Reducing inpatient opioid consumption after caesarean delivery: effects of an opioid stewardship programme and racial impact in a community hospital.

Caesarean section is the most common inpatient surgery in the USA, with more than 1.1 million procedures in 2020. Similar to other surgical procedures, healthcare providers rely on opioids for postoperative pain management. However, current evidence shows that postpartum patients usually experience less pain due to pregnancy-related physiological changes. Owing to the current opioid crisis, public health agencies urge providers to provide rational opioid prescriptions. In addition, a personalised postoperative opioid prescription may benefit racial minorities since research shows that this population receives fewer opioids despite greater pain levels. Our project aimed to reduce inpatient opioid consumption after caesarean delivery within 6 months of the implementation of an opioid stewardship programme.A retrospective analysis of inpatient opioid consumption after caesarean delivery was conducted to determine the baseline, design the opioid stewardship programme and set goals. The plan-do-study-act method was used to implement the programme, and the results were analysed using a controlled interrupted time-series method.After implementing the opioid stewardship programme, we observed an average of 80% reduction (ratio of geometric means 0.2; 95% CI 0.2 to 0.3; p<0.001) in inpatient opioid consumption. The institution designated as control did not experience relevant changes in inpatient opioid prescriptions during the study period. In addition, the hospital where the programme was implemented was unable to reduce the difference in inpatient opioid demand between African Americans and Caucasians.Our project showed that an opioid stewardship programme for patients undergoing caesarean delivery can effectively reduce inpatient opioid use. PDSA, as a quality improvement method, is essential to address the problem, measure the results and adjust the programme to achieve goals.

Leveraging advanced preparation of oncology medications: Decreasing turnaround-times in an outpatient infusion center.

INTRODUCTION: Many oncology infusions are provided in hospital-based infusion centers. With hospital-based infusion centers seeing increased volumes, patient wait times continue to be a priority. Extended wait times for oncology infusions have been shown to lead to patient dissatisfaction. METHODS: Advanced Preparation of oncology infusion medications allows pharmacy to verify and prepare specific medications the day before a patient's infusion appointment. Our study targeted lower cost, commonly used medications to prepare in advance. Data analyzed included turnaround time (TAT), medication waste, and oncology infusion preparation volumes. Implementation took place in two phases to allow time for the healthcare team to adjust to the new workflow. Phase I medications include a small amount of medications prepared manually by pharmacy technicians. Phase II medications included all phase I medications plus additional medications that were compounded in the intravenous (IV) robotic compounding system. RESULTS: Our study demonstrated significant decrease in median TAT for medications prepared in advance. 537 infusions were prepared using the Advanced Preparation module with a median TAT of 24.2 minutes (IQR, 18.0-34.0). The pre-implementation median TAT was 45.0 minutes (IQR, 36.0-56.0), which represents a decrease of 20.8 minutes (46.2%) following implementation of the program, (p<0.001). There were a total of 149 advanced preparation doses that were wasted (21.7% of doses). CONCLUSION: We have seen a statistically significant reduction in TAT for Advanced Preparation medications. Low volume of Advanced Preparation medications compared to total infusion volume limited impact on overall TAT.

Effect of Detailed Titration Instructions on Time to Hemodynamic Stability in ICU Patients Requiring Norepinephrine.

BACKGROUND: This study was conducted to assess the effect of titration instructions on patients receiving norepinephrine. METHODS: In a single-center, retrospective cohort of patients who received at least 24 hours of norepinephrine as their first vasopressor (n = 1,303), patients were classified by whether they received norepinephrine before (n = 616) or after (n = 687) titration instructions were added. RESULTS: Patients in the two groups had significant differences at baseline. On univariate analysis, time to hemodynamic stability was significantly longer in the post group (32 minutes [interquartile range (IQR): 12-65] vs. 10 minutes [IQR: 0-26]; p < 0.01). On multivariate analysis, addition of titration instructions was associated with an increase of 24 minutes in time to hemodynamic stability after accounting for differences in baseline systolic blood pressure, fluid boluses before norepinephrine, baseline arrhythmia, and number of other vasopressors or titratable infusions (p = 0.02). CONCLUSION: In this evaluation, time to hemodynamic stability was significantly longer after addition of norepinephrine titration instructions even when accounting for differences in baseline characteristics.

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring.

BACKGROUND: Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy. METHODS AND FINDINGS: We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure. CONCLUSIONS: This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Impact of a quality-assessment dashboard on the comprehensive review of pharmacist performance.

PURPOSE: The impact of a quality-assessment dashboard and individualized pharmacist performance feedback on the adherence of order verification was evaluated. METHODS: A before-and-after study was conducted at a 1,440-bed academic medical center. Adherence of order verification was defined as orders verified according to institution-derived, medication-related guidelines and policies. Formulas were developed to assess the adherence of verified orders to dosing guidelines using patient-specific height, weight, and serum creatinine clearance values from the electronic medical record at the time of pharmacist verification. A total of 5 medications were assessed by the formulas for adherence and displayed on the dashboard: ampicillin-sulbactam, ciprofloxacin, piperacillin-tazobactam, acyclovir, and enoxaparin. Adherence of order verification was assessed before (May 1-July 31, 2015) and after (November 1, 2015-January 31, 2016) individualized performance feedback was given based on trends identified by the quality-assessment dashboard. RESULTS: There was a significant increase in the overall adherence rate postintervention (90.1% versus 91.9%, p = 0.040). Among the 34 pharmacists who participated, the percentage of pharmacists with at least 90% overall adherence increased postintervention (52.9% versus 70.6%, p = 0.103). Time to verification was similar before and after the study intervention (median, 6.0 minutes; interquartile range, 3-13 minutes). The rate of documentation for nonadherent orders increased significantly postintervention (57.1% versus 68.5%, p = 0.019). CONCLUSION: The implementation of the quality-assessment dashboard, educational sessions, and individualized performance feedback significantly improved pharmacist order-verification adherence to institution-derived, medication-related guidelines and policies and the documentation rate of nonadherent orders.