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Schnipper JL, Reyes Nieva H, Mallouk M, Mixon A, Rennke S, Chu E, Mueller S, Smith GRR, Williams MV, Wetterneck TB, Stein J, Dalal A, Labonville S, Sridharan A, Stolldorf DP, Orav EJ, Levin B, Gresham M, Yoon C, Goldstein J, Platt S, Nyenpan CT, Howell E, Kripalani S. Effects of a refined evidence-based toolkit and mentored implementation on medication reconciliation at 18 hospitals: results of the MARQUIS2 study. BMJ Qual Saf 2022; 31:278-286. [PMID: 33927025 PMCID: PMC10964422 DOI: 10.1136/bmjqs-2020-012709] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/26/2021] [Accepted: 04/10/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND The first Multicenter Medication Reconciliation Quality Improvement (QI) Study (MARQUIS1) demonstrated that mentored implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals, but results varied by site. The objective of this study was to determine the effects of a refined toolkit on a larger group of hospitals. METHODS We conducted a pragmatic quality improvement study (MARQUIS2) at 18 North American hospitals or hospital systems from 2016 to 2018. Incorporating lessons learnt from MARQUIS1, we implemented a refined toolkit, offering 17 system-level and 6 patient-level interventions. One of eight physician mentors coached each site via monthly calls and performed one to two site visits. The primary outcome was number of unintentional medication discrepancies in admission or discharge orders per patient. Time series analysis used multivariable Poisson regression. RESULTS A total of 4947 patients were sampled, including 1229 patients preimplementation and 3718 patients postimplementation. Both the number of system-level interventions adopted per site and the proportion of patients receiving patient-level interventions increased over time. During the intervention, patients experienced a steady decline in their medication discrepancy rate from 2.85 discrepancies per patient to 0.98 discrepancies per patient. An interrupted time series analysis of the 17 sites with sufficient data for analysis showed the intervention was associated with a 5% relative decrease in discrepancies per month over baseline temporal trends (adjusted incidence rate ratio: 0.95, 95% CI 0.93 to 0.97, p<0.001). Receipt of patient-level interventions was associated with decreased discrepancy rates, and these associations increased over time as sites adopted more system-level interventions. CONCLUSION A multicentre medication reconciliation QI initiative using mentored implementation of a refined best practices toolkit, including patient-level and system-level interventions, was associated with a substantial decrease in unintentional medication discrepancies over time. Future efforts should focus on sustainability and spread.
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Affiliation(s)
- Jeffrey L Schnipper
- Hospital Medicine Unit, Brigham Health, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Harry Reyes Nieva
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Meghan Mallouk
- Center for Quality Improvement, Society of Hospital Medicine, Philadelphia, PA, USA
| | - Amanda Mixon
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Geriatric Research, Education, and Clinical Center, VA Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Stephanie Rennke
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco Medical Center, San Francisco, CA, USA
| | - Eugene Chu
- Division of Hospital Medicine, Parkland Health and Hospital System and Department of Internal Medicine, University of Texas Southwestern School of Medicine, Dallas, TX, USA
| | - Stephanie Mueller
- Hospital Medicine Unit, Brigham Health, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Gregory Randy R Smith
- Division of Hospital Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Mark V Williams
- Division of Hospital Medicine, Department of Internal Medicine, University of Kentucky Medical Center, Lexington, KY, USA
| | - Tosha B Wetterneck
- Division of General Internal Medicine, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | | | - Anuj Dalal
- Hospital Medicine Unit, Brigham Health, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | | | - E John Orav
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA
| | - Brian Levin
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcus Gresham
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Cathy Yoon
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jenna Goldstein
- Center for Quality Improvement, Society of Hospital Medicine, Philadelphia, PA, USA
| | - Sara Platt
- Center for Quality Improvement, Society of Hospital Medicine, Philadelphia, PA, USA
| | | | - Eric Howell
- Society of Hospital Medicine, Philadelphia, PA, USA
- Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Sunil Kripalani
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Center for Clinical Quality and Implementation Research, Nashville, TN, USA
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Baughman AW, Triantafylidis LK, O'Neil N, Norstrom J, Okpara K, Ruopp MD, Linsky A, Schnipper J, Mixon AS, Simon SR. Improving Medication Reconciliation with Comprehensive Evaluation at a Veterans Affairs Skilled Nursing Facility. Jt Comm J Qual Patient Saf 2021; 47:646-653. [PMID: 34244044 DOI: 10.1016/j.jcjq.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/19/2021] [Accepted: 06/04/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND Unintentional medication discrepancies due to inadequate medication reconciliation pose a threat to patient safety. Skilled nursing facilities (SNFs) are an important care setting where patients are vulnerable to unintentional medication discrepancies due to increased medical complexity and care transitions. This study describes a quality improvement (QI) approach to improve medication reconciliation in an SNF setting as part of the Multi-Center Medication Reconciliation Quality Improvement Study 2 (MARQUIS2). METHODS This study was conducted at a 112-bed US Department of Veterans Affairs SNF. The researchers used several QI methods, including data benchmarking, stakeholder surveys, process mapping, and a Healthcare Failure Mode and Effect Analysis (HFMEA) to complete comprehensive baseline assessments. RESULTS Baseline assessments revealed that medication reconciliation processes were error-prone, with high rates of medication discrepancies. Provider surveys and process mapping revealed extremely labor-intensive and highly complex processes lacking standardization. Factors contributing were polypharmacy, limited resources, electronic health record limitations, and patient exposure to multiple care transitions. HFMEA enabled a methodical approach to identify and address challenges. The team validated the best possible medication history (BPMH) process for hospital settings as outlined by MARQUIS2 for the SNF setting and found it necessary to use additional medication lists to account for multiple care transitions. CONCLUSION SNFs represent a critical setting for medication reconciliation efforts due to challenges completing the reconciliation process and the concomitant high risk of adverse drug events in this population. Initial baseline assessments effectively identified existing problems and can be used to guide targeted interventions.
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