Improving Clinical Decisions on T2DM Patients Integrating Clinical, Administrative and Environmental Data

This work describes an integrated informatics system developed to collect and display clinically relevant data that can inform physicians and researchers about Type 2 Diabetes Mellitus (T2DM) patient clinical pathways and therapy adherence. The software we developed takes data coming from the electronic medical record (EMR) of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines the data with administrative, pharmacy drugs (purchased from the local healthcare agency (ASL) of the Pavia area), and open environmental data of the same region. By using different use cases, we explain the importance of gathering and displaying the data types through a single informatics tool: the use of the tool as a calculator of risk factors and indicators to improve current detection of T2DM, a generator of clinical pathways and patients' behaviors from the point of view of the hospital care management, and a decision support tool for follow-up visits. The results of the performed data analysis report how the use of the dashboard displays meaningful clinical decisions in treating complex chronic diseases and might improve health outcomes.

Fusing Heterogeneous Data for Alzheimer's Disease Classification

In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic accuracy and how the methods compare against each other for this problem. Our results indicate that multimodal data fusion improves classification accuracy.

Development of Unified Lab Test Result Master for Multiple Facilities

A clinical study requires massive amounts of of lab test data, especially for rare diseases. Before creating a protocol, the hypothesis if the protocol will work with enough amount of patients' dataset has to be proved. However, a single facility, such as a university hospital, often faces a lack of number of patients for specific target diseases. Even if collecting datasets from several facilities, there is no active master table that can merge lab test results between the facility datasets. Therefore, the authors develop a unified lab test result master. Because test master standards such as JLAC10 and LOINC are provided from a viewpoint of academic classification of laboratory medicine, the classification does not fit clinical classification, which doctors understand with a mind-set of establishing a clinical study protocol. The authors establish a method to unify masters using an active lab test result master from two university hospitals.

A pharmacy inventory management system in saudi arabia: a case study

The objective of this paper is to report on the preliminary findings of the implementation process of a pharmacy inventory management system at a local Saudi hospital. Meeting documents, key informant interviews, and experience of the researcher were part of the data collection sources used in the study. A thematic analysis of the data was conducted. Preliminary findings show that the implementation process of the pharmacy inventory management system needs the involvement and support of senior management and experienced technical expertise. Future research will focus on investigating the impacts of the pharmacy inventory management system on workflow and medication errors.

Clinical application of the integrated multicenter discharge summary database

We performed the multi-year project to collect discharge summary from multiple hospitals and made the big text database to build a common document vector space, and developed various applications. We extracted 243,907 discharge summaries from seven hospitals. There was a difference in term structure and number of terms between the hospitals, however the differences by disease were similar. We built the vector space using TF-IDF method. We performed a cross-match analysis of DPC selection among seven hospitals. About 80% cases were correctly matched. The use of model data of other hospitals reduced selection rate to around 10%; however, integrated model data from all hospitals restored the selection rate.

A Cloud Based Real-Time Collaborative Platform for eHealth

For more than a decade, the eHealth initiative has been a government concern of many countries. In an Electronic Health Record (EHR) System, there is a need for sharing the data with a group of specialists simultaneously. Collaborative platforms alone are just a part of a solution, while a collaborative platform with parallel editing capabilities and with synchronized data streaming are stringently needed. In this paper, the design and implementation of a collaborative platform used in healthcare is introduced by describing the high level architecture and its implementation. A series of eHealth services are identified and usage examples in a healthcare environment are given.

Using business intelligence for efficient inter-facility patient transfer

In the context of inter-facility patient transfer, a transfer operator must be able to objectively identify a destination which meets the needs of a patient, while keeping in mind each facility's limitations. We propose a solution which uses Business Intelligence (BI) techniques to analyze data related to healthcare infrastructure and services, and provides a web based system to identify optimal destination(s). The proposed inter-facility transfer system uses a single data warehouse with an Online Analytical Processing (OLAP) cube built on top that supplies analytical data to multiple reports embedded in web pages. The data visualization tool includes map based navigation of the health authority as well as an interactive filtering mechanism which finds facilities meeting the selected criteria. The data visualization is backed by an intuitive data entry web form which safely constrains the data, ensuring consistency and a single version of truth. The overall time required to identify the destination for inter-facility transfers is reduced from hours to a few minutes with this interactive solution.

An IT Architecture for Systems Medicine

Systems medicine aims to support treatment of complex diseases like cancer by integrating all available data for the disease. To provide such a decision support in clinical practice, a suitable IT architecture is necessary. We suggest a generic architecture comprised of the following three layers: data representation, decision support, and user interface. For the systems medicine research project "Clinically-applicable, omics-based assessment of survival, side effects, and targets in multiple myeloma" (CLIOMMICS) we developed a concrete instance of the generic architecture. We use i2b2 for representing the harmonized data. Since no deterministic model exists for multiple myeloma we use case-based reasoning for decision support. For clinical practice, visualizations of the results must be intuitive and clear. At the same time, they must communicate the uncertainty immanent in stochastic processes. Thus, we develop a specific user interface for systems medicine based on the web portal software Liferay.

Towards lifetime electronic health record implementation

Integrated care concepts can help to diminish demographic challenges. Hereof, the use of eHealth, esp. overarching electronic health records, is recognized as an efficient approach. The article aims at rigorously defining the concept of lifetime electronic health records (LEHRs) and the identification of core factors that need to be fulfilled in order to implement such. A literature review was conducted. Existing definitions were identified and relevant factors were categorized. The derived assessment categories are demonstrated by a case study on Germany. Seven dimensions to differentiate types of electronic health records were found. The analysis revealed, that culture, regulation, informational self-determination, incentives, compliance, ICT infrastructure and standards are important preconditions to successfully implement LEHRs. The article paves the way for LEHR implementation and therewith for integrated care. Besides the expected benefits of LEHRs, there are a number of ethical, legal and social concerns, which need to be balanced.

A Practical Approach to Governance and Optimization of Structured Data Elements

Definition and configuration of clinical content in an enterprise-wide electronic health record (EHR) implementation is highly complex. Sharing of data definitions across applications within an EHR implementation project may be constrained by practical limitations, including time, tools, and expertise. However, maintaining rigor in an approach to data governance is important for sustainability and consistency. With this understanding, we have defined a practical approach for governance of structured data elements to optimize data definitions given limited resources. This approach includes a 10 step process: 1) identification of clinical topics, 2) creation of draft reference models for clinical topics, 3) scoring of downstream data needs for clinical topics, 4) prioritization of clinical topics, 5) validation of reference models for clinical topics, and 6) calculation of gap analyses of EHR compared against reference model, 7) communication of validated reference models across project members, 8) requested revisions to EHR based on gap analysis, 9) evaluation of usage of reference models across project, and 10) Monitoring for new evidence requiring revisions to reference model.

Patient-Centered Outcomes Research in Practice: The CAPriCORN Infrastructure

CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments. The paper describes these policies and mechanisms and discusses two case studies to prove the feasibility and effectiveness of the network.

Integrated Database And Knowledge Base For Genomic Prospective Cohort Study In Tohoku Medical Megabank Toward Personalized Prevention And Medicine

The Tohoku Medical Megabank project is a national project to revitalization of the disaster area in the Tohoku region by the Great East Japan Earthquake, and have conducted large-scale prospective genome-cohort study. Along with prospective genome-cohort study, we have developed integrated database and knowledge base which will be key database for realizing personalized prevention and medicine.

Evaluating a Hierarchical Clinical Event Linkage Model for Clinic-Specific Databases

A relational database model is presented that stores the hierarchical linkages between clinical events with qualifier codes, such that the explicit contextual meaning of an event's attributes is preserved upon retrieval. A retrospective analysis of 302 forms built upon the model showed that 91% of 17,899 data elements requested by clinicians and researchers from 19 clinics were successfully represented, but that 62% were never used more than once. These results reinforce the specificity of clinic-specific databases and the need for unambiguous, explicitly-stored clinical data.

Integrating Clinical Decision Support into EMR and PHR: a Case Study Using Anticoagulation

Clinical decision support (CDS) for atrial fibrillation is expected to ease the implementation of often-complex guidelines for atrial fibrillation and anticoagulation. Most clinical decision support systems (CDSS) for anticoagulation are stand-alone systems that do not integrate with electronic medical records (EMR). We have developed an architecture that consists of a computerized CDS that can integrate with multiple EMRs and multiple patient health records (PHRs). The design process revealed some significant issues that were resolved through systematic business/clinical analysis and creative clinical design in the diagnostic and treatment domains. Key issues identified and resolved include: 1) how to correctly allocate existing patients into various CDSS states (e.g., MAINTENANCE, HOLD, DISCONTINUE, etc), 2) identify when a patient becomes eligible for CDSS guidance over time, 3) how the CDSS maintains information about the patient's anticoagulation state and 4) how to transform vague human-readable concepts to explicit computable concepts. The management of anticoagulation for atrial fibrillation is no easy task and we believe our architecture will improve patient care at all levels and ultimately better balance the reduction of stroke risk while minimizing harms from major bleeding. In addition, the architecture presented is scalable to other treatment guidelines and is scalable to multiple EMRs and PHRs, making it suitable for use in a platform approach.

Automatic extraction of numerical values from unstructured data in EHRs

Clinical data recorded in modern EHRs are very rich, although their secondary use research and medical decision may be complicated (eg, missing and incorrect data, data spread over several clinical databases, information available only within unstructured narrative documents). We propose to address the issue related to the processing of narrative documents in order to detect and extract numerical values and to associate them with the corresponding concepts (or themes) and units. We propose to use a CRF supervised categorisation for the detection of segments (themes, numerical sequences and units) and a rules-based system for the association of these segments among them in order to build semantically meaningful sequences. The average results obtained are competitive (0.96 precision, 0.78 recall, and 0.86 F-measure) and we plan to use the system with larger clinical data.

Cross-institutional document exchange system using clinical document architecture (CDA) with virtual printing method

Recently one patient received care from several hospitals at around the same time. When the patient visited a new hospital, the new hospital's physician tried to get patient information the previous hospital. Thus, patient information is frequently exchanged between them. Many types of healthcare facilities have implemented an electronic medical record system, but in Japan, healthcare information exchange is often done by paper. In other words, after a clinical doctor prints a referral document and sends it to another hospital's physician, another hospital's doctor receives it and scans to store the EMR in his own hospital's system. It is a wasteful way to exchange healthcare information about a patient. In order to solve this problem, we have developed a cross-institutional document exchange system using clinical document architecture (CDA) with a virtual printing method.

Mapping Russian Laboratory Terms to LOINC

The paper presents the results of semiautomatic mapping of Russian laboratory terms to LOINC. Two clinics (A and B) and a laboratory service participated in the project. We were able to map 86% (Clinic A) and 87% (Clinic B) of laboratory terms. The required effort was reasonable and the price of mapping and maintenance was considered as relatively low. We established LOINC as a canonical coding method for a laboratory data exchange. This provided semantic interoperability for the data exchange process.

An ontology for healthcare quality indicators: challenges for semantic interoperability

Semantic interoperability, a popular research area for electronic health records, can also be a challenge for quality indicators. We analysed attributes and relationships in a diverse set of over 200 healthcare quality indicators and created a searchable ontology. The ontology is intended to help reduce duplication of effort in healthcare quality monitoring. We describe issues with coding the indicators and specifying inclusion and exclusion criteria and propose some solutions.

Health Interoperability into Practice: Results of the Development of a Consent Form in a Pilot Project in a Health District in São Paulo, Brazil

Interoperability of health information systems is a centerpiece of the "E-Health" Brazilian Ministry of Health strategy. It aims to solve at least partially the health information technology puzzle that we face today. This paper describes a health information exchange pilot project in a health district of the city of São Paulo. It discusses the results of the development of an informed consent form for health information exchange. This consent form showed excellent results, with median application time of 3 minutes and with 97.8% of patients feeling fully clarified. The patients' perception when faced with options of consent to share their data is also described.

InfoRoute: the CISMeF Context-specific Search Algorithm

OBJECTIVE

The aim of this paper was to present a practical InfoRoute algorithm and applications developed by CISMeF to perform a contextual information retrieval across multiple medical websites in different health domains.

METHODS

The algorithm was developed to treat multiple types of queries: natural, Boolean and advanced. The algorithm also generates multiple types of queries: Boolean query, PubMed query or Advanced query. Each query can be extended via an inter alignments relationship from UMLS and HeTOP portal.

RESULTS

A web service and two web applications have been developed based on the InfoRoute algorithm to generate links-query across multiple websites, i.e.: "PubMed" or "ClinicalTrials.org".

CONCLUSION

The InfoRoute algorithm is a useful tool to perform contextual information retrieval across multiple medical websites in both English and French.

Designing an Innovative Data Architecture for the Los Angeles Data Resource (LADR)

The Los Angeles Data Resource (LADR) is a joint project of major Los Angeles health care provider organizations. The LADR helps clinical investigators to explore the size of potential research study cohorts using operational clinical data across all participating institutions. The Charles R. Drew University of Medicine and Science (CDU) LADR team sought to develop an innovative data architecture that would aggregate de-identified clinical data from safety-net providers in the community into CDU LADR node. This in turn would be federated with the other nodes of LADR for a shared view in a way that was never available before. This led to a self-service system to assess patients matching study criteria at each medical center and to search patients by demographics, ICD-9 codes, lab results and medications.

Bridging the Gap from Bench to Bedside - An Informatics Infrastructure for Integrating Clinical, Genomics and Environmental Data (ICGED)

The abundance of heterogeneous biomedical data from a variety of sources demands the development of strategies to address data integration and management issues, so that the data can be used effectively in clinical practices and biomedical research. This research presents an Informatics Infrastructure for Integrating Clinical, Genomics and Environmental Data (ICGED) and provides a roadmap that envisions utilizing the clinical and biomedical resources in our case study. This work describes a data integration approach, proposed by ICGED, with a two-fold purpose: personalized medicine and biomedical data storage and sharing platform. It describes our experiences integrating disease specific clinical and genomics datasets with Data Integration and Analysis Tools (DIAT)--using Informatics for Integrating Biology and the Bedside, and discusses work in progress and future work for extending DIAT, and the development of Risk Assessment and Prediction Tools, Clinical Decision Support Systems and a Bioinformatics Data Warehouse.

A high-availability architecture for continuous monitoring of sleep disorders

We present a complete technical solution for continuously monitoring vital signs required for observing sleep apnoea events, one of the major sleep respiratory disorders. Based on industry accepted medical devices, we developed a GSM-based remote data acquisition and transfer module that is integrated via a set of web services into the server side of the application. The back-end is responsible with aggregating all the data, and, based on machine learning techniques, it provides a first level of filtering in order to warn about possible abnormalities. The proposed solution is currently under the test phase at the "Victor Babes" Hospital in Timisoara, Romania.

How to organize for a large-scale openEHR-based Electronic Patient Record

This paper reports from a large-scale openEHR-based EPR project 2012-2016 in the North Norway Regional Health Authority. To be able to support patient pathways across the health region's trusts, there is a need for standardized routines and practices. Therefore, the North Norway Regional Health Authority has since 2011 had an increased regional focus to prepare for this new system. This includes a) centralizing its IT portfolio to one location, b) creating regional work standards for using the EPR system, and c) establishing a Regional Department for functional ICT. There are several challenges connected to changing the focus from working as individual health trusts to work as one region. There are issues of power balance and local influence that will need a careful consideration to ensure a successful outcome.

Design and development of an EMR for Ebola Treatment Centers in Sierra Leone using OpenMRS

Ebola treatment presents unique challenges for medical records because strict infection control requirements rule out most conventional record-keeping systems. We used the OpenMRS platform to rapidly develop an EMR system for the recently opened Kerry Town, Sierra Leone Ebola Treatment Centre. This system addresses the need for recording patient data and communicating it between the infectious and non-infectious zones, and is specifically designed for maximum usability by staff wearing cumbersome protective equipment. This platform is interoperable with other key eHealth systems in the country, and is extensible to other sites and diseases.