The impact of EMTALA on medical malpractice framework models: a review

Abstract

The Emergency Medical Treatment & Active Labor Act (EMTALA) is a healthcare law specific to screening, stabilizing, and transferring (or accepting) patients with emergency medical conditions and active labor. This law, contextual to Medicare-participating hospitals, ensures public access to emergency medical services, regardless of the individual’s ability to pay. The Defensive Medicine (DM) model and Physician Responsiveness to Standard-of-care Reforms (PRSRs) model are two medical malpractice frameworks leveraged in this paper. The nodes of these frameworks comprise of the treatment-versus-no-treatment dynamics and cutoff thresholds. Cutoff thresholds are specific to health risks and treatment price rates. Health risks stem from those with treating or not treating a patient as well as those inherent from the patient’s ailment. Treatment price rates are subcategorized into customary and efficient price rates. Given the above nodes of these frameworks, this paper examines how the above medical malpractice models synchronize and sequentially align with the legal obligations of this law. This paper, furthermore, contemplatively describes how the incentivize/penalize dynamics interrelate to the push/pull dynamics of the PRSRs malpractice model. Thereafter, this paper applies the above push/pull dynamics contextual to the three specific obligations of this law, essentially, screening, stabilizing, and transferring (or accepting) emergency care patients. Conclusively, this paper illustrates the above network in a cascading algorithm that ligates the nodes of these frameworks to EMTALA's obligations.

Portfolio Optimization Based on Markowitz Investment Theory and Monte Carlo Simulation

“…Portfolio optimization helps in portfolio selection in volatile market conditions [4]. Numerous intelligent algorithms based on artificial intelligence and machine learning have been used for optimization, such as the Bayesian network model proposed by Shenoy is well suited for scenarios that combine qualitative and quantitative information [5]. Among the classical approaches, there are many fruitful models, like the multi-stage portfolio optimization using var as a risk measure proposed by Xu, et al has advantages in shortterm investment, but for long-term investment, there are more uncertainties [6].…”

Ayurvedic management of renal calculus: A review article

“…According to epidemiological research, the prevalence is higher in men (12%) than in women (6%), peaks between the ages of 20 and 40 for both sexes, and then declines after the age of 50. Numerous causes, including a heated environment, food, metabolic diseases, elevated urine citrate, immobility, insufficient urine drainage, etc., are the cause of it [6] . Calcium containing stones are the most common kidney stones (75-90%) followed by magnesium ammonium phosphate(struvite) (10-15%), uric acid (3-10%) and cystine (0.…”