Architecture of a consent management suite and integration into IHE-based regional health information networks

<p>Abstract</p> <p>Background</p> <p>The University Hospital Heidelberg is implementing a Regional Health Information Network (RHIN) in the Rhine-Neckar-Region in order to establish a shared-care environment, which is based on established Health IT standards and in particular Integrating the Healthcare Enterprise (IHE). Similar to all other Electronic Health Record (EHR) and Personal Health Record (PHR) approaches the chosen Personal Electronic Health Record (PEHR) architecture relies on the patient's consent in order to share documents and medical data with other care delivery organizations, with the additional requirement that the German legislation explicitly demands a patients' opt-in and does not allow opt-out solutions. This creates two issues: firstly the current IHE consent profile does not address this approach properly and secondly none of the employed intra- and inter-institutional information systems, like almost all systems on the market, offers consent management solutions at all. Hence, the objective of our work is to develop and introduce an extensible architecture for creating, managing and querying patient consents in an IHE-based environment.</p> <p>Methods</p> <p>Based on the features offered by the IHE profile Basic Patient Privacy Consent (BPPC) and literature, the functionalities and components to meet the requirements of a centralized opt-in consent management solution compliant with German legislation have been analyzed. Two services have been developed and integrated into the Heidelberg PEHR.</p> <p>Results</p> <p>The standard-based Consent Management Suite consists of two services. The Consent Management Service is able to receive and store consent documents. It can receive queries concerning a dedicated patient consent, process it and return an answer. It represents a centralized policy enforcement point. The Consent Creator Service allows patients to create their consents electronically. Interfaces to a Master Patient Index (MPI) and a provider index allow to dynamically generate XACML-based policies which are stored in a CDA document to be transferred to the first service. Three workflows have to be considered to integrate the suite into the PEHR: recording the consent, publishing documents and viewing documents.</p> <p>Conclusions</p> <p>Our approach solves the consent issue when using IHE profiles for regional health information networks. It is highly interoperable due to the use of international standards and can hence be used in any other region to leverage consent issues and substantially promote the use of IHE for regional health information networks in general.</p

Using the ResearchEHR platform to facilitate the practical application of the EHR standards

Possibly the most important requirement to support co-operative work among health professionals and institutions is the ability of sharing EHRs in a meaningful way, and it is widely acknowledged that standardization of data and concepts is a prerequisite to achieve semantic interoperability in any domain. Different international organizations are working on the definition of EHR architectures but the lack of tools that implement them hinders their broad adoption. In this paper we present ResearchEHR, a software platform whose objective is to facilitate the practical application of EHR standards as a way of reaching the desired semantic interoperability. This platform is not only suitable for developing new systems but also for increasing the standardization of existing ones. The work reported here describes how the platform allows for the edition, validation, and search of archetypes, converts legacy data into normalized, archetypes extracts, is able to generate applications from archetypes and finally, transforms archetypes and data extracts into other EHR standards. We also include in this paper how ResearchEHR has made possible the application of the CEN/ISO 13606 standard in a real environment and the lessons learnt with this experience. © 2011 Elsevier Inc..This work has been partially supported by the Spanish Ministry of Science and Innovation under Grants TIN2010-21388-C02-01 and TIN2010-21388-C02-02, and by the Health Institute Carlos in through the RETICS Combiomed, RD07/0067/2001. Our most sincere thanks to the Hospital of Fuenlabrada in Madrid, including its Medical Director Pablo Serrano together with Marta Terron and Luis Lechuga for their support and work during the development of the medications reconciliation project.Maldonado Segura, JA.; Martínez Costa, C.; Moner Cano, D.; Menárguez-Tortosa, M.; Boscá Tomás, D.; Miñarro Giménez, JA.; Fernández-Breis, JT.... (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics. 45(4):746-762. doi:10.1016/j.jbi.2011.11.004S74676245

Archetype development and governance methodologies for the electronic health record

[ES] La interoperabilidad semántica de la información sanitaria es un requisito imprescindible para la sostenibilidad de la atención sanitaria, y es fundamental para afrontar los nuevos retos sanitarios de un mundo globalizado. Esta tesis aporta nuevas metodologías para abordar algunos de los aspectos fundamentales de la interoperabilidad semántica, específicamente aquellos relacionados con la definición y gobernanza de modelos de información clínica expresados en forma de arquetipo. Las aportaciones de la tesis son: - Estudio de las metodologías de modelado existentes de componentes de interoperabilidad semántica que influirán en la definición de una metodología de modelado de arquetipos. - Análisis comparativo de los sistemas e iniciativas existentes para la gobernanza de modelos de información clínica. - Una propuesta de Metodología de Modelado de Arquetipos unificada que formalice las fases de desarrollo del arquetipo, los participantes requeridos y las buenas prácticas a seguir. - Identificación y definición de principios y características de gobernanza de arquetipos. - Diseño y desarrollo de herramientas que brinden soporte al modelado y la gobernanza de arquetipos. Las aportaciones de esta tesis se han puesto en práctica en múltiples proyectos y experiencias de desarrollo. Estas experiencias varían desde un proyecto local dentro de una sola organización que requirió la reutilización de datos clínicos basados en principios de interoperabilidad semántica, hasta el desarrollo de proyectos de historia clínica electrónica de alcance nacional.[CA] La interoperabilitat semàntica de la informació sanitària és un requisit imprescindible per a la sostenibilitat de l'atenció sanitària, i és fonamental per a afrontar els nous reptes sanitaris d'un món globalitzat. Aquesta tesi aporta noves metodologies per a abordar alguns dels aspectes fonamentals de la interoperabilitat semàntica, específicament aquells relacionats amb la definició i govern de models d'informació clínica expressats en forma d'arquetip. Les aportacions de la tesi són: - Estudi de les metodologies de modelatge existents de components d'interoperabilitat semàntica que influiran en la definició d'una metodologia de modelatge d'arquetips. - Anàlisi comparativa dels sistemes i iniciatives existents per al govern de models d'informació clínica. - Una proposta de Metodologia de Modelatge d'Arquetips unificada que formalitza les fases de desenvolupament de l'arquetip, els participants requerits i les bones pràctiques a seguir. - Identificació i definició de principis i característiques de govern d'arquetips. - Disseny i desenvolupament d'eines que brinden suport al modelatge i al govern d'arquetips. Les aportacions d'aquesta tesi s'han posat en pràctica en múltiples projectes i experiències de desenvolupament. Aquestes experiències varien des d'un projecte local dins d'una sola organització que va requerir la reutilització de dades clíniques basades en principis d'interoperabilitat semàntica, fins al desenvolupament de projectes d'història clínica electrònica d'abast nacional.[EN] Semantic interoperability of health information is an essential requirement for the sustainability of healthcare, and it is essential to face the new health challenges of a globalized world. This thesis provides new methodologies to tackle some of the fundamental aspects of semantic interoperability, specifically those aspects related to the definition and governance of clinical information models expressed in the form of archetypes. The contributions of the thesis are: - Study of existing modeling methodologies of semantic interoperability components that will influence in the definition of an archetype modeling methodology. - Comparative analysis of existing clinical information model governance systems and initiatives. - A proposal of a unified Archetype Modeling Methodology that formalizes the phases of archetype development, the required participants, and the good practices to be followed. - Identification and definition of archetype governance principles and characteristics. - Design and development of tools that provide support to archetype modeling and governance. The contributions of this thesis have been put into practice in multiple projects and development experiences. These experiences vary from a local project inside a single organization that required a reuse on clinical data based on semantic interoperability principles, to the development of national electronic health record projects.This thesis was partially funded by the Ministerio de Economía y Competitividad, ayudas para contratos para la formación de doctores en empresas “Doctorados Industriales”, grant DI-14-06564 and by the Agencia Valenciana de la Innovación, ayudas del Programa de Promoción del Talento – Doctorados empresariales (INNODOCTO), grant INNTA3/2020/12.Moner Cano, D. (2021). Archetype development and governance methodologies for the electronic health record [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16491

Data mining framework

The purpose of this document is building a framework for working with clinical data. Vast amounts of clinical records, stored in health repositories, contain information that can be used to improve the quality of health care. However, the information generated from these records depends vastly on the manner, in which the data is arranged. A number of factors need to be considered, before information can be extracted from the patient records. This document deals with the preparation of a framework for the data, before it can be mined.;One of the issues to deal with is information about the patient contained in the clinical records that can be used for identification purposes. A means to create anonymous records is discussed in this document. Once the records have been de-identified, they can be used for data mining. In addition to storing patient records, the document also discusses the possibility of \u27abstracting\u27 information from these documents and storing them in the repository. Information generated from the combination of patient records and abstracted information, could be used to improve the quality of health care.;This document also discusses the possibility of creating a means to query information from the data repository. A prototype application, which provides all these facilities in a form that can be accessed from any remote location, is discussed. In addition, the prospect of using Clinical Document Architecture format to store the clinical records is explored

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Towards a Learning Health System: a SOA based platform for data re-use in chronic infectious diseases

Abstract Information and Communication Technology (ICT) tools can efficiently support clinical research by providing means to collect automatically huge amount of data useful for the management of clinical trials conduction. Clinical trials are indispensable tools for Evidence-Based Medicine and represent the most prevalent clinical research activity. Clinical trials cover only a restricted part of the population that respond to particular and strictly controlled requirements, offering a partial view of the overall patients\u2019 status. For instance, it is not feasible to consider patients with comorbidities employing only one kind of clinical trial. Instead, a system that have a comprehensive access to all the clinical data of a patient would have a global view of all the variables involved, reflecting real-world patients\u2019 experience. The Learning Health System is a system with a broader vision, in which data from various sources are assembled, analyzed by various means and then interpreted. The Institute of Medicine (IOM) provides this definition: \u201cIn a Learning Health System, progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care\u201d. The final goal of my project is the realization of a platform inspired by the idea of Learning Health System, which will be able to re-use data of different nature coming from widespread health facilities, providing systematic means to learn from clinicians\u2019 experience to improve both the efficiency and the quality of healthcare delivery. The first approach is the development of a SOA-based architecture to enable data collection from sparse facilities into a single repository, to allow medical institutions to share information without an increase in costs and without the direct involvement of users. Through this architecture, every single institution would potentially be able to participate and contribute to the realization of a Learning Health System, that can be seen as a closed cycle constituted by a sequential process of transforming patient-care data into knowledge and then applying this knowledge to clinical practice. Knowledge, that can be inferred by re-using the collected data to perform multi-site, practice-based clinical trials, could be concretely applied to clinical practice through Clinical Decision Support Systems (CDSS), which are instruments that aim to help physicians in making more informed decisions. With 4 this objective, the platform developed not only supports clinical trials execution, but also enables data sharing with external research databases to participate in wider clinical trials also at a national level without effort. The results of these studies, integrated with existing guidelines, can be seen as the knowledge base of a decision support system. Once designed and developed, the adoption of this system for chronical infective diseases management at a regional level helped in unifying data all over the Ligurian territory and actively monitor the situation of specific diseases (like HIV, HCV and HBV) for which the concept of retention in care assumes great importance. The use of dedicated standards is essential to grant the necessary level of interoperability among the structures involved and to allow future extensions to other fields. A sample scenario was created to support antiretroviral drugs prescription in the Ligurian HIV Network setting. It was thoroughly tested by physicians and its positive impact on clinical care was measured in terms of improvements in patients\u2019 quality of life, prescription appropriateness and therapy adherence. The benefits expected from the employment of the system developed were verified. Student\u2019s T test was used to establish if significant differences were registered between data collected before and after the introduction of the system developed. The results were really acceptable with the minimum p value in the order of 10 125 and the maximum in the order of 10 123. It is reasonable to assess that the improvements registered in the three analysis considered are ascribable to this system introduction and not to other factors, because no significant differences were found in the period before its release. Speed is a focal point in a system that provides decision support and it is highly recognized the importance of velocity optimization. Therefore, timings were monitored to evaluate the responsiveness of the system developed. Extremely acceptable results were obtained, with the waiting times of the order of 10 121 seconds. The importance of the network developed has been widely recognized by the medical staff involved, as it is also assessed by a questionnaire they compiled to evaluate their level of satisfaction

Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of the American Medical Informatics Association following peer review. The version of record is available online at: http://dx.doi.org/10.1093/jamia/ocv008[EN] [Objective] This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. [Material and Methods] Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. [Results] Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. [Discussion] Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. [Conclusion] Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.This research has been partially funded by the Instituto de Salud Carlos III (Platform for Innovation in Medical Technologies and Health), grant PT13/0006/0036 and the Spanish Ministry of Economy and Competitiveness, grants TIN2010-21388-C02-01 and PTQ-12-05620.Moreno-Conde, A.; Moner Cano, D.; Da Cruz, WD.; Santos, MR.; Maldonado Segura, JA.; Robles Viejo, M.; Kalra, D. (2015). 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DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care

DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care