Risks associated with clinical databases.

P Y Eleazar

Home/Archive/PubMed

PubMed · 10114766

Risks associated with clinical databases.

P Y Eleazar

View original source ↗Export metadata ↓

Abstract

Providers will succeed who are evaluating themselves, and who are continuously striving to examine who they are and where they are going. Conscientious providers know that countless other agencies have them under the microscope and that they have to work to stay ahead in assessing their actions through their clinical database. "Medical care value purchasing" is what every employer and payor is looking for, and providers need to find ways to illustrate cost in relation to quality. The basics of data security and protection should be in place in order to concentrate on the bigger picture. The knowledge of the risk associated with individual hospital databases as well as the risk associated with comparative databases is critical. The hospital-level clinical database is the hub of the wheel. If the risk there can be minimized, the data headed for various investigative sites will have less inherent risk. When it is really recognized and accepted that all financial decisions are made based upon the clinical data generated at the site of care, then data integrity will become a strategic advantage for the industry. Clinical database goals will, over time, cause minimization of risk at all levels. As this occurs, variation in treatment will be explained artfully.

Explore related subjects

Clinical Medicine Computer Security Hospital Information Systems Planning Techniques Risk Management United States Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

P Y Eleazar. 1991. Risks associated with clinical databases.. https://pubmed.ncbi.nlm.nih.gov/10114766/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

[Sexual medicine].

Explore the source record for details and available documents.

Clinical Medicine↗

PubMed2007

Video and qualitative research: analysing medical practice and interaction.

CONTEXT: Video has long been recognised as providing an important resource within medical education, particularly, perhaps, for training in primary health care. As a resource for research, and more specifically within qualitative social science studies of medical practice, video has proved less pervasive, despite its obvious advantages. METHODS: In this paper, we sketch an approach to using video to inform the analysis of medical practice and the ways in which health care is accomplished through social interaction and collaboration. Drawing on our own research, we discuss two brief examples: the first involves the use of computing technology in primary health care and the second concerns informal instruction during surgery. The examples illustrate the multimodal character of medical work, how activities are accomplished through the interplay of talk, the visual and the use of material artefacts. They also illustrate the ways in which video provides access to the complex forms of social interaction and collaboration that underpin health care. DISCUSSION: We reflect upon the research opportunities afforded by video and the ways in which video-based studies of interaction can contribute to the practice and practicalities of medicine.

Clinical Medicine↗

PubMed2006

Predictive data mining in clinical medicine: current issues and guidelines.

BACKGROUND: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments. PURPOSE: The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine. METHODS: We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons. RESULTS: The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this field. CONCLUSIONS: Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures is mandatory for their deployment and the dissemination of results. Thanks to the integration of molecular and clinical data taking place within genomic medicine, the area has recently not only gained a fresh impulse but also a new set of complex problems it needs to address.

Clinical Medicine↗