Racial disparities in opioid administration and prescribing in the emergency department for pain.

STUDY OBJECTIVE: To investigate the holistic characteristics of patients administered or prescribed opioids to treat pain in the emergency department (ED). METHODS: We used National Hospital Ambulatory Medical Care Survey (NHAMCS) data for 2018 to examine the administration and prescribing of opioids for pain-related ED visits. Weighted logistic regression models were developed to evaluate the association between opioid administration and prescribing (OAP) in the ED and patients' pain/severity of conditions, demographic/socioeconomic factors, behavioral factors, contextual factors, and organizational factors. Then, subgroup analyses were conducted by type of pain. RESULTS: Nearly 55% of the ED visits in 2018 involved pain as a main reason for visiting the ED. The odds of receiving opioids were 45% less in black patients than in white patients when other covariates were adjusted (OR: 0.55; CI: 0.430-0.703). Compared to patients with private insurance, Medicaid beneficiaries and uninsured/self-pay patients had a 45% (OR: 0.55; CI: 0.423-0.706) and 44% (OR: 0.56; CI: 0.386-0.813) lower chance of receiving or being prescribed opioids for a pain-related ED visit when all covariates were adjusted. Other significant predictors of OAP for pain in EDs included older age, higher pain level, ED arrival by ambulance, admission to hospital, ED arrival during a night shift, geographic region of the ED. Behavioral factors, such as ED return within 72 h and whether a patient has substance/alcohol abuse or dependence, were not significantly associated with OAP. The subgroup analysis indicated that black patients had lower odds of OAP than their white counterparts only for certain pain categories. CONCLUSION: Despite increasing awareness of potential implicit bias in managing pain in the ED, racial disparities in OAP still existed. More education and training on implicit bias would help with reduce the disparities. Also, our study result indicated that non-clinical factors may play a role in emergency physicians' decision making in OAP. Increased recognition of the variation and systemic efforts to address factors affecting the variability are needed.

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach.

Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time.

Relationships Among Bone Morphological Parameters and Mechanical Properties of Cadaveric Human Vertebral Cancellous Bone.

Mechanical properties and morphological features of the vertebral cancellous bone are related to resistance to fracture and capability of withstanding surgical treatments. In particular, vertebral strength is related to its elastic properties, whereas the ease of fluid motion, related to the success of incorporation orthopedic materials (eg, bone cement), is regulated by the hydraulic permeability (K). It has been shown that both elastic modulus and permeability of a material are affected by its morphology. The objective of this study was to establish relations between local values of K and the aggregate modulus (H), and parameters descriptive of the bone morphology. We hypothesized that multivariate statistical models, by including the contribution of several morphology parameters at once, would provide a strong correlation with K and H of the vertebral cancellous bone. Hence, µCT scans of human lumbar vertebra were used to determine a set of bone morphology descriptors. Subsequently, indentation tests on the bone samples were conducted to determine local values of K and H. Finally, a multivariate approach supported by principal component analysis was adopted to develop predictive statistical models of bone permeability and aggregate modulus as a function of bone morphology descriptors. It was found that linear combinations of bone volume fraction, trabecular thickness, trabecular spacing, structure model index, connectivity density, and degree of anisotropy provide a strong correlation (R 2 ~ 76%) with K and a weaker correlation (R 2 ~ 47%) with H. The results of this study can be exploited in computational mechanics frameworks for investigating the potential mechanical behavior of human vertebra and to develop strategies to treat or prevent pathological conditions such as osteoporosis, age-related bone loss, and vertebral compression fractures. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.