OBJECTIVE: This study aimed to develop an innovative prioritizing model for conducting medication reconciliation (MR) at a fast-paced workflow emergency department (ED) and to implement an efficient working model for MR. PATIENTS AND METHODS: A total of 276 patients were included at the ED, Diakonhjemmet Hospital, Norway, and medication discrepancies (MDs) between hospital admission records and information on prehospital medication use were recorded. Clinically relevant medication discrepancies (crMDs) were assessed by a multidisciplinary panel. Binary logistic regression was used to construct the prioritizing model from patient characteristics correlated to crMDs, and patient characteristics included in the model should be easily available in the acute situation. A survey among the physicians made up the basis for the working model for conducting MR. RESULTS: In total, 62% of the patients had one or more crMD. The following turned out to be risk factors for having a crMD suitable for inclusion in the model: sex (woman), age (≥60), one or more admission to hospital in the last 12 months and admission causes: surgical, malfunction, cancer. The prioritizing model correctly classified 76.1% of the patients as high-risk patients for having a crMD. In the new working model, in which clinical pharmacists/trained nurses perform MR before the physician did the medication history, was perceived to be more time efficient and also clarified questions related to the medication history early in the admission process. CONCLUSION: This innovative prioritizing model is designed to be practical in the fast-paced workflow at the ED and can identify what patients are at increased risk of having crMDs. The multidisciplinary working model was proven time efficient and could contribute towards increased patient safety.
OBJECTIVE: This study aimed to develop an innovative prioritizing model for conducting medication reconciliation (MR) at a fast-paced workflow emergency department (ED) and to implement an efficient working model for MR. PATIENTS AND METHODS: A total of 276 patients were included at the ED, Diakonhjemmet Hospital, Norway, and medication discrepancies (MDs) between hospital admission records and information on prehospital medication use were recorded. Clinically relevant medication discrepancies (crMDs) were assessed by a multidisciplinary panel. Binary logistic regression was used to construct the prioritizing model from patient characteristics correlated to crMDs, and patient characteristics included in the model should be easily available in the acute situation. A survey among the physicians made up the basis for the working model for conducting MR. RESULTS: In total, 62% of the patients had one or more crMD. The following turned out to be risk factors for having a crMD suitable for inclusion in the model: sex (woman), age (≥60), one or more admission to hospital in the last 12 months and admission causes: surgical, malfunction, cancer. The prioritizing model correctly classified 76.1% of the patients as high-risk patients for having a crMD. In the new working model, in which clinical pharmacists/trained nurses perform MR before the physician did the medication history, was perceived to be more time efficient and also clarified questions related to the medication history early in the admission process. CONCLUSION: This innovative prioritizing model is designed to be practical in the fast-paced workflow at the ED and can identify what patients are at increased risk of having crMDs. The multidisciplinary working model was proven time efficient and could contribute towards increased patient safety.
Authors: Daniel K Nishijima; Samuel Gaona; Trent Waechter; Ric Maloney; Troy Bair; Adam Blitz; Andrew R Elms; Roel D Farrales; Calvin Howard; James Montoya; Jeneita M Bell; Victor C Coronado; David E Sugerman; Dustin W Ballard; Kevin E Mackey; David R Vinson; James F Holmes Journal: Prehosp Emerg Care Date: 2016-09-16 Impact factor: 3.077
Authors: Lisbeth D Nymoen; Therese Tran; Scott R Walter; Elin C Lehnbom; Ingrid K Tunestveit; Erik Øie; Kirsten K Viktil Journal: Int J Clin Pharm Date: 2021-12-23
Authors: Lisbeth Damlien Nymoen; Trude Eline Flatebø; Tron Anders Moger; Erik Øie; Espen Molden; Kirsten Kilvik Viktil Journal: PLoS One Date: 2022-09-19 Impact factor: 3.752