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1. Learners will be able to identify high-yield ways to utilize a PC pharmacist to improve patient care and to support PC providers in medication review and prescribing. Learners will also evaluate ways in which working with a pharmacist might increase the quality and experience of PC clinicians' work. 2. Learners will be able to advocate for integration or collaboration with PC pharmacists at their own medical centers using data demonstrating pharmacists' impact on both patient outcomes and provider experience. Integration of a palliative care (PC) pharmacist into the inpatient PC consult service at an academic medical center resulted in the transdisciplinary team (TDT) reporting increased efficiency and wellbeing and a trend towards decreased 30-day readmissions. These results support the case for wider adoption of PC pharmacists as an essential member of the TDT. Inclusion of a palliative care (PC) pharmacist on the transdisciplinary PC team (TDT) is the gold standard but remains rare, and outcomes merit further characterization. Integrate a PC pharmacist into the inpatient consult service at an academic medical center and evaluate qualitative and quantitative outcomes. A PC pharmacist was integrated into the inpatient consult service for 1-2 days per week. The pharmacist provided patient care as a core member of the TDT. The impact of the pharmacist's work was measured in three ways: 1) tracking all patient interventions provided, 2) conducting a survey of the TDT about their experiences working with the pharmacist, and 3) assessing 30-day readmissions. To account for confounding by indication, a propensity score model predicting pharmacist involvement was created and used in inverse probability of treatment weighted (IPTW) logistic regression to generate a propensity score-adjusted odds ratio. From January 2022-June 2023, the PC pharmacist performed 1135 interventions over 244 patient encounters. The most common interventions provided were reviewing prescription monitoring reports, educating providers on PC pharmacology, and adjusting pain medication regimens. On IPTW logistic regression, there was a trend towards reduction in 30-day readmissions for patients seen by the pharmacist (OR 0.81; 95% CI 0.60-1.09; p=0.161). In an anonymous survey of 15 TDT members, 93% said the team was able to see more patients, 91% experienced improved confidence in medication recommendations, 87% felt more satisfied with work, 71% reported less burnout, and 100% thought that the TDT was stronger with the pharmacist present. Following integration of a PC pharmacist, the TDT reported increased team efficiency and wellbeing and observed a trend towards decreased 30-day readmissions for PC patients. These results reinforce the value of PC pharmacists and support the case for their wider integration as core members of transdisciplinary PC teams. Pharmacotherapeutics / Pharmacopalliation / Interdisciplinary Teamwork / Professionalism [ABSTRACT FROM AUTHOR]

1. Participants will reflect on the process by which patients are brought to inpatient consult services' attention and will imagine alternative systems that would help identify PC appropriate patients more promptly and proactively. 2. Learners will examine the ways in which bias and inequity may affect which patients are referred to subspecialty PC in our current system and compare how interdisciplinary team members may perceive patients' PC needs differently. Due to reliance on referrals for consults, the true quantity and quality of palliative care (PC) needs in an inpatient population are unknown. This work describes the process and outcomes of a screen to characterize PC needs in one medical center. It demonstrated significant unmet need and underscores the need for a more proactive and systematic model for PC delivery. D ue to reliance on referral-based systems for identifying patients, many inpatient palliative care (PC) services are unaware of the true quantity and quality of need at their institutions. Screen all hospitalized patients at a large academic medical center to provide a snapshot of patients' quantity and quality of PC needs at one moment in time. A validated EMR-based algorithm for identifying hospitalized patients with serious illness (1) was applied to the adult population at an academic medical center during one week in September 2021. Each patient's primary provider and bedside nurse were asked to respond to a scripted phone survey about their patient's PC needs. Data analysis was performed to compare Results: from the primary provider and nurse surveys, and both were compared against a novel EMR-based screening algorithm to identify PC needs (discussed in a separate abstract). During the survey week, a total of 268 patients were identified as having serious illness, and at least one clinician was surveyed for 224 of these patients (83.6%). Seventeen patients (7.6%) were followed by the inpatient PC consult team at the time. Providers reported that 60 patients (27%) had an unmet PC need. Sixty patients (27%) were reported as having intolerable and/or uncontrolled physical symptoms, with pain being most common. Primary providers reported that 96 patients and/or their caregivers (43%) were experiencing non-physical suffering. Primary providers were concerned that their patient might die during this hospitalization in 16% of cases. According to a survey of PC needs, over a quarter of seriously ill patients had an unmet PC need, representing approximately 11% of the total hospitalized population. Only a small percentage of patients with physical or psychosocial distress were receiving subspecialty PC services, demonstrating an opportunity for service expansion. Models of Palliative Care DeliveryManaging Suffering and Distress [ABSTRACT FROM AUTHOR]

1. Participants will describe how population-based methods to systematically identify palliative care need make access to palliative care services more equitable by mitigating clinician bias, ensure limited resources are provided to those most likely to benefit, and allow programs to quantify population need to argue for additional resources. 2. Participants will analyze the criteria included in our validated algorithm, identify those criteria that can be directly applied in their own clinical setting, and propose changes to criteria that require tailoring to maximize their utility in their local environment. This validated algorithm could fundamentally change the approach to care for people with serious illness from one in which patients are "lucky" to receive palliative care to one in which their needs are reliably and proactively identified and addressed. It could reduce disparities by mitigating clinician bias in referral and help programs quantify population need to advocate for additional resources. Population-based methods to identify patients with palliative care (PC) needs are necessary to provide equitable and efficient access to PC services. Create a validated algorithm embedded in the electronic medical record (EMR) to identify PC needs among hospitalized patients with serious illness. Interdisciplinary workgroups generated criteria to identify PC needs from either the EMR or clinician interview using literature review and clinical experience. Criteria were validated using a modified Delphi process and used to screen all hospitalized patients in one week in 2021. Workgroups used this survey's results to identify the EMR-based criteria that correlated best with PC need and developed a weighting system to generate a composite needs score. This score was validated against a "gold standard" of clinician assessment of unmet PC need from our 2021 survey. Of 76 criteria initially generated, 56 emerged from our validation process and were used to screen 224 of 268 patients (84%) identified as having serious illness. Of these, 28 criteria in four domains (psychological, physical, and spiritual distress; ACP needs) were found to correlate best with PC need. When compared with clinician assessment of unmet PC need, our composite need score resulted in an acceptable Area Under the Receiver Operating Characteristics Curve of 0.70. Furthermore, we found that patients referred for PC had a significantly higher mean needs score than patients that were not referred (21.6 [17.8-25.4] vs 16.4 [15.9-16.9], p=0.003). This algorithm is the first to successfully utilize the EMR to identify PC needs in hospitalized patients. Application of this algorithm allows programs to target services to those with greatest need and mitigate the potential for bias to result in disparities in referral and access to PC. It also allows programs to report the breadth and depth of PC needs to advocate for additional resources. Models of Palliative Care DeliveryInnovative Technologies [ABSTRACT FROM AUTHOR]