Architectural approaches for implementing Clinical Decision Support Systems in Cloud: A Systematic Review
- 1. Architectural approaches for implementing Clinical Decision Support Systems in Cloud: A Systematic Review Luis Tabares, Jhonatan Hernandez and Ivan Cabezas imcabezas@usbcali.edu.co June 27, 2016 International Workshop on Cloud Connected Health, CCH 2016, Washington D.C . 1
- 3. Content Clinical Decision Support Systems Cloud Computing Systematic Review Protocol Systematic Review Results Final Remarks 3
- 4. Clinical Decision Support Systems CDSS: Systems providing clinicians, staff and patients with intelligently filtered knowledge and person-specific information, presented at appropiate time, to enhance health and health care outcomes Types: • Alerts and Reminders • Knowledge service • Diagnostic, treatment and prescription support • Information Recovery • Image recognition and interpretation 4 Administrative Managing clinical complexity Cost control Decision support (Wyatt & Spiegelhalter, 1991; Berner, 2009; Goertzel, 1969; Coiera, 2005) Knowledge based Non- knowledge based
- 5. Cloud Computing 5 Model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. On-demand self-service Broad network access Resource pooling Rapid elasticity Measured service (Mell & Grance, 2009)
- 6. Systematic Literature Review 6 A systematic literature review (SLR) is a means of identifying, evaluating and interpreting all available research relevant to a particular research question, or topic area, or phenomenon of interest (Kitchenham, 2004; “Exploring Systematic Reviews,” n.d.)
- 8. SLR Planning 8 Identified need: Determine and discuss key issues and approaches involving architectural designs in implementing a CDSS using Cloud Computing. CDSS Cloud Computing Intervention of Cloud Computing in CDSS implementations Identification of the need for a review
- 9. SLR Planning (ii) 9 Research Questions: ID Research Question (RQ) RQ1 What evidence is there about implementing CDSS in the cloud since 2010? What are the major architectural approaches, contributions, limitations and concerns about implementing Cloud CDSS? RQ2 Among health areas, which have more CDSS implementations? RQ3 What types of CDSS are being built? RQ4 What are the quality attributes that are typically driven in CDSS architectural designs? RQ5 What are the main data sources used in cloud-based CDSS? RQ6 What evidence is there that cloud computing is an adequate approach for implementing CDSS? Specifying the research question(s)
- 10. SLR Conducting 10 Search Process Identification of research
- 11. SLR Conducting (ii) 11 Selection of Primary Studies Inclusion Criteria (IC) ID Criteria IC1 Primary studies published between 2010 and 2016 IC2 Journals and conference proceedings IC3 Articles describing the use or intervention of cloud computing on CDSS Exclusion Criteria (EC) ID Criteria EC1 Articles not showing the intervention of cloud computing on CDSS EC2 Duplicated reports of the same study Selection of primary studies
- 12. SLR Conducting (iii) 12 Study Quality Assessment ID Assessment Question (AQ) Score AQ1 Was the method process properly described? 12,5% AQ2 Were the results clearly described? 12,5% AQ3 Was the architectural approach described? 25% AQ4 It is possible to identify key quality attributes or driving design scenarios? 25% AQ5 The article guides a future architectural design to conduct a CDSS implementation? 25% Study quality assessment
- 13. SLR Conducting (iv) 13 Data Extraction & Monitoring Data extraction and monitoring
- 14. SLR Conducting (v) 14 Data Extraction Template Extracted Data General Data: data extractor, extraction date, data checker, checking date, study identifier, title, authors, year of publication, full reference, name of database, type of source, name of source and quality assessment score Summary of the proposed architectural approach Contributions of cloud computing on CDSS Gaps on intervention of cloud computing on CDSS Challenges of computing on CDSS Application area within the domain of health Types of proposed clinical decision support systems List of quality attributes addressed Data sources proposed for the implementation of the CDSS Data extraction and monitoring
- 15. SLR Conducting (vi) 15 Data Analysis ID Synthesis or Tabulations (T) RQ T1 For each study, the proposed architectural approach, its main contributions, gaps and challenges RQ1 T2 Number of studies per outcome about intervention of cloud computing on CDSS RQ6 T3 Number of studies per application area RQ2 T4 Number of studies per type of CDSS RQ3 T5 Number of studies per quality attributes RQ4 T6 Number of studies per data source RQ5 T7 Discussion about intervention of cloud computing on CDSS implementations in terms of main outcomes detected in the literature RQ6 Data synthesis
- 16. SLR Results 16 12 8 4 4 4 1 1 1
- 20. SLR Results (v) 20 Cloud-based CDSS Architectural Drivers Security Compatibility Performance
- 24. SLR Results (ix) 24 Intervention of Cloud Computing on CDSS Cost-efficiency Better patient outcomes “Unlimited resources” Clinical data quality Researching knowledge
- 25. Final Remarks • Healthcare organizations are adopting cloud-based CDSS to provide enhanced patient care comes. • On-premise environments could allow similar advantages but the effort to achieve that in these environments is larger. • Main challenges in cloud-based CDSS: Performance, Compatibility and Reliability. • Main concerns in cloud-based CDSS: Security and Privacy. These concerns may not be being well validated in practice. • May be a lack of formalism regarding to software engineering practice. 25
- 26. Final Remarks (ii) • There is a lack of rigor using the term “Cloud” 26 On-demand self-service Broad network access Resource pooling Rapid elasticity Measured service • It was discussed a web-based, SOA-based or ROA-based proposals without use the term “cloud computing” rigorously. • Not all primary characteristics of Cloud Computing are being strictly implemented in their proposals.
- 27. Final Remarks (iii) 27 Common Cloud-based CDSS Architectural Approach (Oh et al., 2015) 3 Common Components: • Knowledge database • Inference Engine • Interface Server
- 28. Future Work CDSS Triage as a Service 28 Common CDSS Components
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- 36. Architectural approaches for implementing Clinical Decision Support Systems in Cloud: A Systematic Review Iván Cabezas, Luis Tabares and Jhonatan Hernández imcabezas@usbcali.edu.co June 27, 2016 International Workshop on Cloud Connected Health, CCH 2016, Washington D.C . 36