A framework for integrating heterogeneous clinical data for a disease area into a central data warehouse

Structured collection of clinical facts is a common approach in clinical research. Especially in the analysis of rare diseases it is often necessary to aggregate study data from several sites in order to achieve a statistically significant cohort size. In this paper we describe a framework how to approach an integration of heterogeneous clinical data into a central register. This enables site-spanning queries for the occurrence of specific clinical facts and thus supports clinical research. The framework consists of three sequential steps, starting from a formal data harmonization process, to the data transformation methods and finally the integration into a proper data warehouse. We implemented reusable software templates that are based on our best practices in several projects in integrating heterogeneous clinical data. Our methods potentially increase the efficiency and quality for future data integration projects by reducing the implementation effort as well as the project management effort by usage of our approaches as a guideline.

Reporting of drug allergies for use in a national decision support system

In Denmark, there are plans for establishing a national decision support system, providing on-line support for physicians during drug prescribing. This includes establishment of a national database containing information about each patient's drug allergies. Allergy information already exists in medication modules in hospital systems and primary care systems and thus constitutes a potential source for the national allergy database. This paper reports an analysis of local data structure, content and registration policies with the aim to re-use existing allergy data. The result of the analysis is that due to lack of harmonisation most existing cannot be re-used in the national database. The paper propose a common dataset for allergy data where national and international standards were considered.

EHR-based disease registries to support integrated care in a health neighbourhood: an ontology-based methodology

Disease registries derived from Electronic Health Records (EHRs) are widely used for chronic disease management. We approached registries from the perspective of integrated care in a health neighbourhood, considering data quality issues such as semantic interoperability (consistency), accuracy, completeness and duplication. Our proposition is that a realist ontological approach is required to accurately identify patients in an EHR or data repository, assess data quality and fitness for use by the multidisciplinary integrated care team. We report on this approach with routinely collected data in a practice based research network in Australia.

A patient centered electronic health system: an example for cyprus

The design and implementation of a healthcare system needs to follow a specific philosophy regarding its operational structure and must be adapted gradually with one step at a time, depending on the level of maturity of a country on certain key issues. The main goal of this paper is to present an overall recommendations framework for implementing an Electronic Health System at national level, guided by the Patient Centered Philosophy. Certain prerequisites for implementing such systems are analyzed together with guiding principles for identifying the maturity level of an organization or country. The maturity level analysis for Cyprus is presented and is accompanied by some recommendations that determine the steps needed to prepare the ground for a complete patient centered national healthcare system.

Practical implementation of a bridge between legacy EHR system and a clinical research environment

Employing the bridge between Clinical Information System (CIS) and Clinical Research Environment (CRE) can provide functionality, which is not easily, implemented by traditional legacy EHR system. In this paper, the experience of such implementation at the University Hospitals of Geneva is described. General overview of the mapping of extracted from CIS data to the i2b2 Clinical Data Warehouse is provided. The defined implementation manages to provide the interoperability for the CRE.

Towards an automatic harmonization of the representation of medical reports to assess their similarities

Numerous hospitals contain unexploited knowledge deposits. These often take the form of unstructured records with heterogeneous content, which, at various levels of those organizations, register past cases. Those records are for instance patient medical records. Accessing the knowledge and experience they gather would help us to handle present cases. We present here a method to normalize textual reports in foetopathology in order to constitute a proper case base that will be the target of case-based reasoning techniques. Statistics of noise and silence generated by this method on 10 cases are presented.

Ci4SeR--curation interface for semantic resources--evaluation with adverse drug reactions

Evaluation and validation have become a crucial problem for the development of semantic resources. We developed Ci4SeR, a Graphical User Interface to optimize the curation work (not taking into account structural aspects), suitable for any type of resource with lightweight description logic. We tested it on OntoADR, an ontology of adverse drug reactions. A single curator has reviewed 326 terms (1020 axioms) in an estimated time of 120 hours (2.71 concepts and 8.5 axioms reviewed per hour) and added 1874 new axioms (15.6 axioms per hour). Compared with previous manual endeavours, the interface allows increasing the speed-rate of reviewed concepts by 68% and axiom addition by 486%. A wider use of Ci4SeR would help semantic resources curation and improve completeness of knowledge modelling.

Building a common ground on the clinical case: design, implementation and evaluation of an information model for a Handover EHR

Handovers need a common ground on the clinical cases between the members of the successive shifts to establish continuity of care. Conventional electronic patient record systems (EHR) proved to be only insufficiently suitable for supporting the grounding process. Against this background we proposed a basic concept for a handover EHR that extends general EHRs in particular openEHR based systems. The resulting handover information model was implemented in a database and evaluated based on 120 clinical cases. The information items of these cases could be mapped successfully to the model, however, the new class "anticipatory guidance" needed to be introduced. The evaluation also demonstrated the importance of highly aggregated information on the clinical case, opinions and meta-information such as the relevance of an item during handovers. Based on these findings, in particular the handover database, handover EHR applications are currently developed to support the grounding process.

Mapping biomedical vocabularies: a semi-automated term matching approach

Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i.e., different terms in multiple vocabularies referring to the same entity) between the vocabularies increase. Therefore, there is a need for automatically aligning vocabularies. In this paper, we explore and propose new methods for detecting probable matches between two vocabularies. The methods build upon existing string similarity functions, enhancing these functions for the context of semi-automated vocabulary matching.

Interconnection of electronic medical record with clinical data management system by CDISC ODM

EDC system has been used in the field of clinical research. The current EDC system does not connect with electronic medical record system (EMR), thus a medical staff has to transcribe the data in EMR to EDC system manually. This redundant process causes not only inefficiency but also human error. We developed an EDC system cooperating with EMR, in which the data required for a clinical research form (CRF) is transcribed automatically from EMR to electronic CRF (eCRF) and is sent via network. We call this system as "eCRF reporter". The interface module of eCRF reporter can retrieves the data in EMR database including patient biography data, laboratory test data, prescription data and data entered by template in progress notes. The eCRF reporter also enables users to enter data directly to eCRF. The eCRF reporter generates CDISC ODM file and PDF which is a translated form of Clinical data in ODM. After storing eCRF in EMR, it is transferred via VPN to a clinical data management system (CDMS) which can receive the eCRF files and parse ODM. We started some clinical research by using this system. This system is expected to promote clinical research efficiency and strictness.

Data and knowledge in medical distributed applications

Building a clinical decision support system (CDSS) capable to collect process and diagnose data from the patients automatically, based on information, symptoms and investigations is one of the current challenges for researchers and medical science. The purpose of the current study is to design a cloud-based CDSS to improve patient safety, quality of care and organizational efficiency. It presents the design of a cloud-based application system using a medical based approach, which covers different diseases to diagnosis, differentiated on most important pathologies. Using online questionnaires, traditional and new data will be collected from patients. After data input, the application will formulate a presumptive diagnosis and will direct patients to the correspondent department. A questionnaire will dynamically ask questions about the interface, and functionality improvements. Based on the answers, the functionality of the system and the user interface will be improved considering the real needs expressed by the end-users. The cloud-based CDSS, as a useful tool for patients, physicians and healthcare providers involves the computer support in the diagnosis of different pathologies and an accurate automatic differential diagnostic system.

Integrated medication management in mHealth applications

Continuous medication monitoring is essential for successful management of heart failure patients. Experiences with the recently established heart failure network HerzMobil Tirol show that medication monitoring limited to heart failure specific drugs could be insufficient, in particular for general practitioners. Additionally, some patients are confused about monitoring only part of their prescribed drugs. Sometimes medication will be changed without informing the responsible physician. As part of the upcoming Austrian electronic health record system ELGA, the eMedication system will collect prescription and dispensing data of drugs and these data will be accessible to authorized healthcare professionals on an inter-institutional level. Therefore, we propose two concepts on integrated medication management in mHealth applications that integrate ELGA eMedication and closed-loop mHealth-based telemonitoring. As a next step, we will implement these concepts and analyze--in a feasibility study--usability and practicability as well as legal aspects with respect to automatic data transfer from the ELGA eMedication service.

The Israel National Hospital Discharge Register: an essential component of data driven healthcare

The Israel National Hospital Discharge Register (INHDR) is an essential section of healthcare data. It includes record for each admission to hospital wards during the last twenty years, and the data are increasing by digitally updated information from hospitals on continually a monthly or quarterly basis. The register contains encrypted patient identity number, admission number, demographic and geographic data, hospitalization data, diagnoses, procedures and accounting data. The goal of the register is to measure medical and surgical services in hospitals, to compare hospital activity among regions, gender and age and population groups within the country and among other countries, to analyse the difference between periods. This large-scale hospital data helps in planning of the hospital services, analysing the health status of the population, disease and injury surveillance, and helps in performance of quality indicators. It assists decision makers at the Ministry of Health (MOH) in their daily and on-going missions.

Improving EHR semantic interoperability: future vision and challenges

We propose a semantic-driven architecture to improve EHR semantic interoperability. This architecture is constituted by five layers: structured heterogeneous data (i), as found in (un-)standardised clinical information models, which are consumed by a semantic mapping layer (ii), which links the data items to clinical ontologies via user-friendly content patterns. A semantic mediator (iii) then translates these content patterns into ontology-based annotations, which populate a virtual homogeneous data store (iv), which serves the application layer (v).

Enabling Person-Centric Care using linked data technologies

Patient-Centric Care requires comprehensive visibility into the strengths and vulnerabilities of individuals and populations. The systems involved in Patient-Centric Care are numerous and heterogeneous, span medical, behavioral and social domains and must be coordinated across government and NGO stakeholders in Health Care, Social Care and more. We present a system, based on Linked Data technologies, taking first steps in making this cross-domain information accessible and fit-for-use, using minimal structure and open vocabularies. We evaluate our system through user studies.

Separation of personal data in a biobank information system

Separation of different types of personal data has been introduced as an effective measure to improve data protection in the context of medical research. In particular, research associated with human biomaterials requires not only secure technologies but also trustworthy processing of personal data on a need-to-know basis. Web-based information systems make use of a technological infrastructure that is well suited to distributed data repositories and remote processing systems. This approach was successfully applied to develop an information system supporting acquisition, processing and storage of remnant biomaterial from surgical treatment, as well as its allocation to research projects. In order to enhance data protection, the contents of the originally unified database were divided into identification data and medical data. A web application was created for each part and appropriate functionality to maintain and access corresponding data was developed. It is concluded that a distribution of biobanking data across separate databases can be achieved if workflows and staff roles are redesigned accordingly.

Improving inpatient fall prevention strategies using interactive data repository information system

Observing the pattern changes of inpatient fall and validating the Fall Prevention Tool Kit (FPTK) are essential for developing fall prevention strategies. However, the work requires the collection, calculation, and comparison of large amount of data. The information is often scattered in diverse information systems and lack of integration, which makes the work difficult and often neglected. This study demonstrates the development of an Interactive Data Repository System (IDRS) and uses it in the analysis of the pattern changes of inpatient fall within the institute, and validates efficiency of the FPTK across time. This study collected the incident data of year 2011 and compared it with the previous analysis in 2001. The result shows that reasons for patient fall had turned from physical disability to impaired conscious or cognition. The scoring result may be too sensitive in identifying patient falls. Patients with high scores needed to reinforce in functional strength.

Health Information Research Platform (HIReP)--an architecture pattern

Secondary use or single source is still far from routine in healthcare, although lots of data are available either structured or unstructured. As data are stored in multiple systems, using them for biomedical research is difficult. Clinical data warehouses already help overcoming this issue, but currently they are only used for certain parts of biomedical research. A comprehensive research platform based on a generic architecture pattern could increase the benefits of existing data warehouses for both patient care and research by meeting two objectives: serving as a so called single point-of-truth and acting as a mediator between them strengthening interaction and close collaboration. Another effect is to reduce boundaries for the implementation of data warehouses. Taking further settings into account the architecture of a clinical data warehouse supporting patient care and biomedical research needs to be integrated with biomaterial banks and other sources. This work provides a solution conceptualizing a comprehensive architecture pattern of a Health Information Research Platform (HIReP) derived from use cases of the patient care and biomedical research domain. It serves as single IT infrastructure providing solutions for any type of use case.

TeamTreat--a communication platform for concerted cancer treatment

TeamTreat is an internet platform providing a case record for cancer patients across and inside the primary and secondary health care sector. Due to the slow progress of cross-institutional integration in healthcare IT, we created an alternative low-level approach to this problem and put special emphasis on an easy access for healthcare professionals regardless of the specific IT infrastructure they use. Physicians use the platform to share and collect information to achieve a collaborative treatment of cancer. Furthermore, the data in the case record is searchable for clinical researchers to find suitable patients for inclusion in clinical trials. Reading access for patients is also possible.

From generic pathways to ICT-supported horizontally integrated care: the SmartCare approach and convergence with future Internet assembly

Successful service integration in policy and practice requires both technology innovation and service process innovation being pursued and implemented at the same time. The SmartCare project (partially EC-funded under CIP ICT PSP Program) aims to achieve this through development, piloting and evaluation of ICT-based services, horizontally integrating health and social care in ten pilot regions, including Kraljevo region in Serbia. The project has identified and adopted two generic highest-level common thematic pathways in joint consolidation phase - integrated support for long-term care and integrated support after hospital discharge. A common set of standard functional specifications for an open ICT platform enabling the delivery of integrated care is being defined, around the challenges of data sharing, coordination and communication in these two formalized pathways. Implementation and system integration on technology and architecture level are to be based on open standards, multivendor interoperability, and leveraging on the current evolving open specification technology foundations developed in relevant projects across the European Research Area.

Patient identity management for secondary use of biomedical research data in a distributed computing environment

Dealing with data from different source domains is of increasing importance in today's large scale biomedical research endeavours. Within the European Network for Cancer research in Children and Adolescents (ENCCA) a solution to share such data for secondary use will be established. In this paper the solution arising from the aims of the ENCCA project and regulatory requirements concerning data protection and privacy is presented. Since the details of secondary biomedical dataset utilisation are often not known in advance, data protection regulations are met with an identity management concept that facilitates context-specific pseudonymisation and a way of data aggregation using a hidden reference table later on. Phonetic hashing is proposed to prevent duplicated patient registration and re-identification of patients is possible via a trusted third party only. Finally, the solution architecture allows for implementation in a distributed computing environment, including cloud-based elements.

Generation of ELGA-compatible radiology reports from the Vienna Hospital Association's EHR system

In the course of setting up the upcoming Austrian national shared EHR system ELGA, adaptors will have to be implemented for the local EHR systems of all participating healthcare providers. These adaptors must be able to transform EHR data from the internal format of the particular local EHR system to the specified format of the ELGA document types and vice versa. In the course of an ongoing diploma thesis we are currently developing a transformation application that shall allow the generation of ELGA-compatible radiology reports from the local EHR system of the Vienna Hospital Association. Up to now a first prototype has been developed that was tested with six radiology reports. It generates technically valid ELGA radiology reports apart from two errors yielded by the ELGA online validator that rather seem to be bugs of the validator. A medical validation of the reports remains to be done.

Case-based visualization of a patient cohort using SEER epidemiologic data

Data from cancer registries can be used to track the epidemiology of cancer and can potentially serve to guide individual diagnostic and treatment decisions. Even though some cancer registry datasets have been made publicly available for scientific and clinical use, few applications have so far provided direct access to these data from within the patient context of an electronic patient record. The goal of this project was to implement a proof-of-concept integration of the public SEER (Surveillance, Epidemiology and End Results) cancer registry dataset with a digital breast cancer tumor board at a German university hospital and to determine its utility in the clinical settings. The integration was successfully established, using data from routine documentation to provide dynamic visualizations of cohort composition and Kaplan-Meier survival plots. Evaluation feedback was favorable regarding the concept and implementation, but highlighted that important data elements, e.g. receptor status data, were missing in the SEER dataset, limiting clinical value of the system.

Can big data transform electronic health records into learning health systems?

In the United States and globally, healthcare delivery is in the midst of an acute transformation with the adoption and use of health information technology (health IT) thus generating increasing amounts of patient care data available in computable form. Secure and trusted use of these data, beyond their original purpose can change the way we think about business, health, education, and innovation in the years to come. "Big Data" is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it.

EMR Database Upgrade from MUMPS to CACHE: Lessons Learned

Over the past few years, Saudi hospitals have been implementing and upgrading Electronic Medical Record Systems (EMRs) to ensure secure data transfer and exchange between EMRs.This paper focuses on the process and lessons learned in upgrading the MUMPS database to a the newer Caché database to ensure the integrity of electronic data transfer within a local Saudi hospital. This paper examines the steps taken by the departments concerned, their action plans and how the change process was managed. Results show that user satisfaction was achieved after the upgrade was completed. The system was stable and offered better healthcare quality to patients as a result of the data exchange. Hardware infrastructure upgrades improved scalability and software upgrades to Caché improved stability. The overall performance was enhanced and new functions were added (CPOE) during the upgrades. The essons learned were: 1) Involve higher management; 2) Research multiple solutions available in the market; 3) Plan for a variety of implementation scenarios.

The data model for social welfare in Finland

A client data model for social welfare was gradually developed in the National Project of IT in Social Services in Finland. The client data model describes the nationally uniformed data structures and relationships between the data elements needed in production of social services. It contains the structures of social care client records, unique core components and distinct classifications. The modeling method guaranteed the coverage, integrity, flexibility and device independency of the model. The model is maintained and developed by the National Institute for Health and Welfare (THL) together with the social workers and other experts of social welfare. It forms the basis of the electronic information management of the social services. Implementation of the data model in information systems enables the availability of the client data where and when ever a client has to be helped.

Integrating personalized health information from MedlinePlus in a patient portal

The objective of this paper is to describe the implementation and use of context aware information in Spanish from MedlinePlus embedded in a Patient Portal. Personalized information can help patients solve problems, make treatment decisions, gain confidence in their ability to care for themselves and communicate with providers. To integrate MedlinePlus information in our institutional PHR we used the HL7 Context-Aware Knowledge Retrieval Standard, also known as the Infobutton Standard. After analysing one year of use, patients accessed MedlinePlus information in Spanish in a similar rate to other personalized information generated locally. Infobuttons associated to laboratory test results were used in approximately 10% of patients portal sessions when reviewing lab results.

Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective

The paper presents an overview of approaches to encoding uncertain causal knowledge in formal ontologies and demonstrates how these approaches can be used in a semantic-driven application for public health using the Population Health Record (PopHR) platform as an example.

Routine data for disease surveillance in the undeveloped region of the OR Tambo district of the Eastern Cape Province

The research team needed to upsize the solution previously tested so that it could expand the routine data collected via tablet computers. The research team identified the general flow of data within clinics. Data was mainly collected from registers, which were later converted to electronic form and checked for duplication. A database was designed for the collection of demographic data (Patient Master Index), which was aimed at eliminating duplication of patients' data in several registers. Open Data Kit (ODK) Collect was setup on Android tablets for collecting disease related routine data, while ODK Aggregate as the storage and aggregates of data captured by ODK Collect and the Patient Master Index for demographic data, were setup on an Apple Mini Mac server. Data collection is in progress. The expected results include improved data quality, reliability and quick access to summary data. Secondly, instant retrieval of patient demographic details and clinic numbers are included. Thirdly, ability to form standard reporting from the SQL database and lastly exporting data into the TIER.net and DHIS systems via CVS files thus eliminating the need for data capturers are shown.

Electronic Lab Information Exchange (ELIE) in Saudi Arabia

The purpose of this paper is to discuss the Saudi experience in implementing Electronic Lab Information Exchange (ELIE) within healthcare organizations in Saudi Arabia. This paper reviews the benefits, challenges and achievements that Saudi Arabia has gone through over the last five years in implementing ELIE. Data sources included academic literature, websites, and informant interviews. Results show that various Saudi healthcare organizations are participating in ELIE and improvements in laboratory department workflow and patient care have been reported. Future work includes projects to be implemented in different laboratories within the Kingdom to link the various laboratory information systems to Electronic Health Records. Various challenges stand in the way of implementing ELIE including weakness of the information infrastructure, staff resistance, recruiting qualified staff to develop and implement ELIE, producing clear policy and procedures to ensure staff compliance with the data entry, ensuring the privacy and integrity of patient data, and the lack of awareness on the importance of ELIE.

A Hybrid EAV-Relational Model for Consistent and Scalable Capture of Clinical Research Data

Many clinical research databases are built for specific purposes and their design is often guided by the requirements of their particular setting. Not only does this lead to issues of interoperability and reusability between research groups in the wider community but, within the project itself, changes and additions to the system could be implemented using an ad hoc approach, which may make the system difficult to maintain and even more difficult to share. In this paper, we outline a hybrid Entity-Attribute-Value and relational model approach for modelling data, in light of frequently changing requirements, which enables the back-end database schema to remain static, improving the extensibility and scalability of an application. The model also facilitates data reuse. The methods used build on the modular architecture previously introduced in the CURe project.

User needs and requirements analysis for big data healthcare applications

The realization of big data applications that allow improving the quality and efficiency of healthcare care delivery is challenging. In order to take advantage of the promising opportunities of big data technologies, a clear understanding of user needs and requirements of the various stakeholders of healthcare, such as patients, clinicians and physicians, healthcare provider, payors, pharmaceutical industry, medical product suppliers and government, is needed. Our study is based on internet, literature and market study research as well as on semi-structured interviews with major stakeholder groups of healthcare delivery settings. The analysis shows that big data technologies could be used to align the opposing user needs of improved quality with improved efficiency of care. However, this requires the integrated view of various heterogeneous data sources, legal frameworks for data sharing and incentives that foster collaboration.

The design and implementation of a ubiquitous personal health record system for South Africa

Doctors can experience difficulty in accessing medical information of new patients. One reason for this is that, the management of medical records is mostly institution-centred. The lack of access to medical information may affect patients in several ways, such as: new medical tests may be carried out at a cost to the patient, and doctors may prescribe drugs to which the patient is allergic. This paper presents the design and implementation of a ubiquitous Personal Health Record system for South Africa. The design was informed by a literature review of existing personal health record standards, applications and the need to ensure patient privacy. Three medical practices in Port Elizabeth were interviewed with the aim of contextualizing the personal health record standards from the literature study. The findings of this research provide an insight as to how patients can bridge the gap created by institution-centred management of medical records.

Mining the ultimate phenome repository

Combining genotyping and the data locked in medical records yields a large number of known genotype-phenotype associations.

The EHR-ARCHE project: satisfying clinical information needs in a Shared Electronic Health Record system based on IHE XDS and Archetypes

PURPOSE

While contributing to an improved continuity of care, Shared Electronic Health Record (EHR) systems may also lead to information overload of healthcare providers. Document-oriented architectures, such as the commonly employed IHE XDS profile, which only support information retrieval at the level of documents, are particularly susceptible for this problem. The objective of the EHR-ARCHE project was to develop a methodology and a prototype to efficiently satisfy healthcare providers' information needs when accessing a patient's Shared EHR during a treatment situation. We especially aimed to investigate whether this objective can be reached by integrating EHR Archetypes into an IHE XDS environment.

METHODS

Using methodical triangulation, we first analysed the information needs of healthcare providers, focusing on the treatment of diabetes patients as an exemplary application domain. We then designed ISO/EN 13606 Archetypes covering the identified information needs. To support a content-based search for fine-grained information items within EHR documents, we extended the IHE XDS environment with two additional actors. Finally, we conducted a formative and summative evaluation of our approach within a controlled study.

RESULTS

We identified 446 frequently needed diabetes-specific information items, representing typical information needs of healthcare providers. We then created 128 Archetypes and 120 EHR documents for two fictive patients. All seven diabetes experts, who evaluated our approach, preferred the content-based search to a conventional XDS search. Success rates of finding relevant information was higher for the content-based search (100% versus 80%) and the latter was also more time-efficient (8-14min versus 20min or more).

CONCLUSIONS

Our results show that for an efficient satisfaction of health care providers' information needs, a content-based search that rests upon the integration of Archetypes into an IHE XDS-based Shared EHR system is superior to a conventional metadata-based XDS search.

Personalized-detailed clinical model for data interoperability among clinical standards

OBJECTIVE

Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalized-detailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems.

MATERIALS AND METHODS

We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimer's disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards.

RESULTS

For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimer's disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts.

CONCLUSIONS

The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.

Use of web-based shared medical records among patients with HIV

OBJECTIVES

To compare use of 7 shared electronic medical record (SMR) features by adult HIV patients.

STUDY DESIGN

Observational cohort study of adult HIV-positive patients in the first 36 months following implementation of the SMR at Group Health and Kaiser Permanente Northern California.

METHODS

Automated data from the 36 months following SMR implementation were assessed in 2 integrated delivery systems. Cox proportional hazards analysis identified factors associated with any SMR use.

RESULTS

Most (3888/7398) patients used the SMR at least once. Users were most likely to view medical test results (49%), use secure messaging (43%), or request appointments (31%) or medication refills (30%). Initial use was associated with new prescription for antiretroviral therapy (rate ratio [RR] 1.65, P <.001), recent change to a CD4+ count of fewer than 200 cells per microliter (RR = 1.34, P <.02), new HIV RNA of 75 or more copies per milliliter (RR = 1.63, P <.001), or recent increase in non-HIV comorbidity score (RR = 1.49, P = .0001). Users were less likely to be women (RR = 0.49, P = .0001), injection drug users (RR = 0.59, P = .0001), or from lower-socioeconomic status neighborhoods (RR = 0.68, P = .0001), and were less likely to be black (RR = 0.38, P = .0001), Hispanic (RR = 0.52, P = .0001) or Asian/Pacific Islander (RR = 0.59, P = .001).

CONCLUSIONS

SMR use was higher among HIV patients who had indicators of recent increases in healthcare needs and lower among several vulnerable populations.

Are women with major depression in pregnancy identifiable in population health data?

BACKGROUND

Although record linkage of routinely collected health datasets is a valuable research resource, most datasets are established for administrative purposes and not for health outcomes research. In order for meaningful results to be extrapolated to specific populations, the limitations of the data and linkage methodology need to be investigated and clarified. It is the objective of this study to investigate the differences in ascertainment which may arise between a hospital admission dataset and a dispensing claims dataset, using major depression in pregnancy as an example. The safe use of antidepressants in pregnancy is an ongoing issue for clinicians with around 10% of pregnant women suffer from depression. As the birth admission will be the first admission to hospital during their pregnancy for most women, their use of antidepressants, or their depressive condition, may not be revealed to the attending hospital clinicians. This may result in adverse outcomes for the mother and infant.

METHODS

Population-based de-identified data were provided from the Western Australian Data Linkage System linking the administrative health records of women with a delivery to related records from the Midwives' Notification System, the Hospital Morbidity Data System and the national Pharmaceutical Benefits Scheme dataset. The women with depression during their pregnancy were ascertained in two ways: women with dispensing records relating to dispensed antidepressant medicines with an WHO ATC code to the 3rd level, pharmacological subgroup, 'N06A Antidepressants'; and, women with any hospital admission during pregnancy, including the birth admission, if a comorbidity was recorded relating to depression.

RESULTS

From 2002 to 2005, there were 96698 births in WA. At least one antidepressant was dispensed to 4485 (4.6%) pregnant women. There were 3010 (3.1%) women with a comorbidity related to depression recorded on their delivery admission, or other admission to hospital during pregnancy. There were a total of 7495 pregnancies identified by either set of records. Using data linkage, we determined that these records represented 6596 individual pregnancies. Only 899 pregnancies were found in both groups (13.6% of all cases). 80% of women dispensed an antidepressant did not have depression recorded as a comorbidity on their hospital records. A simple capture-recapture calculation suggests the prevalence of depression in this population of pregnant women to be around 16%.

CONCLUSION

No single data source is likely to provide a complete health profile for an individual. For women with depression in pregnancy and dispensed antidepressants, the hospital admission data do not adequately capture all cases.

[Methodological considerations for data linkage of primary and secondary data in occupational epidemiology studies]

Within the scope of an aging population, the topic age, work and health becomes more and more important. So far, research in occupational epidemiology utilizes various primary or secondary data sources. However, data linkage has rarely been used as an instrument in this field. The study presented here combines two large databases within a so-called "age-work matrix", stratified by sex, age group and occupational group. This matrix is based on the German classification of occupations and uses its occupation codes as a key variable. The first database is the representative BIBB/BAuA employment survey for employees of all occupations in Germany 2005/06. The second database consists in sickness absence data of the insurees of a German statutory health insurance fund. Using the matrix approach, the study investigates associations of reported subjectively perceived psychosocial work strains and health impairments provided by health insurance claims data. These claims data offer sickness absence data as an indicator for health impairments. Usability of sickness absence data for studies in occupational epidemiology, their methodological challenges and the solutions realized in this study are discussed.