Digital clinical guidelines modelling

Oliveira T., Costa A., Neves J., Novais P., Digital Clinical Guidelines Modelling, Modelling and Simulation 2011, Novais P., Machado J., Analide C., Abelha A., (Eds.) (ESM’2011 – The 2011 European Simulation and Modelling Conference, Guimarães, Portugal) EUROSIS Publisher, ISBN: 978-9077381-66-3, pp 392-398, 2011.Healthcare environments are very demanding, because practitioners are required to consult many patients in a short period of time, increasing the levels of stress which usually harms the outcome of healthcare processes. The short time practitioners have with their patients does not facilitate informed decision making and checking all possibilities. A possible solution is the use of guideline-based applications, because they have the potential of being an effective means of both changing the process of healthcare and improving its outcomes. However, current Clinical Guidelines are available in text format as long documents, which render them difficult to consult and to integrate in clinical Decision Support Systems. With this paper we present a new model for guideline interpretation, in order to facilitate de development of guideline-based Decision Support Systems and to increase the availability of Clinical Guidelines at the moment of the clinical process. This model will also provide mechanisms to comply with cases where incomplete and uncertain information is present. The development and implementation of this model will be presented in the following pages

Comparison of clinical knowledge management capabilities of commercially-available and leading internally-developed electronic health records

<p>Abstract</p> <p>Background</p> <p>We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commercially-available EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined "meaningful use" criteria.</p> <p>Methods</p> <p>We developed and fielded a 17-question survey to representatives from nine commercially available EHR vendors and four leading internally developed EHRs. The first part of the survey asked basic questions about the vendor's EHR. The second part asked specifically about the CDS-related system tools and capabilities that each vendor provides. The final section asked about clinical content.</p> <p>Results</p> <p>All of the vendors and institutions have multiple modules capable of providing clinical decision support interventions to clinicians. The majority of the systems were capable of performing almost all of the key knowledge management functions we identified.</p> <p>Conclusion</p> <p>If these well-designed commercially-available systems are coupled with the other key socio-technical concepts required for safe and effective EHR implementation and use, and organizations have access to implementable clinical knowledge, we expect that the transformation of the healthcare enterprise that so many have predicted, is achievable using commercially-available, state-of-the-art EHRs.</p

Document Automation Architectures: Updated Survey in Light of Large Language Models

This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically creating and integrating input from different sources and assembling documents conforming to defined templates. There have been reviews of commercial solutions of DA, particularly in the legal domain, but to date there has been no comprehensive review of the academic research on DA architectures and technologies. The current survey of DA reviews the academic literature and provides a clearer definition and characterization of DA and its features, identifies state-of-the-art DA architectures and technologies in academic research, and provides ideas that can lead to new research opportunities within the DA field in light of recent advances in generative AI and large language models.Comment: The current paper is the updated version of an earlier survey on document automation [Ahmadi Achachlouei et al. 2021]. Updates in the current paper are as follows: We shortened almost all sections to reduce the size of the main paper (without references) from 28 pages to 10 pages, added a review of selected papers on large language models, removed certain sections and most of diagrams. arXiv admin note: substantial text overlap with arXiv:2109.1160

The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

Guias clínicas : representação e raciocínio

Dissertação de integrado em Engenharia BiomédicaOs ambientes de cuidados de saúde são extremamente exigentes para os profissionais. Nestes ambientes há uma grande exposição a situações de tensão, que se repercutem na qualidade da prática clínica. O stress ocupacional origina situações de erro médico, de variações indesejadas na prática clínica e de medicina defensiva. As Guias Clínicas (GCs), como recomendações clínicas baseadas em investigação científica sólida, podem solucionar tais problemas, fornecendo um suporte para a prática da medicina, baseado na evidência, e preenchendo eventuais vazios de conhecimento dos profissionais de saúde. Contudo, o seu formato actual não responde às exigências de um processo clínico que obriga a tomar decisões rápidas, com segurança. A solução pode passar pela implementação de formatos informáticos de GCs, as chamadas Guias Interpretáveis por Computador (Computer-Interpretable Guidelines - CIGs), em sistemas de apoio à decisão. As abordagens actuais de CIGs concentram-se sobretudo em aspectos relacionados com a modelação de tarefas, restrições temporais à execução de guiase integrações com sistemas de informação locais. No entanto, não providenciam um tratamento de casos de Informação Imperfeita, que são comuns nos processos clínicos. Há necessidade de uma representação de guias, que combine a capacidade de modelação de tarefas das abordagens actuais de CIGs, com linguagens de programação que permitam expressar casos de Informação Imperfeita e métodos que permitam quantificá-los. Para o efeito, recolheram-se as principais características das abordagens de CIGs actuais e propôs-se um modelo, utilizando a Extensão à Programação em Lógica (EPL) e o método da Qualidade da Informação (Quality of Information - QoI). A aplicabilidade deste modelo foi estudada através de um caso de estudo com uma GC para detecção e tratamento de elevados níveis de colesterol. Concluiu-se que, embora careça de melhoramentos ao nível de um suporte para o estado do paciente e ao nível da estruturação da informação, o modelo apresenta potencial para melhorar os resultados do processo clínico.Healthcare environments are very demanding. In these environments healthcare professionals are exposed to many stressful situations that affect negatively the quality of clinical practice. Occupational stress is among the causes of medical errors, undesirable variations in clinical practice and defensive medicine. The use of Clinical Guidelines may be a solution to these issues. They are evidence based recommendations that support good clinical practice and may compensate for knowledge gaps of healthcare professionals. However, their current format does not meet the requirements of real time decision support in the clinical process. Implementing Computer-Interpretable Guidelines (CIGs) in clinical decision support systems shows promises of both changing the process of healthcare delivery and improving its outcomes. The existing CIG approaches focus mainly on task modeling, temporal constraints to the execution of guidelines and integration with local information systems. Yet, they fail to address the issue of Imperfect Information, which is common in many clinical cases. There is a need for a guideline representation, which combines the task modeling capabilities of the existing CIG approaches with programing languages that enable the expression of cases of Imperfect Information and methods to quantify them. For this purpose we collected the main features of the existing approaches and proposed a model that uses the Extension to Logic Programming (ELP) and the method of Quality of Information. The applicability of this model was studied with a guideline for detection and treatment of elevated levels of cholesterol. Although the model needs improvements in the support for the patient state and the structuring of information, it has the potential to improve clinical results