Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

Clinical Decision Support at Intermountain Healthcare

book chapterBiomedical Informatic

Development of Decision Support Systems

journal articleBiomedical Informatic

Modelling clinical goals: a corpus of examples and a tentative ontology

Knowledge of clinical goals and the means to achieve them are either not represented in most current guideline representation systems or are encoded procedurally (e.g. as clinical algorithms, condition-action rules). There would be a number of major benefits if guideline enactment systems could reason explicitly about clinical objectives (e.g. whether a goal has been successfully achieved or not, whether it is consistent with prevailing conditions, or how the system should adapt to circumstances where a recommended action has failed to achieve the intended result). Our own guideline specification language, PROforma, includes a simple goal construct to address this need, but the interpretation is unsatisfactory in current enactment engines, and goals have yet to be included in the language semantics. This paper discusses some of the challenges involved in developing an explicit, declarative formalism for goals. As part of this, we report on a study we have undertaken which has identified over 200 goals in the routine management of breast cancer, and outline a tentative formal structure for this corpus

Computer support for protocol-based treatment of cancer

Cancer treatment is often carried out within protocol-based clinical trials. An oncology clinic may take part in many trials each of which requires data to be collected for monitoring efficacy and toxicity of treatment. Subsequently, this data is analysed statistically to evaluate clinical objectives of the trial. To be scientifically valid, such analysis must be based on data that is both complete and correct. This is one motivating factor for introducing computer support for trial management. Further motivation is provided by concern that treatment is consistent with the protocol and the well-being of the patient. The complexity of many protocols, the life-threatening nature of cancer and the toxicity of treatment side-effects emphasise the safety-critical nature of oncology. The OaSiS system provides decision support for the protocol-based treatment of cancer patients with emphasis on the safety aspects of the advice it gives. It offers a highly graphical interface, employs integrity constraint checking techniques from logic databases to monitor compliance with a protocol and is implemented in PROLOG. The paper describes the main features of OaSiS and indicates work in progress and planned. 1

Characteristics and impact of interventions to support healthcare providers’ compliance with guideline recommendations for breast cancer: a systematic literature review

BackgroundBreast cancer clinical practice guidelines (CPGs) offer evidence-based recommendations to improve quality of healthcare for patients. Suboptimal compliance with breast cancer guideline recommendations remains frequent, and has been associated with a decreased survival. The aim of this systematic review was to characterize and determine the impact of available interventions to support healthcare providers' compliance with CPGs recommendations in breast cancer healthcare.MethodsWe searched for systematic reviews and primary studies in PubMed and Embase (from inception to May 2021). We included experimental and observational studies reporting on the use of interventions to support compliance with breast cancer CPGs. Eligibility assessment, data extraction and critical appraisal was conducted by one reviewer, and cross-checked by a second reviewer. Using the same approach, we synthesized the characteristics and the effects of the interventions by type of intervention (according to the EPOC taxonomy), and applied the GRADE framework to assess the certainty of evidence.ResultsWe identified 35 primary studies reporting on 24 different interventions. Most frequently described interventions consisted in computerized decision support systems (12 studies); educational interventions (seven), audit and feedback (two), and multifaceted interventions (nine). There is low quality evidence that educational interventions targeted to healthcare professionals may improve compliance with recommendations concerning breast cancer screening, diagnosis and treatment. There is moderate quality evidence that reminder systems for healthcare professionals improve compliance with recommendations concerning breast cancer screening. There is low quality evidence that multifaceted interventions may improve compliance with recommendations concerning breast cancer screening. The effectiveness of the remaining types of interventions identified have not been evaluated with appropriate study designs for such purpose. There is very limited data on the costs of implementing these interventions.ConclusionsDifferent types of interventions to support compliance with breast cancer CPGs recommendations are available, and most of them show positive effects. More robust trials are needed to strengthen the available evidence base concerning their efficacy. Gathering data on the costs of implementing the proposed interventions is needed to inform decisions about their widespread implementation

Doctor of Philosophy

dissertationThe task of comparing and evaluating the performance of different computer-based clinical protocols is difficult and expensive to accomplish. This dissertation explores methods to compare and evaluate computer-based insulin infusion protocols based on an in silico analytical framework iteratively developed for this study, using data from the intensive care unit (ICU). In Methods for Aim 1, we used a pairwise comparative technique to evaluate two computer-based insulin infusion protocols. Our result showed that the pairwise method can rapidly identify a promising computer-based clinical protocol but with limitations. In Methods for Aim 2, we used a ranking strategy to evaluate six computer-based insulin infusion protocols. The ranking method enabled us to overcome a key limitation in Methods for Aim 1, making it possible to compare multiple computer-based clinical protocols simultaneously. In Methods for Aim 3, we developed a more comprehensive in silico method based on multiple-criteria decision analysis that included user-defined performance evaluation criteria examining different facets of the computer-based insulin infusion protocols. The in silico method appears to be an efficient way for identifying promising computer-based clinical protocols suitable for clinical evaluation. We discuss the advantages and disadvantages for each of the presented methods. We also discuss future research work and the generalizability of the framework to other potential clinical areas

Healthcare Process Support: Achievements, Challenges, Current Research

Healthcare organizations are facing the challenge of delivering high-quality services to their patients at affordable costs. To tackle this challenge, the Medical Informatics community targets at formalisms for developing decision-support systems (DSSs) based on clinical guidelines. At the same time, business process management (BPM) enables IT support for healthcare processes, e.g., based on workflow technology. By integrating aspects from these two fields, promising perspectives for achieving better healthcare process support arise. The perspectives and limitations of IT support for healthcare processes provided the focus of three Workshops on Process-oriented Information Systems (ProHealth). These were held in conjunction with the International Conference on Business Process Management in 2007-2009. The ProHealth workshops provided a forum wherein challenges, paradigms, and tools for optimized process support in healthcare were debated. Following the success of these workshops, this special issue on process support in healthcare provides extended papers by research groups who contributed multiple times to the ProHealth workshop series. These works address issues pertaining to healthcare process modeling, process-aware healthcare information system, workflow management in healthcare, IT support for guideline implementation and medical decision support, flexibility in healthcare processes, process interoperability in healthcare and healthcare standards, clinical semantics of healthcare processes, healthcare process patterns, best practices for designing healthcare processes, and healthcare process validation, verification, and evaluation