A role for pharmacists in community-based post-discharge warfarin management: protocol for the 'the role of community pharmacy in post hospital management of patients initiated on warfarin' study

<p>Abstract</p> <p>Background</p> <p>Shorter periods of hospitalisation and increasing warfarin use have placed stress on community-based healthcare services to care for patients taking warfarin after hospital discharge, a high-risk period for these patients. A previous randomised controlled trial demonstrated that a post-discharge service of 4 home visits and point-of-care (POC) International Normalised Ratio (INR) testing by a trained pharmacist improved patients' outcomes. The current study aims to modify this previously trialled service model to implement and then evaluate a sustainable program to enable the smooth transition of patients taking warfarin from the hospital to community setting.</p> <p>Methods/Design</p> <p>The service will be trialled in 8 sites across 3 Australian states using a prospective, controlled cohort study design. Patients discharged from hospital taking warfarin will receive 2 or 3 home visits by a trained 'home medicines review (HMR)-accredited' pharmacist in their 8 to 10 days after hospital discharge. Visits will involve a HMR, comprehensive warfarin education, and POC INR monitoring in collaboration with patients' general practitioners (GPs) and community pharmacists. Patient outcomes will be compared to those in a control, or 'usual care', group. The primary outcome measure will be the proportion of patients experiencing a major bleeding event in the 90 days after discharge. Secondary outcome measures will include combined major bleeding and thromboembolic events, death, cessation of warfarin therapy, INR control at 8 days post-discharge and unplanned hospital readmissions from any cause. Stakeholder satisfaction will be assessed using structured postal questionnaire mailed to patients, GPs, community pharmacists and accredited pharmacists at the completion of their study involvement.</p> <p>Discussion</p> <p>This study design incorporates several aspects of prior interventions that have been demonstrated to improve warfarin management, including POC INR testing, warfarin education and home visits by trained pharmacists. It faces several potential challenges, including the tight timeframe for patient follow-up in the post-discharge period. Its strengths lie in a strong multidisciplinary team and the utilisation of existing healthcare frameworks. It is hoped that this study will provide the evidence to support the national roll-out of the program as a new Australian professional community pharmacy service.</p> <p>Trial Registration</p> <p>Australian New Zealand Clinical Trials Registry Number <a href="http://www.anzctr.org.au/trial_view.aspx?ID=82959">12608000334303</a>.</p

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao-Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics

Spatio-temporal tracking from natural language statements using outer probability theory

© 2018 Elsevier Inc. This work considers a target tracking problem where the observed information is in the form of natural language-type statements. More specifically, the focus is on a spatio-temporal tracking problem where each uttered expression may involve both spatial, motion and temporal uncertainty, and a general modelling framework for natural language statements of a rather general semantic form is developed. This framework involves the definition of some tuple that allows one to extract the common semantics from arbitrary parsed expressions conveying some canonical information. Given this tuple, an estimation and tracking method based on the concept of outer probability measures is introduced and an estimation algorithm for handling this temporal uncertainty, along with delayed and out-of-sequence information arrival, is developed. This framework allows for modelling imprecise information in a more general and realistic sense

Search for targets in a risky environment using multi-objective optimisation

This study develops an online unmanned aerial vehicle (UAV) path-planning strategy for autonomous search and localisation of targets in a risky environment that simultaneously optimises two objectives: (i) search and (ii) survival. The authors formulate two rewards (objective) functions corresponding to the two objectives and compute the Pareto front, formed by taking convex combinations of these rewards. In the extreme case of pure search, using entropy reduction as the reward, the trajectory of the UAV is reminiscent of a systematic search pattern. When combined with the survival objective, the search pattern appears more random, as the UAV intelligently trades off the reward of finding targets with the risk of being destroyed

Sensor scheduling for target tracking in large multistatic sonobuoy fields

Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. A sensor scheduling algorithm is required in order to optimise tracking performance by selecting which emitter sonobuoy should transmit in each time interval, and which waveform it should use. In this paper we describe a new long term sensor scheduling algorithm for sonobuoy fields, called the continuous probability states algorithm. This algorithm reduces the scheduling search space by keeping track of the probability that a target is undetected, rather than modelling all possible detection outcomes, which reduces the computation complexity of the algorithm. It is shown that this approach results in high quality tracking for multiple targets in a simulated sonobuoy field

Covariance Cost Functions for Scheduling Multistatic Sonobuoy Fields

Sonobuoy fields, comprising a network of sonar transmitters and receivers, are used to find and track underwater targets. For a given environment and sonobuoy field layout, the performance of such a field depends on the scheduling, that is, deciding which source should transmit, and which waveform should be transmitted at any given time. In this paper, we explore the choice of cost function used in myopic scheduling and its effect on tracking performance. Specifically, we consider 5 different cost functions derived from the predicted error covariance matrix of the track. Importantly, our cost functions combine both positional and velocity covariance information to allow the scheduler to choose the optimum source-waveform action. Using realistic multistatic sonobuoy simulations, we demonstrate that each cost function results in a different choice of source-waveform actions, which in turn affects the performance of the scheduler. In particular, we show there is a trade-off between position and velocity error performance such that no one cost function is superior in both

RRT* Trajectory Scheduling Using Angles-Only Measurements for AUV Recovery

Sensor trajectory optimisation involves extensive search over the sensor motion space against an optimisation criterion. The search under dynamic programming or fixed grid is often computationally nontrivial even for a myopic search scenario. In this paper, we study the problem of an autonomous underwater vehicle planning its return route to a moving recovery vessel. To complicate the issue, the AUV needs to localize the vessel using angle-only measurements. Accordingly, we propose a random sampling based trajectory planning algorithm that incorporates both a dynamic goal and the need to localize that goal. More precisely, we incorporate an information theoretic cost into a rapid-exploring random tree trajectory planning framework thus allowing the AUV to both localize and reach the recovery vessel. Our experimental results show that the proposed method may achieve the same trajectory optimisation performance as that under dynamic programming method but with greater computational efficiency