Person: Schnipper, Jeffrey
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Schnipper
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Jeffrey
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Schnipper, Jeffrey
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Publication The association of post-discharge adverse events with timely follow-up visits after hospital discharge(Public Library of Science, 2017) Tsilimingras, Dennis; Ghosh, Samiran; Duke, Ashley; Zhang, Liying; Carretta, Henry; Schnipper, JeffreyObjective: There has been little research to examine the association of post-discharge adverse events (AEs) with timely follow-up visits after hospital discharge. We aimed to examine whether having a timely follow-up outpatient visit would reduce the risk for post-discharge AEs. Methods: This was a methods study of patients at risk for post-discharge AEs from December 2011 through October 2012. Five hundred and forty-five patients who were under the care of hospitalist physicians and were discharged home from a community hospital, spoke English, and could be contacted after discharge were evaluated. The aim of the study was to examine the association of post-discharge AEs with timely follow-up visits after hospital discharge based on structured telephone interviews, health record review, and adjudication by two blinded, trained physicians using a previously established methodology. Results: We observed a higher incidence of AEs with patients that had their first follow-up visit within 7 days after hospital discharge (33.5% vs. 23.0%, p = 0.007). This effect was attenuated somewhat but remained significant when adjusted for several patient factors (adjusted OR 1.33, 95% confidence interval 1.16–2.71). Conclusion: This observational study paradoxically showed an increase in post-discharge AEs with early follow-up, likely a result of confounding by indication and/or information bias that could not be completely adjusted for. This study illustrates the potential hazards with conducting observational studies to determine the efficacy of various transitional care interventions, such as early follow-up, where risk for confounding by indication is high.Publication Causes and patterns of readmissions in patients with common comorbidities: retrospective cohort study(BMJ Publishing Group Ltd., 2013) Donze, Jacques; Lipsitz, Stuart; Bates, David; Schnipper, JeffreyObjective To evaluate the primary diagnoses and patterns of 30 day readmissions and potentially avoidable readmissions in medical patients with each of the most common comorbidities. Design: Retrospective cohort study. Setting: Academic tertiary medical centre in Boston, 2009-10. Participants: 10 731 consecutive adult discharges from a medical department. Main outcome measures Primary readmission diagnoses of readmissions within 30 days of discharge and potentially avoidable 30 day readmissions to the index hospital or two other hospitals in its network. Results: Among 10 731 discharges, 2398 (22.3%) were followed by a 30 day readmission, of which 858 (8.0%) were identified as potentially avoidable. Overall, infection, neoplasm, heart failure, gastrointestinal disorder, and liver disorder were the most frequent primary diagnoses of potentially avoidable readmissions. Almost all of the top five diagnoses of potentially avoidable readmissions for each comorbidity were possible direct or indirect complications of that comorbidity. In patients with a comorbidity of heart failure, diabetes, ischemic heart disease, atrial fibrillation, or chronic kidney disease, the most common diagnosis of potentially avoidable readmission was acute heart failure. Patients with neoplasm, heart failure, and chronic kidney disease had a higher risk of potentially avoidable readmissions than did those without those comorbidities. Conclusions: The five most common primary diagnoses of potentially avoidable readmissions were usually possible complications of an underlying comorbidity. Post-discharge care should focus attention not just on the primary index admission diagnosis but also on the comorbidities patients have.Publication Rationale and design of the Multicenter Medication Reconciliation Quality Improvement Study (MARQUIS)(BioMed Central, 2013) Salanitro, Amanda H; Kripalani, Sunil; Resnic, JoAnne; Mueller, Stephanie; Wetterneck, Tosha B; Haynes, Katherine Taylor; Stein, Jason; Kaboli, Peter J; Labonville, Stephanie; Etchells, Edward; Cobaugh, Daniel J; Hanson, David; Greenwald, Jeffrey; Williams, Mark V; Schnipper, JeffreyBackground: 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 NCT01337063Publication Hospital Readmission in General Medicine Patients: A Prediction Model(Springer-Verlag, 2009) Hasan, Omar; Meltzer, David O.; Shaykevich, Shimon A.; Bell, Chaim M.; Kaboli, Peter J.; Auerbach, Andrew; Wetterneck, Tosha B.; Arora, Vineet M.; Zhang, James; Schnipper, JeffreyBackground: 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.Publication Association of Communication Between Hospital-Based Physicians and Primary Care Providers with Patient Outcomes(Springer-Verlag, 2008) Bell, Chaim M.; Schnipper, Jeffrey; Auerbach, Andrew D.; Kaboli, Peter J.; Wetterneck, Tosha B.; Gonzales, David V.; Arora, Vineet M.; Zhang, James X.; Meltzer, David O.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.