Sociodemographic disparities in the use of cardiovascular ambulatory care and telemedicine during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic accelerated adoption of telemedicine in cardiology clinics. Early in the pandemic, there were sociodemographic disparities in telemedicine use. It is unknown if these disparities persisted and whether they were associated with changes in the population of patients accessing care. METHODS: We examined all adult cardiology visits at an academic and an affiliated community practice in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). We compared patient sociodemographic characteristics between these periods. We used logistic regression to assess the association of patient/visit characteristics with visit modality (in-person vs telemedicine and video- vs phone-based telemedicine) during the COVID period. RESULTS: There were 54,948 pre-COVID and 58,940 COVID visits. Telemedicine use increased from <1% to 70.7% of visits (49.7% video, 21.0% phone) during the COVID period. Patient sociodemographic characteristics were similar during both periods. In adjusted analyses, visits for patients from some sociodemographic groups were less likely to be delivered by telemedicine, and when delivered by telemedicine, were less likely to be delivered by video versus phone. The observed disparities in the use of video-based telemedicine were greatest for patients aged ≥80 years (vs age <60, OR 0.24, 95% CI 0.21, 0.28), Black patients (vs non-Hispanic White, OR 0.64, 95% CI 0.56, 0.74), patients with limited English proficiency (vs English proficient, OR 0.52, 95% CI 0.46-0.59), and those on Medicaid (vs privately insured, OR 0.47, 95% CI 0.41-0.54). CONCLUSIONS: During the first year of the pandemic, the sociodemographic characteristics of patients receiving cardiovascular care remained stable, but the modality of care diverged across groups. There were differences in the use of telemedicine vs in-person care and most notably in the use of video- vs phone-based telemedicine. Future studies should examine barriers and outcomes in digital healthcare access across diverse patient groups.

The Association Between Central Line-Associated Bloodstream Infection and Central Line Access.

OBJECTIVES: Identifying modifiable risk factors associated with central line-associated bloodstream infections (CLABSIs) may lead to modifications to central line (CL) management. We hypothesize that the number of CL accesses per day is associated with an increased risk for CLABSI and that a significant fraction of CL access may be substituted with non-CL routes. DESIGN: We conducted a retrospective cohort study of patients with at least one CL device day from January 1, 2015, to December 31, 2019. A multivariate mixed-effects logistic regression model was used to estimate the association between the number of CL accesses in a given CL device day and prevalence of CLABSI within the following 3 days. SETTING: A 395-bed pediatric academic medical center. PATIENTS: Patients with at least one CL device day from January 1, 2015, to December 31, 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 138,411 eligible CL device days across 6,543 patients, with 639 device days within 3 days of a CLABSI (a total of 217 CLABSIs). The number of per-day CL accesses was independently associated with risk of CLABSI in the next 3 days (adjusted odds ratio, 1.007; 95% CI, 1.003-1.012; p = 0.002). Of medications administered through CLs, 88% were candidates for delivery through a peripheral line. On average, these accesses contributed a 6.3% increase in daily risk for CLABSI. CONCLUSIONS: The number of daily CL accesses is independently associated with risk of CLABSI in the next 3 days. In the pediatric population examined, most medications delivered through CLs could be safely administered peripherally. Efforts to reduce CL access may be an important strategy to include in contemporary CLABSI-prevention bundles.

Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press.

Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.

Individualized risk trajectories for iron-related adverse outcomes in repeat blood donors.

BACKGROUND: Despite a fingerstick hemoglobin requirement and 56-day minimum donation interval, repeat blood donation continues to cause and exacerbate iron deficiency. STUDY DESIGN AND METHODS: Using data from the REDS-II Donor Iron Status Evaluation study, we developed multiclass prediction models to estimate the competing risk of hemoglobin deferral and collecting blood from a donor with sufficient hemoglobin but low or absent underlying iron stores. We compared models developed with and without two biomarkers not routinely measured in most blood centers: ferritin and soluble transferrin receptor. We generated and analyzed "individual risk trajectories": estimates of how each donors' risk developed as a function of the time interval until their next donation attempt. RESULTS: With standard biomarkers, the top model had a multiclass area under the receiver operator characteristic curve (AUC) of 77.6% (95% CI [77.3%-77.8%]). With extra biomarkers, multiclass AUC increased to 82.8% (95% CI [82.5%-83.1%]). In the extra biomarkers model, ferritin was the single most important variable, followed by the donation interval. We identified three risk archetypes: "fast recoverers" (<10% risk of any adverse outcome on post-donation day 56), "slow recoverers" (>60% adverse outcome risk on day 56 that declines to <35% by day 250), and "chronic high-risk" (>85% risk of the adverse outcome on day 250). DISCUSSION: A longer donation interval reduced the estimated risk of iron-related adverse outcomesfor most donors, but risk remained high for some. Tailoring safeguards to individual risk estimates could reduce blood collections from donors with low or absent iron stores.

Assessment of physician training and prediction of workforce needs in paediatric cardiac intensive care in the United States.

OBJECTIVE: To assess the training and the future workforce needs of paediatric cardiac critical care faculty. DESIGN: REDCap surveys were sent May-August 2019 to medical directors and faculty at the 120 US centres participating in the Society of Thoracic Surgeons Congenital Heart Surgery Database. Faculty and directors were asked about personal training pathway and planned employment changes. Directors were additionally asked for current faculty numbers, expected job openings, presence of training programmes, and numbers of trainees. Predictive modelling of the workforce was performed using respondents' data. Patient volume was projected from US Census data and compared to projected provider availability. MEASUREMENTS AND MAIN RESULTS: Sixty-six per cent (79/120) of directors and 62% (294/477) of contacted faculty responded. Most respondents had training that incorporated critical care medicine with the majority completing training beyond categorical fellowship. Younger respondents and those in dedicated cardiac ICUs were more significantly likely to have advanced training or dual fellowships in cardiology and critical care medicine. An estimated 49-63 faculty enter the workforce annually from various training pathways. Based on modelling, these faculty will likely fill current and projected open positions over the next 5 years. CONCLUSIONS: Paediatric cardiac critical care training has evolved, such that the majority of faculty now have dual fellowship or advanced training. The projected number of incoming faculty will likely fill open positions within the next 5 years. Institutions with existing or anticipated training programmes should be cognisant of these data and prepare graduates for an increasingly competitive market.

Target-Based Care: An Intervention to Reduce Variation in Postoperative Length of Stay.

OBJECTIVES: To derive care targets and evaluate the impact of displaying them at the point of care on postoperative length of stay (LOS). STUDY DESIGN: A prospective cohort study using 2 years of historical controls within a freestanding, academic children's hospital. Patients undergoing benchmark cardiac surgery between May 4, 2014, and August 15, 2016 (preintervention) and September 6, 2016, to September 30, 2018 (postintervention) were included. The intervention consisted of displaying at the point of care targets for the timing of extubation, transfer from the intensive care unit (ICU), and hospital discharge. Family satisfaction, reintubation, and readmission rates were tracked. RESULTS: The postintervention cohort consisted of 219 consecutive patients. There was a reduction in variation for ICU (difference in SD -2.56, P < .01) and total LOS (difference in SD -2.84, P < .001). Patients stayed on average 0.97 fewer days (P < .001) in the ICU (median -1.01 [IQR -2.15, -0.39]), 0.7 fewer days (P < .001) on mechanical ventilation (median -0.54 [IQR -0.77, -0.50]), and 1.18 fewer days (P < .001) for the total LOS (median -2.25 [IQR -3.69, -0.15]). Log-transformed multivariable linear regression demonstrated the intervention to be associated with shorter ICU LOS (ß coefficient -0.19, SE 0.059, P < .001), total postoperative LOS (ß coefficient -0.12, SE 0.052, P = .02), and ventilator duration (ß coefficient -0.21, SE 0.048, P < .001). Balancing metrics did not differ after the intervention. CONCLUSIONS: Target-based care is a simple, novel intervention associated with reduced variation in LOS and absolute LOS across a diverse spectrum of complex cardiac surgeries.

Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health.

OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset. CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.

Baseline creatinine determination method impacts association between acute kidney injury and clinical outcomes.

BACKGROUND: Current consensus definition for acute kidney injury (AKI) does not specify how baseline serum creatinine should be determined. We assessed how baseline determination impacted AKI incidence and association between AKI and clinical outcomes. METHODS: We retrospectively applied empirical (measured serum creatinine) and imputed (age/height) baseline estimation methods to pediatric patients discharged between 2014 and 2019 from an academic hospital. Using each method, we estimated AKI incidence and assessed area under ROC curve (AUROC) for AKI as a predictor of three clinical outcomes: application of AKI billing code (proxy for more clinically overt disease), inpatient mortality, and post-hospitalization chronic kidney disease. RESULTS: Incidence was highly variable across baseline methods (12.2-26.7%). Incidence was highest when lowest pre-admission creatinine was used if available and Schwartz bedside equation was used to impute one otherwise. AKI was more predictive of application of an AKI billing code when baseline was imputed universally, regardless of pre-admission values (AUROC 80.7-84.9%) than with any empirical approach (AUROC 64.5-76.6%). AKI was predictive of post-hospitalization CKD when using universal imputation baseline methods (AUROC 67.0-74.6%); AKI was not strongly predictive of post-hospitalization CKD when using empirical baseline methods (AUROC 46.4-58.5%). Baseline determination method did not affect the association between AKI and inpatient mortality. CONCLUSIONS: Method of baseline determination influences AKI incidence and association between AKI and clinical outcomes, illustrating the need for standard criteria. Imputing baseline for all patients, even when preadmission creatinine is available, may identify a more clinically relevant subset of the disease.

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determine higher-risk patients. We assessed the ability of a variety of machine-learning algorithms to predict adolescents at risk of POUS and to identify factors associated with this risk. METHODS: A retrospective cohort study was conducted using a national insurance claims database of adolescents aged 12-21 years who underwent 1 of 1297 surgeries, with general anesthesia, from January 1, 2011 to December 30, 2017. Logistic regression with an L2 penalty and with a logistic regression with an L1 lasso (Lasso) penalty, random forests, gradient boosting machines, and extreme gradient boosted models were trained using patient and provider characteristics to predict POUS (≥1 opioid prescription fill within 90-180 days after surgery) risk. Predictive capabilities were assessed using the area under the receiver-operating characteristic curve (AUC)/C-statistic, mean average precision (MAP); individual decision thresholds were compared using sensitivity, specificity, Youden Index, F1 score, and number needed to evaluate. The variables most strongly associated with POUS risk were identified using permutation importance. RESULTS: Of 186,493 eligible patient surgical visits, 8410 (4.51%) had POUS. The top-performing algorithm achieved an overall AUC of 0.711 (95% confidence interval [CI], 0.699-0.723) and significantly higher AUCs for certain surgeries (eg, 0.823 for spinal fusion surgery and 0.812 for dental surgery). The variables with the strongest association with POUS were the days' supply of opioids and oral morphine milligram equivalents of opioids in the year before surgery. CONCLUSIONS: Machine-learning models to predict POUS risk among adolescents show modest to strong results for different surgeries and reveal variables associated with higher risk. These results may inform health care system-specific identification of patients at higher risk for POUS and drive development of preventative measures.

The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).

Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional point forecasts of patient demand are commonly available, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the 'second wave' of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased and unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.

A personalized decision aid for prostate cancer shared decision making.

BACKGROUND: A shared decision-making model is preferred for engaging prostate cancer patients in treatment decisions. However, the process of assessing an individual's preferences and values is challenging and not formalized. The purpose of this study is to develop an automated decision aid for patient-centric treatment decision-making using decision analysis, preference thresholds and value elicitations to maximize the compatibility between a patient's treatment expectations and outcome. METHODS: A template for patient-centric medical decision-making was constructed. The inputs included prostate cancer risk group, pre-treatment health state, treatment alternatives (primarily focused on radiation in this model), side effects (erectile dysfunction, urinary incontinence, nocturia and bowel incontinence), and treatment success (5-year freedom from biochemical failure). A linear additive value function was used to combine the values for each attribute (side effects, success and the alternatives) into a value for all prospects. The patient-reported toxicity probabilities were derived from phase II and III trials. The probabilities are conditioned on the starting state for each of the side effects. Toxicity matrices for erectile dysfunction, urinary incontinence, nocturia and bowel incontinence were created for the treatment alternatives. Toxicity probability thresholds were obtained by identifying the patient's maximum acceptable threshold for each of the side effects. Results are represented as a visual. R and Rstudio were used to perform analyses, and R Shiny for application creation. RESULTS: We developed a web-based decision aid. Based on preliminary use of the application, every treatment alternative could be the best choice for a decision maker with a particular set of preferences. This result implies that no treatment has determinist dominance over the remaining treatments and that a preference-based approach can help patients through their decision-making process, potentially affecting compliance with treatment, tolerance of side effects and satisfaction with the decision. CONCLUSIONS: We present a unique patient-centric prostate cancer treatment decision aid that systematically assesses and incorporates a patient's preferences and values to rank treatment options by likelihood of achieving the preferred outcome. This application enables the practice and study of personalized medicine. This model can be expanded to include additional inputs, such as genomics, as well as competing, concurrent or sequential therapies.

Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed-methods approach.

AIM: To identify, simulate and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift. BACKGROUND: Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels. METHODS: Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels. RESULTS: Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift. CONCLUSION: Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand. IMPLICATIONS FOR NURSING MANAGEMENT: Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.

Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance.

OBJECTIVE: Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance. METHODS: We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and body mass index were used to calculate adjusted mean and delta HbA1c. RESULTS: Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]), whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]). CONCLUSIONS: Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance.

Hospitalization Patterns for Inpatient Pediatric Surgery and Procedures in California: 2000-2016.

BACKGROUND: We report hospitalization patterns from 2000 to 2016 for young children (ages 0-5 years old) in California who underwent 1 of the 20 most common inpatient procedures that required general anesthesia and evaluate the estimated probability of treatment at a tertiary care children's hospital (CH) by year. METHODS: We hypothesized that children ≤5 years old increasingly undergo care at tertiary care CHs for common inpatient surgeries or other procedures that require general anesthesia. Data from the California Office of Statewide Health Planning and Development dataset were used to determine procedure, patient age, year of procedure, and hospital name. Hospitals were designated as either tertiary care CHs, children's units within general hospitals (CUGHs), or general hospitals (GHs) based on the California Children's Services Provider List. A tertiary care CH was defined using the California Children's Services definition as a referral hospital that provides comprehensive, multidisciplinary, regionalized pediatric care to children from birth up to 21 years of age with a full range of medical and surgical care for severely ill children. We report the unadjusted percentage of patients treated at each hospital type and, after controlling for patient covariates and comorbidities, the estimated probability of undergoing care at a tertiary care CH from 2000 to 2016. RESULTS: There were 172,318 treatment episodes from 2000 to 2016. The estimated probability of undergoing care at a tertiary care CH increased from 63.4% (95% confidence interval [CI], 62.4%-64.4%) in 2000 to 78.3% (95% CI, 77.3%-79.4%) in 2016. CONCLUSIONS: Children ≤5 years old undergoing common inpatient procedures that require general anesthesia increasingly receive care at tertiary care CHs in California.

Improving the efficiency of the operating room environment with an optimization and machine learning model.

The operating room is a major cost and revenue center for most hospitals. Thus, more effective operating room management and scheduling can provide significant benefits. In many hospitals, the post-anesthesia care unit (PACU), where patients recover after their surgical procedures, is a bottleneck. If the PACU reaches capacity, patients must wait in the operating room until the PACU has available space, leading to delays and possible cancellations for subsequent operating room procedures. We develop a generalizable optimization and machine learning approach to sequence operating room procedures to minimize delays caused by PACU unavailability. Specifically, we use machine learning to estimate the required PACU time for each type of surgical procedure, we develop and solve two integer programming models to schedule procedures in the operating rooms to minimize maximum PACU occupancy, and we use discrete event simulation to compare our optimized schedule to the existing schedule. Using data from Lucile Packard Children's Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital.

The Pediatric Anesthesiology Workforce: Projecting Supply and Trends 2015-2035.

BACKGROUND: A workforce analysis was conducted to predict whether the projected future supply of pediatric anesthesiologists is balanced with the requirements of the inpatient pediatric population. The specific aims of our analysis were to (1) project the number of pediatric anesthesiologists in the future workforce; (2) project pediatric anesthesiologist-to-pediatric population ratios (0-17 years); (3) project the mean number of inpatient pediatric procedures per pediatric anesthesiologist; and (4) evaluate the effect of alternative projections of individual variables on the model projections through 2035. METHODS: The future number of pediatric anesthesiologists is determined by the current supply, additions to the workforce, and departures from the workforce. We previously compiled a database of US pediatric anesthesiologists in the base year of 2015. The historical linear growth rate for pediatric anesthesiology fellowship positions was determined using the Accreditation Council for Graduate Medical Education Data Resource Books from 2002 to 2016. The future number of pediatric anesthesiologists in the workforce was projected given growth of pediatric anesthesiology fellowship positions at the historical linear growth rate, modeling that 75% of graduating fellows remain in the pediatric anesthesiology workforce, and anesthesiologists retire at the current mean retirement age of 64 years old. The baseline model projections were accompanied by age- and gender-adjusted anesthesiologist supply, and sensitivity analyses of potential variations in fellowship position growth, retirement, pediatric population, inpatient surgery, and market share to evaluate the effect of each model variable on the baseline model. The projected ratio of pediatric anesthesiologists to pediatric population was determined using the 2012 US Census pediatric population projections. The projected number of inpatient pediatric procedures per pediatric anesthesiologist was determined using the Kids' Inpatient Database historical data to project the future number of inpatient procedures (including out of operating room procedures). RESULTS: In 2015, there were 5.4 pediatric anesthesiologists per 100,000 pediatric population and a mean (±standard deviation [SD]) of 262 ±8 inpatient procedures per pediatric anesthesiologist. If historical trends continue, there will be an estimated 7.4 pediatric anesthesiologists per 100,000 pediatric population and a mean (±SD) 193 ±6 inpatient procedures per pediatric anesthesiologist in 2035. If pediatric anesthesiology fellowship positions plateau at 2015 levels, there will be an estimated 5.7 pediatric anesthesiologists per 100,000 pediatric population and a mean (±SD) 248 ±7 inpatient procedures per pediatric anesthesiologist in 2035. CONCLUSIONS: If historical trends continue, the growth in pediatric anesthesiologist supply may exceed the growth in both the pediatric population and inpatient procedures in the 20-year period from 2015 to 2035.

A quality improvement initiative to optimize dosing of surgical antimicrobial prophylaxis.

BACKGROUND: The risk of surgical site infections is reduced with appropriate timing and dosing of preoperative antimicrobials. Based on evolving national guidelines, we increased the preoperative dose of cefazolin from 25 to 30 mg·kg-1 . This quality improvement project describes an improvement initiative to develop standard work processes to ensure appropriate dosing. AIMS: The primary aim was to deliver cefazolin 30 mg·kg-1 to at least 90% of indicated patients. The secondary aim was to determine differences between accuracy of cefazolin doses when given as an electronic order compared to a verbal order. METHODS: Data were collected from January 1, 2012 to May 31, 2016. A quality improvement team of perioperative physicians, nurses, and pharmacists implemented a series of interventions including new electronic medical record order sets, personal provider antibiotic dose badges, and utilization of pharmacists to prepare antibiotics to increase compliance with the recommended dose. Process compliance was measured using a statistical process control chart, and dose compliance was measured through electronic analysis of the electronic medical record. Secondary aim data were displayed as percentage of dose compliance. An unpaired t-test was used to determine differences between groups. RESULTS: Between January 1, 2012 and May 31, 2016, cefazolin was administered to 9086 patients. The mean compliance of cefazolin at 30 mg·kg-1 from May 2013 to March 2014 was 40%, which prompted initiation of this project. From April 2014 to May 2016, a series of interventions were deployed. The mean compliance from September 2015 to May 2016 was 93% with significantly reduced variation and no special cause variation, indicating that the process was in control at the target primary aim. There were 649 cefazolin administrations given verbally and 1929 given with an electronic order between October 1, 2014 and May 31, 2016. During this time period, the rate of compliance of administering cefazolin at 30 mg·kg-1 was significantly higher when given after an electronic order than when given verbally, 94% vs 76%. CONCLUSION: This comprehensive quality improvement project improved practitioner compliance with evidence-based preoperative antimicrobial dosing recommendations to reduce the risk of surgical site infections.