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There is increasing pressure to effectively treat patients with complex care needs from the moment of admission to the general hospital. In this study, the authors developed a measurement strategy for hospital-based care complexity. The authors' four-factor model describes the interrelations between complexity indicators, highlighting differences between length of stay (LOS), objective complexity (such as medications or consultations), complexity ratings by the nurse, and complexity ratings by the doctor. Their findings illustrate illustrate limitations in the use of LOS as a sole indicator for care complexity. The authors show how objective and subjective complexity indicators can be used for early and valid detection of patients needing interdisciplinary care.

The authors linked admission risk factors to a series of indicators for complex care delivery to enable detection of patients in need of care coordination at the moment of admission to the general hospital. The authors found 13 risk factors to be predictive of more than one indicator of care complexity. An admission risk screening procedure to detect patients in need of care coordination should focus on these risk factors and should include predictions made by doctors and nurses at admission and information collected from the patient and the medical chart.

Aims and objectives. The relationship between mental disturbances - anxiety and depression, somatization and alcohol abuse - on admission to internal medicine units and perceived complexity of care as indicated by the nurse at discharge was studied. The goal was to study the utility of short screeners for mental disturbances to select patients for case-management on admission. Design. The study had a cohort design: patients were included on admission and followed through their hospital stay until discharge. The study was conducted within the framework of the European Biomed 1 Risk Factor study. Research methods and instruments. In the first 3 days of admission the patients were interviewed by a trained health care professional, who scored the SCL-8D, a somatization questionnaire based on the Whiteley-7 and the CAGE. At discharge, nurses rated the complexity of the patient's care. Results. Patients with high scores on anxiety and depression (SCL-8D) and on somatization received higher ratings on perceived nursing complexity than those with low scores, with and without control for age, severity of illness and chronicity. The actual nursing intensity and medical care utilization, as measured daily by means of a checklist, could not explain these relations. No differences were found between patients with high or low scores on alcohol abuse. Conclusions. The study shows a potential use of screeners for mental disturbances to detect patients for whom nurses might need additional help. However, mental disturbance is not the sole criterion: functional status and other variables that predict medical and nursing care utilization should be included in a screening strategy for case-management programmes.

DICOM is today's de-facto standard for exchanging medical images. Since new image acquisition devices produce more and more image and non-image data, image compression has become an important part of the standard. However, the compression of non-pixel data also stored in DICOM data sets has been disregarded up to now. In the scope of an EU research project we have examined a large amount of real-world DICOM images to test whether or not there is a potential for compressing the non-pixel attributes. As a result we have found out that it is indeed possible to reduce the size of header information in DICOM data sets significantly. However, the contribution to the overall compression ratio is very small. We conclude that compression of DICOM header information is only interesting for data sets which mainly consist of non-pixel data. Since new DICOM modalities introduce more and more of such data, the importance of this issue is likely to increase. Also many DICOM network services are based on more or less large 'textual' data structures. Especially for use with narrow-band networks extensions as proposed in this paper could be a solution to save valuable bandwidth.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Purpose The aim of the study was to determine the risk of mortality, predicting by the Refined Multiscale Entropy (RMSE) analysis of heart failure patients with ejection fraction lower than 0.35. Method Refined Multi-Scale Entropy (RMSE) was proposed as a refinement of MSE, since it offers a way to solve two shortcomings that produce the MSE dependence on variance and on the shape of the power spectrum of the considered series. MSE is based on three steps: 1) progressive elimination of the fast time scales; 2) coarse graining procedure necessary to assess entropy rate; 3) calculation of the entropy rate. 168 patients (aged 65.7±1.2 years, M/F: 76/92 86.9%) were enrolled in this study. Patients were followed for 36 months. 24 h Holter registrations were performed in every 2 weeks. The high risk group (HRG) contains 27 patients, who had either sudden cardiac death, or death due to progressive heart failure, while the low risk group (LRG) contains 141 alive patients. Beside the descriptive statistics of the entropy values at the different scales, a multivariate discriminant model was developed, where the input variables were the change of the individual entropy/scale in time (72 Holter measurement for each patient), the output parameters were death (HRG) or alive (LRG). Results All RMSE curves had a minimum at short time scale, followed by an exponential increment with t. Risk groups showed significant differences over a large interval of scales (t=4–15), being the entropy-based complexity smaller in HRG than in LRG group. These results indicate a reduction of the HRV complexity in patients of death group. The largest statistical differentiation was obtained at the time scale t=8 with a p-value=0.0024 (LRG: SE=1.312±0.015 and HRG: SE=1.107±0.022). The Wilks9 statistic showed an excellent prediction (Wilks9 λ was 0.002 p

The authors developed a screening instrument to detect patients in need of complex care coordination at admission to a general hospital. On the basis of a series of risk factors for care complexity, the authors constructed a short, care complexity prediction instrument (COMPRI) and assessed its qualities. The COMPRI is an easily administered screening instrument that detects patients at risk for complex care needs for whom care coordination is indicated. COMPRI's predictive power exceeds all currently available case-mix instruments.