SEARCH · PubMed Health
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
This project examined which child health data would be appropriate and useful to integrate with an existing real-time Michigan Department of Community Health (MDCH) application, such as the Michigan Childhood Immunization Registry. A consultant was secured to conduct data gathering analysis activities. An advisory committee of MDCH Administrators convened to guide the project. Interviews were conducted with MDCH administrators, program managers and representatives from the public and private health care provider community. These interviews focused on answering three main questions: (1) What MDCH data resources do you currently use? (2) How do you use and access these data? and (3) What is your vision for expanding, enhancing, and linking these data to meet Michigan's future health goals? Acceptance of the Michigan Childhood Immunization Registry by the pediatric and family practice community demonstrates the utility of providing more information electronically to the medical community in Michigan. The MDCH has completed a stakeholders analysis and is moving forward with requirements gathering sessions in order to create an integrated child health data system. The integrated child health data system will include data from immunizations, newborn screening, newborn hearing, lead, the Women, Infants, and Children program, and Medicaid.
Explore the source record for details and available documents.
The concept of the common medical information space proposes that there are available health data kept in the data bases of various automatic medical systems. This data integration is achieved by integrating computers into the network, which will make a regular exchange of data among the functionally related medical institutions. The above concept of the common medical informational space is a basis for organizing a comprehensive monitoring for the health status of children (including the ecomedical aspect) at the basically new technological level, creating conditions for practical implementation of the concept of the continuum of the developing body's states, which will take into account the influence of many interacting factors on the developing fetus and child.
Five key surveillance issues faced by the National Center for Prevention Services for the next 10 years are: 1) developing surveillance for risk factors, 2) agreeing on and standardizing definitions, 3) integrating surveillance systems into general public health information systems, 4) integrating surveillance and public health information systems into patient care data systems, and 5) integrating health service and programmatic data into surveillance and public health information systems. Current technology and public health needs combine to make development of integrated, compatible surveillance and information systems essential, not just desirable.
Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.
Today, the process of mental health assessments and treatment are difficult to describe and assimilate into other medical areas. Integrated patient summaries and treatment descriptions are poorly standardized. Any aggregate data analysis must rely on the very few standardized patient data points that may include some demographic information, diagnosis and codable procedures. This paper describes one of the first published attempts to use business process reengineering (BPR) Integrated Computer Assisted Manufacturing Definition (IDEF) activity and data modeling within a focused clinical setting. The clinician's desire to focus on patient care has been used to create both a current and an idealized activity and data model. Clinical patient information was used to build an analyzable database. This provides the potential to track a patient in data throughout a continuum of care. Conceptually, outcomes management is able to use clinical, rather than administrative or claims, data. These models were used to create a prototype for a computer-based patient record which would allow outcomes management. It was tested successfully as a proof of concept.
PURPOSE: In the development of integrated care, there is an increasing need for knowledge about the actual degree of integration between different providers of health services. The purpose of this article is to describe the conceptualisation and validation of a practical model for measurement, which can be used by managers to implement and sustain integrated care. THEORY: The model is based on a continuum of integration, extending from full segregation through intermediate forms of linkage, coordination and cooperation to full integration. METHODS: The continuum was operationalised into a ratio scale of functional clinical integration. This scale was used in an explorative study of a local health authority in Sweden. Data on integration were collected in self-assessment forms together with estimated ranks of optimum integration between the different units of the health authority. The data were processed with statistical methods and the results were discussed with the managers concerned. RESULTS: Judging from this explorative study, it seems that the model of measurement collects reliable and valid data of functional clinical integration in local health care. The model was also regarded as a useful instrument for managers of integrated care. DISCUSSION: One of the main advantages with the model is that it includes optimum ranks of integration beside actual ranks. The optimum integration rank between two units is depending on the needs of both differentiation and integration.
The PAIN indicators create an excellent platform for identifying appropriate patients for whom pain management should be improved within a health plan. The PAIN indicators can be applied efficiently to standard health plan integrated claims data from which a health plan can implement a flexible, phased approach to improving pain management. The program also can be expanded beyond OA and persistent LBP to affect a broader high-risk group with evidence of poorly controlled pain. Pain management has traditionally been an area that has been difficult to target; therefore, pain-specific quality-management initiatives have not been implemented in most health plans. The PAIN indicators can serve as a valuable health plan tool that can be readily implemented and will allow a health plan to advance its overall quality of care in the important area of pain management.
OBJECTIVE: To outline the main issues related to the impact of the data generated by the Human Genome Project on health information systems. A major challenge for medical informatics is identified, consisting of adapting traditional systems to new genetic-based diagnostic and therapeutic tools. METHODS: Reviewing and analysing the different health information levels from an organisational complexity point of view. A model is proposed to explain the interactions between health informatics, bioinformatics and molecular medicine. RESULTS: We suggest a new framework that integrates genetic data into health information systems. Using this model, new topics for future research and development are identified. CONCLUSIONS: We are witnessing the birth of a new era (post-genomics). In this era technological advancements in genomics offer new opportunities for clinical applications. Medical informaticians should play an important role in this new endeavour.
BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.
The ETS-domain transcription factor family can be divided into a series of subfamilies. Elk-1 represents the founding member of the ternary complex factor (TCF) subfamily. By focusing on the TCF subfamily, we can demonstrate the complexities that exist in the function and regulation of ETS-domain transcription factors. This article focuses on Elk-1 in detail and summarizes the functions of other TCFs. The key themes covered include the domain structure of the TCFs, the mechanisms of complex formation with serum response factor, regulation of TCFs by mitogen-activated protein kinase cascades, and transcriptional regulatory properties of the TCFs. Finally, the emerging role of the TCFs in vivo is discussed. A picture is developing indicating that, while these proteins exhibit significant sequence and functional conservation, key differences in their structure and regulation are being identified which may relate to unique functions of these proteins in vivo.
This paper attempted to identify whether access to reproductive health services partly explains use of modern contraception in Malawi. Recent changes in Malawi's population policy have brought the state's population ambitions into alignment with the consensus reached at the International Conference on Population and Development held in Cairo in 1994. Concurrently, Malawi witnessed a large increase in the use of modern contraceptives from 7% in 1992 to 26% in 2000. A geographic information system (GIS) was employed to integrate health facility data from the Malawi health facilities inventory and global positioning data from the 2000 Malawi demographic and health survey. An effort to detect a plausible causal pathway was made by using distance to health services as a proxy variable for access to services. In a multivariate logistic regression analysis, after controlling for background variables traditionally associated with use of modern contraception, access could not be shown to explain use of modern contraception in Malawi.
School nurses and health office employees are the creators and caretakers of legal documentation. School nurses have an ethical and legal obligation to protect the integrity of electronic student health records. Although there are many threats to data integrity, from inadequate hardware to electrical surges, one of the most pervasive threats to data is computer viruses. There are many precautions that can be taken to protect electronic student health data from viruses in the school health office.
Explore the source record for details and available documents.
AE signal detection and its techniques are part of the continuum of public health surveillance, borrowing from both its theory and application (171). Like public health surveillance networks, whose major goals are to identify early signs of new outbreaks, pinpoint new organisms, and monitor designated illnesses, AE signaling and surveillance systems attempt to provide early warnings of previously unsuspected product-AE pairs, hypothesize potential drug-event relations, identify populations "at risk," and facilitate case ascertainment and definition. In both examples, definitive research is often subsequently undertaken to quantify the strength of relations that may be proposed. As with any public health surveillance effort, AE surveillance provides an infrastructure for the ongoing collection of health data and its direct integration into the health regulatory policy (172), including its keystone role in risk assessment and management. However, unlike many surveillance systems, postmarketing AE systems collect case information that is often relatively incomplete and imperfect, estimate exposure based on surrogate values (e.g., sales data), and are used by both governmental and the private sector for preventive planning. These factors make AE signaling and surveillance more ambiguous, regulatory oriented, and complex than its disease counterparts (173). Despite such issues, AE signaling methods continue to evolve in extent, complexity, and acceptance (4, 131, 174). Undoubtedly, this is largely due to the widespread practical experience that has been gained with spontaneous reporting systems over the past 4 decades and the cumulative usefulness that has been demonstrated.
Data Insight: Rising health care costs and high rates of comorbidity between psychological and chronic physical illnesses are prompting some organizations to integrate behavioral health services in the primary care setting, prompting a need for new models of reimbursement and risk-sharing.
BACKGROUND: Little is known about the relative reliability of medical record and clinical automated data, sources commonly used to assess diabetes quality of care. The agreement between diabetes quality measures constructed from clinical automated versus medical record data sources was compared, and the performance of hybrid measures derived from a combination of the two data sources was examined. METHODS: Medical records were abstracted for 1,032 patients with diabetes who received care from 21 facilities in 4 Veterans Integrated Service Networks. Automated data were obtained from a central Veterans Health Administration diabetes registry containing information on laboratory tests and medication use. RESULTS: Success rates were higher for process measures derived from medical record data than from automated data, but no substantial differences among data sources were found for the intermediate outcome measures. Agreement for measures derived from the medical record compared with automated data was moderate for process measures but high for intermediate outcome measures. Hybrid measures yielded success rates similar to those of medical record-based measures but would have required about 50% fewer chart reviews. CONCLUSIONS: Agreement between medical record and automated data was generally high. Yet even in an integrated health care system with sophisticated information technology, automated data tended to underestimate the success rate in technical process measures for diabetes care and yielded different quartile performance rankings for facilities. Applying hybrid methodology yielded results consistent with the medical record but required less data to come from medical record reviews.