7 research outputs found
Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes
The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides
personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a
mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients
to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus
follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system
Validating archetypes for the Multiple Sclerosis Functional Composite
Background Numerous information models for electronic health records, such as
openEHR archetypes are available. The quality of such clinical models is
important to guarantee standardised semantics and to facilitate their
interoperability. However, validation aspects are not regarded sufficiently
yet. The objective of this report is to investigate the feasibility of
archetype development and its community-based validation process, presuming
that this review process is a practical way to ensure high-quality information
models amending the formal reference model definitions. Methods A standard
archetype development approach was applied on a case set of three clinical
tests for multiple sclerosis assessment: After an analysis of the tests, the
obtained data elements were organised and structured. The appropriate
archetype class was selected and the data elements were implemented in an
iterative refinement process. Clinical and information modelling experts
validated the models in a structured review process. Results Four new
archetypes were developed and publicly deployed in the openEHR Clinical
Knowledge Manager, an online platform provided by the openEHR Foundation.
Afterwards, these four archetypes were validated by domain experts in a team
review. The review was a formalised process, organised in the Clinical
Knowledge Manager. Both, development and review process turned out to be time-
consuming tasks, mostly due to difficult selection processes between
alternative modelling approaches. The archetype review was a straightforward
team process with the goal to validate archetypes pragmatically. Conclusions
The quality of medical information models is crucial to guarantee standardised
semantic representation in order to improve interoperability. The validation
process is a practical way to better harmonise models that diverge due to
necessary flexibility left open by the underlying formal reference model
definitions. This case study provides evidence that both community- and tool-
enabled review processes, structured in the Clinical Knowledge Manager, ensure
archetype quality. It offers a pragmatic but feasible way to reduce variation
in the representation of clinical information models towards a more unified
and interoperable model
DETAILED CLINICAL MODELS AND THEIR RELATION WITH ELECTRONIC HEALTH RECORDS
Tesis por compendio[EN] Healthcare domain produces and consumes big quantities of people's health data. Although data exchange is the norm rather than the exception, being able to access to all patient data is still far from achieved. Current developments such as personal health records will introduce even more data and complexity to the Electronic Health Records (EHR). Achieving semantic interoperability is one of the biggest challenges to overcome in order to benefit from all the information contained in the distributed EHR. This requires that the semantics of the information can be understood by all involved parties. It has been stablished that three layers are needed to achieve semantic interoperability: Reference models, clinical models (archetypes), and clinical terminologies.
As seen in the literature, information models (reference models and clinical models) are lacking methodologies and tools to improve EHR systems and to develop new systems that can be semantically interoperable. The purpose of this thesis is to provide methodologies and tools for advancing the use of archetypes in three different scenarios:
- Archetype definition over specifications with no dual model architecture native support. Any EHR architecture that directly or indirectly has the notion of detailed clinical models (such as HL7 CDA templates) can be potentially used as a reference model for archetype definition. This allows transforming single-model architectures (which contain only a reference model) into dual-model architectures (reference model with archetypes). A set of methodologies and tools has been developed to support the definition of archetypes from multiple reference models.
- Data transformation. A complete methodology and tools are proposed to deal with the transformation of legacy data into XML documents compliant with the archetype and the underlying reference model. If the reference model is a standard then the transformation is a standardization process. The methodologies and tools allow both the transformation of legacy data and the transformation of data between different EHR standards.
- Automatic generation of implementation guides and reference materials from archetypes. A methodology for the automatic generation of a set of reference materials is provided. These materials are useful for the development and use of EHR systems. These reference materials include data validators, example instances, implementation guides, human-readable formal rules, sample forms, mindmaps, etc. These reference materials can be combined and organized in different ways to adapt to different types of users (clinical or information technology staff). This way, users can include the detailed clinical model in their organization workflow and cooperate in the model definition.
These methodologies and tools put clinical models as a key part of the system. The set of presented methodologies and tools ease the achievement of semantic interoperability by providing means for the semantic description, normalization, and validation of existing and new systems.[ES] El sector sanitario produce y consume una gran cantidad de datos sobre la salud de las personas. La necesidad de intercambiar esta informaciĂłn es una norma más que una excepciĂłn, aunque este objetivo está lejos de ser alcanzado. Actualmente estamos viviendo avances como la medicina personalizada que incrementarán aĂşn más el tamaño y complejidad de la Historia ClĂnica ElectrĂłnica (HCE). La consecuciĂłn de altos grados de interoperabilidad semántica es uno de los principales retos para aprovechar al máximo toda la informaciĂłn contenida en las HCEs. Esto a su vez requiere una representaciĂłn fiel de la informaciĂłn de tal forma que asegure la consistencia de su significado entre todos los agentes involucrados. Actualmente está reconocido que para la representaciĂłn del significado clĂnico necesitamos tres tipos de artefactos: modelos de referencia, modelos clĂnicos (arquetipos) y terminologĂas.
En el caso concreto de los modelos de informaciĂłn (modelos de referencia y modelos clĂnicos) se observa en la literatura una falta de metodologĂas y herramientas que faciliten su uso tanto para la mejora de sistemas de HCE ya existentes como en el desarrollo de nuevos sistemas con altos niveles de interoperabilidad semántica. Esta tesis tiene como propĂłsito proporcionar metodologĂas y herramientas para el uso avanzado de arquetipos en tres escenarios diferentes:
- DefiniciĂłn de arquetipos sobre especificaciones sin soporte nativo al modelo dual. Cualquier arquitectura de HCE que posea directa o indirectamente la nociĂłn de modelos clĂnicos detallados (por ejemplo, las plantillas en HL7 CDA) puede ser potencialmente usada como modelo de referencia para la definiciĂłn de arquetipos. Con esto se consigue transformar arquitecturas de HCE de modelo Ăşnico (solo con modelo de referencia) en arquitecturas de doble modelo (modelo de referencia + arquetipos). Se han desarrollado metodologĂas y herramientas que faciliten a los editores de arquetipos el soporte a mĂşltiples modelos de referencia.
- TransformaciĂłn de datos. Se propone una metodologĂa y herramientas para la transformaciĂłn de datos ya existentes a documentos XML conformes con los arquetipos y el modelo de referencia subyacente. Si el modelo de referencia es un estándar entonces la transformaciĂłn será un proceso de estandarizaciĂłn de datos. La metodologĂa y herramientas permiten tanto la transformaciĂłn de datos no estandarizados como la transformaciĂłn de datos entre diferentes estándares.
- GeneraciĂłn automática de guĂas de implementaciĂłn y artefactos procesables a partir de arquetipos. Se aporta una metodologĂa para la generaciĂłn automática de un conjunto de materiales de referencia de utilidad en el desarrollo y uso de sistemas de HCE, concretamente validadores de datos, instancias de ejemplo, guĂas de implementaciĂłn , reglas formales legibles por humanos, formularios de ejemplo, mindmaps, etc. Estos materiales pueden ser combinados y organizados de diferentes modos para facilitar que los diferentes tipos de usuarios (clĂnicos, tĂ©cnicos) puedan incluir los modelos clĂnicos detallados en el flujo de trabajo de su sistema y colaborar en su definiciĂłn.
Estas metodologĂas y herramientas ponen los modelos clĂnicos como una parte clave en el sistema. El conjunto de las metodologĂas y herramientas presentadas facilitan la consecuciĂłn de la interoperabilidad semántica al proveer medios para la descripciĂłn semántica, normalizaciĂłn y validaciĂłn tanto de sistemas nuevos como ya existentes.[CA] El sector sanitari produeix i consumeix una gran quantitat de dades sobre la salut de les persones. La necessitat d'intercanviar aquesta informaciĂł Ă©s una norma mĂ©s que una excepciĂł, encara que aquest objectiu estĂ lluny de ser aconseguit. Actualment estem vivint avanços com la medicina personalitzada que incrementaran encara mĂ©s la grandĂ ria i complexitat de la Història ClĂnica Electrònica (HCE). La consecuciĂł d'alts graus d'interoperabilitat semĂ ntica Ă©s un dels principals reptes per a aprofitar al mĂ xim tota la informaciĂł continguda en les HCEs. Açò, per la seua banda, requereix una representaciĂł fidel de la informaciĂł de tal forma que assegure la consistència del seu significat entre tots els agents involucrats. Actualment estĂ reconegut que per a la representaciĂł del significat clĂnic necessitem tres tipus d'artefactes: models de referència, models clĂnics (arquetips) i terminologies.
En el cas concret dels models d'informaciĂł (models de referència i models clĂnics) s'observa en la literatura una mancança de metodologies i eines que en faciliten l'Ăşs tant per a la millora de sistemes de HCE ja existents com per al desenvolupament de nous sistemes amb alts nivells d'interoperabilitat semĂ ntica. Aquesta tesi tĂ© com a propòsit proporcionar metodologies i eines per a l'Ăşs avançat d'arquetips en tres escenaris diferents:
- DefiniciĂł d'arquetips sobre especificacions sense suport natiu al model dual. Qualsevol arquitectura de HCE que posseĂŻsca directa o indirectament la nociĂł de models clĂnics detallats (per exemple, les plantilles en HL7 CDA) pot ser potencialment usada com a model de referència per a la definiciĂł d'arquetips. Amb açò s'aconsegueix transformar arquitectures de HCE de model Ăşnic (solament amb model de referència) en arquitectures de doble model (model de referència + arquetips). S'han desenvolupat metodologies i eines que faciliten als editors d'arquetips el suport a mĂşltiples models de referència.
- Transformació de dades. Es proposa una metodologia i eines per a la transformació de dades ja existents a documents XML conformes amb els arquetips i el model de referència subjacent. Si el model de referència és un està ndard llavors la transformació serà un procés d'estandardització de dades. La metodologia i eines permeten tant la transformació de dades no estandarditzades com la transformació de dades entre diferents està ndards.
- GeneraciĂł automĂ tica de guies d'implementaciĂł i artefactes processables a partir d'arquetips. S'hi inclou una metodologia per a la generaciĂł automĂ tica d'un conjunt de materials de referència d'utilitat en el desenvolupament i Ăşs de sistemes de HCE, concretament validadors de dades, instĂ ncies d'exemple, guies d'implementaciĂł, regles formals llegibles per humans, formularis d'exemple, mapes mentals, etc. Aquests materials poden ser combinats i organitzats de diferents maneres per a facilitar que els diferents tipus d'usuaris (clĂnics, tècnics) puguen incloure els models clĂnics detallats en el flux de treball del seu sistema i col·laborar en la seua definiciĂł.
Aquestes metodologies i eines posen els models clĂnics com una part clau del sistemes. El conjunt de les metodologies i eines presentades faciliten la consecuciĂł de la interoperabilitat semĂ ntica en proveir mitjans per a la seua descripciĂł semĂ ntica, normalitzaciĂł i validaciĂł tant de sistemes nous com ja existents.Boscá Tomás, D. (2016). DETAILED CLINICAL MODELS AND THEIR RELATION WITH ELECTRONIC HEALTH RECORDS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62174TESISCompendi
An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks
Healthcare informatics still lacks wide-scale adoption of intelligent decision
support methods, despite continuous increases in computing power and
methodological advances in scalable computation and machine learning, over
recent decades. The potential has long been recognised, as evidenced in the
literature of the domain, which is extensively reviewed.
The thesis identifies and explores key barriers to adoption of clinical decision
support, through computational experiments encompassing a number of technical
platforms. Building on previous research, it implements and tests a novel platform
architecture capable of processing and reasoning with clinical data. The key
components of this platform are the now widely implemented openEHR electronic
health record specifications and Bayesian Belief Networks.
Substantial software implementations are used to explore the integration of
these components, guided and supplemented by input from clinician experts and
using clinical data models derived in hospital settings at Moorfields Eye Hospital.
Data quality and quantity issues are highlighted. Insights thus gained are used to
design and build a novel graph-based representation and processing model for the
clinical data, based on the openEHR specifications. The approach can be
implemented using diverse modern database and platform technologies.
Computational experiments with the platform, using data from two clinical
domains – a preliminary study with published thyroid metabolism data and a
substantial study of cataract surgery – explore fundamental barriers that must be
overcome in intelligent healthcare systems developments for clinical settings. These
have often been neglected, or misunderstood as implementation procedures of
secondary importance. The results confirm that the methods developed have the
potential to overcome a number of these barriers.
The findings lead to proposals for improvements to the openEHR
specifications, in the context of machine learning applications, and in particular for
integrating them with Bayesian Networks. The thesis concludes with a roadmap for
future research, building on progress and findings to date
Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record
[EN] Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.This study was partially funded by the European Commission 7th Framework Program, grant #287811. 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