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PubMed1997

Demystifying mental health information needs through integrated definition (IDEF) activity and data modeling.

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.

Ambulatory Care Information Systems↗
PubMed2002

Integrating genomics into health information systems.

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.

Databases, Genetic↗
PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

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.

Biosample identifiers↗
PubMed2002

Complexities in ETS-domain transcription factor function and regulation: lessons from the TCF (ternary complex factor) subfamily. The Colworth Medal Lecture.

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.

Amino Acid Sequence↗
PubMed2001

Spontaneous adverse event signaling methods: classification and use with health care treatment products.

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.

Adverse Drug Reaction Reporting Systems↗
PubMed2000

Study makes case for integrating behavioral health, primary care.

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.

Antipsychotic Agents↗
PubMed2002

Comparing clinical automated, medical record, and hybrid data sources for diabetes quality measures.

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.

Aged↗
PubMedDate not supplied

Maintaining data integrity in randomized clinical trials.

BACKGROUND: The process of attaining and maintaining data integrity is critical to ensure a successful randomized clinical trial. Methodologic strategies to achieve data integrity when repeated measures are used has not been discussed in detail in the literature. The National Institutes of Health requires that data integrity and safety monitoring boards or plans be established for randomized clinical trials. OBJECTIVES: The objectives of this paper are to (a) examine important data collection issues nurse scientists often encounter in randomized clinical trials and (b) present a process that researchers can apply to achieve data integrity. METHODS: The process to achieve data integrity is based on strategies that were developed by an interdisciplinary hospice research team involved in an ongoing National Institutes of Health-funded clinical trial. The process and key issues are illustrated with methodologic examples from the randomized clinical trial and supporting literature. RESULTS: The process of achieving data integrity involves developing protocols in three key areas: data collection, training of data collectors, and data monitoring. The use of these protocols will increase the rigor of the clinical trial and assist in maintaining study validity. CONCLUSIONS: Investigators conducting clinical trials need to consider all issues involved in achieving data integrity and have tested protocols in place throughout the study. These approaches will not only help maintain study validity but also help ensure data of sufficient quantity and quality to achieve the desired statistical power.

Bias↗
PubMed1996

The health belief model and consumer information searches: toward an integrated model.

Some health data organizations (HDOs) are producing consumer-level health services information. National reform proposals would suggest that competition between health plans will be developed through the use of outcome information. Policy makers have paid little attention to how consumers might use that information or how that information might be most effectively packaged for consumer use. This paper argues that marketing literature developed over the last ten to fifteen years could prove to be an informative resource for policy makers and the health services provider community alike. This paper suggests that combining a consumer decision model (CDM) with the health belief model (HBM) will provide an important step toward an increased understanding of consumer information search behavior. This integrated model could form the basis of future research in this important area.

Attitude to Health↗
PubMed1985

Costs of mandates for outpatient mental health care in private health insurance.

Various methods for estimating the cost of mandated mental health benefits have been devised, each resulting in substantially different estimates. These methods neglect to distinguish between the two components of cost to the insurer: social cost (due to increased utilization) and shifted cost (from other sources of payment). We apply a method we developed for estimating the two types of costs of mandates for outpatient mental health services that integrates data from insurers with information from the literature on financing of mental health services. We applied our method to legislation recently proposed in Massachusetts that would double the mandated minimum benefit level from +500 to +1,000. We expect payments by the largest carrier in the state to increase by a factor of 1.65. More than half of this increase represents shifted costs rather than new costs to society.

Ambulatory Care↗
PubMed2002

GIS and health care.

GIS and related spatial analysis methods provide a set of tools for describing and understanding the changing spatial organization of health care, for examining its relationship to health outcomes and access, and for exploring how the delivery of health care can be improved. This review discusses recent literature on GIS and health care. It considers the use of GIS in analyzing health care need, access, and utilization; in planning and evaluating service locations; and in spatial decision support for health care delivery. The adoption of GIS by health care researchers and policy-makers will depend on access to integrated spatial data on health services utilization and outcomes and data that cut across human service systems. We also need to understand better the spatial behaviors of health care providers and consumers in the rapidly changing health care landscape and how geographic information affects these dynamic relationships.

Bioterrorism↗
PubMed2000

An integrated data warehouse system: development, implementation, and early outcomes.

This paper describes a generic vision of global information flow and the development of an integrated data warehouse system, using clinical data on all patient encounters and administrative data on all operating transactions as part of an integrated health care system. This new integrated data warehouse system has been successfully used for multiple purposes, including patient care, health services research, resource utilization and feasibility studies. During 1999, core analyses included the electronic abstraction, aggregation, and analysis of data on over 400,000 patients. This approach to building a centralized data system comprised of multiple repositories efficiently meets a variety of individual and aggregate information needs, while reducing the need to create duplicate databases.

Database Management Systems↗
PubMed1991

The positive known association design: a quality assurance method for occupational health surveillance data.

Quality control must be an integral component of an occupational health surveillance program. The positive known association design offers the occupational health physician a method to test, on a population basis (ie, high periodic medical surveillance examination participation rates by the employees), the quality of periodic medical surveillance data. Several well-established biological associations were evaluated and observed in this study, including a dramatic relation between white blood cell counts and smoking. We highly recommend that the positive known association design be incorporated in the quality assurance procedures of occupational health surveillance programs.

Adult↗
PubMed1991

Special report. Integrating managed care. When managed care is a system.

In a comprehensive managed care system, all participants benefit from the improved quality and cost efficiency. When payers, beneficiaries, and providers communicate, share data, and participate in decision making, the result is more appropriate and more cost-efficient care. The following articles take a look at how each player in the health care system benefits from a comprehensive approach to health care. Payers (employers) benefit by being able to track the cost effectiveness of their health care through integrated data systems. Beneficiaries (employees and dependents) benefit by becoming more involved in the decisions being made by providers and third party payers about their care through patient education and advocacy programs. And health care providers benefit by gaining an increased leadership role in directing the management of their patients' care through practice guidelines. These guidelines will help doctors make decisions that lead to high quality care as well as build a foundation for standardized review criteria throughout the industry.

Clinical Protocols↗
PubMed1995

Drug utilization review in the managed care environment.

Traditional drug use evaluation (DUE) and Medicaid drug utilization review (DUR) prototypes do not meet the needs of managed care organizations. yet, creating entirely new systems for measuring quality drug use in managed care, such as HEDIS, needlessly discards the good clinical foundations already built in the other health care settings. A better solution would be for managed care to apply its hallmark traits, namely state-of-the-art electronic data management systems, integrated health care system interchange, and strong customer communication, to make the DUR process better. A strong union between clinical criteria and sophisticated health care management can revolutionize the DUR/DUE process.

Consumer Behavior↗
PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗