Suchergebnisse

Volltext // Exemplar mit der Signatur: München, Bayerische Staatsbibliothek -- H.eccl. 353 h

OBJECTIVES. Given the many profound health care problems facing Russia and the other former Soviet republics, there are a number of fundamental policy questions that deserve close attention as part of the reform process. METHODS. Summary data regarding Soviet health care issues were drawn from government agency reports, scholarly books and journals, recent press reports, and the authors' personal research. RESULTS. Smoking, alcohol, accidents, poor sanitation, inadequate nutrition, and extensive environmental pollution contribute to illness and premature mortality in Russia and the other newly independent states. Hospitals and clinics are poorly maintained and equipped; most physicians are poorly trained and inadequately paid; and there is essentially no system of quality management. While efforts at reform, which emphasize shifting to a system of "insurance medicine," have been largely unsuccessful, they have raised several important policy issues that warrant extensive research and discussion. CONCLUSIONS. Without considering the implications and consequences of alternative policy directions, Russia and the other states face the very real possibility of developing health care systems that improve the overall level of care but also incorporate limited access and escalating costs. Russian health care reform leaders can learn from the health care successes in the West and avoid repeating our mistakes.

OBJECTIVES: Given the declining health status of the Russian population and the negative social impact of ongoing economic reforms, it is important to understand the nature and scope of Russia's innovations in health care financing. METHODS: Data on Russian health care and its financing were gathered from Russian newspapers and journals. US government agency reports, recent press accounts, and the authors' observations and interviews in Russia. RESULTS: The 1991 statutory basis for the Russian mandatory medical insurance system replaced the traditional, state-funded medical care system with a regional system principally reliant on an enterprise-based with-holding tax plus supplementation by local government and, to a minor extent, federal funds. The regional agent for distribution and management of these funds is a series of Territorial Health Insurance Funds. Implementation thus far has been highly uneven among territories. CONCLUSIONS: An insurance model patterned after the Western example may not be the optimal solution to Russia's current health financing problems. Given the chaotic nature of political and economic reform, Russia may simply not be ready for market-based medical insurance.

On May 22, 2009, Massey University's Wellington campus hosted many speakers addressing the conference on war reporting jointly organised by the International Committee of the Red Cross. Media speakers included Television New Zealand's Sunday programme reporter Cameron Bennett; Radio NZ political editor Brent Edwards; Fairfax NZ reporter Michael Field; Fairfax Media editorial development manager Clive Lind; Pacific Media Centre director and AUT University associate professor Dr David Robie; freelance foreign correspondent Jon Stephenson; and Radio NZ International news editor Walter Zweifel. Commentaries, in some cases transcribed from recordings of proceedings, have been abridged. This transcript was compiled by Massey journalism programme lecturer Alan Samson.

Introduction There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. Methods A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. Results The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024). Conclusion Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.