Background:The role of clinical pharmacists in the care of hospitalized patients has evolved over time, with increased emphasis on collaborative care and patient interaction. The purpose of this review was to evaluate the published literature on the effects of interventions by clinical pharmacists on processes and outcomes of care in hospitalized adults.

BackgroundPrevious studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models.ObjectiveTo identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk.DesignProspective observational cohort study.PatientsParticipants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts.MeasurementsWe identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk.ResultsApproximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively.ConclusionsSelect patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission.

Inappropriate medication use and underuse were common in older people taking five or more medications, with both simultaneously present in more than 40% of patients. Inappropriate medication use is most frequent in patients taking many medications, but underuse is also common and merits attention regardless of the total number of medications taken.

Context Total hip arthroplasty (THA) is a common surgical procedure but little is known about longitudinal trends. Objective To examine demographics and outcomes of patients undergoing primary and revision THA between 1991 and 2008. Design, Setting, and Participants Observational cohort of 1,453,493 Medicare beneficiaries who underwent primary THA and 348,596 who underwent revision THA. Outcomes Changes in patient demographics and comorbidity; hospital length of stay (LOS); mortality; discharge disposition; and all-cause readmission rates. Results Between 1991 and 2008 the mean age for primary THA increased from 74.1 (95% CI, 74.0-74.1) years to 75.1 (95% CI, 75.1-75.2)(P=0.01) and 75.8 (95% CI, 75.7-75.9) to 77.3 (95% CI, 77.2-77.4) for revision THA (P<.001). The mean number of comorbid illnesses per patient increased from 1.0 (95% CI, 1.0-1.0) to 2.0 (95% CI, 2.0-2.0) for primary and 1.1 (95% CI, 1.1-1.1) to 2.3 (95% CI, 2.3-2.3) for revision THA (P<.001 for both). For primary THA, LOS decreased from 9.1 days (95% CI, 9.1-9.2) in 1991–1992 to 3.7 days (95% CI, 3.7-3.7) in 2007–2008 (P=0.002); unadjusted in-hospital and 30-day mortality decreased from 0.5% (95% CI, 0.5%-0.5%) to 0.2% (95% CI, 0.2%-0.2%)(P< 0.001) and 0.7% (95% CI, 0.7%-0.7%) to 0.4% respectively (95% CI, 0.4%-0.4%)(P< 0.001). The proportion of primary THA patients discharged home declined from 68.0% (95% CI, 67.8%-68.3%) to 48.2% (95% CI, 48.0%-48.4%)(P<.001); the proportion discharged to skilled care increased from 17.8% (95% CI, 17.6%-18.1%) to 34.3% (95% CI, 34.1%-34.5%) (P<.001); 30-day all-cause readmission increased from 5.9% (95% CI, 5.8%-6.1%) to 8.5% (95% CI, 8.4%-8.6%) (P<.001). For revision THA similar trends were observed in hospital LOS, in-hospital mortality, discharge disposition, and hospital readmission rates. Conclusions Among Medicare beneficiaries who underwent primary and revision hip arthroplasty between 1991 and 2008, there was a decrease in hospital LOS, but an increase in the rates of post-acute care and readmission.

Many e-health technologies are available to promote virtual patient–provider communication outside the context of face-to-face clinical encounters. Current digital communication modalities include cell phones, smartphones, interactive voice response, text messages, e-mails, clinic-based interactive video, home-based web-cams, mobile smartphone two-way cameras, personal monitoring devices, kiosks, dashboards, personal health records, web-based portals, social networking sites, secure chat rooms, and on-line forums. Improvements in digital access could drastically diminish the geographical, temporal, and cultural access problems faced by many patients. Conversely, a growing digital divide could create greater access disparities for some populations. As the paradigm of healthcare delivery evolves towards greater reliance on non-encounter-based digital communications between patients and their care teams, it is critical that our theoretical conceptualization of access undergoes a concurrent paradigm shift to make it more relevant for the digital age. The traditional conceptualizations and indicators of access are not well adapted to measure access to health services that are delivered digitally outside the context of face-to-face encounters with providers. This paper provides an overview of digital “encounterless” utilization, discusses the weaknesses of traditional conceptual frameworks of access, presents a new access framework, provides recommendations for how to measure access in the new framework, and discusses future directions for research on access.

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Office of Rural Health and the Health Services Research & Development Service, Veterans Health Administration, U.S. Department of Veterans Affairs.

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