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PubMed1998

An ontological analysis of the UMLS Metathesaurus.

Paper-based terminology systems cannot satisfy anymore the new desiderata of healthcare information systems: the demand for re-use and sharing of patient data, their transmission and the need of semantic-based criteria for purposive statistical aggregation. The unambiguous communication of complex and detailed medical concepts is now a crucial feature of medical information systems. Ontologies can support a more effective data and knowledge sharing in medicine. In this paper we briefly survey our ontological analysis and integration of various top-levels of terminologies and we report the main results of the ontological analysis of the UMLS Metathesaurus.

Semantics↗
PubMed2005

Use of Internet audience measurement data to gauge market share for online health information services.

BACKGROUND: The transition to a largely Internet and Web-based environment for dissemination of health information has changed the health information landscape and the framework for evaluation of such activities. A multidimensional evaluative approach is needed. OBJECTIVE: This paper discusses one important dimension of Web evaluation-usage data. In particular, we discuss the collection and analysis of external data on website usage in order to develop a better understanding of the health information (and related US government information) market space, and to estimate the market share or relative levels of usage for National Library of Medicine (NLM) and National Institutes of Health (NIH) websites compared to other health information providers. METHODS: The primary method presented is Internet audience measurement based on Web usage by external panels of users and assembled by private vendors-in this case, comScore. A secondary method discussed is Web usage based on Web log software data. The principle metrics for both methods are unique visitors and total pages downloaded per month. RESULTS: NLM websites (primarily MedlinePlus and PubMed) account for 55% to 80% of total NIH website usage depending on the metric used. In turn, NIH.gov top-level domain usage (inclusive of NLM) ranks second only behind WebMD in the US domestic home health information market and ranks first on a global basis. NIH.gov consistently ranks among the top three or four US government top-level domains based on global Web usage. On a site-specific basis, the top health information websites in terms of global usage appear to be WebMD, MSN Health, PubMed, Yahoo! Health, AOL Health, and MedlinePlus. Based on MedlinePlus Web log data and external Internet audience measurement data, the three most heavily used cancer-centric websites appear to be www.cancer.gov (National Cancer Institute), www.cancer.org (American Cancer Society), and www.breastcancer.org (non-profit organization). CONCLUSIONS: Internet audience measurement has proven useful to NLM, with significant advantages compared to sole reliance on usage data from Web log software. Internet audience data has helped NLM better understand the relative usage of NLM and NIH websites in the intersection of the health information and US government information market sectors, which is the primary market intersector for NLM and NIH. However important, Web usage is only one dimension of a complete Web evaluation framework, and other primary research methods, such as online user surveys, usability tests, and focus groups, are also important for comprehensive evaluation that includes qualitative elements, such as user satisfaction and user friendliness, as well as quantitative indicators of website usage.

Humans↗
PubMed2005

Design considerations for a web-based database system of ELISpot assay in immunological research.

The enzyme-linked immunospot (ELISpot) assay has been a primary means in immunological researches (such as HIV-specific T cell response). Due to huge amount of data involved in ELISpot assay testing, the database system is needed for efficient data entry, easy retrieval, secure storage, and convenient data process. Besides, the NIH has recently issued a policy to promote the sharing of research data (see http://grants.nih.gov/grants/policy/data_sharing). The Web-based database system will be definitely benefit to data sharing among broad research communities. Here are some considerations for a database system of ELISpot assay (DBSEA).

Access to Information↗
PubMed2005

Discussion suppers as a means for community engagement.

PURPOSE: This paper describes how Lehigh Valley Hospital and Health Network (LVHHN), a large tertiary care urban hospital, used discussion suppers as a means for community engagement designed to lead to community health improvement. An overview of the implementation of the project is described. PROJECT: In 1996, with an awareness of the need to address population-based health improvement, the Dorothy Rider Pool Health Care Trust and LVHHN undertook a multiyear, multidimensional effort to improve health and quality of life in the Lehigh Valley of Pennsylvania. Data were obtained via a series of community and health assessments. Action-Oriented Community Diagnosis and the Behavioral Risk Factor Surveillance System survey, a national instrument, are 2 assessments discussed. The community was engaged through a series of discussion suppers in which community data were shared in a friendly, interactive fashion. The process included community definition of priorities from the data and the subsequent determination of corresponding actions (programs). CONCLUSIONS: The success of these activities demonstrates the discussion suppers were an effective approach and that data can be shared with rural areas in ways that build partnerships and provide a basis for joint actions. This is increasingly important as communities expect our health care systems to provide care both within the hospital as well as outside its walls.

Communication↗
PubMedDate not supplied

Probabilistic population estimation of the size and overlap of data sets based on date of birth.

Probabilistic population estimation is a statistical procedure for deriving unduplicated counts of the number of people represented in data sets that do not include unique person identifiers and the number of people shared by data sets that do not share personal identifiers. Because the procedure relies on anonymous data sets, the personal privacy of individuals and the confidentiality of medical records is protected. This paper describes the mathematics of probabilistic population estimation, and applies the procedure to an important contemporary public policy issue.

Age Factors↗
PubMed2026

Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.

Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access repositories, federated analysis, and synthetic data. This narrative review synthesizes the scientific and societal rationale for greater openness with the ethical, legal, and governance constraints that shape what "open" can realistically mean in healthcare. We examine how data sharing supports reproducibility, machine learning, and more efficient research, while also enabling public health surveillance and learning health systems. Against these benefits, we analyze privacy and re-identification risks, consent challenges in large-scale secondary use, inequities including data colonialism, and tensions introduced by commercialization. We integrate lessons from prominent case examples spanning pandemic data sharing, genomic initiatives, population registries, patient-led rare disease infrastructures, and regional data spaces. Across these domains, experience suggests that durable progress depends less on unrestricted openness than on calibrated access, privacy-preserving architectures, clear accountability, and sustained public engagement. We conclude by proposing a pragmatic ethical orientation for healthcare open data: treat openness as a spectrum of controlled sharing arrangements, embed equity and reciprocity into governance, and institutionalize trust-building measures that can persist beyond emergencies and political cycles.

Data colonialism↗
PubMed2006

Development of the MAGIC congenital heart disease catheterization database for interventional outcome studies.

As the field of catheter-based therapies for congenital heart disease continues to expand, we lack the evidence-based data to make appropriate therapeutic decisions in the catheterization laboratory. A stumbling block to the determination of evidence-based therapies is our inability to simply and reliably share outcome data across multiple centers. We investigated whether a commonly used congenital heart disease catheterization database program (PedCath) could be used as an automatic catheterization data submission tool to a central database for outcome analysis. To test the feasibility of such a tool for collaborative outcomes research we formed a national group of seven congenital heart disease centers, the Mid-Atlantic Group of Interventional Cardiology, to warehouse and analyze catheterization data. We successfully modified PedCath to transfer the results of catheter-based therapies on 256 therapeutic procedures for atrial septal defect (ASD) closure, coarctation of the aorta angioplasty and stenting, and pulmonary and aortic balloon valvuloplasties over a 13-month pilot period. Short-term follow-up within the 13-month period was received on 31 patients. This study demonstrated the successful development of a simple process, requiring minimal data entry for investigators from around the world to share cardiac catheterization data for long-term outcome determination of catheter-based therapies for congenital heart disease.

Adolescent↗
PubMed2005

Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI.

The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10-20 system without using the subject's magnetic resonance image (MRI). In the current study, we demonstrate our method with a new statistical model to facilitate group studies and provide information on different components of variability. We adopt an analysis similar to the single-factor one-way classification analysis of variance based on random effects model to examine the variability involved in our improvised method of probabilistic registration of fNIRS data. We tested this method by registering head surface data of twelve subjects to seventeen reference MRI data sets and found that the standard deviation in probabilistic registration thus performed for given head surface points is approximately within the range of 4.7 to 7.0 mm. This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community.

Adult↗
PubMed2006

The politics of end-of-life decision-making: computerised decision-support tools, physicians' jurisdiction and morality.

With the increasing corporate and governmental rationalisation of medical care, the mandate of efficiency has caused many to fear that concern for the individual patient will be replaced with impersonal, rule-governed allocation of medical resources. Largely ignored is the role of moral principles in medical decision-making. This analysis comes from an ethnographic study conducted from 1999-2001 in three US Intensive Care Units, two of which were using the computerised decision-support tool, APACHE III (Acute Physiological and Chronic Health Evaluation III), which notably predicts the probability that a patient will die. It was found that the use of APACHE presents a paradox regarding concern for the individual patient. To maintain jurisdiction over the care of patients, physicians share the data with the payers and regulators of care to prove they are using resources effectively and efficiently, yet they use the system in conjunction with moral principles to justify treating each patient as unique. Thus, concern for the individual patient is not lessened with the use of this system. However, physicians do not share the data with patients or surrogate decision-makers because they fear they will be viewed as more interested in profits than patients.

APACHE↗
PubMed1995

Health care report cards. Validity of case definitions.

Administrative databases are increasingly being used to construct health care report cards. We analyzed information from one of the original report cards, the Medicare Hospital Information Project. Assessment of mortality statistics for three clinical entities--coronary artery bypass surgery, hip reconstruction, and treatment of sepsis--demonstrated widespread outcome variances that reflected imperfect definitions rather than performance issues in clinical care. The use of administrative data sets to design report cards requires clinical expertise to ensure validity of the data. Designers of report card measures should share preliminary data with providers to enable feedback in methods and uncover definitional and validity concerns before widespread dissemination.

Bias↗
PubMed2002

Regression models for allele sharing: analysis of accumulating data in affected sib pair studies.

Advances in human genome mapping have led to the identification of large numbers of genetic markers that allow systematic searches for multiple disease susceptibility genes for complex traits. A common design involves the recruitment of families with at least two children affected with the disease of interest. The objective is to find chromosomal regions that harbour susceptibility genes for the disease. The affected children, their parents if available, and sometimes other, unaffected, siblings are genotyped using sets of microsatellite DNA markers representing chromosomal sites distributed across the genome. Each marker can occur in several different variants known as alleles, and a pair of alleles constitutes the marker genotype. Each child randomly inherits one of their mother's two alleles and one of their father's two alleles. If a marker is close to a disease susceptibility gene, then affected siblings are expected to have more sharing of the same maternal and/or paternal marker alleles. Statistical methods are used to estimate the distribution of allele sharing in each affected sib pair (ASP) using the set of markers typed across each chromosome, and to test for the presence of excess sharing in the families as a group at each point across the genome. Regression models that allow the allele sharing proportions to depend on characteristics of the family such as diagnostic subtype or ethnic background have been developed to address the heterogeneity that is characteristic of complex disease, but these have not yet been widely applied. In this paper, we apply regression modelling to investigate variation associated with family-level covariates and with the order in which families are recruited and genotyped. We also discuss how some of the concepts of group sequential analysis apply to accumulating data from genome scans of complex disease.

Adolescent↗
PubMed2002

Using Internet GIS technology for sharing health and health related data for the West Midlands Region.

Recent government legislation highlights the need for co-operative working by government agencies to improve the overall health of people and to help reduce the existing health inequalities in England. To effectively tackle health inequalities, access to a range of timely and relevant data sets about a region is vital. The Multi-Agency Internet Geographic Information Service (MAIGIS) project is a 3-yr pilot project funded by the Public Health Development Fund to establish an interactive map-based web site for sharing health and health related data for the West Midlands Region (http://maigis.wmpho.org.uk). Data sets within the MAIGIS project follow three broad themes of health, socio-economic and environmental information. Data are made available by different organisations and shared using geography as the linking theme. This paper discusses the use of Internet GIS technology for sharing health and health related data based on the issues that arose during the formative period of the MAIGIS project. Issues such as data confidentiality, amalgamation and copyright are discussed and the technical development of the project is outlined. The links that MAIGIS has formed with other regional and national initiatives for the sharing of health and health related information are also presented. Finally, the future work programme for the MAIGIS project is summarised.

Data Collection↗
PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗
PubMed2003

Foodborne disease in Australia: incidence, notifications and outbreaks. Annual report of the OzFoodNet network, 2002.

In 2002, OzFoodNet continued to enhance surveillance of foodborne diseases across Australia. The OzFoodNet network expanded to cover all Australian states and territories in 2002. The National Centre for Epidemiology and Population Health together with OzFoodNet concluded a national survey of gastroenteritis, which found that there were 17.2 (95% C.I. 14.5-19.9) million cases of gastroenteritis each year in Australia. The credible range of gastroenteritis that may be due to food each year is between 4.0-6.9 million cases with a mid-point of 5.4 million. During 2002, there were 23,434 notifications of eight bacterial diseases that may have been foodborne, which was a 7.7 per cent increase over the mean of the previous four years. There were 14,716 cases of campylobacteriosis, 7,917 cases of salmonellosis, 505 cases of shigellosis, 99 cases of yersiniosis, 64 cases of typhoid, 62 cases of listeriosis, 58 cases of shiga toxin producing E. coli and 13 cases of haemolytic uraemic syndrome. OzFoodNet sites reported 92 foodborne disease outbreaks affecting 1,819 persons, of whom 5.6 per cent (103/1,819) were hospitalised and two people died. There was a wide range of foods implicated in these outbreaks and the most common agent was Salmonella Typhimurium. Sites reported two outbreaks with potential for international spread involving contaminated tahini from Egypt resulting in an outbreak of Salmonella Montevideo infection and an outbreak of suspected norovirus infection associated with imported Japanese oysters. In addition, there were three outbreaks associated with animal petting zoos or poultry hatching programs and 318 outbreaks of suspected person-to-person transmission. Sites conducted 100 investigations into clusters of gastrointestinal illness where a source could not be identified, including three multi-state outbreaks of salmonellosis. OzFoodNet identified important risk factors for foodborne disease infection, including: Salmonella infections due to chicken and egg consumption, bakeries as a source of Salmonella infection, and problems associated with spit roast meals served by mobile caterers. There were marked improvements in surveillance during 2002, with all jurisdictions contributing to national cluster reports, increasing use of analytical studies to investigate outbreaks and 96.9 per cent of Salmonella notifications on state and territory surveillance databases recording complete information about serotype and phage type. During 2002, there were several investigations that showed the benefits of national collaboration to control foodborne disease. Sharing surveillance data from animals, humans and foods and rapid sharing of molecular typing information for human isolates of potentially foodborne organisms could further improve surveillance of foodborne disease in Australia.

Adolescent↗