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A Spanish Pillbox App for Elderly Patients Taking Multiple Medications: Randomized Controlled Trial

Background Nonadherence and medication errors are common among patients with complex drug regimens. Apps for smartphones and tablets are effective for improving adherence, but they have not been tested in elderly patients with complex chronic conditions and who typically have less experience with this type of technology. Objective The objective of this study was to design, implement, and evaluate a medication self-management app (called ALICE) for elderly patients taking multiple medications with the intention of improving adherence and safe medication use. Methods A single-blind randomized controlled trial was conducted with a control and an experimental group (N=99) in Spain in 2013. The characteristics of ALICE were specified based on the suggestions of 3 nominal groups with a total of 23 patients and a focus group with 7 professionals. ALICE was designed for Android and iOS to allow for the personalization of prescriptions and medical advice, showing images of each of the medications (the packaging and the medication itself) together with alerts and multiple reminders for each alert. The randomly assigned patients in the control group received oral and written information on the safe use of their medications and the patients in the experimental group used ALICE for 3 months. Pre and post measures included rate of missed doses and medication errors reported by patients, scores from the 4-item Morisky Medication Adherence Scale (MMAS-4), level of independence, self-perceived health status, and biochemical test results. In the experimental group, data were collected on their previous experience with information and communication technologies, their rating of ALICE, and their perception of the level of independence they had achieved. The intergroup intervention effects were calculated by univariate linear models and ANOVA, with the pre to post intervention differences as the dependent variables. Results Data were obtained from 99 patients (48 and 51 in the control and experimental groups, respectively). Patients in the experimental group obtained better MMAS-4 scores (P<.001) and reported fewer missed doses of medication (P=.02). ALICE only helped to significantly reduce medication errors in patients with an initially higher rate of errors (P<.001). Patients with no experience with information and communication technologies reported better adherence (P<.001), fewer missed doses (P<.001), and fewer medication errors (P=.02). The mean satisfaction score for ALICE was 8.5 out of 10. In all, 45 of 51 patients (88%) felt that ALICE improved their independence in managing their medications. Conclusions The ALICE app improves adherence, helps reduce rates of forgetting and of medication errors, and increases perceived independence in managing medication. Elderly patients with no previous experience with information and communication technologies are capable of effectively using an app designed to help them take their medicine more safely. Trial Registration Clinicaltrials.gov NCT02071498; http://clinicaltrials.gov/ct2/show/NCT02071498 (Archived by WebCite at http://www.webcitation.org/6OJjdHVhD).

The Ex-Post Evaluation of the Minimum Local Reliability Level: An Enhanced Probabilistic Location Set Covering Model

In order to evaluate the behavior of three probabilistic location set covering models we propose a new method that allows the ex-post measurement of the so called “minimum local reliability level”, both under the independence and under the dependence assumption. We show experimentally, by means of a set of test problems, that the proposed loss-system version of Ball and Lins model (1993) does almost always achieve the required reliability level. Moreover, if we compare this new version with the other two already known probabilistic models and consider as a second additional criteria the least number of required vehicles, we show that the new version has a better behavior both under the independence and the dependence assumption. In this paper we further propose a new model with the aim of reducing the number of required vehicles while satisfying the fixed reliability level. Our new model is formulated like Ball and Lins model incorporating the servers workload estimate of the Binomial PLSCP of ReVelle and Hogan (1988). Finally, we check the precision of our ex-post evaluation method over the four considered models through a simulation study.

Decomposing profit inefficiency in DEA through the weighted additive model

An issue that has received little attention in the Data Envelopment Analysis literature is the decomposition of profit inefficiency by means of measures that account all sources of technical inefficiency. In this paper we introduce a new way to measure and decompose profit inefficiency through weighted additive models. All our results are derived from a new Fenchel-Mahler inequality using duality theory.

Accounting for slacks to measure and decompose revenue efficiency in the Spanish Designation of Origin wines with DEA

In this paper, we show how Data Envelopment Analysis (DEA) may be used to measure and decompose revenue inefficiency, taking into account all sources of technical waste in the context of an application to assess the Spanish quality wine sector, in particular Designation of Origin (DO) wines. We try to go beyond the standard approaches, which use Shephard distance functions or directional distance functions, to provide decomposition that incorporates slacks as a source of technical inefficiency. To accomplish this, we will base our analysis on a recent approach introduced in Cooper et al. (2011a). In particular, we show how an output-oriented version of the Weighted Additive model can be used to properly identify revenue, technical, and allocative inefficiencies in Spanish DOs. In the application, we conclude that the main source of revenue inefficiency in this sector is technical waste, and that Cava can be highlighted as the DO that performs as a benchmark for more numbers of units.

A MONTE CARLO EVALUATION OF SEVERAL TESTS FOR THE SELECTION OF VARIABLES IN DEA MODELS

Data Envelopment Analysis (DEA) is a recently developed methodology that is widely used for estimating relative efficiency scores of Decision Making Units (DMUs) that use several inputs to produce several outputs. Model specification in DEA includes aspects such as the choice of inputs and outputs or the adoption of a returns to scale assumption. As pointed out by many authors, it is obvious that the specification of a model is the key to having reliable efficiency scores. In this paper, we are particularly concerned with the selection of variables in DEA models. To be specific, we investigate the performance of several statistical tests existing in the literature that can be used for the selection of variables. In particular, the behaviour of the well-known tests proposed by Banker2 and the nonparametric tests recently developed by Pastor et al.13 is analyzed in relation to several factors such as sample size, model size, the specification of returns to scale and the type and level of inefficiency. We have drawn some conclusions that will be of help for practical uses, since the observed behaviour of the tests in the different scenarios determined by the specifications of the mentioned factors may provide some useful insight into the choice of an adequate statistical test in the particular context of a given DEA application.

Benchmarking in Healthcare: An Approach Based on Closest Targets

This chapter examines the process of benchmarking in healthcare. In particular, we focus on hospital performance. We are especially interested in showing that the determination of closest targets as a benchmarking technique has significant advantages over traditional Data Envelopment Analysis (DEA) methods for signaling keys for the inefficient hospitals to improve their performance. In doing so, we use a sample of hospital located in the eastern region of Spain. We show the computational problems related to the estimation of this type of targets and the ways that exist to solve such problems. Finally, we suggest in the application some guidance in relation to determining potential improvement targets for each of the inefficient units.

Safer virtual pillbox: assuring medication adherence to elderly patients

In this paper, we describe a new mobile application for assisting to elderly patients and their caregivers (familiars, physicians, pharmacist) in the medication management at their homes. This mobile application will help patients to maintain medication adherence in a reliable and correctly intake of their drugs. The main target users of this application are polymedicated, polypathology and over 65 years old patients. The application was designed using User Centered Design (UCD) methods, taking always into account the characteristics of these target users, so usability and accessibility principles were applied in the design of interfaces. The design has been tested with a pre-test group of selected patients and we have confirmed that patients using this mobile application have increased their medication adherence and have reduced the frequency of adverse drug events.

Loss Distance Functions and Profit Function: General Duality Results

The concept of loss distance functions is introduced and compared with other functional representations of the technology including the Holder metric distance functions (Briec and Lesourd (1999)), the directional distance functions due to Chambers et al. (1996, 1998), and the Shephard’s input and output distance functions as particular cases of the directional distance functions. Specifically, it is shown that, under appropriate normalization conditions defined over the (intrinsic) input and output prices, the loss distance functions encompass a wide class of well-known and much less known distance functions. Additionally, a dual correspondence is developed between the loss distance functions and the profit function, and it is shown that all previous dual connections appeared in the literature are special cases of this general correspondence. Finally, we obtain several interesting results assuming differentiability.

Evaluating the usability and acceptance of an AR app in learning Chemistry for Secondary Education

Teaching concepts that require the manipulation or visualization of objects that are not available to everyone because of their abstract, scientific or spatial nature present some limitations. Augmented reality and mobile devices are tools available today that allow solving these shortcomings and offer the possibility of interacting with virtual objects in a three-dimensional space. In this paper, we present an author tool for teaching Chemistry in Secondary Education, using augmented reality and then we provide the results of the evaluation of usability that we conducted with 12 high school students in order to evaluate the usability and user experience of the developed app. We have found in this study that, surprisingly, it seems that users of iOS mobiles are better to manipulate AR objects in 3D using the Android mobile we used in this experiment.