Background: Adverse drug events (ADEs) result in excess hospitalizations. Thorough admission medication histories (AMHs) may prevent ADEs; however, the resources required oftentimes outweigh what is available in large hospital settings. Previous risk prediction models embedded into the Electronic Medical Record (EMR) have been used at hospitals to aid in targeting delivery of scarce resources. Objective: To determine if an AMH scoring tool used to allocate resources can decrease 30-day hospital readmissions. Design Setting and Participants: Propensity-matched cohort study, Medicine/Surgery patients in large academic safety-net hospital. Intervention or Exposure: Pharmacy-conducted AMHs identified by risk model versus standard of care AMH. Main Outcomes and Measures: A total of 30-day hospital readmissions and inpatient ADE prevention. Results: The model screened 87 240 hospitalizations between June 2017 and June 2019 and 4027 patients per group were included. There were significantly less 30 day readmissions among high-risk identified patients that received a pharmacy-conducted AMH compared to controls (11% vs 15%; P = 0.004) and no significant difference in readmission rates for low-risk patients. While there was significantly higher documentation of major ADE prevention in the pharmacy-led AMH group versus control (1656 vs 12; P < 0.001), there was no difference in electronically-detected inpatient ADEs between groups. Conclusions: A risk tool embedded into the EMR can be used to identify patients whom pharmacy teams can easily target for AMHs. This study showed significant reductions in readmissions for patients identified as high-risk. However, the same benefit in readmissions was not seen in those identified at low-risk, which supports allocating resources to those that will benefit the most.
Background: Adverse drug events (ADEs) result in excess hospitalizations. Thorough admission medication histories (AMHs) may prevent ADEs; however, the resources required oftentimes outweigh what is available in large hospital settings. Previous risk prediction models embedded into the Electronic Medical Record (EMR) have been used at hospitals to aid in targeting delivery of scarce resources. Objective: To determine if an AMH scoring tool used to allocate resources can decrease 30-day hospital readmissions. Design Setting and Participants: Propensity-matched cohort study, Medicine/Surgery patients in large academic safety-net hospital. Intervention or Exposure: Pharmacy-conducted AMHs identified by risk model versus standard of care AMH. Main Outcomes and Measures: A total of 30-day hospital readmissions and inpatient ADE prevention. Results: The model screened 87 240 hospitalizations between June 2017 and June 2019 and 4027 patients per group were included. There were significantly less 30 day readmissions among high-risk identified patients that received a pharmacy-conducted AMH compared to controls (11% vs 15%; P = 0.004) and no significant difference in readmission rates for low-risk patients. While there was significantly higher documentation of major ADE prevention in the pharmacy-led AMH group versus control (1656 vs 12; P < 0.001), there was no difference in electronically-detected inpatient ADEs between groups. Conclusions: A risk tool embedded into the EMR can be used to identify patients whom pharmacy teams can easily target for AMHs. This study showed significant reductions in readmissions for patients identified as high-risk. However, the same benefit in readmissions was not seen in those identified at low-risk, which supports allocating resources to those that will benefit the most.
Authors: Nadine Shehab; Maribeth C Lovegrove; Andrew I Geller; Kathleen O Rose; Nina J Weidle; Daniel S Budnitz Journal: JAMA Date: 2016-11-22 Impact factor: 56.272
Authors: D W Bates; D J Cullen; N Laird; L A Petersen; S D Small; D Servi; G Laffel; B J Sweitzer; B F Shea; R Hallisey Journal: JAMA Date: 1995-07-05 Impact factor: 56.272
Authors: Jeffrey L Schnipper; Amanda Mixon; Jason Stein; Tosha B Wetterneck; Peter J Kaboli; Stephanie Mueller; Stephanie Labonville; Jacquelyn A Minahan; Elisabeth Burdick; Endel John Orav; Jenna Goldstein; Nyryan V Nolido; Sunil Kripalani Journal: BMJ Qual Saf Date: 2018-08-20 Impact factor: 7.035
Authors: Michelle Baker; Chaim M Bell; Wei Xiong; Edward Etchells; Peter G Rossos; Kaveh G Shojania; Kelly Lane; Tim Tripp; Mary Lam; Kimindra Tiwana; Derek Leong; Gary Wong; Jin-Hyeun Huh Huh; Emily Musing; Olavo Fernandes Journal: J Hosp Med Date: 2017-10-04 Impact factor: 2.960
Authors: Ruben Amarasingham; Parag C Patel; Kathleen Toto; Lauren L Nelson; Timothy S Swanson; Billy J Moore; Bin Xie; Song Zhang; Kristin S Alvarez; Ying Ma; Mark H Drazner; Usha Kollipara; Ethan A Halm Journal: BMJ Qual Saf Date: 2013-07-31 Impact factor: 7.035
Authors: Anne Burden; Nicolas Roche; Cristiana Miglio; Elizabeth V Hillyer; Dirkje S Postma; Ron Mc Herings; Jetty A Overbeek; Javaria Mona Khalid; Daniela van Eickels; David B Price Journal: Pragmat Obs Res Date: 2017-03-22