Sandra F Simmons’s research while affiliated with Vanderbilt University and other places

Background In post-acute and long-term care (PALTC) facilities, challenges exist to optimizing medication management. During the COVID-19 pandemic, nursing homes (NHs) sought ways to streamline medication administration and deprescribe medications. Objective This quality improvement (QI) initiative aimed to implement medication-related projects in three NHs. Methods Each NH was assigned a nurse practitioner (NP) QI mentor who led the facilities through the QI efforts using validated tools during the COVID-19 pandemic. Each facility selected their medication-related topic of interest. The QI efforts were implemented by the NH staff including the director of nursing, providers, and consultant pharmacists. Results Two facilities focused on general medication deprescribing, and the third facility focused specifically on antipsychotic deprescribing. Successful deprescribing interventions occurred in all three facilities; however, they did not achieve all QI goals. Conclusion Successful deprescribing can occur using mentored implementation of QI tools. However, it is imperative to have key stakeholders within NHs who are supportive and engaged in the deprescribing process, such as facility staff (leadership and front-line staff), facility providers, consultant pharmacists, and residents/families.

Background Many hospitals have acute care for elders (ACE) units or engage in programs to enhance care for older inpatients. However, few studies have objectively evaluated nurse staffing models to support care for older inpatients. Methods This study applied discrete event simulation (DES) to an ACE unit to objectively evaluate registered nurse (RN) and nursing assistant (NA) staffing allocations. Research staff collected standardized, objective data related to nursing tasks and time requirements to model the ACE unit clinical care environment and evaluate varying RN and NA staffing allocations on measures of nursing workload, care quality, and care efficiency. Results On a 22‐bed ACE unit, 85% of patients were aged 65 or older, 37% had cognitive impairment, and 89% required toileting and/or mobility assistance. Nurse care routines were interrupted frequently by unscheduled patient care requests, with an average frequency of 6.1 (±1.6) requests per hour. DES was used to simulate four different RN and NA staffing allocations. Results showed the most common staffing (four RNs and one NA) resulted in the highest nursing workload rates (89% and 88% for RNs and NAs, respectively) and the highest rate of predicted care omissions (6.2%). Additionally, RNs were predicted to help with 83% of NA care tasks related to toileting and mobility assistance. Alternative allocations of four RNs and three NAs or five RNs and two NAs resulted in more feasible workload rates, lower rates of care omissions, and less reliance on RNs for NA care tasks. Conclusions DES provides an objective method to identify nurse staffing needs for an ACE hospital unit. This approach can be used to safely evaluate the potential impact of varying nurse staffing allocations. The DES model for the ACE unit is adaptable to other types of hospital units that care for older patients.

Polypharmacy is common in home health care (HHC), but no deprescribing intervention has been tested in HHC. In a prior study, we identified essential components of deprescribing by interviewing patients, primary, acute and post-acute care providers, pharmacists, and HHC nurses in 12 U.S. states. This study assessed the feasibility and acceptability of deprescribing in HHC through the collaboration of an HHC nurse and a pharmacist to conduct home medication review, deprescribing assessment, primary care provider discussion, patient education, deprescribing implementation, and safety monitoring. In 11 patients who received HHC following hospital discharge, we assessed feasibility and acceptability of deprescribing through surveys and qualitative interviews with patients, providers, and study interventionists. Patients received deprescribing components over 3-4 home visits. On average, the HHC nurse spent 1 hour/home visit/patient, the pharmacist spent 1 hour on deprescribing assessment/patient, and the provider spent 5 minutes / patient to review and implement deprescribing recommendations. Over 70% of deprescribing recommendations were accepted by providers, with 30% not accepted mostly due to patient preference. Providers found deprescribing “very feasible,” but noted barriers such as time constraints and patient preferences. The HHC nurse found deprescribing components “very feasible,” citing the multidisciplinary approach as a facilitator but noting communication challenges with providers. Providers and patients both generally reported that deprescribing components were helpful and led to patients feeling empowered. In conclusion, deprescribing components were feasible in HHC and highly acceptable to patients and providers. Effective communication between patients, HHC nurses, and providers is crucial for deprescribing in HHC.

This cross-sectional study of data from the US Veterans Health Administration examines the availability of services provided through community care networks by specialty and clinical characteristics.

Background Deprescribing is the planned/supervised method of dose reduction or cessation of medications that might be harmful, or no longer be beneficial. Though benefits of deprescribing are debatable in improving clinical outcomes, it has been associated with decreased number of potentially inappropriate medications, which may potentially reduce the risk of adverse events among hospitalized older adults. With unclear evidence for deprescribing in this population, this study aimed to examine time‐to‐first unplanned healthcare utilization, which included 90‐day emergency department (ED) visits or hospital readmission and associated predictors, during a deprescribing intervention. Methods A secondary data analysis of a clinical trial (Shed‐MEDS NCT02979353) was performed. Cox regression was used to compare the time‐to‐first 90‐day ED visit/readmission/death from hospital discharge for the intervention and control groups. Additionally, we performed exploratory analysis of predictors (comorbidities, functional health status, drug burden index (DBI), hospital length of stay, health literacy, food insecurity, and financial burden) associated with the time‐to‐first 90‐day ED visit/readmission/death. Results The hazard of first 90‐day ED visits/readmissions/death was 15% lower in the intervention versus the control group (95% CI: 0.61–1.19, p = 0.352, respectively); however, this difference was not statistically significant. For every additional number of comorbidities (Hazard ratio (HR): 1.12, 95% CI: 1.04–1.21) and each additional day of hospital length of stay (HR: 1.04, 95% CI: 1.01–1.07) were significantly associated with a higher hazard of 90‐day ED visit/readmission/death in the intervention group; whereas for each unit of increase in pre‐hospital DBI score (HR: 1.08 and HR 1.16, respectively) was significantly associated with a higher hazard of 90‐day ED visit/readmission/death in the control group. Conclusions The intervention and control groups had comparable time‐to‐first 90‐day ED visit/readmission/death during a deprescribing intervention. This finding suggests that deprescribing did not result in a higher risk of ED visit/readmission/death during the 90‐day period following hospital discharge.