Do Hospitalists or Physicians with Greater Inpatient HIV Experience Improve HIV Care in the Era of Highly Active Antiretroviral Therapy? Results from a Multicenter Trial of Academic Hospitalists

Background. Little is known about the effect of provider type and experience on outcomes, resource use, and processes of care of hospitalized patients with human immunodeficiency virus (HIV) infection. Hospitalists are caring for this population with increasing frequency. Methods. Data from a natural experiment in which patients were assigned to physicians on the basis of call cycle was used to study the effects of provider type—that is, hospitalist versus non hospitalist—and HIV-specific inpatient experience on resource use, outcomes, and selected measures of processes of care at 6 academic institutions. Administrative data, inpatient interviews, 30-day follow-up interviews, and the National Death Index were used to measure outcomes. Results. A total of 1207 patients were included in the analysis. There were few differences in resource use, outcomes, and processes of care by provider type and experience with HIV-infected inpatients. Patients who received hospitalist care demonstrated a trend toward increased length of hospital stay compared with patients who did not receive hospitalist care (6.0 days vs. 5.2 days; Pp .13). Inpatient providers with moderate experience with HIV-infected patients were more likely to coordinate care with outpatient providers (odds ratio, 2.40; Pp .05) than were those with the least experience with HIV-infected patients, but this pattern did not extend to providers with the highest level of experience. Conclusion. Provider type and attending physician experience with HIV-infected inpatients had minimal effect on the quality of care of HIV-infected inpatients. Approaches other than provider experience, such as the use of multidisciplinary inpatient teams, may be better targets for future studies of the outcomes, processes of care, and resource use of HIV-infected inpatients

Rationale and design of the Multicenter Medication Reconciliation Quality Improvement Study (MARQUIS)

Background: Unresolved medication discrepancies during hospitalization can contribute to adverse drug events, resulting in patient harm. Discrepancies can be reduced by performing medication reconciliation; however, effective implementation of medication reconciliation has proven to be challenging. The goals of the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) are to operationalize best practices for inpatient medication reconciliation, test their effect on potentially harmful unintentional medication discrepancies, and understand barriers and facilitators of successful implementation. Methods: Six U.S. hospitals are participating in this quality improvement mentored implementation study. Each hospital has collected baseline data on the primary outcome: the number of potentially harmful unintentional medication discrepancies per patient, as determined by a trained on-site pharmacist taking a “gold standard” medication history. With the guidance of their mentors, each site has also begun to implement one or more of 11 best practices to improve medication reconciliation. To understand the effect of the implemented interventions on hospital staff and culture, we are performing mixed methods program evaluation including surveys, interviews, and focus groups of front line staff and hospital leaders. Discussion At baseline the number of unintentional medication discrepancies in admission and discharge orders per patient varies by site from 2.35 to 4.67 (mean=3.35). Most discrepancies are due to history errors (mean 2.12 per patient) as opposed to reconciliation errors (mean 1.23 per patient). Potentially harmful medication discrepancies averages 0.45 per patient and varies by site from 0.13 to 0.82 per patient. We discuss several barriers to implementation encountered thus far. In the end, we anticipate that MARQUIS tools and lessons learned have the potential to decrease medication discrepancies and improve patient outcomes. Trial registration Clinicaltrials.gov identifier NCT0133706

Association of Communication Between Hospital-based Physicians and Primary Care Providers with Patient Outcomes

Background: Patients admitted to general medicine inpatient services are increasingly cared for by hospital-based physicians rather than their primary care providers (PCPs). This separation of hospital and ambulatory care may result in important care discontinuities after discharge. We sought to determine whether communication between hospital-based physicians and PCPs influences patient outcomes. Methods: We approached consecutive patients admitted to general medicine services at six US academic centers from July 2001 to June 2003. A random sample of the PCPs for consented patients was contacted 2 weeks after patient discharge and surveyed about communication with the hospital medical team. Responses were linked with the 30-day composite patient outcomes of mortality, hospital readmission, and emergency department (ED) visits obtained through follow-up telephone survey and National Death Index search. We used hierarchical multi-variable logistic regression to model whether communication with the patient’s PCP was associated with the 30-day composite outcome. Results: A total of 1,772 PCPs for 2,336 patients were surveyed with 908 PCPs responses and complete patient follow-up available for 1,078 patients. The PCPs for 834 patients (77%) were aware that their patient had been admitted to the hospital. Of these, direct communication between PCPs and inpatient physicians took place for 194 patients (23%), and a discharge summary was available within 2 weeks of discharge for 347 patients (42%). Within 30 days of discharge, 233 (22%) patients died, were readmitted to the hospital, or visited an ED. In adjusted analyses, no relationship was seen between the composite outcome and direct physician communication (adjusted odds ratio 0.87, 95% confidence interval 0.56 – 1.34), the presence of a discharge summary (0.84, 95% CI 0.57–1.22), or PCP awareness of the index hospitalization (1.08, 95% CI 0.73–1.59). Conclusion: Analysis of communication between PCPs and inpatient medical teams revealed much room for improvement. Although communication during handoffs of care is important, we were not able to find a relationship between several aspects of communication and associated adverse clinical outcomes in this multi-center patient sample

Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To 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. Design: Prospective observational cohort study. Patients: Participants 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. Measurements: We 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. Results: Approximately 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. Conclusions: Select 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