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Item Asymptomatic Bacteriuria versus Symptom Underreporting in Older Emergency Department Patients with Suspected Urinary Tract Infection(Wiley, 2020-11) Caterino, Jeffrey M.; Stephens, Julie A.; Camargo, Carlos A., Jr.; Wexler, Randell; Hebert, Courtney; Southerland, Lauren T.; Hunold, Katherine M.; Hains, David S.; Bischof, Jason J.; Wei, Lai; Wolfe, Alan J.; Schwaderer, Andrew; Pediatrics, School of MedicineItem The Avoidable Transfer Scale: A New Tool for Identifying Potentially Avoidable Hospital Transfers of Nursing Home Residents(Oxford University Press, 2022-05-11) Carnahan, Jennifer L.; Unroe, Kathleen T.; Evans, Russell; Klepfer, Sarah; Stump, Timothy E.; Monahan, Patrick O.; Torke, Alexia M.; Medicine, School of MedicineBackground and objectives: Prior approaches to identifying potentially avoidable hospital transfers (PAHs) of nursing home residents have involved detailed root cause analyses that are difficult to implement and sustain due to time and resource constraints. They relied on the presence of certain conditions but did not identify the specific issues that contributed to avoidability. We developed and tested an instrument that can be implemented using review of the electronic medical record. Research design and methods: The OPTIMISTIC project was a Centers for Medicare and Medicaid Services demonstration to reduce avoidable hospital transfers of nursing home residents. The OPTIMISTIC team conducted a series of root cause analyses of transfer events, leading to development of a 27-item instrument to identify common characteristics of PAHs (Stage 1). To refine the instrument, project nurses used the electronic medical record (EMR) to score the avoidability of transfers to the hospital for 154 nursing home residents from 7 nursing homes from May 2019 through January 2020, including their overall impression of whether the transfer was avoidable (Stage 2). Each transfer was rated independently by 2 nurses and assessed for interrater reliability with a kappa statistic. Results: Kappa scores ranged from -0.045 to 0.556. After removing items based on our criteria, 12 final items constituted the Avoidable Transfer Scale. To assess validity, we compared the 12-item scale to nurses' overall judgment of avoidability of the transfer. The 12-item scale scores were significantly higher for submissions rated as avoidable than those rated unavoidable by the nurses (mean 5.3 vs 2.6, p < .001). Discussion and implications: The 12-item Avoidable Transfer Scale provides an efficient approach to identify and characterize PAHs using available data from the EMR. Increased ability to quantitatively assess the avoidability of resident transfers can aid nursing homes in quality improvement initiatives to treat more acute changes in a resident's condition in place.Item Brief Educational Video plus Telecare to Enhance Recovery for Older Emergency Department Patients with Acute Musculoskeletal Pain: an update to the study protocol for a randomized controlled trial(Springer Nature, 2022-05-12) Hurka‑Richardson, Karen; Platts‑Mills, Timothy F.; McLean, Samuel A.; Weinberger, Morris; Stearns, Sally C.; Bush, Montika; Quackenbush, Eugenia; Chari, Srihari; Aylward, Aileen; Kroenke, Kurt; Kerns, Robert D.; Weaver, Mark A.; Keefe, Francis J.; Berkoff, David; Meyer, Michelle L.; Medicine, School of MedicineBackground: This update describes changes to the Brief Educational Tool to Enhance Recovery (BETTER) trial in response to the COVID-19 pandemic. Methods/design: The original protocol was published in Trials. Due to the COVID-19 pandemic, the BETTER trial converted to remote recruitment in April 2020. All recruitment, consent, enrollment, and randomization now occur by phone within 24 h of the acute care visit. Other changes to the original protocol include an expansion of inclusion criteria and addition of new recruitment sites. To increase recruitment numbers, eligibility criteria were expanded to include individuals with chronic pain, non-daily opioid use within 2 weeks of enrollment, presenting musculoskeletal pain (MSP) symptoms for more than 1 week, hospitalization in past 30 days, and not the first time seeking medical treatment for presenting MSP pain. In addition, recruitment sites were expanded to other emergency departments and an orthopedic urgent care clinic. Conclusions: Recruiting from an orthopedic urgent care clinic and transitioning to remote operations not only allowed for continued participant enrollment during the pandemic but also resulted in some favorable outcomes, including operational efficiencies, increased enrollment, and broader generalizability.Item Brief Educational Video plus Telecare to Enhance Recovery for Older Emergency Department Patients with Acute Musculoskeletal Pain: an update to the study protocol for a randomized controlled trial(Springer, 2022-05-12) Hurka-Richardson, Karen; Platts-Mills, Timothy F.; McLean, Samuel A.; Weinberger, Morris; Stearns, Sally C.; Bush, Montika; Quackenbush, Eugenia; Chari, Srihari; Aylward, Aileen; Kroenke, Kurt; Kerns, Robert D.; Weaver , Mark A.; Keefe, Francis J.; Berkoff, David; Meyer, Michelle L.; Medicine, School of MedicineBackground This update describes changes to the Brief Educational Tool to Enhance Recovery (BETTER) trial in response to the COVID-19 pandemic. Methods/design The original protocol was published in Trials. Due to the COVID-19 pandemic, the BETTER trial converted to remote recruitment in April 2020. All recruitment, consent, enrollment, and randomization now occur by phone within 24 h of the acute care visit. Other changes to the original protocol include an expansion of inclusion criteria and addition of new recruitment sites. To increase recruitment numbers, eligibility criteria were expanded to include individuals with chronic pain, non-daily opioid use within 2 weeks of enrollment, presenting musculoskeletal pain (MSP) symptoms for more than 1 week, hospitalization in past 30 days, and not the first time seeking medical treatment for presenting MSP pain. In addition, recruitment sites were expanded to other emergency departments and an orthopedic urgent care clinic. Conclusions Recruiting from an orthopedic urgent care clinic and transitioning to remote operations not only allowed for continued participant enrollment during the pandemic but also resulted in some favorable outcomes, including operational efficiencies, increased enrollment, and broader generalizability.Item Complexities of care: Common components of models of care in geriatrics(Wiley, 2022) McNabney, Matthew K.; Green, Ariel R.; Burk, Meg; Le, Stephanie T.; Butler, Dawn; Chun, Audrey K.; Elliott, David P.; Fulton, Ana Tuya; Hyer, Kathryn; Setters, Belinda; Shega, Joseph W.; Medicine, School of MedicineAs people age, they are more likely to have an increasing number of medical diagnoses and medications, as well as healthcare providers who care for those conditions. Health professionals caring for older adults understand that medical issues are not the sole factors in the phenomenon of this “care complexity.” Socioeconomic, cognitive, functional, and organizational factors play a significant role. Care complexity also affects family caregivers, providers, and healthcare systems and therefore society at large. The American Geriatrics Society (AGS) created a work group to review care to identify the most common components of existing healthcare models that address care complexity in older adults. This article, a product of that work group, defines care complexity in older adults, reviews healthcare models and those most common components within them and identifies potential gaps that require attention to reduce the burden of care complexity in older adults.Item Creation and Implementation of a Large-Scale Geriatric Interprofessional Education Experience(Hindawi, 2020-07-25) Mcquown, Colleen; Ahmed, Rami A.; Hughes, Patrick G.; Ortiz-Figueroa, Fabiana; Drost, Jennifer C.; Brown, Diane K.; Fosnight, Sue; Hazelett, Susan; Emergency Medicine, School of MedicineThe care of the older adult requires an interprofessional approach to solve complex medical and social problems, but this approach is difficult to teach in our educational silos. We developed an interprofessional educational session in response to national requests for innovative practice models that use collaborative interprofessional teams. We chose geriatric fall prevention as our area of focus as our development of the educational session coincided with the development of an interprofessional Fall Risk Reduction Clinic. Our aim of this study was to evaluate the number and type of students who attended a pilot and 10 subsequent educational sessions. We also documented the changes that occurred due to a Plan-Do-Study-Act (PDSA) rapid-cycle improvement model to modify our educational session. The educational session evolved into an online presession self-study didactic and in-person educational session with a poster/skill section, an interprofessional team simulation, and simulated patient experience. The simulated patient experience included an interprofessional fall evaluation, team meeting, and presentation to an expert panel. The pilot session had 83 students from the three sponsoring institutions (hospital system, university, and medical university). Students were from undergraduate nursing, nurse practitioner graduate program, pharmacy, medicine, social work, physical therapy, nutrition, and pastoral care. Since the pilot, 719 students have participated in various manifestations of the online didactic plus in-person training sessions. Ten separate educational sessions have been given at three different institutions. Survey data with demographic information were available on 524 participants. Students came from ten different schools and represented thirteen different health care disciplines. A large-scale interprofessional educational session is possible with rapid-cycle improvement, inclusion of educators from a variety of learning institutions, and flexibility with curriculum to accommodate learners in various stages of training.Item Decision Support System For Geriatric Care(Office of the Vice Chancellor for Research, 2010-04-09) Palakal, Mathew; Pandit, Yogesh; Jones, Josette; Xia, Yuni; Bandos, Jean; Geesaman, Jerry; Pecenka, Dave; Tinsley, EricGeriatrics is a branch in medicine that focuses on the healthcare of the elderly. We propose to build a decision support system for the elderly care based on a knowledgebase system that incorporates best practices that are reported in the literature. A Bayesian network model is then used for decision support for the geriatric care tool that we develop.Item Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department(Biomed Central, 2019-07-22) Patterson, Brian W.; Jacobsohn, Gwen C.; Shah, Manish N.; Song, Yiqiang; Maru, Apoorva; Venkatesh, Arjun K.; Zhong, Monica; Taylor, Katherine; Hamedani, Azita G.; Mendonça, Eneida A.; Pediatrics, IU School of MedicineBACKGROUND: Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification. METHODS: In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results. RESULTS: The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders. CONCLUSIONS: Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.Item Effectiveness of the VA-Geriatric Resources for Assessment and Care of Elders (VA-GRACE) program: An observational cohort study(Wiley, 2022) Schubert, Cathy C.; Perkins, Anthony J.; Myers, Laura J.; Damush, Teresa M.; Penney, Lauren S.; Zhang, Ying; Schwartzkopf, Ashley L.; Preddie, Alaina K.; Riley, Sam; Menen, Tetla; Bravata, Dawn M.; Medicine, School of MedicineBackground: As the Department of Veterans Affairs (VA) healthcare system seeks to expand access to comprehensive geriatric assessments, evidence-based models of care are needed to support community-dwelling older persons. We evaluated the VA Geriatric Resources for Assessment and Care of Elders (VA-GRACE) program's effect on mortality and readmissions, as well as patient, caregiver, and staff satisfaction. Methods: This retrospective cohort included patients admitted to the Richard L. Roudebush VA hospital (2010-2019) who received VA-GRACE services post-discharge and usual care controls who were potentially eligible for VA-GRACE but did not receive services. The VA-GRACE program provided home-based comprehensive, multi-disciplinary geriatrics assessment, and ongoing care. Primary outcomes included 90-day and 1-year all-cause readmissions and mortality, and patient, caregiver, and staff satisfaction. We used propensity score modeling with overlapping weighting to adjust for differences in characteristics between groups. Results: VA-GRACE patients (N = 683) were older than controls (N = 4313) (mean age 78.3 ± 8.2 standard deviation vs. 72.2 ± 6.9 years; p < 0.001) and had greater comorbidity (median Charlson Comorbidity Index 3 vs. 0; p < 0.001). VA-GRACE patients had higher 90-day readmissions (adjusted odds ratio [aOR] 1.55 [95%CI 1.01-2.38]) and higher 1-year readmissions (aOR 1.74 [95%CI 1.22-2.48]). However, VA-GRACE patients had lower 90-day mortality (aOR 0.31 [95%CI 0.11-0.92]), but no statistically significant difference in 1-year mortality was observed (aOR 0.88 [95%CI 0.55-1.41]). Patients and caregivers reported that VA-GRACE home visits reduced travel burden and the program linked Veterans and caregivers to needed resources. Primary care providers reported that the VA-GRACE team helped to reduce their workload, improved medication management for their patients, and provided a view into patients' daily living situation. Conclusions: The VA-GRACE program provides comprehensive geriatric assessments and care to high-risk, community-dwelling older persons with high rates of satisfaction from patients, caregivers, and providers. Widespread deployment of programs like VA-GRACE will be required to support Veterans aging in place.Item Expanding Access to Comprehensive Geriatric Evaluation via Telehealth: Development of Hybrid-Virtual Home Visits(Springer, 2024) Schubert, Cathy C.; Penney, Lauren S.; Schwartzkopf, Ashley L.; Damush, Teresa M.; Preddie, Alaina; Flemming, Soyna; Myers, Jennifer; Myers, Laura J.; Perkins, Anthony J.; Zhang, Ying; Bravata, Dawn M.; Medicine, School of MedicineBackground: In response to the aging population, the Department of Veterans Affairs (VA) seeks to expand access to evidence-based practices which support community-dwelling older persons such as the Geriatric Resources for Assessment and Care of Elders (GRACE) program. GRACE is a multidisciplinary care model which provides home-based geriatric evaluation and management for older Veterans residing within a 20-mile drive radius from the hospital. We sought to expand the geographic reach of VA-GRACE by developing a hybrid-virtual home visit (TeleGRACE). Objectives: The objectives were to: (1) describe challenges encountered and solutions implemented during the iterative, pre-implementation program development process; and (2) illustrate potential successes of the program with two case examples. Design: Quality improvement project with longitudinal qualitative data collection. Program description: The hybrid-virtual home visit involved a telehealth technician travelling to patients' homes and connecting virtually to VA-GRACE team members who participated remotely. Approach & participants: We collected multiple data streams throughout program development: TeleGRACE staff periodic reflections, fieldnotes, and team meeting notes; and VA-GRACE team member interviews. Key results: The five program domains that required attention and problem-solving were: telehealth connectivity and equipment, virtual physical examination, protocols and procedures, staff training, and team integration. For each domain, we describe several challenges and solutions. An example from the virtual physical examination domain: several iterations were required to identify the combination of telehealth stethoscope with dedicated headphones that allowed remote nurse practitioners to hear heart and lung sounds. The two cases illustrate how this hybrid-virtual home visit model provided care for patients who would not otherwise have received timely healthcare services. Conclusions: These results provide a blueprint to translate an in-person home-based geriatrics program into a hybrid-virtual model and support the feasibility of using hybrid-virtual home visits to expand access to comprehensive geriatric evaluation and ongoing care for high-risk, community-dwelling older persons who reside geographically distant from the primary VA facility.