The Next Generation EMR

Electronic medical/health record (EMR) usage in North America has increased significantly in the last half decade. But there is widespread dissatisfaction with the technologies that are currently available in the market place. Our hypothesis is that EMR vendors and the market place alone cannot solve the issue of poor technology. We propose an architecture for the next generation of electronic records that solves current concerns of end users and addresses the needs of additional stakeholders, including health system funders, patients, researchers and guideline implementers. By including additional stakeholders, we believe that additional resources, competencies and functionality can be unleashed to solve the larger problems of the current generation of EMRs. The architecture also addresses future requirements that are likely to arise from technological developments such as mobile apps and PHRs and from innovations in medicine, including genomics, artificial intelligence and personalized medicine. The paper makes a call to action for informatics researchers to play a greater role in R&D on EMRs.

Optimal Care Mother-Baby and Outcomes through Community-wide Data Sharing, Interoperability and Connectivity

The power of interoperable systems with data/information integration, central to achieving the goals of Telehealth, is illustrated through mutually beneficial sharing between Labor & Delivery (L&D) and Obstetrics (OBs) Clinics. Data shared between L&D and OB brought improved practice patterns and outcomes, and increased satisfaction at both. Staffing and skillsets were significantly improved by knowing complications arriving and anticipated volumes. OBs increased clinic efficiencies and improved patient-direct care time with improved clinical and cost outcomes.

Client-side Medical Image Colorization in a Collaborative Environment

The paper presents an application related to collaborative medicine using a browser based medical visualization system with focus on the medical image colorization process and the underlying open source web development technologies involved. Browser based systems allow physicians to share medical data with their remotely located counterparts or medical students, assisting them during patient diagnosis, treatment monitoring, surgery planning or for educational purposes. This approach brings forth the advantage of ubiquity. The system can be accessed from a any device, in order to process the images, assuring the independence towards having a specific proprietary operating system. The current work starts with processing of DICOM (Digital Imaging and Communications in Medicine) files and ends with the rendering of the resulting bitmap images on a HTML5 (fifth revision of the HyperText Markup Language) canvas element. The application improves the image visualization emphasizing different tissue densities.

Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics

Metformin is a commonly prescribed diabetes medication whose mechanism of action is poorly understood. In this study we utilized EHR-linked biobank data to elucidate the impact of genomic variation on glycemic response to metformin. Our study found significant gene- and SNP-level associations within the beta-2 subunit of the heterotrimeric adenosine monophosphate-activated protein kinase complex. Using EHR phenotypes where were able to add additional clarity to ongoing metformin pharmacogenomic dialogue.

Design, Implementation and Evaluation of an Architecture based on the CDA R2 Document Repository to Provide Support to the Contingency Plan

The pervasive use of electronic records in healthcare increases the dependency on technology due to the lack of physical backup for the records. Downtime in the Electronic Health Record system is unavoidable, due to software, infrastructure and power failures as well as natural disasters, so there is a need to develop a contingency plan ensuring patient care continuity and minimizing risks for health care delivery. To mitigate these risks, two applications were developed allowing healthcare delivery providers to retrieve clinical information using the Clinical Document Architecture Release 2 (CDA R2) document repository as the information source. In this paper we describe the strategy, implementation and results; and provide an evaluation of effectiveness.

Building a Semantic Interoperability Framework for Care and Research in Fibromuscular Dysplasia

Identifying patients with Fibromuscular Dysplasia (FMD) at the international level will have considerable value for understanding the epidemiology, clinical manifestations and susceptible genes in this arterial disease, but also for identifying eligible patients in clinical trials or cohorts. We present a two-step methodology to create a general semantic interoperability framework allowing access and comparison of distributed data over various nations, languages, formats and databases.

METHODS

The first step is to develop a pivot multidimensional model based on a core dataset to harmonize existing heterogeneous data sources. The second step is to align the model to additional data, semantically related to FMD and collected currently in various registries. We present the results of the first step that has been fully completed with the validation and implementation of the model in a dedicated information system (SIR-FMD). We discuss the current achievements for step 2 and the extensibility of the methodology in the context of other rare diseases.

Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic

By the use of extended intelligent information technology tools for fully automated healthcare-associated infection (HAI) surveillance, clinicians can be informed and alerted about the emergence of infection-related conditions in their patients. Moni--a system for monitoring nosocomial infections in intensive care units for adult and neonatal patients--employs knowledge bases that were written with extensive use of fuzzy sets and fuzzy logic, allowing the inherent un-sharpness of clinical terms and the inherent uncertainty of clinical conclusions to be a part of Moni's output. Thus, linguistic as well as propositional uncertainty became a part of Moni, which can now report retrospectively on HAIs according to traditional crisp HAI surveillance definitions, as well as support clinical bedside work by more complex crisp and fuzzy alerts and reminders. This improved approach can bridge the gap between classical retrospective surveillance of HAIs and ongoing prospective clinical-decision-oriented HAI support.

Normalization of Phenotypic Data from a Clinical Data Warehouse: Case Study of Heterogeneous Blood Type Data with Surprising Results

Clinical data warehouses often contain analogous data from disparate sources, resulting in heterogeneous formats and semantics. We have developed an approach that attempts to represent such phenotypic data in its most atomic form to facilitate aggregation. We illustrate this approach with human blood antigen typing (ABO-Rh) data drawn from the National Institutes of Health's Biomedical Translational Research Information System (BTRIS). In applying the method to actual patient data, we discovered a 2% incidence of changed blood types. We believe our approach can be applied to any institution's data to obtain comparable patient phenotypes. The actual discrepant blood type data will form the basis for a future study of the reasons for blood typing variation.

Development and practice of ISMS at a Radiotherapy Hospital by using IHE Integration profiles

Our hospital is specialized for radiation therapy and has many information devices. Various job categories are working. When we implemented an EMR, we aimed to enforce ISMS by using IHE profiles. To solve the already existed system-related problems, we selected and use some profiles (EUA, PSA, ATNA and PAM). After implementation, we audited and then some findings were pointed out. These findings are being settled by the PDCA cycle. We also found that appropriate IHE profiles were effective in the building of ISMS.

Proposal for a European Public Health Research Infrastructure for Sharing of health and Medical administrative data (PHRIMA)

In Europe, health and medical administrative data is increasingly accumulating on a national level. Looking further than re-use of this data on a national level, sharing health and medical administrative data would enable large-scale analyses and European-level public health projects. There is currently no research infrastructure for this type of sharing. The PHRIMA consortium proposes to realise the Public Health Research Infrastructure for Sharing of health and Medical Administrative data (PHRIMA) which will enable and facilitate the efficient and secure sharing of healthcare data.

Integration of Disease Specific Clinical and Genomics Datasets using I2B2 Framework

The availability of a patient's genomic profile along with the clinical profile for providing individualized care and treatment is paving the road for a new era of personalized medicine, and is an important area of focus in current biomedical research. One of the prominent and globally implemented solutions for clinical and genomics data integration in biomedical research is an NIH funded NCBC initiative--Informatics for Integrating Biology and the Beside (I2B2). This paper presents the development of a pilot prototype for integrating patient's clinical and genomics datasets using open source and scalable I2B2 Framework. It focuses on disease specific clinical data and genomic variants, when combined together can be used for informed decision making in clinical practices by healthcare professionals and for further investigations by biomedical researchers. The research was carried out using a case study of King Abdullah International Medical Research Center (KAIMRC) in collaboration with King Fahad National Guard Hospital (KFNGH).

Assessing the Collective Population Representativeness of Related Type 2 Diabetes Trials by Combining Public Data from ClinicalTrials.gov and NHANES

Randomized controlled trials generate high-quality medical evidence. However, the use of unjustified inclusion/exclusion criteria may compromise the external validity of a study. We have introduced a method to assess the population representativeness of related clinical trials using electronic health record (EHR) data. As EHR data may not perfectly represent the real-world patient population, in this work, we further validated the method and its results using the National Health and Nutrition Examination Survey (NHANES) data. We visualized and quantified the differences in the distributions of age, HbA1c, and BMI among the target population of Type 2 diabetes trials, diabetics in NHANES databases, and a convenience sample of patients enrolled in selected Type 2 diabetes trials. The results are consistent with the previous study.

The implementation experiences of a pharmacy automation drug dispensing system in saudi arabia

The objective of this paper is to investigate the experiences of implementing a pharmacy automation drug dispensing system in Saudi Arabia. Key informant interviews, meeting documents, and experience of the researcher were the data collection sources used in the study. A thematic analysis of the data was conducted. Study results discuss the organizational challenges prior to implementation as well as details of the implementation process. Preliminary results show improvements in the services provided by the pharmaceutical department. Lessons learned are also discussed. The work presented in this paper is preliminary and more research is needed to evaluate the overall impact of the new pharmacy automation system on services provided by the pharmaceutical department.

Analysis and Typology of Global eHealth Platforms - A Survey on Five Continents

Driven by the diverse needs for exchanging patient, other healthcare and health system data with the aim to improve the overall quality and efficiency of healthcare provision, regions and countries globally have been developing electronic platforms to gather and exchange such data. Based on an initial sample of more than 50 potential cases, eight such platforms were analysed in detail. This covered issues like core public health policy goals pursued, and major patient and other healthcare data access and exchange characteristics driving the platforms surveyed. This allows for arriving at an initial, pragmatic typology of such platforms. It provides for a better understanding of the main objective(s) and the major thrust of the underlying national (or district-related) health policy to develop and implement such infrastructures.

An adaptive scheduling model for a multi-agent based VEPR data collection actions

With the purpose of improving the access to departmental legacy information systems, a multi agent based Virtual Electronic Patient Record (VEPR) was deployed at a major Portuguese Hospital. The agent module (MAID) is in charge of identifying new data produced (reports), collecting and making it available through an integrated web interface. The deployed MAID system uses a static interval for checking the existence of new data, however from the gathered data regarding each department data production it is observable a variable rate throughout the day. In order to address this variability an adaptive model was developed and tested in a simulated environment with real data. The model takes in consideration the past report production profiles for determining a variable query frequency in order to reduce the average time to make data available minimizing the number of departmental requests.

Supporting in- and off-Hospital Patient Management Using a Web-based Integrated Software Platform

In this paper, a Web-based software platform appropriately designed to support the continuity of health care information and management for both in and out of hospital care is presented. The system has some additional features as it is the formation of continuity of care records and the transmission of referral letters with a semantically annotated web service. The platform's Web-orientation provides significant advantages, allowing for easily accomplished remote access.

ADVISE: First Results of a European Interoperative Information System Network Developed for the ADenoVirus Initiative Study in Epidemiology

Adenoviral conjunctivitis is extremely contagious, causes a form of conjunctivitis. Therefore, it is important to identify patients who suffer from adenoviral conjunctivitis, as early as possible, in order to contain the disease. We present the 6 independent but interoperable platforms developed for the purpose of a large European epidemiologic study (ADVISE), which has been implemented independently in France, Germany, Spain, Italy and the UK. ADVISE is a non-interventional, observational epidemiology study with the objectives of assessing clinical characteristics and incidence of adenovirus conjunctivitis. One of the challenges faced in developing this network of European epidemiology platforms has been the multilingual context. Actually, we have established independent platforms fully dedicated to each of the participating countries. Similar protocols have been submitted across these countries, allowing individual and pooled analyses of the data. A standardized questionnaire is used to collect patient ocular medical history. The electronic questionnaire contains 151 items with automatic coherence control. The first platform ADVISE was set up in France. Development, evaluation and validation of this platform were carried out between January and July 2013. The Medical Informatics and Knowledge Engineering Laboratory (LIMICS), proposes through this project, a tool that allows the installation and application of epidemiologic monitoring in any part of the world.

A socio-technical analytical framework on the EHR-organizational innovation interplay: Insights from a public hospital in Greece

The healthcare sector globally is confronted with increasing internal and external pressures that urge for a radical reform of health systems' status quo. The role of technological innovations such as Electronic Health Records (EHR) is recognized as instrumental in this transition process as it is expected to accelerate organizational innovations. This is why the widespread uptake of EHR systems is a top priority in the global healthcare agenda. The successful co-deployment though of EHR systems and organizational innovations within the context of secondary healthcare institutions is a complex and multifaceted issue. Existing research in the field has made little progress thus emphasizing the need for further research contribution that will incorporate a holistic perspective. This paper presents insights about the EHR-organizational innovation interplay from a public hospital in Greece into a socio-technical analytical framework providing a multilevel set of action points for the eHealth roadmap with worldwide relevance.

A health analytics semantic ETL service for obesity surveillance

The increasingly large amount of data produced in healthcare (e.g. collected through health information systems such as electronic medical records - EMRs or collected through novel data sources such as personal health records - PHRs, social media, web resources) enable the creation of detailed records about people's health, sentiments and activities (e.g. physical activity, diet, sleep quality) that can be used in the public health area among others. However, despite the transformative potential of big data in public health surveillance there are several challenges in integrating big data. In this paper, the interoperability challenge is tackled and a semantic Extract Transform Load (ETL) service is proposed that seeks to semantically annotate big data to result into valuable data for analysis. This service is considered as part of a health analytics engine on the cloud that interacts with existing healthcare information exchange networks, like the Integrating the Healthcare Enterprise (IHE), PHRs, sensors, mobile applications, and other web resources to retrieve patient health, behavioral and daily activity data. The semantic ETL service aims at semantically integrating big data for use by analytic mechanisms. An illustrative implementation of the service on big data which is potentially relevant to human obesity, enables using appropriate analytic techniques (e.g. machine learning, text mining) that are expected to assist in identifying patterns and contributing factors (e.g. genetic background, social, environmental) for this social phenomenon and, hence, drive health policy changes and promote healthy behaviors where residents live, work, learn, shop and play.

Case Study: Applying OpenEHR Archetypes to a Clinical Data Repository in a Chinese Hospital

openEHR is a flexible and scalable modeling methodology for clinical information and has been widely adopted in Europe and Australia. Due to the reasons of differences in clinical process and management, there are few research projects involving openEHR in China. To investigate the feasibility of openEHR methodology for clinical information modelling in China, this paper carries out a case study to apply openEHR archetypes to Clinical Data Repository (CDR) in a Chinese hospital. The results show that a set of 26 archetypes are found to cover all the concepts used in the CDR. Of all these, 9 (34.6%) are reused without change, 10 are modified and/or extended, and 7 are newly defined. The reasons for modification, extension and newly definition have been discussed, including granularity of archetype, metadata-level versus data-level modelling, and the representation of relationships between archetypes.

Acquisition of Character Translation Rules for Supporting SNOMED CT Localizations

Translating huge medical terminologies like SNOMED CT is costly and time consuming. We present a methodology that acquires substring substitution rules for single words, based on the known similarity between medical words and their translations, due to their common Latin / Greek origin. Character translation rules are automatically acquired from pairs of English words and their automated translations to German. Using a training set with single words extracted from SNOMED CT as input we obtained a list of 268 translation rules. The evaluation of these rules improved the translation of 60% of words compared to Google Translate and 55% of translated words that exactly match the right translations. On a subset of words where machine translation had failed, our method improves translation in 56% of cases, with 27% exactly matching the gold standard.

Development of text mining based classification of written communication within a telemedical collaborative network

Chronic diseases like Heart Failure are widespread in the ageing population. Affected patients can be treated with the aid of a disease management program, including a telemedical collaborative network. Evaluation of a currently used system has shown that the information of the textual communication is of pivotal importance for the collaboration in the network. Thus, the challenge is to make this unstructured information useable, potentially leading to a better understanding of the collaboration so as to optimize the processes. This paper presents the setup of an analysis pipeline for processing textual information automatically, and, how this pipeline can be utilized to train a model that is able to automatically classify the written messages into a set of meaningful task and status categories.

Standardized data sharing in a paediatric oncology research network--a proof-of-concept study

Data that has been collected in the course of clinical trials are potentially valuable for additional scientific research questions in so called secondary use scenarios. This is of particular importance in rare disease areas like paediatric oncology. If data from several research projects need to be connected, so called Core Datasets can be used to define which information needs to be extracted from every involved source system. In this work, the utility of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) as a format for Core Datasets was evaluated and a web tool was developed which received Source ODM XML files and--via Extensible Stylesheet Language Transformation (XSLT)--generated standardized Core Dataset ODM XML files. Using this tool, data from different source systems were extracted and pooled for joined analysis in a proof-of-concept study, facilitating both, basic syntactic and semantic interoperability.

Integrating data from multiple sources for data completeness in a web-based registry for pediatric renal transplantation--the CERTAIN Registry

Patient registries are a useful tool to measure outcomes and compare the effectiveness of therapies in a specific patient population. High data quality and completeness are therefore advantageous for registry analysis. Data integration from multiple sources may increase completeness of the data. The pediatric renal transplantation registry CERTAIN identified Eurotransplant (ET) and the Collaborative Transplant Study (CTS) as possible partners for data exchange. Import and export interfaces with CTS and ET were implemented. All parties reached their projected goals and benefit from the exchange.

Personalised Medicine Possible With Real-Time Integration of Genomic and Clinical Data To Inform Clinical Decision-Making

Despite widespread use of genomic sequencing in research, there are gaps in our understanding of the performance and provision of genomic sequencing in clinical practice. The Melbourne Genomics Health Alliance (the Alliance), has been established to determine the feasibility, performance and impact of using genomic sequencing as a diagnostic tool. The Alliance has partnered with BioGrid Australia to enable the linkage of genomic sequencing, clinical treatment and outcome data for this project. This integrated dataset of genetic, clinical and patient sourced information will be used by the Alliance to evaluate the potential diagnostic value of genomic sequencing in routine clinical practice. This project will allow the Alliance to provide recommendations to facilitate the integration of genomic sequencing into clinical practice to enable personalised disease treatment.