Applying openEHR's Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study

Business Process Model and Notation and openEHR Task Planning for Clinical Pathway Standards in Infections: Critical Analysis

Background

Clinical pathways (CPs) are usually expressed by means of workflow formalisms, providing health care personnel with an easy-to-understand, high-level conceptual model of medical steps in specific patient conditions, thereby improving overall health care process quality in clinical practice. From a standardized perspective, the business process model and notation (BPMN), a widely spread general-purpose process formalism, has been used for conceptual modeling in clinical domains, mainly because of its easy-to-use graphical notation, facilitating the common understanding and communication of the parties involved in health care. However, BPMN is not particularly oriented toward the peculiarities of complex clinical processes such as infection diagnosis and treatment, in which time plays a critical role, which is why much of the BPMN clinical-oriented research has revolved around how to extend the standard to address these special needs. The shift from an agnostic, general-purpose BPMN notation to a natively clinical-oriented notation such as openEHR Task Planning (TP) could constitute a major step toward clinical process improvement, enhancing the representation of CPs for infection treatment and other complex scenarios.

Objective

Our work aimed to analyze the suitability of a clinical-oriented formalism (TP) to successfully represent typical process patterns in infection treatment, identifying domain-specific improvements to the standard that could help enhance its modeling capabilities, thereby promoting the widespread adoption of CPs to improve medical practice and overall health care quality.

Methods

Our methodology consisted of 4 major steps: identification of key features of infection CPs through literature review, clinical guideline analysis, and BPMN extensions; analysis of the presence of key features in TP; modeling of relevant process patterns of catheter-related bloodstream infection as a case study; and analysis and proposal of extensions in view of the results.

Results

We were able to easily represent the same logic applied in the extended BPMN-based process models in our case study using out-of-the-box standard TP primitives. However, we identified possible improvements to the current version of TP to allow for simpler conceptual models of infection CPs and possibly of other complex clinical scenarios.

Conclusions

Our study showed that the clinical-oriented TP specification is able to successfully represent the most complex catheter-related bloodstream infection process patterns depicted in our case study and identified possible extensions that can help increase its adequacy for modeling infection CPs and possibly other complex clinical conditions.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

Standardised data reporting from pre-hospital advanced airway management - a nominal group technique update of the Utstein-style airway template

Background

Pre-hospital advanced airway management with oxygenation and ventilation may be vital for managing critically ill or injured patients. To improve pre-hospital critical care and develop evidence-based guidelines, research on standardised high-quality data is important. We aimed to identify which airway data were most important to report today and to revise and update a previously reported Utstein-style airway management dataset.

Methods

We recruited sixteen international experts in pre-hospital airway management from Australia, United States of America, and Europe. We used a five-step modified nominal group technique to revise the dataset, and clinical study results from the original template were used to guide the process.

Results

The experts agreed on a key dataset of thirty-two operational variables with six additional system variables, organised in time, patient, airway management and system sections. Of the original variables, one remained unchanged, while nineteen were modified in name, category, definition or value. Sixteen new variables were added. The updated dataset covers risk factors for difficult intubation, checklist and standard operating procedure use, pre-oxygenation strategies, the use of drugs in airway management, airway currency training, developments in airway devices, airway management strategies, and patient safety issues not previously described.

Conclusions

Using a modified nominal group technique with international airway management experts, we have updated the Utstein-style dataset to report standardised data from pre-hospital advanced airway management. The dataset enables future airway management research to produce comparable high-quality data across emergency medical systems. We believe this approach will promote research and improve treatment strategies and outcomes for patients receiving pre-hospital advanced airway management.

Trial registration

The Regional Committee for Medical and Health Research Ethics in Western Norway exempted this study from ethical review (Reference: REK-Vest/2017/260).

Electronic supplementary material

The online version of this article (10.1186/s13049-018-0509-y) contains supplementary material, which is available to authorized users.

An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR

BACKGROUND

Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well-established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS.

OBJECTIVE

We aim at developing and evaluating an openEHR based approach to achieve interoperability in CDSS by designing and implementing an exemplary system for automated systemic inflammatory response syndrome (SIRS) detection in pediatric intensive care.

METHODS

We designed an interoperable concept, which enables an easy integration of the CDSS across different institutions, by using openEHR Archetypes, terminology bindings and the Archetype Query Language (AQL). The practicability of the approach was tested by (1) implementing a prototype, which is based on an openEHR based data repository of the Hannover Medical School (HaMSTR), and (2) conducting a first pilot study.

RESULTS

We successfully designed and implemented a CDSS with interoperable knowledge bases and interfaces by reusing internationally agreed-upon Archetypes, incorporating LOINC terminology and creating AQL queries, which allowed retrieving dynamic facts in a standardized and unambiguous form. The technical capabilities of the system were evaluated by testing the prototype on 16 randomly selected patients with 129 days of stay, and comparing the results with the assessment of clinical experts (leading to a sensitivity of 1.00, a specificity of 0.94 and a Cohen's kappa of 0.92).

CONCLUSIONS

We found the use of openEHR Archetypes and AQL a feasible approach to bridge the interoperability gap between local infrastructures and CDSS. The designed concept was successfully transferred into a clinically evaluated openEHR based CDSS. To the authors' knowledge, this is the first openEHR based CDSS, which is technically reliable and capable in a real context, and facilitates clinical decision-support for a complex task. Further activities will comprise enrichments of the knowledge base, the reasoning processes and cross-institutional evaluations.