Performance Monitoring and Competence Assessment in Health Services

Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.     

Modeling Variability Order: A Semiparametric Bayesian Approach

In comparing two populations, sometimes a model incorporating a certain probability order is desired. In this setting, Bayesian modeling is attractive since a probability order restriction imposed a priori on the population distributions is retained a posteriori. Extending the work in Gelfand and Kottas (2001) for stochastic order specifications, we formulate modeling for distributions ordered in variability. We work with Dirichlet process mixtures resulting in a fully Bayesian semiparametric approach. The details for simulation-based model fitting and prior specification are provided. An example, based on two small subsets of time intervals between eruptions of the Old Faithful geyser, illustrates the methodology.     

A graph search algorithm for indoor pursuit/evasion

Using concepts from both robotics and graph theory, we formulate the problem of indoor pursuit/evasion in terms of searching the nodes of a graph for a mobile evader. We present the IGNS (Iterative Greedy Node Search) algorithm, which performs offline guaranteed search (i.e. no matter how the evader moves, it will eventually be captured). Furthermore, the algorithm produces an internal search (the searchers move only along the edges of the graph; “teleporting” is not used) and exploits non-monotonicity, extended visibility and finite evader speed to reduce the number of searchers required to clear an environment. We present search experiments for several indoor environments, in all of which the algorithm succeeds in clearing the graph (i.e. capturing the evader).     

Water sorption and electrical/dielectric properties of organic-inorganic polymer blends

Organic-inorganic polymer blends (OIPB) were obtained by reaction of organic and inorganic oligomers. The organic oligomer was synthesized with 2,4-toluene diisocyanate (TDI) and oligooxypropylene glycols (OPG) with various molecular weights (MW). The inorganic component was a water solution of sodium silicate. The OIPB obtained are hydrophilic and have great water sorption ability (the relative weight of sorbed water reaches 2000 %). The kinetics of water sorption and the changes of electrical conductivity during sorption were studied. Sorption ability, and mechanical, electrical and dielectric properties of OIPB depend on molecular weight of OPG: conductivity increases with increasing MW, whereas the sorption ability correlates with the mechanical properties. The influence of the inorganic phase content on the electrical and dielectric properties was studied as well.