In a time of decreasing resources, managers need a tool to manage their resources effectively, support clinical requirements, and replace aging equipment in order to ensure adequate clinical care. To do this successfully, one must be able to perform technology assessment and capital equipment asset management. The lack of a commercial system that adequately performed technology needs assessment and addressed the unique needs of the military led to the development of an in-house Technology Assessment and Requirements Analysis (TARA) program. The TARA is a tool that provides an unbiased review of clinical operations and the resulting capital equipment requirements for military hospitals. The TARA report allows for the development of acquisition strategies for new equipment, enhances personnel management, and improves and streamlines clinical operations and processes.
A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications—namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.
Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), chemical shift spectroscopy imaging (CSI), diffusion tensor imaging (DTI), and perfusion-weighted imaging (PWI) create novel challenges in terms of data storage and management: huge amounts of raw data are generated, the results of analysis may depend on the software and settings that have been used, and most often intermediate files are inherently not compliant with the current DICOM (digital imaging and communication in medicine) standard, as they contain multidimensional complex and tensor arrays and various other types of data structures. A software architecture, referred to as Bio-Image Warehouse System (BIWS), which can be used alongside a radiology information system/picture archiving and communication system (RIS/PACS) system to store neuroimaging data for research purposes, is presented. The system architecture is conceived with the purpose of enabling to query by diagnosis according to a predefined two-layered classification taxonomy. The operational impact of the system and the time needed to get acquainted with the web-based interface and with the taxonomy are found to be limited. The development of modules enabling automated creation of statistical templates is proposed.
PACS; image repository; data warehouse; web-based; neuroradiology
This purpose of this paper is to introduce the status of the Asan Medical Center (AMC) medical information system with respect to healthcare quality improvement.
Asan Medical Information System (AMIS) is projected to become a completely electronic and digital information hospital. AMIS has played a role in improving the health care quality based on the following measures: safety, effectiveness, patient-centeredness, timeliness, efficiency, privacy, and security.
AMIS consisted of several distinctive systems: order communication system, electronic medical record, picture archiving communication system, clinical research information system, data warehouse, enterprise resource planning, IT service management system, and disaster recovery system. The most distinctive features of AMIS were the high alert-medication recognition & management system, the integrated and severity stratified alert system, the integrated patient monitoring system, the perioperative diabetic care monitoring and support system, and the clinical indicator management system.
AMIS provides IT services for AMC, 7 affiliated hospitals and over 5,000 partners clinics, and was developed to improve healthcare services. The current challenge of AMIS is standard and interoperability. A global health IT strategy is needed to get through the current challenges and to provide new services as needed.
Hospital Information Systems; Quality of Health Care; Clinical Decision Support Systems; Electronic Medical Record
The use of electronic medical record (EMR) data is necessary to improve clinical research efficiency. However, it is not easy to identify patients who meet research eligibility criteria and collect the necessary information from EMRs because the data collection process must integrate various techniques, including the development of a data warehouse and translation of eligibility criteria into computable criteria. This research aimed to demonstrate an electronic medical records retrieval system (ERS) and an example of a hospital-based cohort study that identified both patients and exposure with an ERS. We also evaluated the feasibility and usefulness of the method.
The system was developed and evaluated.
In total, 800 000 cases of clinical information stored in EMRs at our hospital were used.
Primary and secondary outcome measures
The feasibility and usefulness of the ERS, the method to convert text from eligible criteria to computable criteria, and a confirmation method to increase research data accuracy.
To comprehensively and efficiently collect information from patients participating in clinical research, we developed an ERS. To create the ERS database, we designed a multidimensional data model optimised for patient identification. We also devised practical methods to translate narrative eligibility criteria into computable parameters. We applied the system to an actual hospital-based cohort study performed at our hospital and converted the test results into computable criteria. Based on this information, we identified eligible patients and extracted data necessary for confirmation by our investigators and for statistical analyses with our ERS.
We propose a pragmatic methodology to identify patients from EMRs who meet clinical research eligibility criteria. Our ERS allowed for the efficient collection of information on the eligibility of a given patient, reduced the labour required from the investigators and improved the reliability of the results.
Oral & Maxillofacial Surgery; Public Health; Statistics & Research Methods
The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative.
neuroimaging database framework; image processing; query interface; data management; data query; neuroimaging collaboration and workflows; web 2.0 application
The computer system used by the Microbiology Service of the Clinical Pathology Department, Clinical Center, National Institutes of Health is discussed. This microbiology subsystem is a part of a dedicated on-line laboratory computer system used by the entire department. The laboratory computer is connected on-line to a hospital computer which provides patient admission, transfer, and discharge data. Mark sense worksheets and cathode ray tube terminals are used for result entry and correction. Cumulative patient reports are printed. Results for both active and completed accessions can be easily retrieved on cathode ray terminals in the laboratory. All laboratory data are archived on magnetic tape from which a research data base and microfiched laboratory records are generated. The manner in which the system is integrated in the routine operation of the microbiology laboratory is emphasized. In addition, some of the costs, benefits, liabilities, and pitfalls associated with the introduction of the computer in the laboratory are reviewed. Finally, we have presented our concept of some of the future enhancements to our present system and some of the directions in which any future microbiology system might develop.
Existing data stored in a hospital's transactional servers have enormous potential to improve performance measurement and health care quality. Accessing, organizing, and using these data to support research and quality improvement projects are evolving challenges for hospital systems. The authors report development of a clinical data warehouse that they created by importing data from the information systems of three affiliated public hospitals. They describe their methodology; difficulties encountered; responses from administrators, computer specialists, and clinicians; and the steps taken to capture and store patient-level data. The authors provide examples of their use of the clinical data warehouse to monitor antimicrobial resistance, to measure antimicrobial use, to detect hospital-acquired bloodstream infections, to measure the cost of infections, and to detect antimicrobial prescribing errors. In addition, they estimate the amount of time and money saved and the increased precision achieved through the practical application of the data warehouse.
The effectiveness of weight loss therapies is commonly measured using body mass index and other obesity-related variables. Although these data are often stored in electronic health records (EHRs) and potentially very accessible, few studies on obesity and weight loss have used data derived from EHRs. We developed processes for obtaining data from the EHR in order to construct a database on patients undergoing Roux-en-Y gastric bypass (RYGB) surgery.
Clinical data obtained as part of standard of care in a bariatric surgery program at an integrated health delivery system were extracted from the EHR and deposited into a data warehouse. Data files were extracted, cleaned, and stored in research datasets. To illustrate the utility of the data, Kaplan-Meier analysis was used to estimate length of post-operative follow-up.
Demographic, laboratory, medication, co-morbidity, and survey data were obtained from 2028 patients who had undergone RYGB at the same institution since 2004. Pre-and post-operative diagnostic and prescribing information were available on all patients, while survey laboratory data were available on a majority of patients. The number of patients with post-operative laboratory test results varied by test. Based on Kaplan-Meier estimates, over 74% of patients had post-operative weight data available at 4 years.
A variety of EHR-derived data related to obesity can be efficiently obtained and used to study important outcomes following RYGB.
EHR; Database; Weight loss; Modeling; Obesity
Purpose: To identify practical issues surrounding delivering digital images from picture archiving and communication systems (PACS) for research and teaching purposes. The complexity of Digital Imaging and Communications in Medicine (DICOM) access methods, security, patient confidentiality, PACS database integrity, portability, and scalability are discussed. A software prototype designed to resolve these issues is described.System Architecture: A six-component, three-tier, client server software application program supporting DICOM query/retrieve services was developed in the JAWA language. This software was interfaced to a large GE (Mt Prospect, IL) Medical Systems clinical PACS at Northwestern Memorial Hospital (NMH).Conclusion: Images can be delivered from a clinical PACS for research and teaching purposes. Concerns for security, patient confidentiality, integrity of the PACS database, and management of the transactions can be addressed. The described software is one such solution for achieving this goal.
The Information Warehouse at the Ohio State University Medical Center is a comprehensive repository of business, clinical, and research data from various source systems. Data collected here is a valuable resource that facilitates both translational research and personalized healthcare. The use of such data in research is governed by federal privacy regulations with oversight by the Institutional Review Board. In 2006, the Information Warehouse was recognized by the OSU IRB as an “Honest Broker” of clinical data, providing investigators with de-identified or limited datasets under stipulations contained in a signed data use agreement. In order to streamline this process even further, the Information Warehouse is developing a de-identified data warehouse that is suitable for direct user access through a controlled query tool that is aimed to support both research and education activities. In this paper we report our findings on performance evaluation of different de-identification schemes that may be used to ensure regulatory compliance while also facilitating practical database updating and querying. We also discuss how date-shifting in the de-identification process can impact other data elements such as diagnosis and procedure codes and consider a possible solution to those problems.
Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.
The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.
Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.
Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.
Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.
Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.
Human subjects are indispensable for clinical and translational research. Federal and local agencies issue regulations governing the conduct of research involving human subjects in order to properly protect study participants. Institutional Review Boards (IRBs) have the authority to review human subject research to ensure concordance with these regulations. One of the primary goals of the IRB oversight is to protect research participants’ privacy by carefully reviewing the data used and disclosed during a study. However, there are major challenges for IRBs in the typical research process. Due to the information disconnect between the data providers (e.g., a clinical data warehouse) and the IRB, it is often impossible to tell exactly what data has been disclosed to investigators. This causes time-consuming, inefficient, and often ineffective monitoring of clinical studies. This paper proposes an integrated architecture that interconnects a federated healthcare data query platform with an electronic IRB system.
To meet the educational needs of a medical imaging department with a strong teaching commitment, a teaching file that uses digital data supplied by the institutional picture archiving and communications system (PACS) was required. This teaching file had to be easily used by the end users, have a simple submission process, be able to support multiple users, be searchable on all data fields, and implementing the teaching file must not incur any additional software or hardware costs. The teaching file developed to address this problem takes advantage of the database structure and capabilities of several components included in the commercial PACS installed at the hospital. MS Access is used to seamlessly integrate with the digital imaging and communication in medicine (DICOM) database of a normal work station that is part of the PACS. This integration allows relevant patient and study demographics to be copied from images of interest and then to be stored in a separate database as the back-end of the digital teaching file. When images for a particular teaching file case need to be reviewed, they are automatically retrieved and displayed from the main PACS database using an open application programming interface (API) connection defined on the PACS web server. Utilizing this open API connection means the teaching file contains only the relevant demographic information of each teaching file case; no image data is stored locally. The open API connection allows access to imaging data usually not encountered in a teaching file, allowing more comprehensive imaging case files to be developed by the radiologist. Other advantages of this teaching file design are that it does not duplicate image data, it is small allowing simple ongoing backup, and it can be opened with multiple users accessing the database without compromising data access or integrity.
Electronic teaching files; DICOM; PACS
The purpose of this presentation is to review and evaluate computerized workflow of selected sites that have integrated systems of the hospital information system (HIS), radiology information system (RIS), and picture archiving and communications system (PACS). We then focus on some essential points of integration of those systems, such as avoiding multiple entries of patients demographic data, prefetching current and previous images to the correspondent workstations, and workflow management. To realize them by integrating multiple subsystems such as HIS/RIS/PACS integration, there must be exchange of the workflow control information, and consistency of the information between subsystems.
It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.
Diagnosis; computer-assisted; content-based image retrieval (CBIR); picture archiving and communication system (PACS (radiology); digital imaging and communications in medicine (DICOM) structured reporting (SR); health Level 7 (HL7); RWTH; helmholtz; radiology; image processing; data management; interfaces; protocols
The purpose of this literature review is to present the concepts surrounding the issue of communication between imaging systems and information systems in radiology and the literature about them. Picture archiving and communication systems (PACS) were developed to combine viewing of modality images, archiving, and distribution of images. When PACS is integrated/interfaced with radiology information systems (RIS) or hospital information systems (HIS), it can merge patient demographics, medical records, and images. To address several issues surrounding communication between PACS and HIS/RIS and to make interface development easier and faster, various organizations have developed standards for the formatting and transfer of clinical data. Additional work continues to better handle these issues. Communication protocol Health Level 7 (HL7) is a standard application protocol used for electronic text data exchange in health care by most HIS/RIS. The imaging communication protocol for PACS is the Digital Imaging and Communications in Medicine (DICOM) standard specification protocol that describes the means of formatting and exchanging images and associated information.
HL7; DICOM; PACS; radiology information systems; hospital information systems; communication protocol standards; interface
STRIDE (Stanford Translational Research Integrated Database Environment) is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research. STRIDE consists of three integrated components: a clinical data warehouse, based on the HL7 Reference Information Model (RIM), containing clinical information on over 1.3 million pediatric and adult patients cared for at Stanford University Medical Center since 1995; an application development framework for building research data management applications on the STRIDE platform and a biospecimen data management system. STRIDE’s semantic model uses standardized terminologies, such as SNOMED, RxNorm, ICD and CPT, to represent important biomedical concepts and their relationships. The system is in daily use at Stanford and is an important component of Stanford University’s CTSA (Clinical and Translational Science Award) Informatics Program.
A clinical information management system using dBASE III on a micro computer has been developed for a Geriatric Evaluation Unit (GEU). This application provides clinicians with information and computing access not available on the hospital's computers. Two major functions are routinely performed: 1) longitudinal archival of in-patient and out-patient data, providing supportive clinical documentation for clinic visits, pre-admission review, discharge notes and summaries, and 2) education and research support services to GEU team members including management of research datasets, statistical analysis, literature searching, word processing, drug interactions, and intra-ward mail. The system is menu driven and takes minutes to learn. Higher levels of capability are available to experienced dBASE users. The demonstration will cover program/data file arrangement and logistics of integrating the application into the Geriatrics program. Programming techniques will be discussed and source code will be available.
In order to support the analysis of the information transformation process, quality, outcome, and cost of care delivered at a medical institution, an environment that integrates data from multiple clinical, administrative and financial systems is necessary. This integration becomes essential to facilitate the access and retrieval of the varieties of information. We propose how to rapidly specify a clinical data warehouse taking into account the value chain of a health care institution and covering three different layers of data analysis. In order to assess our proposal, we have developed an environment called FBCDataWare with data warehouse and clinical data repository capabilities that has been validated at the Unit of Cardiology and Cardiovascular Surgery/Fundação Bahiana de Cardiologia.
An archive is a location containing a collection of records, documents, or other materials of historical importance. An integral part of Picture Archiving and Communication System (PACS) is archiving. When a hospital needs to migrate a PACS vendor, the complete earlier data need to be migrated in the format of the newly procured PACS. It is both time and money consuming. To address this issue, the new concept of vendor neutral archive (VNA) has emerged. A VNA simply decouples the PACS and workstations at the archival layer. This is achieved by developing an application engine that receives, integrates, and transmits the data using the different syntax of a Digital Imaging and Communication in Medicine (DICOM) format. Transferring the data belonging to the old PACS to a new one is performed by a process called migration of data. In VNA, a number of different data migration techniques are available to facilitate transfer from the old PACS to the new one, the choice depending on the speed of migration and the importance of data. The techniques include simple DICOM migration, prefetch-based DICOM migration, medium migration, and the expensive non-DICOM migration. “Vendor neutral” may not be a suitable term, and “architecture neutral,” “PACS neutral,” “content neutral,” or “third-party neutral” are probably better and preferred terms. Notwithstanding this, the VNA acronym has come to stay in both the medical IT user terminology and in vendor nomenclature, and radiologists need to be aware of its impact in PACS across the globe.
Archive; content neutral; architecture neutral; archival layer; data migration; DICOM; non-DICOM migration; PACS; PACS neutral; PACS vendor; patient data; third-party neutral; vendor neutral archive; VNA; workstations
Ultrasound scanning uses the medical imaging format, DICOM, for electronically storing the images and data associated with a particular scan. Large health care facilities typically use a picture archiving and communication system (PACS) for storing and retrieving such images. However, these systems are usually not suitable for managing large collections of anonymized ultrasound images gathered during a clinical screening trial.
We have developed a system enabling the accurate archiving and management of ultrasound images gathered during a clinical screening trial. It is based upon a Windows application utilizing an open-source DICOM image viewer and a relational database. The system automates the bulk import of DICOM files from removable media by cross-validating the patient information against an external database, anonymizing the data as well as the image, and then storing the contents of the file as a field in a database record. These image records may then be retrieved from the database and presented in a tree-view control so that the user can select particular images for display in a DICOM viewer or export them to external media.
This system provides error-free automation of ultrasound image archiving and management, suitable for use in a clinical trial. An open-source project has been established to promote continued development of the system.
Development of a Picture Archiving and Communications System (PACS) with a large and diverse set of medical images will lead to large digital libraries that can be accessed to provide improved support for patient care, research and education. New representational and retrieval models for clinical images are required to address these issues. The PACS at the Georges Pompidou Hospital (GPH) is integrated in the hospital information system (HIS), and several modalities from medical imaging departments have been attached to it. The two main axes of the GPH PACS project were 1) HIS-integration to allow hospital-wide access to the images based on demographic and procedure-type information and 2) the development of content-based image retrieval to enhance the medical impact of image retrieval in daily practice.
Environmental Polymorphisms Registry (EPR) is a large-scale phenotype-by-genotype registry developed by National Institute of Environmental Health Sciences to facilitate translational research. The link between personal identity and collected genomic data was preserved in EPR which creates opportunities for EPR to be linked to phenotype-rich databases, such as the Carolina Data Warehouse for Health (CDW-H) located at the University of North Carolina hospital system. CDW-H contains clinically-relevant data for patients who have been admitted to UNC healthcare system. To validate the feasibility of linking EPR with CDWH, the number of matching records between the two databases had to be established. To that end, combinations of subjects’ demographic identifiers from both databases were converted to anonymized hash codes, which were then matched to determine the number of overlapping records. Preliminary results showed that combination of last name, gender, data of birth and zip code would generate over 2,700 matches between the two databases.