Using Semantic Components to Represent Dynamics of an Interdisciplinary Healthcare Team in a Multi-Agent Decision Support System

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

Ontologies Applied in Clinical Decision Support System Rules: Systematic Review

BACKGROUND

Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.

OBJECTIVE

Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules.

METHODS

The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively.

RESULTS

CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules.

CONCLUSIONS

Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.

Specialization of Business Process Model and Notation Applications in Medicine—A Review

Process analysis and process modeling are a current topic that extends to many areas. This trend of using optimization and modeling techniques in various specific areas has led to the question of how widespread these approaches are overall in medical specializations. We compiled a list of 272 medical disciplines that we used as a search string with the Business Process Model and Notation (BPMN) for a Web of Science database search. Thus, we found a total of 485 documents that we subjected to the exclusion criteria. We analyzed the remaining 108 articles using bibliometric and content analyses to find answers to three research questions. This systematic review was carried out using the procedure proposed by Kitchenham and following the Preferred Items of the Systematic Review and Meta-Analysis Report (PRISMA). Due to the broad scope of the medical field, it was no surprise that for almost 85% of the sought-after medical specializations, we could not identify any publications in the given database when applying the BPMN. We analyzed the impact of upgrades to the BPMN on publishing. The keyword analysis showed a diametrical difference between the authors’ keywords and the so-called “Keywords Plus”, and we categorized the publications according to the purpose of applying the BPMN. However, the growing interest in combining BPMN with other approaches brings new challenges in practice.

An ontology-driven framework to support the dynamic formation of an interdisciplinary healthcare team

BACKGROUND AND PURPOSE

Teamwork has become a modus operandi in healthcare and delivery of patient care by an interdisciplinary healthcare team (IHT) is now a prevailing modality of care. We argue that a formal and automated support framework is needed for an IHT to properly leverage information technology resources. Such a framework should allow for patient preferences and expand a representation of a clinical workflow with a formal model of dynamic formation of a team, especially with regards to team leader- and membership, and the assignment of tasks to team members. Our goal was to develop such a support framework, present its prototype software implementation and verify the implementation using a proof-of-concept use case. Specifically, we focused on clinical workflows for in-patient tertiary care and on patient preferences with regards to selecting team members and team leaders.

MATERIALS AND METHODS

Drawing on the research on clinical teamwork we defined the conceptual foundations for the proposed framework. Then, we designed its architecture and used ontology-driven design and first-order logic with associated reasoning methods to create and operationalize architectural elements. Finally, we incorporated existing solutions for business workflow modeling and execution as a backend for implementing the proposed framework.

RESULTS

We developed a Team and Workflow Management Framework (TWMF) with semantic components that allow for formalizing and operationalizing team formation in in-patient tertiary care setting and support provider-related patient preferences. We also created a prototype software implementation of TWMF using the IBM Business Process Manager platform. This implementation was evaluated in several simulated patient scenarios.

CONCLUSIONS

TWMF integrates existing workflow technologies and extends them with the capabilities to support dynamic formation of an IHT. Results of this research can be used to support real-time execution of clinical workflows, or to simulate their execution in order to assess the impact of various conditions (e.g., patterns of work shifts, staffing) on IHT operations.

Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis

OBJECTIVE

The study sought to describe the literature describing clinical reasoning ontology (CRO)-based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research.

METHODS

MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles.

RESULTS

We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment.

CONCLUSION

We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians' during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines

In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process. We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation, and who are managed according to CPGs for these diseases. While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS "insulates" clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment.

Computerized Clinical Decision Support: Contributions from 2015

OBJECTIVE

To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.

METHOD

A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection.

RESULTS

Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians' decisions.

CONCLUSIONS

While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate the benefits that they promise.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.