Surgical Outcomes Improvement and Health Inequity in a Regional Quality Collaborative.

BACKGROUND: Surgical quality improvement initiatives may impact sociodemographic groups differentially. The objective of this analysis was to assess the trajectory of surgical morbidity by race and age over time within a Regional Collaborative Quality Initiative. STUDY DESIGN: Adults undergoing eligible general surgery procedures in South Carolina Surgical Quality Collaborative hospitals were analyzed for the presence of at least 1 of 22 morbidities between August 2015 and February 2020. Surgery-level multivariable logistic regression assessed the racial differences in morbidity over time, stratified by age group (18 to 64 years, 65 years and older), and adjusting for potential patient- and surgical-level confounders. RESULTS: A total of 30,761 general surgery cases were analyzed, of which 28.4% were performed in Black patients. Mean morbidity rates were higher for Black patients than non-Black patients (8.5% vs 6.0%, p < 0.0001). After controlling for race and other confounders, a significant decrease in monthly mean morbidity through time was observed in each age group (odds ratio [95% CI]: age 18 to 64 years, 0.986 [0.981 to 0.990]; age 65 years and older, 0.991 [0.986 to 0.995]). Comparing morbidity rates from the first 4 months of the collaborative to the last 4 months reveals older Black patients had an absolute decrease in morbidity of 6.2% compared with 3.6% for older non-Black patients. Younger Black patients had an absolute decrease in morbidity of 4.7% compared with a 3.0% decrease for younger non-Black patients. CONCLUSIONS: Black patients had higher morbidity rates than non-Black patients even when controlling for confounders. The reasons for these disparities are not apparent. Morbidity improved over time in all patients with older Black patients seeing a larger absolute decrease in morbidity.

Preventive Care Utilization by Patients Who Use Virtual Urgent Care.

Introduction: The use of direct to patient (DTP) telemedicine for common acute conditions is widespread. It provides certain advantages over in-person visits, but has led to concerns about fragmentation of care. It is unknown whether use of DTP telemedicine decreases use of primary care services in a way that leads to missed preventive screenings and immunizations. Methods: Virtual urgent care (VUC) is a DTP telemedicine service to treat common acute conditions. All VUC encounters completed at an academic health system from July 2018 to December 2019 were evaluated and analyzed in 2020. Only patients established with primary care (at least one primary care visit in the same year as VUC encounter) were included. Specific preventive screenings (breast cancer, gonorrhea/chlamydia, and cervical cancer) and immunizations (tetanus and influenza) were characterized as up to date based on national guidelines. Chi-squares and multivariate logistic regressions were used to assess receipt of screenings and immunizations. Regressions included VUC and primary care utilization and demographic factors. Results: Patients evaluated (N = 1025) were mostly 25-50 years old (69.7%), women (81.8%), and white (74.9%). More than half (56.5%) had only used VUC once. In multivariate analyses, VUC utilization was not negatively associated with any of the preventive services evaluated, whereas primary care utilization was associated with receipt of both immunizations and gonorrhea/chlamydia screening. Conclusions: Higher VUC utilization is not negatively associated with receipt of preventive services, as long as a primary care relationship is established. VUC may provide a useful method of encouraging receipt of preventive services, especially for younger patients.

High-risk opioid analgesic dispensing to adolescents 12-18 years old in South Carolina: 2010-2017.

PURPOSE: To evaluate "high-risk" opioid dispensing to adolescents, including daily morphine milligram equivalents (MME) above recommended amounts, the percentage of extended-release opioid prescriptions dispensed to opioid-naïve adolescents, and concurrent use of opioids and benzodiazepines, and to evaluate changes in those rates over time. METHODS: Retrospective cohort study of one state's prescription drug monitoring program data (2010-2017), evaluating adolescents 12-18 years old dispensed opioid analgesic prescriptions. Outcomes of interest were the quarterly frequencies of the high-risk measures. We utilized generalized linear regression to determine whether the rate of the outcomes changed over time. RESULTS: The quarterly percentage of adolescents ages 12-18 years old dispensed an opioid who received ≥90 daily MME declined from 4.1% in the first quarter (Q1) of 2010 to 3.4% in the final quarter (Q4) of 2017 (p < 0.0001). The frequency of adolescents dispensed ≥50 daily MME changed little over time. In 2010, the percentage of adolescents receiving an extended-release opioid who were opioid naïve was 60.7%, declining to 50.6% by Q4 of 2017 (p > 0.10 overall change 2010-2017). The percentage of adolescent opioid days overlapping with benzodiazepine days was 1.6% in Q1 of 2010, declining to 1.1% by Q4 of 2017 (p < 0.001). CONCLUSIONS: Among persons 12-18 years old dispensed an opioid analgesic, receipt of ≥90 daily MME declined during the years 2010-2017, as did the percentage of adolescent opioid days that overlapped with benzodiazepines. More than half of the individuals who received extended-release opioid analgesics were identified as opioid naïve and, counter to guidelines, received products intended for opioid-tolerant individuals.

Trends in Dispensed Opioid Analgesic Prescriptions to Children in South Carolina: 2010-2017.

BACKGROUND AND OBJECTIVES: Despite published declines in opioid prescribing and dispensing to children in the past decade, in few studies have researchers evaluated all children in 1 state or examined changes in mean daily opioid dispensed. In this study, we evaluated changes in the rate of dispensed opioid analgesics and the mean daily opioid dispensed to persons 0 to 18 years old in 1 state over an 8-year period. METHODS: We identified opioid analgesics dispensed to children 0 to 18 years old between 2010 and 2017 using South Carolina prescription drug monitoring program data. We used generalized linear regression analyses to examine changes over time in the following: (1) rate of dispensed opioid prescriptions and (2) mean daily morphine milligram equivalents (MMEs) per prescription. RESULTS: From the first quarter of 2010 to the end of the fourth quarter of 2017, the quarterly rate of opioids dispensed decreased from 18.68 prescriptions per 1000 state residents to 12.03 per 1000 residents (P < .0001). The largest declines were among the oldest individuals, such as the 41.2% decline among 18-year-olds. From 2010 through 2017, the mean daily MME dispensed declined by 7.6%, from 40.7 MMEs per day in 2010 to 37.6 MMEs per day in 2017 (P < .0001), but the decrease was limited to children 0 to 9 years old. CONCLUSIONS: The rate of opioid analgesic prescriptions dispensed to children 0 to 18 years old in South Carolina declined by 35.6% over the years 2010-2017; however, the MME dispensed per day declined minimally, suggesting that more can be done to improve opioid prescribing and dispensing.

Facilitated Regional Collaboration and In-Hospital Surgical Complication.

BACKGROUND: Surgical quality improvement efforts are challenging due to the multidisciplinary nature of care, difficulties obtaining reliable data, and variability in quality metrics. The objective of this analysis was to assess whether participation in a regional collaborative quality initiative was associated with decreased in-hospital surgical complication in South Carolina. STUDY DESIGN: In-hospital surgical complication rates were determined using a statewide all-payer claims data set. Retrospective, univariate, and longitudinal multivariable analyses were performed and adjustments were made to account for aggregated hospital-level patient characteristics. RESULTS: The analysis included 275,387 general surgery cases performed in South Carolina hospitals between January 2016 and December 2018. Eight hospitals involved in the South Carolina Surgical Quality Collaborative (SCSQC) performed 56,179 cases and 51 non-SCSQC hospitals performed 219,208 cases. Univariate analysis revealed SCSQC hospitals performed operations in older patients (p < 0.0001) and patients with higher mean Charlson Comorbidity Index scores (p < 0.0001). SCSQC hospitals had higher mean in-hospital surgical complication rates at the surgery level compared with non-SCSQC hospitals (8.3% vs 7.0%; p < 0.0001). However, in multivariable analyses, the rate ratio for in-hospital surgical complication in SCSQC hospitals was 0.994 (95% CI, 0.989 to 0.998; p = 0.008) per month compared with non-SCSQC hospitals. This suggests a 21.6% (95% CI, 7.2% to 39.6%) proportional decrease in the rate of in-hospital surgical complication during 3 years associated with participation in the regional collaborative quality initiative. CONCLUSIONS: Structured collaboration between facilities, reliable data abstraction support, timely data review, and active member participation resulted in outcomes improvements for participating hospitals compared with hospitals that did not participate in a regional collaborative quality initiative.

Chaos to complexity: leveling the playing field for measuring value in primary care.

RATIONALE, AIMS AND OBJECTIVES: Develop a risk-stratification model that clusters primary care patients with similar co-morbidities and social determinants and ranks 'within-practice' clusters of complex patients based on likelihood of hospital and emergency department (ED) utilization. METHODS: A retrospective cohort analysis was performed on 10 408 adults who received their primary care at the Medical University of South Carolina University Internal Medicine clinic. A two-part generalized linear regression model was used to fit a predictive model for ED and hospital utilization. Agglomerative hierarchical clustering was used to identify patient subgroups with similar co-morbidities. RESULTS: Factors associated with increased risk of utilization included specific disease clusters {e.g. renal disease cluster [rate ratio, RR = 5.47; 95% confidence interval (CI; 4.54, 6.59) P < 0.0001]}, low clinic visit adherence [RR = 0.33; 95% CI (0.28, 0.39) P < 0.0001] and census measure of high poverty rate [RR = 1.20; 95% CI (1.11, 1.28) P < 0.0001]. In the cluster model, a stable group of four clusters remained regardless of the number of additional clusters forced into the model. Although the largest number of high-utilization patients (top 20%) was in the multiple chronic condition cluster (1110 out of 4728), the largest proportion of high-utilization patients was in the renal disease cluster (67%). CONCLUSIONS: Risk stratification enhanced with disease clustering organizes a primary care population into groups of similarly complex patients so that care coordination efforts can be focused and value of care can be maximized.

Identification of High Utilization Inpatients on Internal Medicine Services.

BACKGROUND: As healthcare reform moves toward value based care, hospitals must reduce costs. As a first step, here we developed a predictive model to identify high-cost patients on admission. METHODS: We performed a retrospective observational study of 7,571 adults admitted to internal medicine services from July 1, 2013 to June 30, 2014. We compared the top 10% highest cost patients to other patients (controls) and identified clinical variables associated with high inpatient costs. Using logistic regression analyses, we developed a predictive model that could be used on admission to identify potential high utilization patients. RESULTS: In the 757 high utilizer patients, the median total hospital cost was $53,430 ± 60,679 compared to $8,431 ± 7,245 in the control group (P < 0.0001). The median length of stay for high utilization patients was 19.5 ± 32.5 days compared to 3.8 ± 3.9 days in the control group (P < 0.001). Variables associated with high utilization included transfer from an outside hospital (odds ratio [OR] = 1.6), admission to the pulmonary or medical intensive care unit (OR = 2.4), admission to cardiology (OR = 1.8), coagulopathy (OR = 2.6) and fluid and electrolyte disorders (OR = 2.1). A multivariate logistic regression model was used to fit a predictive model for high utilizers. The receiver operating characteristics curve of this prediction model yielded an area under the curve of 0.80. CONCLUSIONS: High resource utilization patients appear to have a specific phenotype that can be predicted with commonly available clinical variables. Our predictive formula holds promise as a tool that may help ultimately reduce hospital costs.

Point of care experience with pneumococcal and influenza vaccine documentation among persons aged ≥65 years: high refusal rates and missing information.

Missed opportunities to vaccinate and refusal of vaccine by patients have hindered the achievement of national health care goals. The meaningful use of electronic medical records should improve vaccination rates, but few studies have examined the content of these records. In our vaccine intervention program using an electronic record with physician prompts, paper prompts, and nursing standing orders, we were unable to achieve national vaccine goals, due in large part to missing information and patient refusal.