RESUMO
OBJECTIVE: The primary objective was to evaluate if the percentage of patients with missing or inaccurate code status documentation at a Trauma Level 1 hospital could be reduced through daily updates. The secondary objective was to examine if patient preferences for DNR changed during the COVID-19 pandemic. METHODS: This retrospective study, spanning March 2019 to December 2022, compared the code status in ICU and ED patients drawn from two data sets. The first was based on historical electronic medical records (EHR), and the second involved daily updates of code status following patient admission. RESULTS: Implementing daily updates upon admission was more effective in ICUs than in the ED in reducing missing code status documentation. Around 20% of patients without a specific code status chose DNR under the new system. During COVID-19, a decrease in ICU patients choosing DNR and an increase in full code (FC) choices were observed. CONCLUSION: This study highlights the importance of regular updates and discussions regarding code status to enhance patient care and resource allocation in ICU and ED settings. The COVID-19 pandemic's influence on shifting patient preferences towards full code status underscores the need for adaptable documentation practices. Emphasizing patient education about DNR implications and benefits is key to supporting informed decisions that reflect individual health contexts and values. This approach will help balance the considerations for DNR and full code choices, especially during health care crises.
RESUMO
The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina's prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation.
