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      Effect of Pharmacy-Supported Transition-of-Care Interventions on 30-Day Readmissions: A Systematic Review and Meta-analysis

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          The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010

          Background The escalating use of prescribed drugs has increasingly raised concerns about polypharmacy. This study aims to examine changes in rates of polypharmacy and potentially serious drug-drug interactions in a stable geographical population between 1995 and 2010. Methods This is a repeated cross-sectional analysis of community-dispensed prescribing data for all 310,000 adults resident in the Tayside region of Scotland in 1995 and 2010. The number of drug classes dispensed and the number of potentially serious drug-drug interactions (DDIs) in the previous 84 days were calculated, and age-sex standardised rates in 1995 and 2010 compared. Patient characteristics associated with receipt of ≥10 drugs and with the presence of one or more DDIs were examined using multilevel logistic regression to account for clustering of patients within primary care practices. Results Between 1995 and 2010, the proportion of adults dispensed ≥5 drugs doubled to 20.8%, and the proportion dispensed ≥10 tripled to 5.8%. Receipt of ≥10 drugs was strongly associated with increasing age (20–29 years, 0.3%; ≥80 years, 24.0%; adjusted OR, 118.3; 95% CI, 99.5–140.7) but was also independently more common in people living in more deprived areas (adjusted OR most vs. least deprived quintile, 2.36; 95% CI, 2.22–2.51), and in people resident in a care home (adjusted OR, 2.88; 95% CI, 2.65–3.13). The proportion with potentially serious drug-drug interactions more than doubled to 13% of adults in 2010, and the number of drugs dispensed was the characteristic most strongly associated with this (10.9% if dispensed 2–4 drugs vs. 80.8% if dispensed ≥15 drugs; adjusted OR, 26.8; 95% CI 24.5–29.3). Conclusions Drug regimens are increasingly complex and potentially harmful, and people with polypharmacy need regular review and prescribing optimisation. Research is needed to better understand the impact of multiple interacting drugs as used in real-world practice and to evaluate the effect of medicine optimisation interventions on quality of life and mortality. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0322-7) contains supplementary material, which is available to authorized users.
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            Readmissions, Observation, and the Hospital Readmissions Reduction Program.

            The Hospital Readmissions Reduction Program, which is included in the Affordable Care Act (ACA), applies financial penalties to hospitals that have higher-than-expected readmission rates for targeted conditions. Some policy analysts worry that reductions in readmissions are being achieved by keeping returning patients in observation units instead of formally readmitting them to the hospital. We examined the changes in readmission rates and stays in observation units over time for targeted and nontargeted conditions and assessed whether hospitals that had greater increases in observation-service use had greater reductions in readmissions.
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              Pharmacist participation on physician rounds and adverse drug events in the intensive care unit.

              Pharmacist review of medication orders in the intensive care unit (ICU) has been shown to prevent errors, and pharmacist consultation has reduced drug costs. However, whether pharmacist participation in the ICU at the time of drug prescribing reduces adverse events has not been studied. To measure the effect of pharmacist participation on medical rounds in the ICU on the rate of preventable adverse drug events (ADEs) caused by ordering errors. Before-after comparison between phase 1 (baseline) and phase 2 (after intervention implemented) and phase 2 comparison with a control unit that did not receive the intervention. A medical ICU (study unit) and a coronary care unit (control unit) in a large urban teaching hospital. Seventy-five patients randomly selected from each of 3 groups: all admissions to the study unit from February 1, 1993, through July 31, 1993 (baseline) and all admissions to the study unit (postintervention) and control unit from October 1, 1994, through July 7, 1995. In addition, 50 patients were selected at random from the control unit during the baseline period. A senior pharmacist made rounds with the ICU team and remained in the ICU for consultation in the morning, and was available on call throughout the day. Preventable ADEs due to ordering (prescribing) errors and the number, type, and acceptance of interventions made by the pharmacist. Preventable ADEs were identified by review of medical records of the randomly selected patients during both preintervention and postintervention phases. Pharmacists recorded all recommendations, which were then analyzed by type and acceptance. The rate of preventable ordering ADEs decreased by 66% from 10.4 per 1000 patient-days (95% confidence interval [CI], 7-14) before the intervention to 3.5 (95% CI, 1-5; P<.001) after the intervention. In the control unit, the rate was essentially unchanged during the same time periods: 10.9 (95% CI, 6-16) and 12.4 (95% CI, 8-17) per 1000 patient-days. The pharmacist made 366 recommendations related to drug ordering, of which 362 (99%) were accepted by physicians. The presence of a pharmacist on rounds as a full member of the patient care team in a medical ICU was associated with a substantially lower rate of ADEs caused by prescribing errors. Nearly all the changes were readily accepted by physicians.
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                Author and article information

                Journal
                Annals of Pharmacotherapy
                Ann Pharmacother
                SAGE Publications
                1060-0280
                1542-6270
                June 10 2017
                October 2017
                June 09 2017
                October 2017
                : 51
                : 10
                : 866-889
                Affiliations
                [1 ]JPS Health Network, Fort Worth, TX, USA
                [2 ]University of Arizona, Tucson, AZ, USA
                [3 ]Midwestern University, Glendale, AZ, USA
                [4 ]Idaho State University, Pocatello, ID, USA
                [5 ]Eastern Idaho Regional Medical Center, Idaho Falls, ID, USA
                [6 ]SinfoníaRx, Phoenix, AZ, USA
                Article
                10.1177/1060028017712725
                28599601
                36a48fde-12c9-46c2-b2ed-1f4321a0d254
                © 2017

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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