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1.
Contemp Clin Trials ; 106: 106419, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33932574

RESUMO

BACKGROUND: Older adults commonly face challenges in understanding, obtaining, administering, and monitoring medication regimens after hospitalization. These difficulties can lead to avoidable morbidity, mortality, and hospital readmissions. Pharmacist-led peri-discharge interventions can reduce adverse drug events, but few large randomized trials have examined their effectiveness in reducing readmissions. Demonstrating reductions in 30-day readmissions can make a financial case for implementing pharmacist-led programs across hospitals. METHODS/DESIGN: The PHARMacist Discharge Care, or the PHARM-DC intervention, includes medication reconciliation at admission and discharge, medication review, increased communication with caregivers, providers, and retail pharmacies, and patient education and counseling during and after discharge. The intervention is being implemented in two large hospitals: Cedars-Sinai Medical Center and the Brigham and Women's Hospital. To evaluate the intervention, we are using a pragmatic, randomized clinical trial design with randomization at the patient level. The primary outcome is utilization within 30 days of hospital discharge, including unforeseen emergency department visits, observation stays, and readmissions. Randomizing 9776 patients will achieve 80% power to detect an absolute reduction of 2.5% from an estimated baseline rate of 27.5%. Qualitative analysis will use interviews with key stakeholders to study barriers to and facilitators of implementing PHARM-DC. A cost-effectiveness analysis using a time-and-motion study to estimate time spent on the intervention will highlight the potential cost savings per readmission. DISCUSSION: If this trial demonstrates a business case for the PHARM-DC intervention, with few barriers to implementation, hospitals may be much more likely to adopt pharmacist-led peri-discharge medication management programs. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04071951.


Assuntos
Farmacêuticos , Cuidado Transicional , Idoso , Feminino , Hospitalização , Humanos , Reconciliação de Medicamentos , Alta do Paciente , Readmissão do Paciente
2.
BMJ Qual Saf ; 27(7): 512-520, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-28986515

RESUMO

BACKGROUND: Admission medication history (AMH) errors frequently cause medication order errors and patient harm. OBJECTIVE: To quantify AMH error reduction achieved when pharmacy staff obtain AMHs before admission medication orders (AMO) are placed. METHODS: This was a three-arm randomised controlled trial of 306 inpatients. In one intervention arm, pharmacists, and in the second intervention arm, pharmacy technicians, obtained initial AMHs prior to admission. They obtained and reconciled medication information from multiple sources. All arms, including the control arm, received usual AMH care, which included variation in several common processes. The primary outcome was severity-weighted mean AMH error score. To detect AMH errors, all patients received reference standard AMHs, which were compared with intervention and control group AMHs. AMH errors and resultant AMO errors were independently identified and rated by ≥2 investigators as significant, serious or life threatening. Each error was assigned 1, 4 or 9 points, respectively, to calculate severity-weighted AMH and AMO error scores for each patient. RESULTS: Patient characteristics were similar across arms (mean±SD age 72±16 years, number of medications 15±7). Analysis was limited to 278 patients (91%) with reference standard AMHs. Mean±SD AMH errors per patient in the usual care, pharmacist and technician arms were 8.0±5.6, 1.4±1.9 and 1.5±2.1, respectively (p<0.0001). Mean±SD severity-weighted AMH error scores were 23.0±16.1, 4.1±6.8 and 4.1±7.0 per patient, respectively (p<0.0001). These AMH errors led to a mean±SD of 3.2±2.9, 0.6±1.1 and 0.6±1.1 AMO errors per patient, and mean severity-weighted AMO error scores of 6.9±7.2, 1.5±2.9 and 1.2±2.5 per patient, respectively (both p<0.0001). CONCLUSIONS: Pharmacists and technicians reduced AMH errors and resultant AMO errors by over 80%. Future research should examine other sites and patient-centred outcomes. TRIAL REGISTRATION NUMBER: NCT02026453.


Assuntos
Erros de Medicação/prevenção & controle , Erros de Medicação/estatística & dados numéricos , Relações Profissional-Paciente , Centros Médicos Acadêmicos , Idoso , Idoso de 80 Anos ou mais , Análise de Variância , Serviço Hospitalar de Emergência , Feminino , Humanos , Entrevistas como Assunto , Los Angeles , Masculino , Reconciliação de Medicamentos/métodos , Pessoa de Meia-Idade , Farmacêuticos , Técnicos em Farmácia
3.
Patient Prefer Adherence ; 11: 801-810, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28461742

RESUMO

PURPOSE: The aim of this study was to test whether patient medication adherence, a modifiable risk factor obtainable at hospital admission, predicts readmission within 30 days. PATIENTS AND METHODS: We used a retrospective cohort study design to test whether patient medication adherence to all chronic medications, as determined by the 4-item Morisky Medication Adherence Scale (MMAS-4) administered by a pharmacist at the time of hospital admission, predicts 30-day readmissions. We compared readmission rates among 385 inpatients who had their adherence assessed from February 1, 2013, to January 31, 2014. Multiple logistic regression was used to examine the benefit of adding medication adherence to previously published variables that have been shown to predict 30-day readmissions. RESULTS: Patients with low and intermediate adherence (combined) had readmission rates of 20.0% compared to a readmission rate of 9.3% for patients with high adherence (P=0.005). By adding MMAS-4 data to previously published variables that have been shown to predict 30-day readmissions, we found that patients with low and intermediate medication adherence had an adjusted 2.54-fold higher odds of readmission compared to those in patients with high adherence (95% confidence interval [CI]: 1.32-4.90, P=0.005). The model's predictive power, as measured by the c-statistic, improved from 0.65 to 0.70 after adding adherence. CONCLUSION: Because medication adherence assessed at hospital admission was independently associated with 30-day readmission risk, it offers potential for targeting interventions to improve adherence.

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