Pilot study to develop and test palliative care quality indicators for nursing homes

An increasingly frail population in nursing homes accentuates the need for high quality care at the end of life and better access to palliative care in this context. Implementation of palliative care and its outcomes can be monitored by using quality indicators. Therefore, we developed a quality indicator set for palliative care in nursing homes and a tailored measurement procedure while using a mixed-methods design. We developed the instrument in three phases: (1) literature search, (2) interviews with experts, and (3) indicator and measurement selection by expert consensus (RAND/UCLA). Second, we pilot tested and evaluated the instrument in nine nursing homes in Flanders, Belgium. After identifying 26 indicators in the literature and expert interviews, 19 of them were selected through expert consensus. Setting-specific themes were advance care planning, autonomy, and communication with family. The quantitative and qualitative analyses showed that the indicators were measurable, had good preliminary face validity and discriminative power, and were considered to be useful in terms of quality monitoring according to the caregivers. The quality indicators can be used in a large implementation study and process evaluation in order to achieve continuous monitoring of the access to palliative care for all of the residents in nursing homes

Homeomorphic Embedding for Online Termination of Symbolic Methods

Well-quasi orders in general, and homeomorphic embedding in particular, have gained popularity to ensure the termination of techniques for program analysis, specialisation, transformation, and verification. In this paper we survey and discuss this use of homeomorphic embedding and clarify the advantages of such an approach over one using well-founded orders. We also discuss various extensions of the homeomorphic embedding relation. We conclude with a study of homeomorphic embedding in the context of metaprogramming, presenting some new (positive and negative) results and open problems

Forward Slicing by Conjunctive Partial Deduction and Argument Filtering

Program slicing is a well-known methodology that aims at identifying the program statements that (potentially) affect the values computed at some point of interest. Within imperative programming, this technique has been successfully applied to debugging, specialization, merging, reuse, maintenance, etc. Due to its declarative nature, adapting the slicing notions and techniques to a logic programming setting is not an easy task. In this work, we define the first, semantics-preserving, forward slicing technique for logic programs. Our approach relies on the application of a conjunctive partial deduction algorithm for a precise propagation of information between calls. We do not distinguish between static and dynamic slicing since partial deduction can naturally deal with both static and dynamic data. Furthermore, this approach can quite easily be implemented by adding a new code generator on top of existing partial deduction systems. A slicing tool has been implemented in ECCE, where a post-processing transformation to remove redundant arguments has been added. Experiments conducted on a wide variety of programs are encouraging and demonstrate the usefulness of our approach, both as a classical slicing method and as a technique for code size reduction