Transitions in Care: Medication Reconciliation in the Community Pharmacy Setting After Discharge

Objective: To assess the feasibility of a workflow process in which pharmacists in an independent community pharmacy group conduct medication reconciliation for patients undergoing transitions in care.Methods: Three workflow changes were made to improve the medication reconciliation process in a group of three independent community pharmacies. Analysis of the process included workflow steps performed by pharmacy staff, pharmacist barriers encountered during the medication reconciliation process, number of medication discrepancies identified, and pharmacist comfort level while performing each medication reconciliation service.Key Findings: Sixty patient medication reconciliation services met the inclusion criteria for the study. Pharmacists were involved in all steps associated with the medication reconciliation workflow, and were the sole performer in four of the steps: verifying discharge medications with the pharmacy medication profile, resolving discrepancies, contacting the prescriber, and providing patient counseling. Pharmacists were least involved in entering medications into the pharmacy management system, performing that workflow step 13% of the time. The most common barriers were the absence of a discharge medication list (24%) and patient not present during consultation (11%). A total of 231 medication discrepancies were identified, with an average of 3.85 medication discrepancies per discharge. Pharmacists’ comfort level performing medication reconciliation improved through the 13 weeks of the study.Conclusions: These findings suggest that medication reconciliation for patients discharged from hospitals and long term care facilities can be successfully performed in an independent community pharmacy setting. Because many medication discrepancies were identified during this transition of care, it is highly valuable for community pharmacists to perform medication reconciliation services

A boosting method for maximizing the partial area under the ROC curve

<p>Abstract</p> <p>Background</p> <p>The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function combining multiple markers. The area under the ROC curve (AUC) for a score function measures the intrinsic ability for the score function to discriminate between the controls and cases. Recently, the partial AUC (pAUC) has been paid more attention than the AUC, because a suitable range of the false positive rate can be focused according to various clinical situations. However, existing pAUC-based methods only handle a few markers and do not take nonlinear combination of markers into consideration.</p> <p>Results</p> <p>We have developed a new statistical method that focuses on the pAUC based on a boosting technique. The markers are combined componentially for maximizing the pAUC in the boosting algorithm using natural cubic splines or decision stumps (single-level decision trees), according to the values of markers (continuous or discrete). We show that the resulting score plots are useful for understanding how each marker is associated with the outcome variable. We compare the performance of the proposed boosting method with those of other existing methods, and demonstrate the utility using real data sets. As a result, we have much better discrimination performances in the sense of the pAUC in both simulation studies and real data analysis.</p> <p>Conclusions</p> <p>The proposed method addresses how to combine the markers after a pAUC-based filtering procedure in high dimensional setting. Hence, it provides a consistent way of analyzing data based on the pAUC from maker selection to marker combination for discrimination problems. The method can capture not only linear but also nonlinear association between the outcome variable and the markers, about which the nonlinearity is known to be necessary in general for the maximization of the pAUC. The method also puts importance on the accuracy of classification performance as well as interpretability of the association, by offering simple and smooth resultant score plots for each marker.</p

Self-assembly of Microcapsules via Colloidal Bond Hybridization and Anisotropy

Particles with directional interactions are promising building blocks for new functional materials and may serve as models for biological structures. Mutually attractive nanoparticles that are deformable due to flexible surface groups, for example, may spontaneously order themselves into strings, sheets and large vesicles. Furthermore, anisotropic colloids with attractive patches can self-assemble into open lattices and colloidal equivalents of molecules and micelles. However, model systems that combine mutual attraction, anisotropy, and deformability have---to the best of our knowledge---not been realized. Here, we synthesize colloidal particles that combine these three characteristics and obtain self-assembled microcapsules. We propose that mutual attraction and deformability induce directional interactions via colloidal bond hybridization. Our particles contain both mutually attractive and repulsive surface groups that are flexible. Analogous to the simplest chemical bond, where two isotropic orbitals hybridize into the molecular orbital of H2, these flexible groups redistribute upon binding. Via colloidal bond hybridization, isotropic spheres self-assemble into planar monolayers, while anisotropic snowman-like particles self-assemble into hollow monolayer microcapsules. A modest change of the building blocks thus results in a significant leap in the complexity of the self-assembled structures. In other words, these relatively simple building blocks self-assemble into dramatically more complex structures than similar particles that are isotropic or non-deformable

A qualitative investigation of breast cancer survivors’ experiences with breastfeeding

This is an exploratory, qualitative investigation of breast cancer survivors’ experiences with breastfeeding. Previous studies have focused on the physiology of lactation after surgery and treatment, but have not explored factors influencing breastfeeding decisions and behavior. We used purposeful sampling to identify 11 breast cancer survivors who had a child after their diagnosis and treatment. Participants were recruited from among those in the Women’s Healthy Eating and Living (WHEL) study and a Young Survival Coalition (YSC) affiliate. We conducted semi-structured, open-ended telephone interviews lasting 45–75 min. We used social cognitive theory (SCT) to structure questions regarding influences on breastfeeding behavior. We transcribed interviews and used cross-case, inductive analysis to identify themes. Ten of 11 participants initiated breastfeeding. The following main themes emerged: 1) Cautiously hopeful, 2) Exhausting to rely on one breast, 3) Motivated despite challenges, 4) Support and lack of support, and 5) Encouraging to others. Study participants were highly motivated to breastfeed but faced considerable challenges. Participants described problems that are not unique to women with breast cancer, but experienced these to a much greater degree because they relied mostly or entirely on one lactating breast. This study revealed a need for improved access to information and support and greater sensitivity to the obstacles faced by breast cancer survivors. Results of this qualitative analysis indicate that interventions to support the efforts of breast cancer survivors who are interested in breastfeeding are warranted. Additional research would aid in the development of such interventions

Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain

his paper proposes a compromise programming (CP) model to help investors decide whether to construct photovoltaic power plants with government financial support. For this purpose, we simulate an agreement between the government, who pursues political prices (guaranteed prices) as low as possible, and the project sponsor who wants returns (stochastic cash flows) as high as possible. The sponsor s decision depends on the positive or negative result of this simulation, the resulting simulated price being compared to the effective guaranteed price established by the country legislation for photovoltaic energy. To undertake the simulation, the CP model articulates variables such as ranges of guaranteed prices, tech- nical characteristics of the plant, expected energy to be generated over the investment life, investment cost, cash flow probabilities, and others. To determine the CP metric, risk aver- sion is assumed. As an actual application, a case study on photovoltaic power investment in Extremadura, western Spain, is developed in detail.Garcia-Bernabeu, A.; Benito Benito, A.; Bravo Selles, M.; Pla Santamaría, D. (2015). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain. Annals of Operations Research. 1-12. doi:10.1007/s10479-015-1836-2S112Andrews, R. W., Pollard, A., & Pearce, J. M. (2012). Improved parametric empirical determination of module short circuit current for modelling and optimization of solar photovoltaic systems. Solar Energy, 86(9), 2240–2254.Anwar, Y., & Mulyadi, M. S. (2011). Income tax incentives on renewable energy industry: Case of geothermal industry in USA and Indonesia. African Journal of Business Management, 5(31), 12264–12270.Aouni, B., & Kettani, O. (2001). Goal programming model: A glorious history and a promising future. European Journal of Operational Research, 133(2), 225–231.Ballestero, E. (1997). Selecting the CP metric: A risk aversion approach. European Journal of Operational Research, 97(3), 593–596.Ballestero, E. (2000). Project finance: A multicriteria approach to arbitration. Journal of Operational Research Society, 51, 183–197.Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., Pérez-Gladish, B., Arenas-Parra, M., & BilbaoTerol, A. (2009). Selecting portfolios given multiple Eurostoxx-based uncertainty scenarios: A stochastic goal programming approach from fuzzy betas. INFOR: Information Systems and Operational Research, 47(1), 59–70.Ballestero, E., & Plà-Santamaría, D. (2003). Portfolio selection on the Madrid exchange: A compromise programming model. International Transactions in Operational Research, 10(1), 33–51.Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.Ballestero, E., & Pla-Santamaria, D. (2005). Grading the performance of market indicators with utility benchmarks selected from Footsie: A 2000 case study. Applied Economics, 37(18), 2147–2160.Ballestero, E., & Romero, C. (1996). Portfolio selection: A compromise programming solution. Journal of the Operational Research Society, 47, 1377–1386.Bastian-Pinto, C., Brandão, L., & de Lemos Alves, M. (2010). Valuing the switching flexibility of the ethanol–gas flex fuel car. Annals of Operations Research, 176(1), 333–348.Branker, K., Pathak, M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.Casares, F., Lopez-Luque, R., Posadillo, R., & Varo-Martinez, M. (2014). Mathematical approach to the characterization of daily energy balance in autonomous photovoltaic solar systems. Energy, 72, 393–404.Chatterji, A. K., Levine, D. I., & Toffel, M. W. (2009). How well do social ratings actually measure corporate social responsibility? Journal of Economics & Management Strategy, 18(1), 125–169.Copeland, T. E., & Weston, J. (1988). Financial theory and corporate policy. Reading, Massachusetts: Addison-Wesley.Gallagher, K. S. (2013). Why & how governments support renewable energy. Daedalus, 142(1), 59–77.García-Cascales, M. S., Lamata, M. T., & Sánchez-Lozano, J. M. (2012). Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373–391.Gupta, S. (2012). Financing renewable energy. In F. L. Toth (Ed.), Energy for development (pp. 171–186). Springer.Karaarslan, A. (2012). Obtaining renewable energy from piezoelectric ceramics using Sheppard–Taylor converter. International Review of Electrical Engineering, 7(2), 3949–3956.Koellner, T., Weber, O., Fenchel, M., & Scholz, R. (2005). Principles for sustainability rating of investment funds. Business Strategy and the Environment, 14(1), 54–70.Lorenzo, E., & Navarte, L. (2000). On the usefulness of stand-alone PV sizing methods. Progress in Photovoltaics: Research and Applications, 8(4), 391–409.Lüdeke-Freund, F., & Loock, M. (2011). Debt for brands: Tracking down a bias in financing photovoltaic projects in Germany. Journal of Cleaner Production, 19(12), 1356–1364.Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined MCDA-IP approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.Mendez-Rodriguez, P., Garcia Bernabeu, A., Hilario, A., & Perez-Gladish, B. (2013). Some effects on the efficient frontier of the investment strategy: A preliminary approach. Recta, 14, 131–144.Michelson, G., Wailes, N., Van Der Laan, S., & Frost, G. (2004). Ethical investment processes and outcomes. Journal of Business Ethics, 52(1), 1–10.Mills, S. J. (1994). Project finance for renewable energy. Renewable energy, 5(1–4), 700–708.ORourke, A. (2003). The message and methods of ethical investment. Journal of Cleaner Production, 11(6), 683–693.Pla-Santamaria, D., & Bravo, M. (2013). Portfolio optimization based on downside risk: A mean-semivariance efficient frontier from Dow Jones blue chips. Annals of Operations Research, 205(1), 189–201.Richter, N. (2009). Renewable project finance options: ITC, PTC, or cash grant? Power, 153(5), 90–92.Schrader, U. (2006). Ignorant advice-customer advisory service for ethical investment funds. Business Strategy and the Environment, 15(3), 200–214.Sitarz, S. (2013). Compromise programming with tehebycheff norm for discrete stochastic orders. Annals of Operations Research, 211(1), 433–446.van de Kaa, G., Rezaei, J., Kamp, L., & de Winter, A. (2014). Photovoltaic technology selection: A fuzzy MCDM approach. Renewable and Sustainable Energy Reviews, 32, 662–670.Yaqub, M., Shahram Sarkni, P., & Mazzuchi, T. (2012). Feasibility analysis of solar photovoltaic commercial power generation in California. Engineering Management Journal, 24(4), 36–49.Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Moini, S. H. H. (2013). Selecting the optimal renewable energy using multi criteria decision making. Journal of Business Economics and Management, 14(5), 957–978.Yu, P. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Springer.Zeleny, M. (1982). Multiple criteria decision making (Vol. 25). New York: McGraw-Hill.Zhao, R., Shi, G., Chen, H., Ren, A., & Finlow, D. (2011). Present status and prospects of photovoltaic market in China. Energy Policy, 39(4), 2204–2207

Quantum adiabatic machine learning

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. We apply and illustrate this approach in detail to the problem of software verification and validation.Comment: 21 pages, 9 figure

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Challenges in supporting lay carers of patients at the end of life: results from focus group discussions with primary healthcare providers

Background: Family caregivers (FCGs) of patients at the end of life (EoL) cared for at home receive support from professional and non-professional care providers. Healthcare providers in general practice play an important role as they coordinate care and establish contacts between the parties concerned. To identify potential intervention targets, this study deals with the challenges healthcare providers in general practice face in EoL care situations including patients, caregivers and networks. Methods: Focus group discussions with general practice teams in Germany were conducted to identify barriers to and enablers of an optimal support for family caregivers. Focus group discussions were analysed using content analysis. Results: Nineteen providers from 11 general practices took part in 4 focus group discussions. Participants identified challenges in communication with patients, caregivers and within the professional network. Communication with patients and caregivers focused on non-verbal messages, communicating at an appropriate time and perceiving patient and caregiver as a unit of care. Practice teams perceive themselves as an important part of the healthcare network, but also report difficulties in communication and cooperation with other healthcare providers. Conclusion: Healthcare providers in general practice identified relational challenges in daily primary palliative care with potential implications for EoL care. Communication and collaboration with patients, caregivers and among healthcare providers give opportunities for improving palliative care with a focus on the patient-caregiver dyad. It is insufficient to demand a (professional) support network; existing structures need to be recognized and included into the care