The quality of nasal anesthesia obtained with two local anesthetic solutions (2% lidocaine in oxymetazoline and 1% tetracaine in oxymetazoline) was evaluated in this double-blind, randomized study. Each local anesthetic mixture was applied to the nasal septum of healthy volunteers with medication-soaked pledgets. Measurements of anesthetic effect (sensation threshold and pain perception) were made with Semmes-Weinstein monofilaments (North Coast Medical, San Jose, Calif.). Measurements were performed before local anesthetic application and at 10 and 70 minutes after local anesthetic application. Subjects had greater increases in sensation threshold with tetracaine than with lidocaine at both 10 and 70 minutes (p = 0.0005 and p = 0.0001, respectively). Subjects had greater decreases in pain perception with tetracaine than with lidocaine at both time intervals (p = 0.0003 and p < 0.0001, respectively). Tetracaine mixed with oxymetazoline appears to be a superior topical anesthetic for nasal procedures.
The quality of nasal anesthesia obtained with three local anesthetic solutions (4% cocaine, 2% lidocaine in oxymetazoline, and 1% tetracaine in oxymetazoline) was evaluated in a randomized study. Each local anesthetic mixture was applied to the nasal septum of healthy volunteers using medication-soaked pledgets. Measurements of anesthetic effect (sensation threshold and pain perception) were made with Semmes-Weinstein monofilaments. Measurements were performed prior to local anesthetic application and 10 and 70 min after local anesthetic application. Subjects had greater increases in sensation threshold with tetracaine than with lidocaine or cocaine at both 10 and 70 min (P < 0.05). Subjects had greater decreases in pain perception with tetracaine than with lidocaine or cocaine at both time intervals (P < 0.05). Tetracaine mixed with oxymetazoline appears to be a superior topical anesthetic for nasal procedures.
Preparing patients for surgery is critical for achieving the best possible surgical outcomes. To do this effectively, care must be coordinated across several types of specialists, and potentially across multiple settings. In this paper, we develop a Patient-Centered Surgical Home (PCSH) for outpatient surgery based on the concept of the Perioperative Surgical Home proposed by the American Society of Anesthesiologists. A key feature of the PCSH is to have an anesthesiology preoperative assessment clinic (APC) serve as system coordinator and information integrator. Based on a study of outpatient surgery at the University of Texas Health Science Center at San Antonio and its primary teaching hospital using statistical analysis and simulation, we demonstrate how this can be accomplished. We show that for the PCSH to succeed, APC must see the right patients with the right information by overcoming improper triaging of patients and patient information deficiencies. Our analysis shows that with the proper screening tool and modifications to the way triage is handled, it is possible to increase the number of patients that the APC sees each day with a modest increase in resources. Much of the potential benefits rest on the cooperation of the referring clinics as well as closing the gap between the current level of patient information and what is needed for optimizing medical decisions. Estimated cost savings are over one million dollars annually with a PCSH. Since APC-like clinics are common, our findings have great potential for widespread implementation of similar PCSH models with commensurate benefits.
Many parts of the healthcare system remain fragmented and outpatient surgical care is no exception. In this study, we develop a coordinated pre‐operative scheduling approach between Anesthesiology and Internal Medicine to optimize patients’ medical conditions prior to surgery. Coordinating these two services has conceptual appeal because any health issues discovered by the anesthesiologist can often be addressed by a general internist. We design a patient‐centered approach that allows the patient to see both providers, if necessary, on a single visit. This problem is modeled as a two‐station stochastic network, where each station (or clinic) may be staffed by multiple parallel providers and patients see the first available provider. To solve the scheduling problem, we formulate an optimization model to maximize the net benefit of scheduling patients. The objective balances benefits against patient waiting time and clinic overtime costs. We develop a scheduling method with a booking limit to create a balanced network schedule. Due to the complexity of this problem, the solution approach is myopic. In addition, we develop a hybrid method that combines analytical calculation and simulation‐based optimization. We demonstrate our approach on a healthcare network at The University of Texas Health Sciences Center in San Antonio. We compare our method against other policies and show that it yields high quality and robust results. Based on the level of generality of our model and results, the insights gained are not limited to the particular application, but can be applied to other patient‐centered models where scheduling coordination can be used.
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