Suchergebnisse

Illustrates the sharp increase in bank lending to UK property
companies, which has occurred between 1985 and 1989. Considers some of
the reasons, not all of which relate to the property market. Also
examines the varying attitudes banks will have to different types of
lending proposals, and this is expanded to look at the variety of roles
which a bank may assume, be it as lender, arranger, or partner. Provides
insights into how banks will analyse the risk inherent in any particular
proposal. Finally, suggests how prospective borrowers might find a
suitable bank and present their proposals.

All 223 psychiatric clinical tutors in the UK were surveyed on their use of DSM-111 in their postgraduate training programmes. Results indicate that DSM-III is widely used in clinical training to some extent in the majority of schemes (73%) and in the schemes where it is used it is rated as moderately useful by nearly everyone (93%). DSM-III is generally perceived to have positive effects on learning basic elements of psychopathology and in offering a common language for diagnostic discussion. Interestingly only 16% of users felt that DSM-III was so complicated that it impeded rather than facilitated the teaching of diagnosis. Few schemes provide specific teaching courses and case conferences provide the major forum for teaching DSM- III.

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.