Dataset Information

Can the targeted use of a discharge pharmacist significantly decrease 30-day readmissions?

ABSTRACT: BACKGROUND: The prevalence and cost of hospital readmissions have gained attention. The ability to identify patients at high risk for hospital readmission has implications for quality and costs of care. Medication errors have been shown to increase the risk for readmission. OBJECTIVE: To study the impact of a pharmacist-based predischarge medication reconciliation and counseling program on 30-day readmission rates and determine whether polypharmacy and problem medications are important screening criteria. METHODS: A prospective, nonrandomized cohort study performed at a single medical-surgical unit with telemetry capability at a single academic medical center. The participants were 729 patients, aged 18 years and older, who were discharged between July 1 and October 29, 2010. The intervention was pharmacist medication reconciliation and counseling based on a screening tool. The primary outcome was 30-day readmission rate. Secondary outcomes were the presence of polypharmacy and problem medications and their relationship with observed 30-day readmission rate, including calculation of a problem med/polypharmacy score. RESULTS: The pharmacy review group (n = 537) had a lower 30-day readmission rate than the group receiving usual care (n = 192) (16.8% vs 26.0%; odds ratio [OR] 0.572; 95% CI, 0.387-0.852; P = .006). Polypharmacy, defined as either 5 or more or 10 or more scheduled medications, alone and in combination with at least one problem medication had higher 30-day readmission rates. A score of no factors present exhibited good negative predictive value. CONCLUSIONS: Medication reconciliation and counseling by a pharmacist reduced the 30-day readmission rate. Polypharmacy and problem medications appear to have value individually and together. A pharmacist, guided by a screening tool in predischarge medication reconciliation, is one option to effectively reduce 30-day readmissions.

SUBMITTER: Pal A

PROVIDER: S-EPMC3839464 | biostudies-other | 2013 May

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundAlthough early follow-up after discharge from an index admission (IA) has been postulated to reduce 30-day readmission, some researchers have questioned its efficacy, which may depend upon the likelihood of readmission at a given time and the health conditions contributing to readmissions.ObjectiveTo investigate the relationship between post-discharge services utilization of different types and at different timepoints and unplanned 30-day readmission, length of stay (LOS), and inpatient costs.Design, setting, and participantsThe study sample included 583,199 all-cause IAs among 2014 Medicare fee-for-service beneficiaries that met IA inclusion criteria.Main measuresThe outcomes were probability of 30-day readmission, average readmission LOS per IA discharge, and average readmission inpatient cost per IA discharge. The primary independent variables were 7 post-discharge health services (institutional outpatient, primary care physician, specialist, non-physician provider, emergency department (ED), home health care, skilled nursing facility) utilized within 7 days, 14 days, and 30 days of IA discharge. To examine the association with post-discharge services utilization, we employed multivariable logistic regressions for 30-day readmissions and two-part models for LOS and inpatient costs.Key resultsAmong all IA discharges, the probability of unplanned 30-day readmission was 0.1176, the average readmission LOS per discharge was 0.67 days, and the average inpatient cost per discharge was $5648. Institutional outpatient, home health care, and primary care physician visits at all timepoints were associated with decreased readmission and resource utilization. Conversely, 7-day and 14-day specialist visits were positively associated with all three outcomes, while 30-day visits were negatively associated. ED visits were strongly associated with increases in all three outcomes at all timepoints.ConclusionPost-discharge services of different types and at different timepoints have varying impacts on 30-day readmission, LOS, and costs. These impacts should be considered when coordinating post-discharge follow-up, and their drivers should be further explored to reduce readmission throughout the health care system.

Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

Dataset Information

Can the targeted use of a discharge pharmacist significantly decrease 30-day readmissions?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets