6 research outputs found
A sensitive data access model in support of learning health systems
Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR
Comparative study of healthcare messaging standards for interoperability in ehealth systems
Advances in the information and communication technology have created the field of "health informatics," which amalgamates healthcare, information technology and business. The use of information systems in healthcare organisations dates back to 1960s, however the use of technology for healthcare records, referred to as Electronic Medical Records (EMR), management has surged since 1990’s (Net-Health, 2017) due to advancements the internet and web technologies. Electronic Medical Records (EMR) and sometimes referred to as Personal Health Record (PHR) contains the patient’s medical history, allergy information, immunisation status, medication, radiology images and other medically related billing information that is relevant. There are a number of benefits for healthcare industry when sharing these data recorded in EMR and PHR systems between medical institutions (AbuKhousa et al., 2012). These benefits include convenience for patients and clinicians, cost-effective healthcare solutions, high quality of care, resolving the resource shortage and collecting a large volume of data for research and educational needs. My Health Record (MyHR) is a major project funded by the Australian government, which aims to have all data relating to health of the Australian population stored in digital format, allowing clinicians to have access to patient data at the point of care. Prior to 2015, MyHR was known as Personally Controlled Electronic Health Record (PCEHR). Though the Australian government took consistent initiatives there is a significant delay (Pearce and Haikerwal, 2010) in implementing eHealth projects and related services. While this delay is caused by many factors, interoperability is identified as the main problem (Benson and Grieve, 2016c) which is resisting this project delivery. To discover the current interoperability challenges in the Australian healthcare industry, this comparative study is conducted on Health Level 7 (HL7) messaging models such as HL7 V2, V3 and FHIR (Fast Healthcare Interoperability Resources). In this study, interoperability, security and privacy are main elements compared. In addition, a case study conducted in the NSW Hospitals to understand the popularity in usage of health messaging standards was utilised to understand the extent of use of messaging standards in healthcare sector. Predominantly, the project used the comparative study method on different HL7 (Health Level Seven) messages and derived the right messaging standard which is suitable to cover the interoperability, security and privacy requirements of electronic health record. The issues related to practical implementations, change over and training requirements for healthcare professionals are also discussed
A Fast Healthcare Interoperability Resources (FHIR) layer implemented over i2b2
Abstract Background Standards and technical specifications have been developed to define how the information contained in Electronic Health Records (EHRs) should be structured, semantically described, and communicated. Current trends rely on differentiating the representation of data instances from the definition of clinical information models. The dual model approach, which combines a reference model (RM) and a clinical information model (CIM), sets in practice this software design pattern. The most recent initiative, proposed by HL7, is called Fast Health Interoperability Resources (FHIR). The aim of our study was to investigate the feasibility of applying the FHIR standard to modeling and exposing EHR data of the Georges Pompidou European Hospital (HEGP) integrating biology and the bedside (i2b2) clinical data warehouse (CDW). Results We implemented a FHIR server over i2b2 to expose EHR data in relation with five FHIR resources: DiagnosisReport, MedicationOrder, Patient, Encounter, and Medication. The architecture of the server combines a Data Access Object design pattern and FHIR resource providers, implemented using the Java HAPI FHIR API. Two types of queries were tested: query type #1 requests the server to display DiagnosticReport resources, for which the diagnosis code is equal to a given ICD-10 code. A total of 80 DiagnosticReport resources, corresponding to 36 patients, were displayed. Query type #2, requests the server to display MedicationOrder, for which the FHIR Medication identification code is equal to a given code expressed in a French coding system. A total of 503 MedicationOrder resources, corresponding to 290 patients, were displayed. Results were validated by manually comparing the results of each request to the results displayed by an ad-hoc SQL query. Conclusion We showed the feasibility of implementing a Java layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources. An important part of this work was the structural and semantic mapping between the i2b2 model and the FHIR RM. To accomplish this, developers must manually browse the specifications of the FHIR standard. Our source code is freely available and can be adapted for use in other i2b2 sites
Software integrador de información médica utilizando Health Level Seven y FHIR
Fast Healthcare Interoperability Resources (FHIR) y Health Level 7 (HL7) son
dos estándares que ayudan el intercambio de datos clÃnicos y que en los últimos años
han ganado bastante protagonismo en la industria médica, ambos son estándares de
información médica cuya propuesta es alcanzar la interoperabilidad entre los diferentes
sistemas de proveedores de servicios médicos. FHIR y HL7 permiten reducir los
esfuerzos de la industria médica a la hora de interoperar con otros entes médicos,
puesto que brinda una sola estructura de datos que pretende ser universal, además
FHIR se construyó con el enfoque de transferencia de estado representacional o REST
que es la interfaz para conectar varios sistemas a través de HTTP.
Sin embargo, en la industria médica latinoamericana y en especÃfico en Ecuador,
los esfuerzos para alcanzar la interoperabilidad de los sistemas médicos se han visto
opacados por muchos aspectos, el más evidente es la inversión que conllevarÃa
construir nuevos sistemas médicos que hagan uso de estos estándares y dejar
obsoletos a los anteriores sistemas, o al menos a gran parte de su extracción de datos.
Sin embargo, este proceso es necesario, para poder cumplir con la universalidad
propuesta por el estado ecuatoriano donde se define la necesidad de extender la
cobertura de los beneficios del sistema de salud, a toda la población. En este trabajo de
titulación se presenta el diseño e implementación de un middleware de información
médica usando estándares HL7 y FHIR, que permite la interoperabilidad de sistemas de
información médica.Fast Healthcare Interoperability Resources (FHIR) and Health Level 7 (HL7) in
recent years have gained considerable prominence in the medical industry, both are
medical information standards whose purpose is to achieve interoperability between the
different systems of medical service providers. FHIR and HL7 reduce the efforts of the
medical industry when it comes to interoperating with other medical entities, since they
provide a single data structure that aims to be universal, and FHIR is built with the REST
approach.
However, in the Latin American medical industry and specifically in Ecuador, the
efforts to achieve medical interoperability have been overshadowed by many aspects,
the most evident being the investment that would entail building new medical systems
that make use of these standards and render obsolete to previous systems, or at least
to much of their data mining. However, this process is necessary in order to comply with
the universality proposed by the Ecuadorian state, where it is defined as extending the
coverage of the benefits of the health system to the entire population. For this reason, in
this degree work, the design and implementation of a medical information middleware is
presented using the HL7 and FHIR standards, which allows the interoperability of
medical information systems.Ingeniero de SistemasCuenc
Recommended from our members
Addressing Semantic Interoperability and Text Annotations. Concerns in Electronic Health Records using Word Embedding, Ontology and Analogy
Electronic Health Record (EHR) creates a huge number of databases which are
being updated dynamically. Major goal of interoperability in healthcare is to
facilitate the seamless exchange of healthcare related data and an environment
to supports interoperability and secure transfer of data. The health care
organisations face difficulties in exchanging patient’s health care information
and laboratory reports etc. due to a lack of semantic interoperability. Hence,
there is a need of semantic web technologies for addressing healthcare
interoperability problems by enabling various healthcare standards from various
healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its
semantics which can be understood by both machines and humans. Thus, a
framework with a similarity analyser has been proposed in the thesis that dealt
with semantic interoperability. While dealing with semantic interoperability,
another consideration was the use of word embedding and ontology for
knowledge discovery. In medical domain, the main challenge for medical
information extraction system is to find the required information by considering
explicit and implicit clinical context with high degree of precision and accuracy.
For semantic similarity of medical text at different levels (conceptual, sentence
and document level), different methods and techniques have been widely
presented, but I made sure that the semantic content of a text that is presented
includes the correct meaning of words and sentences. A comparative analysis
of approaches included ontology followed by word embedding or vice-versa
have been applied to explore the methodology to define which approach gives
better results for gaining higher semantic similarity. Selecting the Kidney Cancer
dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word
embedding to enrich data first has shown better results. Apart from enriching
the EHR, extracting relevant information is also challenging. To solve this
challenge, the concept of analogy has been applied to explain similarities
between two different contents as analogies play a significant role in
understanding new concepts. The concept of analogy helps healthcare
professionals to communicate with patients effectively and help them
understand their disease and treatment. So, I utilised analogies in this thesis to
support the extraction of relevant information from the medical text. Since
accessing EHR has been challenging, tweets text is used as an alternative for
EHR as social media has appeared as a relevant data source in recent years.
An algorithm has been proposed to analyse medical tweets based on analogous
words. The results have been used to validate the proposed methods. Two
experts from medical domain have given their views on the proposed methods
in comparison with the similar method named as SemDeep. The quantitative
and qualitative results have shown that the proposed analogy-based method
bring diversity and are helpful in analysing the specific disease or in text
classification