A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

OBJECTIVE: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. MATERIALS AND METHODS: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. RESULTS: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. DISCUSSION: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS. CONCLUSION: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Leveraging workflow control patterns in the domain of clinical practice guidelines.

BACKGROUND: Clinical practice guidelines (CPGs) include recommendations describing appropriate care for the management of patients with a specific clinical condition. A number of representation languages have been developed to support executable CPGs, with associated authoring/editing tools. Even with tool assistance, authoring of CPG models is a labor-intensive task. We aim at facilitating the early stages of CPG modeling task. In this context, we propose to support the authoring of CPG models based on a set of suitable procedural patterns described in an implementation-independent notation that can be then semi-automatically transformed into one of the alternative executable CPG languages. METHODS: We have started with the workflow control patterns which have been identified in the fields of workflow systems and business process management. We have analyzed the suitability of these patterns by means of a qualitative analysis of CPG texts. Following our analysis we have implemented a selection of workflow patterns in the Asbru and PROforma CPG languages. As implementation-independent notation for the description of patterns we have chosen BPMN 2.0. Finally, we have developed XSLT transformations to convert the BPMN 2.0 version of the patterns into the Asbru and PROforma languages. RESULTS: We showed that although a significant number of workflow control patterns are suitable to describe CPG procedural knowledge, not all of them are applicable in the context of CPGs due to their focus on single-patient care. Moreover, CPGs may require additional patterns not included in the set of workflow control patterns. We also showed that nearly all the CPG-suitable patterns can be conveniently implemented in the Asbru and PROforma languages. Finally, we demonstrated that individual patterns can be semi-automatically transformed from a process specification in BPMN 2.0 to executable implementations in these languages. CONCLUSIONS: We propose a pattern and transformation-based approach for the development of CPG models. Such an approach can form the basis of a valid framework for the authoring of CPG models. The identification of adequate patterns and the implementation of transformations to convert patterns from a process specification into different executable implementations are the first necessary steps for our approach.

Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility.

Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support patient recruitment in the framework of a clinical trial for colorectal cancer screening. The utilisation of archetypes not only has proved satisfactory to achieve interoperability between CDSSs and EHRs but also offers various advantages, in particular from a data model perspective. First, the VHR/data models we work with are of a high level of abstraction and can incorporate semantic descriptions. Second, archetypes can potentially deal with different EHR architectures, due to their deliberate independence of the reference model. Third, the archetype instances we obtain are valid instances of the underlying reference model, which would enable e.g. feeding back the EHR with data derived by abstraction mechanisms. Lastly, the medical and technical validity of archetype models would be assured, since in principle clinicians should be the main actors in their development.

A model-driven transformation approach for the modelling of processes in clinical practice guidelines.

Clinical Practice Guidelines (CPGs) include recommendations aimed at optimising patient care, informed by a review of the available clinical evidence. To achieve their potential benefits, CPG should be readily available at the point of care. This can be done by translating CPG recommendations into one of the languages for Computer-Interpretable Guidelines (CIGs). This is a difficult task for which the collaboration of clinical and technical staff is crucial. However, in general CIG languages are not accessible to non-technical staff. We propose to support the modelling of CPG processes (and hence the authoring of CIGs) based on a transformation, from a preliminary specification in a more accessible language into an implementation in a CIG language. In this paper, we approach this transformation following the Model-Driven Development (MDD) paradigm, in which models and transformations are key elements for software development. To demonstrate the approach, we implemented and tested an algorithm for the transformation from the BPMN language for business processes to the PROforma CIG language. This implementation uses transformations defined in the ATLAS Transformation Language. Additionally, we conducted a small experiment to assess the hypothesis that a language such as BPMN can facilitate the modelling of CPG processes by clinical and technical staff.

CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows.

BACKGROUND AND OBJECTIVE: Effective sharing and reuse of Electronic Health Records (EHR) requires technological solutions which deal with different representations and different models of data. This includes information models, domain models and, ideally, inference models, which enable clinical decision support based on a knowledge base and facts. Our goal is to develop a framework to support EHR interoperability based on transformation and reasoning services intended for clinical data and knowledge. METHODS: Our framework is based on workflows whose primary components are reusable mappings. Key features are an integrated representation, storage, and exploitation of different types of mappings for clinical data transformation purposes, as well as the support for the discovery of new workflows. The current framework supports mappings which take advantage of the best features of EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. RESULTS: We have implemented CLIN-IK-LINKS, a web-based platform that enables users to create, modify and delete mappings as well as to define and execute workflows. The platform has been applied in two use cases: semantic publishing of clinical laboratory test results; and implementation of two colorectal cancer screening protocols. Real data have been used in both use cases. CONCLUSIONS: The CLIN-IK-LINKS platform allows the composition and execution of clinical data transformation workflows to convert EHR data into EHR and/or semantic web standards. Having proved its usefulness to implement clinical data transformation applications of interest, CLIN-IK-LINKS can be regarded as a valuable contribution to improve the semantic interoperability of EHR systems.

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?

A constraint-based approach to medical guidelines and protocols.

Medical guidelines and protocols are documents aimed at improving the quality of medical care by offering support in medical decision making in the form of management recommendations based on scientific evidence. Whereas medical guidelines are intended for nation-wide use, and thus omit medical management details that may differ among hospitals, medical protocols are aimed at local use, e.g., within hospitals, and, therefore, include more detailed information. Although a medical guideline and an associated protocol concerning the management of a particular disorder are related to each other, one question is to what extent they are different. Formal methods are applied to shed light on this issue. A Dutch medical guideline regarding the treatment of breast cancer, and a Dutch protocol based on it, are taken as an example.

Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms.

Clinical Practice Guidelines (CPGs) contain recommendations intended to optimize patient care, produced based on a systematic review of evidence. In turn, Computer-Interpretable Guidelines (CIGs) are formalized versions of CPGs for use as decision-support systems. We consider the enrichment of the CIG by means of an OWL ontology that describes the clinical domain of the CIG, which could be exploited e.g. for the interoperability with the Electronic Health Record (EHR). As a first step, in this paper we describe a method to support the development of such an ontology starting from a CIG. The method uses an alignment algorithm for the automated identification of ontological terms relevant to the clinical domain of the CIG, as well as a web platform to manually review the alignments and select the appropriate ones. Finally, we present the results of the application of the method to a small corpus of CIGs.

Extraction and use of linguistic patterns for modelling medical guidelines.

OBJECTIVE: The quality of knowledge updates in evidence-based medical guidelines can be improved and the effort spent for updating can be reduced if the knowledge underlying the guideline text is explicitly modelled using the so-called linguistic guideline patterns, mappings between a text fragment and a formal representation of its corresponding medical knowledge. METHODS AND MATERIAL: Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of medical guidelines. We illustrate by examples the use of this method for generating and instantiating linguistic patterns in the text of a guideline for treatment of breast cancer, and evaluate the usefulness of these patterns in the modelling of this guideline. RESULTS: We developed a methodology for extracting and using linguistic patterns in guideline formalization, to aid the human modellers in guideline formalization and reduce the human modelling effort. Using automatic transformation rules for simple linguistic patterns, a good recall (between 72% and 80%) is obtained in selecting the procedural knowledge relevant for the guideline model, even though the precision of the guideline model generated automatically covers only between 20% and 35% of the human-generated guideline model. These results indicate the suitability of our method as a pre-processing step in medical guideline formalization. CONCLUSIONS: Modelling and authoring of medical texts can benefit from our proposed method. As pre-requisites for generating automatically a skeleton of the guideline model from the procedural part of the guideline text, to aid the human modeller, the medical terminology used by the guideline must have a good overlap with existing medical thesauri and its procedural knowledge must obey linguistic regularities that can be mapped into the control constructs of the target guideline modelling language.

Improving medical protocols by formal methods.

OBJECTIVES: During the last decade, evidence-based medicine has given rise to an increasing number of medical practice guidelines and protocols. However, the work done on developing and distributing protocols outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical protocols. Recent efforts have tried to address the problem of protocol improvement, but they are not sufficient since they rely on informal processes and notations. Our objective is to improve the quality of medical protocols. APPROACH: The solution we suggest to the problem of quality improvement of protocols consists in the utilisation of formal methods. It requires the definition of an adequate protocol representation language, the development of techniques for the formal analysis of protocols described in that language and, more importantly, the evaluation of the feasibility of the approach based on the formalisation and verification of real-life medical protocols. For the first two aspects we rely on earlier work from the fields of knowledge representation and formal methods. The third aspect, i.e. the evaluation of the use of formal methods in the quality improvement of protocols, constitutes our main objective. The steps with which we have carried out this evaluation are the following: (1) take two real-life reference protocols which cover a wide variety of protocol characteristics; (2) formalise these reference protocols; (3) check the formalisation for the verification of interesting protocol properties; and (4) determine how many errors can be uncovered in this way. RESULTS: Our main results are: a consolidated formal language to model medical practice protocols; two protocols, each both modelled and formalised; a list of properties that medical protocols should satisfy; verification proofs for these protocols and properties; and perspectives of the potentials of this approach. Our results have been evaluated by a panel of medical experts, who judged that the problems we detected in the protocols with the help of formal methods were serious and should be avoided. CONCLUSIONS: We have succeeded in demonstrating the feasibility of formal methods for improving medical protocols.

Using SNOMED CT Expression Constraints to Bridge the Gap Between Clinical Decision-Support Systems and Electronic Health Records.

Clinical decision-support systems (CDSSs) should be able to interact with the electronic health record (EHR) to obtain the patient data they require. A recent solution for the interoperability of CDSSs and EHR systems consists in the use of a mediated schema which provides a unified view of their two schemas. The use of such a mediated schema requires the definition of a mapping between this schema and the EHR one. In this paper we investigate the use of the SNOMED CT Expression Constraint Language to characterize these mappings.

A platform for exploration into chaining of web services for clinical data transformation and reasoning.

The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transformation applications that convert EHR data in proprietary format, first into clinical information models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transformation applications. The platform is built upon a number of web services dealing with transformations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transformation steps in a particular clinical domain. The platform has been used in the development of two different data transformation applications in the area of colorectal cancer.

Combining diagnosis and treatment using ASBRU.

Traditionally, diagnosis and treatment have been seen as two distinct tasks. Consequently, most approaches to computer supported health care focus on one of the two-mostly on diagnosis or rather on the interpretation of measurements which is much better understood and formalised. However, in practice diagnosis and treatment overlap and influence each other in many ways. Combinations range from repeatedly going through the diagnosis-treatment loop over a period of time to permanent monitoring of the patients' health condition as it is done in intensive care units. In this article we describe how to model these combinations using the clinical protocol-representation language ASBRU. It implements treatment steps in a hierarchy of skeletal, time-oriented plans. Diagnosis can either be described in a declarative way in the conditions, under which treatment steps are taken or it can be modelled explicitly as plans of their own right. We demonstrate our approach using examples taken from the American Association of Paediatricians' guideline for the treatment of hyperbilirubinemia in the new-born.

Protocure: supporting the development of medical protocols through formal methods.

Medical guidelines and protocols describe the optimal care for a specific group of patients and therefore, when properly applied, improve the quality of patient care. During the last decade, a large number of medical guidelines and protocols have been published. However, the work done on developing and disseminating them far outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical guidelines and protocols. An approach grounded on a formal representation, can answer these needs, as we have demonstrated in the Protocure project'. The Protocure II project will aim at integrating formal methods in the life cycle of guidelines.

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.

BACKGROUND: The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. OBJECTIVE: To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. MATERIALS AND METHODS: We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. RESULTS: We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. CONCLUSIONS: This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.

A randomized, multicenter, open-label clinical trial to assess the immunogenicity of a meningococcal C vaccine booster dose administered to children aged 14 to 18 months.

BACKGROUND: A booster meningococcal C (MenC) vaccine dose is recommended after the first year of life. The objective of this study was to assess its immunogenicity and factors that modify the immunoresponse. METHODS: An open label study in which 389 children 14 to 18 months of age, previously primed with 3 doses of a MenC vaccine conjugated with CRM197 (MenC-CRM) or with 2 doses of a MenC vaccine conjugated with tetanus toxoid (MenC-TT), were randomized to be boosted with either of these vaccines and a DTaP-IPV-Hib vaccine at the same time. Immunogenicity against MenC and Haemophilus influenzae type b was assessed before and 1 month after the booster dose. RESULTS: Before the second year booster, 44.9% of the studied children had MenC bactericidal (SBA) seroprotection rate of > or =1:8, with no differences related to the vaccine used for priming, whereas the anti Hib antibody concentration was higher in children primed with the MenC-TT (0.59; 95% CI: 0.49-0.71 vs. 0.39; 95% CI: 0.32-0.48).One month after the MenC vaccine booster 99.5% of the children had SBA > or =1:128. Children primed with MenC-TT reached higher SBA titers: 6520 (95% CI: 5359-7932) than those primed with MenC-CRM: 1903 (95% CI: 1600-2262). Children primed with MenC-CRM had SBA titers of 2061 (95% CI: 1599-2627) when boosted with MenC-TT and 1746 (95% CI: 1378-2213) when boosted with MenC-CRM. Children primed with MenC-TT had SBA titers of 6786 (95% CI: 5023-9167) and 6278 (95% CI: 4841-8144) when boosted with MenC-TT or MenC-CRM. There was no difference in the PRP antibody concentration after boosting. CONCLUSIONS: A booster MenC dose induces high SBA and anti Hib response with over 99% of children seroprotected. Children primed with a MenC-TT vaccine reached SBA titers 3.5 times higher no matter which vaccine was used for boosting.

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