Problem: A large percentage of vulnerable older adult (OA) Chicago residents experience risk factors for food insecurity (FI)1. A Chicago community hospital’s program refers OAs who screen positive for FI for assistance in accessing nutritional resources. However, it is unclear how well the program addresses OA’s FI.

Purpose: To evaluate the Chicago community hospital’s FI screening and referral program for OAs. The evaluation plan was based on the Center for Disease Control and Prevention’s framework for program evaluation and the Donabedian framework.

Methods: Retrospective chart reviews were conducted on patients (>60 years old) who evisited the emergency department (ED), were admitted through the ED, or were admitted directly to the post-surgical unit after scheduled surgeries. Semi-structured interviews were conducted with OAs (n=5) to assess correct identification of food insecure OAs and to investigate patient barriers/facilitators in discussing FI with social workers (SWs) and clinical dietitians (CDs). Proportions were tallied and themes identified.

Results: Nearly 80% of the OAs admitted through the ED and 10% of scheduled admissions to the post-surgical unit were screened. Only 4% of OAs visiting the ED were screened, with all screening positive for FI. About 35% of the referrals submitted by SWs from 2019 to May 2021 were for patients who visited the ED. Among patients admitted, CDs documented patients’ FI status more often than SWs. There were discrepancies between SW and CDs’ screenings and between the project evaluator’s screenings and patients’ documented FI status. Patients reported the limited time in discussing FI with SWs/CDs and the challenges in accessing nutritious foods after discharge as barriers.

Recommendations: The hospital should establish a program to systematically screen OAs visiting the ED or admitted directly to the post-surgical unit. CDs and SWs should be trained to screen for FI using an evidence-based screening tool and utilize a common template incorporating FI status. A list of resources should continue to be provided at discharge for food insecure OAs. Findings underscore the importance of establishing systematic evidence-based screening programs to accurately identify and refer food insecure OAs.

References
1) Chicago Department of Public Health. (2019). Healthy Chicago databook: Older adult health. https://www.chicago.gov/content/dam/city/depts/cdph/statistics_and_reports/CCHE-001_OlderAdults_Databook_r5a.pdf

Research question: Does a produce box delivery program have positive impacts on health and wellness measures, including mood, for participants?

Background: Food insecurity (FI) is a growing health problem, worsening during the COVID-19 pandemic. During the height of the COVID-19 pandemic, the Census Household Pulse Survey (2020) revealed that 23% of surveyed households in the United States experienced food insecurity (FI) or the unreliable access to sufficient affordable, quality, and nutritious food. Black and Latinx individuals reported disproportionately higher rates of FI at 36% and 32%, respectively, versus 18% for white individuals. Fresh food prescription programs (FFRx) have been shown to increase healthy eating and decrease FI, but few FFRx are community-informed or theory-based. Our FFRx was a delivery program developed to alleviate FI for families and older adults. It was implemented in an academic medical center and guided by the capabilities, opportunities, motivations, and behaviors and theoretical domains framework.

Sample: 150 participants who lived in Forsyth County and who identified as having food insecurity.

Methods: Our study team tested impacts of a FFRx program using mixed methods research, focusing on pre- and post-intervention changes in FI, fruit and vegetable (FV) intake, depression, and loneliness, measured at six-month intervals. We also evaluated our program via semi-structured interviews and surveys.

Results: Our pilot data showed positive impacts on food insecurity. The themes from the semi-structured interviews and surveys included that the program promoted healthy dietary habits, improved access to high-quality foods, improved well-being, enhanced financial well-being, and alleviated logistical barriers to accessing food and cooking.

Clinical implication: Our pilot program ended in October 2021. We took what we learned from the pilot data and have expanded our work into a USDA-funded multidimensional produce delivery program. We plan to evaluate health and wellness outcomes of this program over time in partnership with the YMCA and The Produce Box, a local produce delivery service. We hope to inform clinical practitioners on ways to implement and evaluate programs such as this utilizing implementation science principles and work to influence larger policy regarding support for food support programs.

References
1) Michie S, van Stralen M, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6: 42. doi:10.1186/1748-5908-6-42.
2) Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7:3
3) Thomson S, Ugwuegbu J, Montez K, et al Qualitative perceptions of an anticipated fresh food prescription program. Health Behav Policy Rev. 2022;9:670–682. Doi:10.
4) Rachel P. Zimmer, Justin B. Moore, Mia Yang, Joni Evans, Scott Best, Sheena McNeill, David Harrison Jr., Heather Martin & Kimberly Montez (2022) Strategies and Lessons Learned from a Home Delivery Food Prescription Program for Older Adults, Journal of Nutrition in Gerontology and Geriatrics, DOI: 10.1080/21551197.2022.2084204
5) Ballard, D. W., Price, M., Fung, V., Brand, R., Reed, M. E., Fireman, B., … Hsu, J. (2010). Validation of an algorithm for categorizing the severity of hospital emergency department visits. Medical Care, 48(1), 58–63. https://doi.org/10.1097/MLR.0b013e3181bd49ad
6) Berkowitz SA, Delahanty LM, Terranova J, et al. Medically Tailored Meal Delivery for Diabetes Patients with Food Insecurity: a Randomized Cross-over Trial. J Gen Intern Med 2019;34:396-404.
7) Pérez-Zepeda MU, Castrejón-Pérez RC, Wynne-Bannister E, García-Peña C. Frailty and food insecurity in older adults. Public Health Nutrition 2016;19:2844-9.

Background: Patients with stroke require long-term rehabilitation to return to the pre-morbid level of functioning. Sleep disturbances are associated with a higher risk of stroke reoccurrence and poor poststroke outcomes. Approximately a third of stroke survivors experience sleep disturbances higher than the general population. Early identification of sleep problems after stroke is crucial to improve stroke outcomes.

Objectives: The project’s aim was to improve sleep outcomes among patients with stroke admitted to inpatient rehabilitation. This project integrated sleep protocol which included insomnia severity index (ISI) to assess sleep disturbances and implemented non-pharmacological and pharmacological interventions to manage sleep disturbances [BP3].

Steps of implementation: ISI scores were obtained on admission and weekly throughout patients’ stay in the rehabilitation unit and before discharge. Non-pharmacological interventions were implemented, and handouts on sleep hygiene were provided to all patients with stroke. Physiatrists were provided with a handout for pharmacological interventions and were utilized for patients scoring high on their ISI. Nurses’ end-of-shift notes were reviewed to assess documentation regarding sleep. The patients’ treatment charts were reviewed for medications prescribed to treat sleep disturbances.

Performance improvement outcomes: This project enrolled 48 patients with stroke who were assessed for sleep disturbances with ISI and were provided with pharmacological and non-pharmacological interventions. Improvement in ISI scores were reported by 29 [BP4] % (N=14) of patients with stroke, 17% (N=8) reported a decline in their ISI scores, and 54% (N=26) reported no change on discharge. Melatonin was scheduled in 35% (N=17) of enrolled patients, 6% (N=3) had trazodone, and 56% (N=27) had no prescribed medications. The documentation rate of sleep by the nurses in the end-of-shift note was 57% [BP5] [bs6].

Implications for future research: Multiple barriers cause sleep disturbances and are difficult to assess because of individual differences and subjective symptoms. The symptom profile should be considered to develop stroke-specific interventions targeting the comorbidities. Additionally, follow-up with their primary care providers should be encouraged for continuity of care to improve their sleep disturbances and stroke outcomes.

References
1) Baylan, S., Griffiths, S., Grant, N., Broomfield, N. M., Evans, J. J., & Gardani, M. (2020). Incidence and prevalence of post-stroke insomnia: A systematic review and meta-analysis. Sleep Med Rev, 49. doi:10.1016/j.Smrv.2019.10122
2) Byun, e., Kohen, R., Becker, K. J., Kirkness, C. J., Khot, S., & Mitchell, P. H. (2019). Stroke impact symptoms are associated with sleep-related impairment. Heart Lung, 49(2), 117-122, doi:10.1016/j.Hrtlng.2019.10.010
3) Rosenthal, l. D., Dolan, D. C., Taylor, D. J., & Grieser, E. (2008). Long-term follow-up with insomnia. Baylor University Medical Center Proceedings, 21(3), 264-265. doi:10.1080/08998280.2008.11928409
4) Winstein, C. J., Stein, J., Arena, R., Bates, B., Cherney, L. R., Cramer, S. C., Deruyter, F., Eng, J. J., Fisher, B., Harvey, R. L., Lang, C. E., Mackay-lyons, M., Ottenbacher, K. J., Pugh, s., Reeves, M. J., Richards, L. G., Stiers, W., & Zorowitzmdon, R. D. (2016). Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 47, e98-e169. https://doi.Org/10.1161/str.0000000000000098

Purpose: The purpose of this project is to promote patient-centered-care (PCC) on an acute care of the elderly (ACE) unit through use of a “get to know me” poster to increase patient satisfaction and nursing perception of providing PCC.

Rationale: As the geriatric population grows, their risk for acute hospitalizations also increases. Instituting PCC increases positive outcomes and satisfaction in geriatric patients. Despite the importance of PCC, adoption into routine acute care practice remains inconsistent (Moore et al., 2017). Older adults who are frail or have cognitive impairment can be easily overlooked in acute care settings. One strategy for implementing PCC is the use of a “get to know me” (GTKM) tool to help improve communication, identify patient values and personalize care interventions (Fick et al., 2013).

Framework: The IHI model for improvement was used as a foundation for the project as it guides development of the plan, do, study, act (PDSA) quality improvement (QI) process (IHI, 2022). Additionally, the IHI’s age-friendly health system provides a foundation for this project with use of the 4 Ms (what matters, medications, mobility, mentation) framework.

Methodology: This is a QI project that will be conducted over two months on an ACE unit in a community hospital. The GTKM poster will be completed using information from patients/family members and posted during the hospital stay. Patient satisfaction will be measured through Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores, specifically reviewing nursing communication percentages pre- and post-implementation of this QI project. Satisfaction with the project will be measured through interviews performed at random selection with 3-4 patients/family members weekly. We will utilize the person-centered care assessment tool (P-CAT) to describe nurse perceptions pre- and post-implementation. Additionally, nursing perceptions of the poster will be collected weekly during audits of completion.

Analysis: An aggregate of responses pre- and post- implementation will be collected for HCAHPS scores and from the P-CAT tool. Poster completion rates will be collected weekly and plotted on a run chart to monitor for trends in poster usage.

Results: The project is ongoing with a plan for completion by July 2023.

References
1) Fick, D. M., DiMeglio, B., McDowell, J. A., & Mathis-Halpin, J. (2013). Do you know your patient? Knowing individuals with dementia combined with evidence-based care promotes function and satisfaction in hospitalized older adults. Journal of gerontological nursing, 39(9), 2–4. https://doi.org/10.3928/00989134-20130809-89
Institute for Healthcare Improvement (2022). How to improve.
https://www.ihi.org/resources/Pages/HowtoImprove
2) Moore, L., Britten, N., Lydahl, D., Naldemirci, Ö., Elam, M., & Wolf, A. (2017). Barriers and facilitators to the implementation of person-centred care in different healthcare contexts. Scandinavian journal of caring sciences, 31(4), 662–673. https://doi.org/10.1111/scs.12376

Background: Frailty is an emerging global concern that has major implications for clinical practice and public health. As people get older and the prevalence of frailty increases, there is an increased need for innovative care models that would address special needs of frail older adults. Implementing standardized frailty screening to determine the prevalence of frailty is the first step in evaluating for potential resources needed to address this problem.

Objective: The objectives of this QI project were to increase providers’ knowledge about frailty, implement standardized frailty assessment, and establish the prevalence and the severity of frailty in a population of homebound older persons enrolled in a home-based primary care program.

Method: The QI project interventions included a teaching module about frailty, implementation of assessment using FRAIL scale, and the analysis of the outcome of the assessment.

Results: There were a total of 97 screenings performed in the period of 12 weeks. The patients’ mean age was 85.9 (SD +/- 8.3). The prevalence of frailty in the older patients who were enrolled in the HBPCP at the time of the QI project’s implementation was 94%. Six (6.4%) patients were found to have severe frailty, 32 (34%) were moderately frail, and 50 (53%) were categorized with mild frailty. No correlation (p = .10) between the frailty score and age was found when the relationship was examined using Pearson correlation (r = .18) or Spearman’s rank correlation (ρ = .18). To determine whether greater severity of frailty correlated with the increased likelihood of making the diagnosis and documenting the diagnosis in the EHR, data were analyzed using the chi-square test. No significant correlations were observed between frailty score (p = .18), age (p = .95), and providers’ compliance with documenting the diagnosis in the EHR.

Conclusion: Most homebound patients who are enrolled in home-based primary care programs are frail. Implementing individualized interventions to address frailty in this population could improve care outcomes, patient satisfaction, and reduce healthcare utilization and costs.

References
1) Ensrud, K. E., Kats, A. M., Schousboe, J. T., Taylor, B. C., Cawthon, P. M., Hillier, T. A., Yaffe, K., Cummings, S.R., Cauley, J.A., Langsetmo, L., for the Study of Osteoporotic Fractures. (2018). Frailty phenotype and healthcare costs and utilization in older women. Journal of the American Geriatrics Society, 66(7), 1276-1283.
https://doi-org.ucsf.idm.oclc.org/10.1111/jgs.15381
2) Ensrud, K. E., Kats, A. M., Schousboe, J. T., Taylor, B. C., Vo, T. N., Cawthon, P. M., Hoffman, A.R., Langsetmo, L., for the Osteoporotic Fractures in Men Study (MrOS). (2020). Frailty phenotype and healthcare costs and utilization in older men. Journal of the American Geriatrics Society, 68(9), 2034-2042.
https://doi-org.ucsf.idm.oclc.org/10.1111/jgs.16522
3) Cesari, M., Calvani, R., & Marzetti, E. (2017). Frailty in Older Persons. Clinics in geriatric medicine, 33(3), 293–303.
https://doi.org/10.1016/j.cger.2017.02.002
4) Cesari, M., Prince, M., Thiyagarajan, J. A., De Carvalho, I. A., Bernabei, R., Chan, P., Gutierrez-Robledo, L. M., Michel, J. P., Morley, J. E., Ong, P., Rodriguez Manas, L., Sinclair, A., Won, C. W., Beard, J., & Vellas, B. (2016). Frailty: An Emerging Public Health Priority. Journal of the American Medical Directors Association, 17(3), 188–192. https://doi.org/10.1016/j.jamda.2015.12.016