Implementing breast cancer decision aids in community sites: barriers and resources
ABSTRACT To assess the feasibility of implementing four patient decision aids (PtDAs) for early stage breast cancer treatment decisions into routine clinical care in community settings.
There is very limited information available about implementing decision aids into routine clinical practice and most of this information is based on academic centres; more information is needed about implementing them into routine clinical practice in community settings.
Structured individual interviews.
Providers from 12 sites, including nine community hospitals, a community oncology centre and two academic centres.
Usage data, barriers to and resources for implementing the PtDAs.
Nine of the 12 sites were using the PtDAs with patients. All of the sites were lending the PtDAs to patients, usually without a formal sign-out system. The keys to successful implementation included nurses' and social workers' interest in distributing the PtDAs and the success of the lending model. Barriers that limited or prevented sites from using the PtDA included a lack of physician support, a lack of an organized system for distributing the PtDAs and nurses' perceptions about patients' attitude towards participation in decision making.
It is feasible to implement PtDAs for early stage breast cancer into routine clinical care in community settings, even with few resources available.
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ABSTRACT: This paper considers the model reduction problem for discrete-time stochastic systems. Two approaches are presented. The first approach is based on viewing the model reduction problem as a reduced order stochastic realization problem. In this approach the state vector for the realization is picked form the canonical decomposition of the Hankel matrix which is obtained from the cross-covariance of the future with the past. Furthermore this choice provides a special ordering for the state vector. Using this ordering and a measure for the mutual information between the past and the future an approximation scheme is developed which leads to the new reduced order realization algorithm. Next the concept of balanced stochastic realization is developed. Using this notion the second approach for model reduction is obtained. In this approach a transformation is derived by appropriately factoring the solutions of algebraic Riccati equations. Use of this transformation then leads to the balanced stochastic realization Whose subsystem gives essentially the same reduced order model as that given by the first approach. Uniqueness and symmetry results for the balanced realization are given.Decision and Control, 1982 21st IEEE Conference on; 01/1983