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At least 145 records · Page 8Linked to original sources
PubMed1993

HIPIN--a generic HIS/RIS-PACS interface based on clinical radiodiagnostic procedures.

Within the EurIPACS HIPIN topic a generic HIS/RIS-PACS interface will be designed, implemented and evaluated. It is generally agreed that integration with the HIS/RIS is essential for the acceptance of PACS in a clinical environment. An interface between HIS/RIS and PACS allows more efficient usage of both systems, better integration of data, better consistency checking on shared data and better security and error handling. Also the PACS performance is improved by using HIS/RIS information to steer the image migration within the PACS. In this paper the functional specifications of the interface are described. These specifications are based on descriptions of clinical radiodiagnostic procedures. The generic interface consists of a common part, and of site specific adapters. The common part is identical for all incarnations and performs message scheduling, processing and logging. The adapters are specific for each communication standard, e.g. ACR-NEMA or HL7, and for each hospital. The interface will be implemented at the radiology department of the Philipps University Hospital in Marburg (Germany) and at the orthopaedic and neuroradiology departments of the hospital of the Free University in Brussels (Belgium).

Computer Communication Networks↗
PubMedDate not supplied

Capturing and using clinical outcome data: implications for information systems design.

There is an urgent need to capture and record data related to clinical outcomes, but there are many barriers. The range of problems includes lack of agreement on conceptualization of the term "outcome," inadequate measures of outcomes, and inadequate information systems to capture and manipulate data that would reflect outcomes. This article focuses on information system requirements to capture, store, and utilize clinical outcome data. For greatest accuracy, outcome data should be captured as close to the source as possible, including direct data capture from patients themselves and from their families. To make maximum use of outcome data, systems must be designed to 1) store data in multipurpose databases; 2) share data across different platforms; 3) link outcome data to other data that might influence or explain outcomes; 4) allow querying of the data by authorized personnel; and 5) protect patient confidentiality.

Decision Support Systems, Management↗
PubMed1998

Pitfalls in neuroepidemiologic research.

In neuroepidemiologic research, there are many pitfalls to trap unwary investigators, whether the project is a survey, a case-control study, or some other type of study. We briefly discuss pitfalls relating to: research preliminaries (e.g., failure to decide on study objectives); personnel and training (e.g., deficient training); data collection (e.g., ineffective supervision); data ownership and data sharing (disagreement about how the data will be used), and report preparation (e.g., failure to interpret results in the context of uncertainties arising from the design and implementation of the research). Awareness of these pitfalls will reduce the likelihood of flawed or ineffective neuroepidemiologic research.

Data Collection↗
PubMed1999

Computing 2010: from black holes to biology.

By 2010, a click on the PC on your desktop will suffice to call up instantly all the computing power you need from what by then will be the world's largest supercomputer, the Internet itself. Supercomputing for the masses will trigger a revolution in the complexity of problems that are tackled, whole disciplines will go digital and, rather than spending time collecting their own data, scientists will organize themselves around shared data sets.

Computers↗
PubMed1991

Use of a microcomputer database system in a statewide effort for data collection in medical genetics.

The Genetics Office Automation System (GOAS) is a database management system for the collection and reporting of medical genetics data. We have previously reported on its implementation in a single university center [1,2]. We report here on its implementation in a coordinated data collection effort for the State of Missouri. We discuss the current status of the data collection activities and procedures to share data collected at an individual center with state, regional, and national data collection efforts.

Data Collection↗
PubMed1992

Case vignette: to share or not to share.

For years, the notion that researchers should share data freely with fellow scientists has been discussed widely. Some argue that this is especially true when the data are generated in federally funded projects. A recent provision in the reauthorization bill for the National Institutes of Health (NIH) would ensconce this principle in law. NIH grantees would be required, on demand, to furnish their data to other researchers. According to the proposed legislation, research data would have to be preserved and made avilable for 3 years after the completion of a project, and for 5 years following publication of the results in a scientific journal. One objective, according to a congressional aide, would be to make it easier for scientists with dissenting views to obtain and reanalyze data collected with public funds. It would also facilitate publication of alternative analyses. Personal records and patent applications would be exempted, but other data from clinical, behavioral, or epidemiological research focused on the evaluation or efficacy of a drug, medical device, or treatment of any sort would be covered immediately. Is such mandatory sharing of data ethical and appropriate scholarship? Does it raise potential for abuse? Should colleagues be required to provide such access to data, whether or not federal support was used in its collection?

Behavioral Research↗
PubMed2026

Individual Differences in Cognitive Aging Rodent Datasets (ID-CARD): A collaborative platform for behavioral analysis across the lifespan.

Understanding cognitive aging requires approaches that capture individual variability while enabling integration across studies. In rodent models, behavioral data are central to this effort, yet cross-laboratory differences in experimental design limit comparability and constrain secondary analysis. To address this gap, we developed the Individual Differences in Cognitive Aging Rodent Datasets (ID-CARD), a first-of-its-kind collaborative repository aggregating trial-level Morris water maze data from multiple laboratories. ID-CARD is designed to support large-scale, integrative analyses and to facilitate secondary use of existing behavioral data in alignment with emerging data-sharing and transparency initiatives. Rather than imposing retrospective harmonization of experimental protocols, we implemented a normalization and modeling framework that enables comparison of learning trajectories while preserving meaningful variation across studies. Behavioral data from > 5000 rats spanning common strains, both sexes, and multiple ages were normalized in training and performance domains and fit with a logarithmic function to derive an error accumulation rate coefficient (EARC) as a measure of spatial learning. Age was strongly associated with increased EARC, indicating attenuated learning, even after adjusting for non-spatial cue performance. Analyses of goodness of fit revealed systematic structure in learning dynamics, where age was associated with reduced learning-curve conformity after accounting for overall performance. Inter-individual variability in spatial learning also increased with age, with strain-specific interactions. These findings demonstrate that integrated analysis of heterogeneous behavioral datasets can yield robust, individual-level insights into cognitive aging. ID-CARD provides a scalable resource and analytic framework to advance discovery in behavioral neuroscience by enabling reuse, integration, and comparative analysis of existing data.

Cognitive aging↗
PubMed2026

Conference report: the third Bacterial Genome Sequencing Pan-European Network conference.

The third Bacterial Genome Sequencing Pan-European Network conference, held in Engelberg, Switzerland (12-15 January 2026), brought together experts from six European countries to discuss the implementation of bacterial genome sequencing in clinical microbiology and public health. Key themes included regulatory frameworks (In Vitro Diagnostic Regulation, General Data Protection Regulation), standardization, quality control, data sharing, economic evaluation, and the integration of artificial intelligence and long-read sequencing into diagnostic workflows. Across presentations, panel discussions, and workshops, participants emphasized that successful implementation of genome sequencing requires more than technical capacity: it depends on robust validation, sustainable funding, interoperable data standards, ethical governance, and interdisciplinary collaboration. The meeting highlighted that sequencing should remain question-driven and clinically meaningful, balancing cost, turnaround time, and public health impact. Overall, the conference reinforced the need for coordinated European efforts to advance responsible, standardized, and sustainable genomic surveillance and diagnostics.

bacterial genome sequencing↗
PubMedDate not supplied

Enhancing performance measurement: NCQA's road map for a health information framework. National Committee for Quality Assurance.

Measuring the quality of health care delivery is one of the most critical challenges facing US health care. Performance measurement can be used to track the quality of care that health plans and medical groups deliver, but effective performance measurement requires timely access to detailed and accurate data. In 1996, the National Committee for Quality Assurance (NCQA) commissioned a report to learn what actions would improve health plans' capacity to electronically report performance data for the Health Plan Employer Data and Information Set (HEDIS). Tracking clinical performance will require not just clinical data stored in information systems, but an integrated health information framework. Seven features are essential to this framework: (1) it specifies data elements; (2) it establishes linkage capability among data elements and records; (3) it standardizes the element definitions; (4) it is automated to the greatest possible extent; (5) it specifies procedures for continually assessing data quality; (6) it maintains strict controls for protecting security and confidentiality of the data; and (7) it specifies protocols for sharing data across institutions under appropriate and well-defined circumstances. Health plans should anticipate the use of computerized patient records and prepare their data management for an information framework by (1) expanding and improving the capture and use of currently available data; (2) creating an environment that rewards the automation of data; (3) improving the quality of currently automated data; (4) implementing national standards; (5) improving clinical data management practices; (6) establishing a clear commitment to protecting the confidentiality of enrollee information; and (7) careful capital planning. Health care purchasers can provide the impetus for implementing the information framework if they demand detailed, accurate data on the quality of care.

Forms and Records Control↗
PubMed2000

Racial disparities in access to simultaneous pancreas-kidney transplantation in the United States.

The purpose of our study is to assess the extent of racial differences in the access to simultaneous pancreas-kidney (SPK) transplantation and evaluate the potential influence of socioeconomic factors on access to transplantation. We performed a retrospective analysis of the US Renal Data System and United Network for Organ Sharing data on all patients with end-stage renal disease (ESRD) due to diabetes mellitus from 1988 to 1996 (n = 562, 814), including all dialysis, wait list, and transplant patients. Racial differences in incidence, prevalence, insurance coverage, employment status, and transplantation rates were calculated. Caucasians had the highest prevalence of ESRD caused by type 1 diabetes (73%), followed by blacks (22%), Hispanics (3%), Native Americans (2%), and others (<1%). Both blacks and Native Americans increased their annual incidence of ESRD caused by insulin-dependent diabetes mellitus by 10% compared with only a 3.5% increase in Caucasians, whereas incidence rates increased annually by almost 8% for both blacks and Native Americans compared with a 3% increase for Caucasians. However, Caucasians received 92% of all SPK transplants, whereas all other racial groups combined received a disproportionate minority of the remaining transplants. Lack of private insurance and unemployment status were associated with annual changes in both incidence of ESRD caused by type 1 diabetes and SPK transplant rates. In conclusion, we observed striking racial disparities for access to SPK transplantation in the United States today, which may be related to employment status, access to private insurance, and subsequent health care. Our preliminary data support current efforts to encourage Medicare and Medicaid coverage for all patients requiring SPK transplantation regardless of racial or financial status.

Black or African American↗
PubMed1992

Structural design of hidden Markov model speech recognizer using multivalued phonetic features: comparison with segmental speech units.

A novel approach to speech recognition, on the basis of a multidimensional multivalued phonetic-feature description of speech signals, is presented and evaluated. The hidden Markov model (HMM) framework is used to provide the recognition algorithm, which assumes that the underlying Markov chain tracks the temporal evolution of the features. It is shown that this approach can naturally accommodate such coarticulatory effects as feature spreading and formant transition in the functionality of the recognizer, and can provide a high degree of acoustic data sharing that makes effective use of training data. Use of phonetic features as the basic speech units creates a framework where the Markov model's state topology in the recognizer can be designed with guidance of detailed speech knowledge. Details of such a design for a stop consonant-vowel vocabulary are described. Experimental results on the task of speaker-dependent stop consonant discrimination, evaluated from speech data from a total of ten male and five female speakers, demonstrate effectiveness of this feature-based recognizer. Over the 15 speakers, the error rates were shown to be reduced by 23%, 37%, 42%, and 38%, respectively, compared with the conventional HMM-based recognition methods using words, phonemes, allophones, and microsegments as the primary speech units.

Communication↗
PubMedDate not supplied

Managing troubled data: coastal data partnerships smooth data integration.

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.

Conservation of Natural Resources↗
PubMed1995

Dynamic patient data bases: the foundation of an integrated approach to outcome measures for the healthcare professionals.

In recent years there has been a tremendous need among healthcare professionals to assess the effectiveness, efficiency, and appropriateness of the patient care services being provided through criteria-based outcome and program evaluation. Although the need for a tool which could evaluate the effectiveness of patient care is widely recognized, such an undertaking has been severely limited due to the lack of any automated means to collect and analyze patient data on a routine, continuous basis within a clinical setting. We have developed and implemented at Mineral Springs Hospital, Banff, Alberta an integrated and automated hospital information system that not only continuously collects administrative, financial, and patient data, but also contains an intelligent component for automated outcome measure and program evaluation. The system collects various non-duplicated data elements from each routine work process within the facility on a continuous basis. Through the creation of a dynamic patient database, data is transformed into information--a powerful decision support tool. The system provides flexible user-defined reports in patient-specific resource utilization, direct and/or indirect specific financial costs, result reporting of each intervention, service provided and user-defined criteria-based outcome, and program evaluation. The system design incorporates expert rules, dynamic data entry forms, quantitative models, and user-defined access control. Using information derived from the dynamic common database, managers and front-line clinicians can easily evaluate and modify management decisions or careplans on a macro or micro level. An external review is planned to evaluate whether the system has helped the assessment of effectiveness, efficiency and appropriateness of healthcare services being provided at the hospital. The fundamental concept behind the system design is that the patient is the center of activity for data collection. The system provides the answers to the 5 W's (who, what, where, when, and why) together with intervention and service result reports. A dynamic common patient database is the center of the system and is accessible to all with proper authorization. Common data elements are collected from routine work flow without extra data entry and this information is subsequently shared. Data collection is a continuous process. We believe that every process is the outcome of another sub-process or event. The design of the dynamic patient database incorporates patient-specific costing and outcome evaluation, user-defined flexible data entry forms, user-defined access control, outcome evaluation rules and information semantic rules. Such a patient database would provide the flexibility needed to accommodate diverse methodologies to evaluate outcomes whether it they be medical, cost, access and/or other combination of measures. The system was developed on a PC-based Network technology, using FOXPRO (XBase) as the database development tool incorporating advanced technology such as distributed processing and fault tolerant computing. We chose PC-based technology because it is economical, having relatively low maintenance costs and requires no major dependency on vendors. The developed system produces patient-specific reports with many dimensions. The reports are user-defined. The system reports general data, CMG, RGN, LOS, Expected LOS, and other user-defined demographic data. Resource utilization, financial costs, and result reportings are produced together with rule-based outcome assessments of any type of measures, including, but not limited to, pre-set functional/health goals, user satisfaction, clinicianUs text or codified comments etc. It provides the framework for continually capturing data at a practical, work-flow level. The incorporation of a dynamic patient database as the driving forece of an integrated, rule-based administration, financial and patient data system will provdie the tools for healthcar

Databases, Factual↗
PubMedDate not supplied

Confidentiality issues for medical data miners.

The first task in any medical data mining effort is ensuring patient confidentiality. In the past, most data mining efforts ensured confidentiality by the dubious policy of withholding their raw data from colleagues and the public. A cursory review of medical informatics literature in the past decade reveals that much of what we have "learned" consists of assertions derived from confidential datasets unavailable for anyone's review. Without access to the original data, it is impossible to validate or improve upon a researcher's conclusions. Without access to research data, we are asked to accept findings as an act of faith, rather than as a scientific conclusion. This special issue of Artificial Intelligence in Medicine is devoted to medical data mining. The medical data miner has an obligation to conduct valid research in a way that protects human subjects. Today, data miners have the technical tools to merge large data collections and to distribute queries over disparate databases. In order to include patient-related data in shared databases, data miners will need methods to anonymize and deidentify data. This article reviews the human subject risks associated with medical data mining. This article also describes some of the innovative computational remedies that will permit researchers to conduct research AND share their data without risk to patient or institution.

Computer Security↗
PubMed1995

Can one patient record accommodate the diversity of specialized care?

Despite a quarter century of developments, few specialists directly use a computerized patient record, that fully replaces the paper chart. Because of the diversity of domains in specialized care, medical decision-making and the continuity of care may suffer from scattering of patient data over various records. The challenge was to develop a computerized patient record, that would be versatile enough to tailor it to specific needs, while keeping it uniform enough to permit physicians to share data on the same patient. In our CPR, the key that reconciles versatility with uniformity lies in the design of the data model. The CPR consists of a mother record with specialized sub-records, that all share the same data model. A physician can enlarge his scope for decision-making by consulting other specialized records on the same patient or by viewing the combined information of all sub-records without the need to convert data or to familiarize himself with different interfaces.

Humans↗
PubMed2001

International surveillance networks and principles of collaboration.

In the face of the multiplication and the development of international surveillance networks for communicable diseases, many questions on the transmission of personal data and information arise. The confidential nature of shared data and their disclosure internationally within and outside the network are therefore potential sources for conflicts. To resolve these problems, Enter-net developed its 'Collaboration Principles' intended to apply to all the participants of the network and to all others potentially involved. These principles propose solutions to questions related to the access to databases created within the framework of the network, to the quality and confidentiality of circulating data, to the individual responsibility in the identification of an incriminated product in case of an outbreak, and even to the transmission of information outside the network. These principles, which are to be regularly reviewed, are primarily aimed at optimising early detection and management of foodborne outbreaks, and at taking the necessary measures for public health. Considering the nature of the problems, however, some of these principles are also of relevance to other surveillance networks.

Communicable Disease Control↗
PubMedDate not supplied

Promoting interactions with basic scientists and clinicians: the NIA Alzheimer's Disease Data Coordinating Center.

To benefit Alzheimer's disease research, a central data co-ordinating centre (CDCC) is planned that will systematically collect data from 27 Alzheimer's disease centres (ADCs) located nationwide. This CDCC will combine, analyse and disseminate epidemiologic, demographic, clinical and neuropathological data to researchers from the ADCs and the broader scientific community. New and larger scale collaborative studies on Alzheimer's disease will be possible through this centre. Since 1 July 1997, an interim data co-ordinating centre (IDCC) has been serving as the agent of the ADCs to begin the data sharing process until a permanent CDCC is established. The data collected by the IDCC are limited to administrative information and to indexing of specimens and clinical material, with future plans for the transfer of the data collected to the CDCC once it is established.

Aged↗