ABSTRACT
Study Objectives: Amid the US opioid epidemic, emergency providers and patients are searching for non-opioid or nonpharmacologic pain treatment options. The challenge of managing pain without opioids was escalated by the COVID-19 pandemic with opioid related overdoses and deaths increasing by 20-40%. Most healthcare professionals have limited knowledge, resources or time for pain education, especially in the emergency department (ED). To address these needs a novel pain coaching program was designed including a menu of nonpharmacologic patient discharge toolkit materials. Study objectives were to determine descriptive patient and toolkit utilization data and challenges in the first 4 months of a novel pain program. Methods: Target population consisted of patients ≥14 years of age seen by a new ED Pain Coaching staff from January 4, 2021- April 30, 2021. The two ED sites consisted of an urban, academic center with trauma center, pediatric ED, etc. and an affiliated community ED. Patients were determined by ED rounding, ED census review and consultation by ED staff, physicians, physical therapy, palliative care and pharmacy. Summary statistics for patient demographics, pain type, REALM-SF score, educational topics, toolkit materials, challenges and other data were ed from coaching and patient notes on a daily basis using a REDCap database for analysis. Upon request, there were select inpatient and repeat coaching encounters. Results: During this 4-month pilot, 296 coaching sessions were completed on 276 unique patients;20 screen outs for severe pain, procedures, violent behavior or other obstacles. Average age was 43 with 85% between 20-70 years of age;62% female;60% African American. Pain was 46% acute, 50% acute on chronic and 4% chronic with patients often having multiple pain etiologies: musculoskeletal (74%), inflammatory (71%), post-trauma (15%), headache (14%), post-surgical (4%) and neuropathic (3%). Education topics provided with accompanying toolkit items: hot/cold gel packs (90%), car with 4 flat tires analogy (90%), pain neuroscience education (88%), aromatherapy inhalers (82%), breathing techniques (69%), virtual reality (51%), exercise (38%), stretching (35%), diet (20%), acupressure (11%). The majority of patients were seen in 2 EDs or associated trauma center (87%);however, the coach received referrals for selected inpatients (13%). Seventeen educational brochures were made available to patients with aromatherapy, managing pain, pain and stress, and nonpharmacologic management being most utilized. Challenges to coaching included medical condition (14%), too much pain (11%), time constraints (7%);52% had no challenges. Regarding patient feedback, 61% indicated the session was helpful and 39% were unsure at the time. Conclusion: Results from this novel ED pain coach and discharge toolkit model provide valuable insights for development of a national pain coach model. Coaching scripts, note template, brochures, videos, inventory and other programmatic materials will be published for further implementation. Future plans include longitudinal patient follow-up, staff satisfaction assessment and addition of new modalities.
ABSTRACT
Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early step-down policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL. The allocation of hospital beds to low-risk and high-risk arrival patients to achieve the goal of reducing the death rate, while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge. © 2021 IEEE.