scholar 

  47. Hales JW, Gadd CS, Lobach DF. Development and use of a Guideline Entry Wizard to convert text clinical practice guidelines to a relational format. Proc AMIA Annu Fall Symp 1997:163–167.

  48. Kuziemsky C, O'Sullivan D, Michalowski W, Wilk S, Farion K. A constraint satisfaction approach to data-driven implementation of clinical practice guidelines. AMIA Annu Symp Proc 2008; 2008:540-544.

    PubMed  PubMed Central  Google Scholar 

  49. Lobach DF, Gadd CS, Hales JW. Structuring clinical practice guidelines in a relational database model for decision support on the Internet. Proc AMIA Annu Fall Symp 1997:158–162.

  50. Moser W, Adlassnig KP. Formal semantics of guarded task structures for clinical practice guidelines. Inform Health Soc Care 2008; 33(3):179-190.

    PubMed  Google Scholar 

  51. Ongenae F, De Backere F, Steurbaut K, et al. Towards computerizing intensive care sedation guidelines: design of a rule-based architecture for automated execution of clinical guidelines. BMC Med Inform Decis Mak 2010; 10:3.

    PubMed  PubMed Central  Google Scholar 

  52. Papageorgiou EI, Roo JD, Huszka C, Colaert D. Formalization of treatment guidelines using Fuzzy Cognitive Maps and semantic web tools. J Biomed Inform 2012; 45(1):45-60.

    PubMed  Google Scholar 

  53. Shiffman RN, Karras BT, Agrawal A, Chen R, Marenco L, Nath S. GEM: a proposal for a more comprehensive guideline document model using XML. J Am Med Inform Assoc 2000; 7(5):488-498.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Musen MA, Tu SW, Das AK, Shahar Y. EON: a component-based approach to automation of protocol-directed therapy. J Am Med Inform Assoc 1996; 3(6):367-388.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Fox J, Johns N, Rahmanzadeh A. Disseminating medical knowledge: the PROforma approach. Artif Intell Med 1998; 14(1-2):157-181.

    CAS  PubMed  Google Scholar 

  56. Shahar Y, Miksch S, Johnson P. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif Intell Med 1998; 14(1-2):29-51.

    CAS  PubMed  Google Scholar 

  57. Ohno-Machado L, Gennari JH, Murphy SN, et al. The guideline interchange format: a model for representing guidelines. J Am Med Inform Assoc 1998; 5(4):357-372.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Purves IN, Sugden B, Booth N, Sowerby M. The PRODIGY project--the iterative development of the release one model. Proc AMIA Symp 1999:359–363.

  59. Skonetzki S, Gausepohl HJ, van der Haak M, Knaebel S, Linderkamp O, Wetter T. HELEN, a modular framework for representing and implementing clinical practice guidelines. Methods Inf Med 2004; 43(4):413-426.

    CAS  PubMed  Google Scholar 

  60. Chen R, Georgii-Hemming P, Ahlfeldt H. Representing a chemotherapy guideline using openEHR and rules. Stud Health Technol Inform 2009; 150:653-657.

    PubMed  Google Scholar 

  61. Shahar Y, Young O, Shalom E, et al. A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. J Biomed Inform 2004; 37(5):325-344.

    PubMed  Google Scholar 

  62. Votruba P, Miksch S, Seyfang A, Kosara R. Tracing the formalization steps of textual guidelines. Stud Health Technol Inform 2004; 101:172-176.

    PubMed  Google Scholar 

  63. Georg G, Cavazza M. Integrating Document-Based and Knowledge-Based Models for Clinical Guidelines Analysis. In: 11th Conference on Artificial Intelligence in Medicine in Europe: July 7–11 2007; Amsterdam, Netherlands; 2007: 421–430.

  64. Peleg M, Boxwala AA, Ogunyemi O, et al. GLIF3: the evolution of a guideline representation format. Proc AMIA Symp 2000:645–649.

  65. Latoszek-Berendsen A, de Clercq P, van den Herik J, Hasman A. Intention-based expressions in GASTINE. Methods Inf Med 2009; 48(4):391-396.

    CAS  PubMed  Google Scholar 

  66. Sordo M, Boxwala AA, Ogunyemi O, Greenes RA. Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support. Stud Health Technol Inform 2004; 107(Pt 1):164-168.

    PubMed  Google Scholar 

  67. Hripcsak G, Ludemann P, Pryor TA, Wigertz OB, Clayton PD. Rationale for the Arden Syntax. Comput Biomed Res 1994; 27(4):291-324.

    CAS  PubMed  Google Scholar 

  68. González-Ferrer A, ten Teije A, Fdez-Olivares J, Milian K. Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artif Intell Med 2013; 57(2):91-109.

    PubMed  Google Scholar 

  69. Grandi F, Mandreoli F, Martoglia R. Efficient management of multi-version clinical guidelines. J Biomed Inform 2012; 45(6):1120-1136.

    PubMed  Google Scholar 

  70. Scott-Wright AO, Fischer RP, Denekamp Y, Boxwala AA. A methodology for modular representation of guidelines. Stud Health Technol Inform 2004; 107(Pt 1):149-153.

    PubMed  Google Scholar 

  71. Quaglini S, Dazzi L, Gatti L, Stefanelli M, Fassino C, Tondini C. Supporting tools for guideline development and dissemination. Artif Intell Med 1998; 14(1-2):119-137.

    CAS  PubMed  Google Scholar 

  72. Tu SW, Campbell J, Musen MA. The SAGE guideline modeling: motivation and methodology. Stud Health Technol Inform 2004; 101:167-171.

    PubMed  Google Scholar 

  73. Gordon C, Herbert I, Johnson P. Knowledge representation and clinical practice guidelines: the DILEMMA and PRESTIGE projects. Stud Health Technol Inform 1996; 34:511-515.

    Google Scholar 

  74. Dart T, Xu Dart T, Xu Y, Chatellier G, Degoulet P. Computerization of guidelines: towards a "guideline markup language". Stud Health Technol Inform 2001; 84(Pt 1):186-190.

    CAS  PubMed  Google Scholar 

  75. Wang D, Shortliffe EH. GLEE--a model-driven execution system for computer-based implementation of clinical practice guidelines. Proc AMIA Symp 2002:855–859.

  76. Young O, Shahar Y. Applying Hybrid-Asbru clinical guidelines using the Spock system. AMIA Annu Symp Proc 2005; 2005:854-858.

    PubMed  PubMed Central  Google Scholar 

  77. Jafarpour B, Abidi SR, Abidi SS. Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines. IEEE J Biomed Health Inform 2016; 20(1):388-398.

    PubMed  Google Scholar 

  78. Moskovitch R, Shahar Y. Vaidurya: a multiple-ontology, concept-based, context-sensitive clinical-guideline search engine. J Biomed Inform 2009; 42(1):11-21.

    PubMed  Google Scholar 

  79. Wang D, Peleg M, Bu D, et al. GESDOR - a generic execution model for sharing of computer-interpretable clinical practice guidelines. AMIA Annu Symp Proc 2003; 2003:694-698.

    PubMed  PubMed Central  Google Scholar 

  80. Seyfang A, Miksch S, Marcos M. Combining diagnosis and treatment using ASBRU. Int J Med Inform 2002; 68(1-3):49-57.

    PubMed  Google Scholar 

  81. Georg G, Séroussi B, Bouaud J. Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism. AMIA Annu Symp Proc 2003; 2003:254-258.

    PubMed  PubMed Central  Google Scholar 

  82. Xiaoze Li, Guoqiang Sun, Yi Zhou, Qiang Lv HH. Construction Process and Rules for Knowledge-driven Visualization Model of Clinical Guideline: Taking Hypertension for Example. Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease 2019; 27(9):14–18.[Chinese]

  83. Anani N, Chen R, Prazeres Moreira T, Koch S. Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR's Guideline Definition Language. BMC Med Inform Decis Mak 2014; 14:39.

    PubMed  PubMed Central  Google Scholar 

  84. Xingmeng Zhan. Research on visualization expression and formal transformation of clinical guidelines. D. Hubei University of Technology, 2014. [Chinese]

  85. Buchtela D, Peleska J, Veselý A, Zvárová J, Zvolský M. Formalization of clinical practice guidelines. Stud Health Technol Inform 2008; 136:151-156.

    PubMed  Google Scholar 

  86. Bouaud J, Séroussi B. Characterizing the dimensions of clinical practice guideline evolution. Stud Health Technol Inform 2008; 136:139-144.

    PubMed  Google Scholar 

  87. Fdez-Olivares J, Onaindia E, Castillo L, Jordán J, Cózar J. Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artif Intell Med 2019; 96:167-186.

    PubMed  Google Scholar 

  88. Piovesan L, Terenziani P. A Constraint-Based Approach for the Conciliation of Clinical Guidelines. In: 15th Ibero-American Conference on Artificial Intelligence (AI): Nov 23–25 2016; San Jose, Costa Rica: Springer-Verlag Berlin; 2016: 77–88.

  89. Pu X, Chen K, Liu J, Wen J, Zhneng S, Li H. [Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020; 37(3):365–372. [Chinese]

  90. Galopin A, Bouaud J, Pereira S, Séroussi B. Clinical practice guidelines consistency for patients with multimorbidity: a case-study in the management of type 2 diabetes and hypertension. Stud Health Technol Inform 2015; 210:344-348.

    PubMed  Google Scholar 

  91. Michalowski M, Wilk S, Michalowski W, Carrier M. MitPlan: A planning approach to mitigating concurrently applied clinical practice guidelines. Artif Intell Med 2021; 112:102002.

    PubMed  Google Scholar 

  92. Wilk S, Michalowski M, Michalowski W, Rosu D, Carrier M, Kezadri-Hamiaz M. Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. J Biomed Inform 2017; 66:52-71.

    PubMed  Google Scholar 

  93. Wilk S, Michalowski W, Michalowski M, Farion K, Hing MM, Mohapatra S. Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. J Biomed Inform 2013; 46(2):341-353.

    PubMed  Google Scholar 

  94. Miller PL. Domain-constrained generation of clinical condition sets to help test computer-based clinical guidelines. J Am Med Inform Assoc 2001; 8(2):131-145.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Duftschmid G, Miksch S. Knowledge-based verification of clinical guidelines by detection of anomalies. Artif Intell Med 2001; 22(1):23-41.

    CAS  PubMed  Google Scholar 

  96. Duftschmid G, Miksch S, Gall W. Verification of temporal scheduling constraints in clinical practice guidelines. Artif Intell Med 2002; 25(2):93-121.

    PubMed  Google Scholar 

  97. Bottrighi A, Giordano L, Molino G, Montani S, Terenziani P, Torchio M. Adopting model checking techniques for clinical guidelines verification. Artif Intell Med 2010; 48(1):1-19.

    PubMed  Google Scholar 

  98. ten Teije A, Marcos M, Balser M, et al. Improving medical protocols by formal methods. Artif Intell Med 2006; 36(3):193-209.

    PubMed  Google Scholar 

  99. Miller DW, Jr., Frawley SJ, Miller PL. Using semantic constraints to help verify the completeness of a computer-based clinical guideline for childhood immunization. Comput Methods Programs Biomed 1999; 58(3):267-280.

    PubMed  Google Scholar 

  100. Bäumler S, Balser M, Dunets A, Reif W, Schmitt J. Verification of Medical Guidelines by Model Checking – A Case Study. In: 13th International SPIN Workshop: March 30 - April 1 2006; Berlin, Heidelberg: Springer Berlin Heidelberg; 2006: 219–233.

  101. Bingfei Wu. Study of knowledge representation and application methods for clinical practice guidelines. D. Zhejiang University; 2010. [Chinese]

  102. Dazzi L, Fassino C, Saracco R, Quaglini S, Stefanelli M. A patient workflow management system built on guidelines. Proc AMIA Annu Fall Symp 1997:146–150.

  103. Fox J, Khan O, Curtis H, et al. Rapid translation of clinical guidelines into executable knowledge: A case study of COVID-19 and online demonstration. Learn Health Syst 2020; 5(1):e10236.

    PubMed  PubMed Central  Google Scholar 

  104. Seitinger A, Fehre K, Adlassnig KP, et al. An Arden-Syntax-based clinical decision support framework for medical guidelines--Lyme borreliosis as an example. Stud Health Technol Inform 2014; 198:125-132.

    PubMed  Google Scholar 

  105. Yu HW, Hussain M, Afzal M, et al. Use of mind maps and iterative decision trees to develop a guideline-based clinical decision support system for routine surgical practice: Case study in thyroid nodules. J Am Med Inform Assoc 2019; 26(6):524-536.

    PubMed  PubMed Central  Google Scholar 

  106. Goldstein MK, Hoffman BB, Coleman RW, et al. Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care. Proc AMIA Symp 2000:300–304.

  107. Hendriks MP, Verbeek XAAM, van Vegchel T, et al. Transformation of the national breast cancer guideline into data-driven clinical decision trees. JCO Clin Cancer Inform 2019; 3:1-14.

    PubMed  Google Scholar 

  108. Keikes L, Kos M, Verbeek XAAM, et al. Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity. Int J Qual Health Care 2021; 33(2):mzab051.

  109. Schriger DL, Baraff LJ, Hassanvand M, Nagda S. EDECS: the Emergency Department Expert Charting System. Medinfo 1995; 8 Pt 2:1665.

    PubMed  Google Scholar 

  110. Choi DJ, Park JJ, Ali T, Lee S. Artificial intelligence for the diagnosis of heart failure. NPJ Digit Med 2020; 3:54.

    PubMed  PubMed Central  Google Scholar 

  111. Colombet I, Dart T, Leneveut L, Zunino S, Ménard J, Chatellier G. A computer decision aid for medical prevention: a pilot qualitative study of the Personalized Estimate of Risks (EsPeR) system. BMC Med Inform Decis Mak 2003; 3:13.

    PubMed  PubMed Central  Google Scholar 

  112. Persson M, Bohlin J, Eklund P. Development and maintenance of guideline-based decision support for pharmacological treatment of hypertension. Comput Methods Programs Biomed 2000; 61(3):209-219.

    CAS  PubMed  Google Scholar 

  113. Shiffman RN, Michel G, Essaihi A, Thornquist E. Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation. J Am Med Inform Assoc 2004; 11(5):418-426.

    PubMed  PubMed Central  Google Scholar 

  114. Domínguez E, Pérez B, Zapata M. Towards a traceable clinical guidelines application. A model-driven approach. Methods Inf Med 2010; 49(6):571-580.

    PubMed  Google Scholar 

  115. Grando MA, Glasspool D, Boxwala A. Argumentation logic for the flexible enactment of goal-based medical guidelines. J Biomed Inform 2012; 45(5):938-949.

    PubMed  Google Scholar 

  116. Lu Tan. The research and application of stroke clinical decision support based on computer-interpretable guidelines. D. Peking Union Medical College; 2020.[Chinese]

  117. Zhao W, Jiang X, Wang K, Sun X, Hu G, Xie G. Construction of Guideline-Based Decision Tree for Medication Recommendation. Stud Health Technol Inform 2020; doi: https://doi.org/10.3233/SHTI200015

    Article  PubMed  Google Scholar 

  118. Becker M, Böckmann B. Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques. Stud Health Technol Inform 2017; 235:271-275.

    PubMed  Google Scholar 

  119. Peleg M, Keren S, Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J Biomed Inform 2008; 41(1):180-201.

    PubMed  Google Scholar 

  120. Correndo G, Terenziani P. Towards a flexible integration of clinical guideline systems with medical ontologies and medical information systems. Stud Health Technol Inform 2004; 101:108-112.

    PubMed  Google Scholar 

  121. Qing Ye. Developing clinical guideline ontology and electronic documents for hypertension. D. The Fourth Military Medical University; 2012. [Chinese]

  122. Quaglini S, Stefanelli M, Cavallini A, Micieli G, Fassino C, Mossa C. Guideline-based careflow systems. Artif Intell Med 2000;20(1):5-22.

    CAS  PubMed  Google Scholar 

  123. Yan Zhao. The research and implementation of open learning tool for clinical guideline based on ontology. D. Xi'an University of Electronic Science and technology; 2013. [Chinese]

  124. Ciccarese P, Caffi E, Quaglini S, Stefanelli M. Architectures and tools for innovative Health Information Systems: the Guide Project. Int J Med Inform 2005; 74(7-8):553-562.

    PubMed  Google Scholar 

  125. Ziming Yin, Fangrui Du, Zitong Zhao, Yujiao Jia, Tian Tian, Dan Xu. Research on Knowledge Graph Construction Technology Based on Clinical Guidelines. Software 2020; 41(9):178–184+197. [Chinese]

  126. Yongbo Wang, Kuang Gao, Xuhui Li, et al. Research on promotion of implementation of clinical practice guidelines(II):framework design of knowledge graph construction based on guidelines for non-muscle invasive bladder cancer. New Medicine 2021; 31(6):419–432. [Chinese]

  127. Jing Guo, Yibei Si, Yongbo Wang, et al. Research on the promotion of implementation of clinical practice guidelines(III): conceptual level design of a knowledge graph for clinical guidelines for Traditional Chinese Medicine/Integrated Traditional Chinese and Western Medicine. New Medicine 2022; 32(1):2–9. [Chinese]

  128. Banjar HR, Alkhatabi H, Alganmi N, Almouhana GI. Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia. Int J Environ Res Public Health 2020; 17(21):8066.

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Alian S, Li J, Pandey V. A Personalized Recommendation System to Support Diabetes Self-Management for American Indians. IEEE Access 2018; 6:73041-73051.

    Google Scholar 

  130. Chen Z, Salazar E, Marple K, et al. An AI-Based Heart Failure Treatment Adviser System. IEEE J Transl Eng Health Med 2018; 6:2800810.

    PubMed  Google Scholar 

  131. Bouaud J, Séroussi B, Antoine EC, Gozy M, Khayat D, Boisvieux JF. Hypertextual navigation operationalizing generic clinical practice guidelines for patient-specific therapeutic decisions. Proc AMIA Symp 1998:488–492.

  132. Chen RC, Huang YH, Bau CT, Chen SM. A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst Appl 2012; 39(4):3995-4006.

    Google Scholar 

  133. Hongyan Liu. Hypertension medical knowledge base design based on clinical guideline. D. Peking Union Medical College; 2017. [Chinese]

  134. Peleg M. Computer-interpretable clinical guidelines: A methodological review. J Biomed Inform 2013; 46(4):744-763.

    PubMed  Google Scholar 

  135. Bui DD, Zeng-Treitler Q. Learning regular expressions for clinical text classification. J Am Med Inform Assoc 2014; 21(5):850-857.

    PubMed  PubMed Central  Google Scholar 

  136. Ning Z, Li Y, Wu ST. Effective Pattern Discovery for Text Mining. IEEE T Knowl Data En 2012; 24(1):30-44.

    Google Scholar 

  137. Yao L, Mao C, Luo Y. Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med Inform Decis Mak 2019; 19(Suppl 3):71.

    CAS  PubMed  PubMed Central  Google Scholar 

  138. de Clercq PA, Blom JA, Korsten HH, Hasman A. Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif Intell Med 2004; 31(1):1-27.

    PubMed  Google Scholar 

  139. Wang D, Peleg M, Tu SW, Shortliffe EH, Greenes RA. Representation of clinical practice guidelines for computer-based implementations. Stud Health Technol Inform 2001; 84(Pt 1):285-289.

    CAS  PubMed  Google Scholar 

  140. Iglesias N, Juarez JM, Campos M. Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines. Artif Intell Med 2020; 103:101741.

    PubMed  Google Scholar 

  141. Gagliardi AR, Marshall C, Huckson S, James R, Moore V. Developing a checklist for guideline implementation planning: review and synthesis of guideline development and implementation advice. Implement Sci 2015; 10:19.

    PubMed  PubMed Central  Google Scholar 

  142. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3:17.

    PubMed  PubMed Central  Google Scholar 

  143. Peiffer-Smadja N, Rawson TM, Ahmad R, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2020; 26(5):584-595.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We express our gratitude to Jean Glover from Tianjin Golden Framework Consulting Company for English editing.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82174230) and the Fundamental Research Funds for the Central Universities (No. 2042022kf1213).

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Author notes

  1. Xu-Hui Li and Jian-Peng Liao contributed equally to this work.

Authors and Affiliations

  1. Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China

    Xu-Hui Li, Yong-Bo Wang, Si-Yu Yan, Qiao Huang, Yun-Yun Wang & Ying-Hui Jin

  2. School of Public Health, Wuhan University, Wuhan, 430071, China

    Jian-Peng Liao

  3. School of Computer Science, Wuhan University, Wuhan, 430071, China

    Mu-Kun Chen, Kuang Gao & Wen-Bin Hu

  4. School of Nursing, Peking University, Beijing, 100191, China

    Yue-Xian Shi

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Contributions

YHJ and WBH conceptualized the study and approved the final manuscript. XHL and JPL retrieved and screened the records, and extracted the data from the eligible articles. MKC, KG, YBW, SYY, QH, YYW and YXS provided methodological consultation. XHL drafted the manuscript and all authors revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wen-Bin Hu or Ying-Hui Jin.

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Li, XH., Liao, JP., Chen, MK. et al. The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review. J Med Syst 48, 6 (2024). https://doi.org/10.1007/s10916-023-02007-1

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