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Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College of Surgeons National Surgical Quality Improvement Program. Predictors from the electronic health records included diagnoses, procedures, and medications. Infectious complications included surgical site infection, urinary tract infection, sepsis, and pneumonia within 30 days of surgery. The knockoff filter, a penalized regression technique that controls type I error, was applied for variable selection. Models were validated in a chronological held-out dataset.Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved0.91 area under the receiver operating characteristic curve,81% specificity,87% sensitivity,99% negative predictive value, and 10% to 15% positive predictive value in a held-out test dataset.Surveillance and reporting of postoperative infection rates can be implemented for all operations with high accuracy using electronic health records data and simple linear regression models.

Implementation of enhanced recovery after surgery (ERAS) pathways for patients undergoing anatomic lung resection have been reported at individual institutions. We hypothesized that an ERAS pathway can be successfully implemented across a large healthcare system including different types of hospital settings (academic, academic-affiliated, community).An expert panel with representation from each hospital within a healthcare system was convened to establish a thoracic ERAS pathway for patients undergoing anatomic lung resection and to develop tools and analytics to ensure consistent application. The protocol was translated into an order set and pathway within the electronic health record (EHR). Iterative implementation was performed with recording of the processes involved. Barriers and facilitators to implementation were recorded.Development and implementation of the protocol took 13 months from conception to rollout. Considerable change management was needed for consensus and incorporation into practice. Facilitators of change included peer accountability, incorporating ERAS care elements into the EHR, and conducting case reviews with timely feedback on protocol deviations. Barriers included institutional cultural differences, agreement in defining mindful deviation from the ERAS protocol, lack of access to specific coded data, and resource scarcity caused by the COVID-19 pandemic. Support from the hospital system's executive leadership and institutional commitment to quality improvement helped overcome barriers and maintain momentum.Development and implementation of a health-system wide thoracic ERAS protocol for anatomic lung resections across a six-hospital health system requires a multidisciplinary team approach. Barriers can be overcome though multidisciplinary team engagement and executive leadership support.

Unplanned hospital admission after intended outpatient surgery is an undesirable outcome. We aimed to develop a prediction model that estimates a patient's risk of conversion from outpatient surgery to inpatient hospitalization.This was a retrospective analysis using the American College of Surgeons National Surgical Quality Improvement Program database, 2005 to 2018. Conversion from outpatient to inpatient surgery was defined as having outpatient surgery and1 day hospital stay. The Surgical Risk Preoperative Assessment System was developed using multiple logistic regression on a training dataset (2005-2016) and compared to a model using the 26 relevant variables in the American College of Surgeons National Surgical Quality Improvement Program. The Surgical Risk Preoperative Assessment System was validated using a testing dataset (2017-2018). Performance statistics and Hosmer-Lemeshow plots were compared. Two high-risk definitions were compared: (1) the maximum Youden index, and (2) the cohort above the tenth decile of risk on the Hosmer-Lemeshow plot. The sensitivities, specificities, positive predictive values, negative predictive values, and accuracies were compared.In all, 2,822,379 patients were included; 3.6% of patients unexpectedly converted to inpatient. The 6-variable Surgical Risk Preoperative Assessment System model performed comparably to the 26-variable American College of Surgeons National Surgical Quality Improvement Program model (c-indices = 0.818 vs. 0.823; Brier scores = 0.0308 vs 0.0306, respectively). The Surgical Risk Preoperative Assessment System performed well on internal validation (c-index = 0.818, Brier score = 0.0341). The tenth decile of risk definition had higher specificity, positive predictive values, and accuracy than the maximum Youden index definition, while having lower sensitivity.The Surgical Risk Preoperative Assessment System accurately predicted a patient's risk of unplanned outpatient-to-inpatient conversion. Patients at higher risk should be considered for inpatient surgery, while lower risk patients could safely undergo operations at ambulatory surgery centers.

Surgical Risk Preoperative Assessment System (SURPAS) estimates patient's preoperative risk of 12 postoperative complications, yet little is known about associations between these probabilities- We sought to examine relationships between predicted probabilities.Risk of 12 postoperative complications was calculated using SURPAS and the 2012-2018 ACS-NSQIP database. Pearson correlation coefficients (r) were computed to examine relationships between predicted outcomes. "High-risk" was predicted risk in the 10th decile.4,777,267 patients were included. 71.1% were not high risk, 10.7% were high risk for 1, and 18.2% were high risk for ≥2 complications. High mortality risk was associated with high risk for pulmonary (r = 0.94), cardiac (r = 0.98), renal (r = 0.93), and stroke (0.96) complications. Patients high-risk for ≥2 complications had the most comorbidities and actual adverse outcomes.High preoperative risk for certain postoperative complications had strong correlations. 18.2% of patients were high-risk for ≥2 complications and could be targeted for risk reduction interventions.

Considerable variability exists between surgeons' assessments of a patient's individual pre-operative surgical risk. Surgical risk calculators are not routinely used despite their validation. We sought to compare thoracic surgeons' prediction of patients' risk of postoperative adverse outcomes versus a surgical risk calculator, the Surgical Risk Preoperative Assessment System (SURPAS). We developed vignettes from 30 randomly selected patients who underwent thoracic surgery in the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) database. Twelve thoracic surgeons estimated patients' preoperative risks of postoperative morbidity and mortality. These were compared to SURPAS estimates of the same vignettes. C-indices and Brier scores were calculated for the surgeons' and SURPAS estimates. Agreement between surgeon estimates was examined using intraclass correlation coefficients (ICCs). Surgeons estimated higher morbidity risk compared to SURPAS for low-risk patients (ASA classes 1-2, 11.5% vs. 5.1%, p=

Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates.This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018. PATOS data were analyzed for the 8 postoperative complications of superficial, deep, and organ space surgical site infection; pneumonia; urinary tract infection; ventilator dependence; sepsis; and septic shock. Unadjusted postoperative complication rates were compared ignoring PATOS vs taking PATOS into account. Observed to expected ratios over time were also compared by calculating expected values using multiple logistic regression analyses with complication as the dependent variable and the 28 nonlaboratory preoperative variables in the ACS NSQIP database as the independent variables.In 5,777,108 patients, observed event rates for each outcome were reduced by between 6.1% (superficial surgical site infection) and 52.5% (sepsis) when PATOS was taken into account. The observed to expected ratios were similar each year for all outcomes, except for sepsis and septic shock in the early years.Taking PATOS into account is important for reporting unadjusted event rates. The effect varied by type of complication-lowest for superficial surgical site infection and highest for sepsis and septic shock. Taking PATOS into account was less important for risk-adjusted outcomes (observed to expected ratios), except for sepsis and septic shock.

Background The universal Surgical Risk Preoperative Assessment System (SURPAS) prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations. Methods The SURPAS models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores. Results The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the ACS NSQIP database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk. Conclusion SURPAS displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where SURPAS was not accurate, developing disease or operation-specific risk models might be appropriate.

Background The Surgical Risk Preoperative Assessment System (SURPAS) uses eight variables to accurately predict postoperative complications but has not been sufficiently studied in emergency surgery. We evaluated SURPAS in emergency surgery, comparing it to the Emergency Surgery Score (ESS). Methods SURPAS and ESS estimates of 30-day mortality and overall morbidity were calculated for emergency operations in the 2009–2018 ACS-NSQIP database and compared using observed-to-expected plots and rates, c-indices, and Brier scores. Cases with incomplete data were excluded. Results In 205,318 emergency patients, SURPAS underestimated (8.1%; 35.9%) while ESS overestimated (10.1%; 43.8%) observed mortality and morbidity (8.9%; 38.8%). Each showed good calibration on observed-to-expected plots. SURPAS had better c-indices (0.855 vs 0.848 mortality; 0.802 vs 0.755 morbidity), while the Brier score was better for ESS for mortality (0.0666 vs. 0.0684) and for SURPAS for morbidity (0.1772 vs. 0.1950). Conclusions SURPAS accurately predicted mortality and morbidity in emergency surgery using eight predictor variables.

A new postoperative esophagectomy care pathway was recently implemented at our institution. Practice pattern change among provider teams can prove challenging; therefore, we sought to study the barriers and facilitators toward pathway implementation at the provider level.This qualitative study was guided by the Theoretical Domains Framework (TDF) to study the adoption and implementation of a post-esophagectomy care pathway. Sixteen in-depth interviews were conducted with providers involved with the pathway. Matrix analysis was used to analyze the data.Providers included attending surgeons (n = 6), advanced practice providers (n = 8), registered dietitian (n = 1), and clinic staff (n = 1). TDF domains that were salient across our findings included knowledge, beliefs about consequences, social influences, and environmental context and resources. Identified facilitators included were electronic health record tools, such as note templates including pathway components and a pathway-specific order set, patient satisfaction, and preliminary data indicating clinical benefits such as a reduced anastomotic leak rate. The major barrier reported was a hesitance to abandon previous practice patterns, most prevalent at the attending surgeon level.The TDF enabled us to identify and understand the individuals' perceived barriers and facilitators toward adoption and implementation of a postoperative esophagectomy pathway. This analysis can help guide and improve adoption of surgical patient care pathways among providers.

Background Implementation researchers recognize the influential role of organizational factors and, thus, seek to assess these factors using quantitative measurement instruments. However, researchers are hindered by instruments that measure similar constructs but rely on different nomenclature and/or definitions. The Consolidated Framework for Implementation Research (CFIR) provides a taxonomy of constructs derived from prior frameworks and empirical studies of implementation-related constructs. The CFIR includes constructs based on the original Promoting Action on Research Implementation in Health Services (PARiHS) framework which highlights the key roles of strength of evidence for a specific evidence-based intervention (EBI), favorability of organizational context for change, and capacities to facilitate implementation of the EBI. Although the CFIR is among the most frequently used implementation frameworks, it does not include quantitative measures. The Organizational Resource and Context Assessment (ORCA) is a quantitative measurement instrument that was developed based on PARiHS, assessing its three domains. Factors within these three domains are conceptually similar to constructs in the CFIR but do not match directly. The aim of this work was to map ORCA survey items to CFIR constructs to enable direct comparisons and syntheses of findings across studies using the CFIR and/or ORCA. Methods Two distinct, independent research teams, each used rigorous constant comparative techniques with deliberation and consensus to map individual items from the ORCA to the five domains and 39 constructs of CFIR. Results ORCA items were mapped primarily to three of five CFIR domains: Inner Setting, Process, and Intervention Characteristics. The two research teams agreed on 88% of mappings at the higher domain level; at the lower construct level, their mappings aligned for 62.2% of the ORCA items. Conclusions Mapping results reveal that the ORCA focuses measurement prominently on Inner Setting, Process, and Intervention Characteristics. This mapping guide can help improve consistency in measurement and reporting, enabling more efficient comparison and synthesis of findings that use either the ORCA instrument or the CFIR framework. The guide helps advance implementation science utilizing mixed methods by providing CFIR users with quantitative measures for selected constructs and enables ORCA users to map their findings to CFIR constructs.

Background Postoperative complications, length of index hospital stay, and unplanned hospital readmissions are important metrics reflecting surgical care quality. Postoperative infections represent a substantial proportion of all postoperative complications. We examined the relationships between identification of postoperative infection prehospital and posthospital discharge, length of stay, and unplanned readmissions in the American College of Surgeons National Surgical Quality Improvement Program database across nine surgical specialties. Methods The 30-day postoperative infectious complications including sepsis, surgical site infections, pneumonia, and urinary tract infection were analyzed in the American College of Surgeons National Surgical Quality Improvement Program inpatient data during the period from 2012 to 2017. General, gynecologic, vascular, orthopedic, otolaryngology, plastic, thoracic, urologic, and neurosurgical inpatient operations were selected. Results Postoperative infectious complications were identified in 5.2% (137,014/2,620,450) of cases; 81,929 (59.8%) were postdischarge. The percentage of specific complications identified postdischarge were 73.4% of surgical site infections (range across specialties 63.7–93.1%); 34.9% of sepsis cases (27.4–58.1%); 26.5% of pneumonia cases (18.9%–36.3%); and 53.2% of urinary tract infections (48.3%–88.0%). The relative risk of readmission among patients with postdischarge versus predischarge surgical site infection, sepsis, pneumonia, or urinary tract infection was 5.13 (95% confidence interval: 4.90–5.37), 9.63 (8.93–10.40), 10.79 (10.15–11.45), and 3.32 (3.07–3.60), respectively. Over time, mean length of stay decreased but postdischarge infections and readmission rates significantly increased. Conclusion Most postoperative infectious complications were diagnosed postdischarge. These were associated with an increased risk of readmission. The trend toward shorter length of stay over time was observed along with an increase both in the percentage of infections detected after discharge and the rate of unplanned related postoperative readmissions over time. Postoperative surveillance of infections should extend beyond hospital discharge of surgical patients.

Background The novel Surgical Risk Preoperative Assessment System (SURPAS) requires entry of five predictor variables (the other three variables of the eight-variable model are automatically obtained from the electronic health record or a table look-up), provides patient risk estimates compared to national averages, is integrated into the electronic health record, and provides a graphical handout of risks for patients. The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). Methods Predicted risk of postoperative mortality and morbidity was calculated using both SURPAS and ACS-SRC for 1,006 randomly selected 2007–2016 ACS National Surgical Quality Improvement Program (NSQIP) patients with known outcomes. C-indexes, Hosmer-Lemeshow graphs, and Brier scores were compared between SURPAS and ACS-SRC. Results ACS-SRC risk estimates for overall morbidity underestimated risk compared to observed postoperative overall morbidity, particularly for the highest risk patients. SURPAS accurately estimates morbidity risk compared to observed morbidity. Conclusions SURPAS risk predictions were more accurate than ACS-SRC's for overall morbidity, particularly for high risk patients. Summary The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). SURPAS risk predictions were more accurate than those of the ACS-SRC for overall morbidity, particularly for high risk patients.

Background: Formal surgical risk assessment tools have been developed to predict risk of adverse postoperative patient outcomes. Such tools accurately predict common postoperative complications, inform patients and providers of likely perioperative outcomes, guide decision making, and improve patient care. However, these are underutilized. We studied the attitudes towards and techniques of how surgeons preoperatively assess risk.Methods: 108 surgical faculty, 95 surgical residents, and 58 affiliate surgeons at a large academic tertiary referral hospital and affiliate community hospitals were emailed a 16-question survey between 8/2019-6/2020. Surveys were analyzed using descriptive statistics (frequency distributions and percentages for categorical variables, means, and standard deviations for continuous variables), and Fisher’s exact test and unpaired t-tests comparing responses by surgical attendings vs. residents.Results: Overall response rates were 50.0% for faculty surgeons, 47.4% for residents, and 36.2% for affiliate surgeons. Only 20.8% of surgeons used risk calculators most or all of the time. Attending surgeons were more likely to use prior experience and current literature while residents used risk calculators more frequently. Risk assessment tools were more likely to be used when predicting major complications and death in older patients with significant risk factors. Greatest barriers for use of risk assessment tools included time, inaccessibility, and trust in accuracy.Conclusions: Even though risk assessment tools are available, few surgeons use them as part of their routine practices. Developers of the tools need to design the tools to better promote the positive features and to avoid the burdens commonly reported by users.

Introduction The Surgical Risk Preoperative Assessment System is a parsimonious, universal surgical risk calculator integrated into our local electronic health record. We determined how many of its eight preoperative risk predictor variables could be automatically obtained from the electronic health record. This has implications for the usability and adoption of Surgical Risk Preoperative Assessment System, serving as an example of use of electronic health record data for populating clinical decision support tools. Methods We quantified the availability and accuracy in the electronic health record of the eight Surgical Risk Preoperative Assessment System predictor variables (patient age, American Society of Anesthesiology physical status classification, functional health status, sepsis, work Relative Value Unit, in-/outpatient operation, surgeon specialty, emergency status) at the patient’s preoperative encounter of 5205 patients entered into the American College of Surgeons National Surgical Quality Improvement Program. Accuracy was determined by comparing the electronic health record data to the same patient’s National Surgical Quality Improvement Program data, used as the “gold standard.” Acceptable accuracy was defined as a Kappa statistic or Pearson correlation coefficient ≥0.8 when comparing electronic health record and National Surgical Quality Improvement Program data. Acceptable availability was defined as presence of the variable in the electronic health record at the preoperative encounter ≥95% of the time. Results Of the eight predictor variables, six had acceptable accuracy. Only preoperative sepsis and functional health status had Kappa statistics Conclusions Processes need to be developed to populate more of the Surgical Risk Preoperative Assessment System preoperative predictor variables in the patient’s electronic health record prior to the preoperative visit to lessen the burden on the busy surgeon and encourage more widespread use of Surgical Risk Preoperative Assessment System.

Background P2Y12 inhibitors are critical following percutaneous coronary intervention (PCI) with stent placement; they reduce the risk of stent thrombosis and myocardial infarction. Despite the importance of the therapy, non-adherence is common among Veterans. Methods and results Our main objective is to conduct a multi-site randomized stepped wedge trial to test the effectiveness of a multi-faceted intervention to improve adherence to P2Y12 inhibitors and PCI outcomes as well as formatively evaluate and refine the intervention implementation process. The primary outcomes of the study are the proportion of patients whose P2Y12 inhibitor prescription was filled at the time of hospital discharge following PCI with stent placement as well as the proportion of patients who were adherent based on the pharmacy refill data in the year after PCI hospital discharge. We will also assess the secondary outcomes such as bleeding, myocardial infarction, stroke, and mortality among these patients, and the cost-effectiveness of the intervention. The study was conducted at Veterans Health Administration (VA) PCI sites. At each site, we enrolled patients over a 6-month period and followed them for 12 months after PCI. Additionally, we collected qualitative data to identify contextual factors and to assess barriers and facilitators to the implementation and maintenance of the intervention. Conclusions The study will add to the current state of knowledge on improving medication adherence in patients receiving PCI with stent implantation. Moreover, the study includes an extensive examination of the implementation process and will contribute to the field of implementation science. Clinical trial registration ClinicalTrials.gov Identifier: NCT01609842 https://clinicaltrials.gov/ct2/show/NCT01609842?term=clopidogrel+adherence&cntry1=NA%3AUS&rank=1

Background Defining a “high risk” surgical population remains challenging. Using the Surgical Risk Preoperative Assessment System (SURPAS), we sought to define “high risk” groups for adverse postoperative outcomes. Materials and Methods We retrospectively analyzed the 2009-2018 American College of Surgeons National Surgical Quality Improvement Program database. SURPAS calculated probabilities of 12 postoperative adverse events. The Hosmer Lemeshow graphs of deciles of risk and maximum Youden index were compared to define “high risk.” Results Hosmer-Lemeshow plots suggested the “high risk” patient could be defined by the 10th decile of risk. Maximum Youden index found lower cutoff points for defining “high risk” patients and included more patients with events. This resulted in more patients classified as “high risk” and higher number needed to treat to prevent one complication. Some specialties (thoracic, vascular, general) had more “high risk” patients, while others (otolaryngology, plastic) had lower proportions. Conclusions SURPAS can define the “high risk” surgical population that may benefit from risk-mitigating interventions.

Background: Implementation researchers recognize the influential role of organizational factors and thus, seek to assess these factors using quantitative measurement instruments. However, researchers are hindered by instruments that measure similar constructs but rely on different nomenclature and/or definitions. The Consolidated Framework for Implementation Research (CFIR) provides a taxonomy of constructs derived from prior frameworks and empirical studies of implementation-related constructs. The CFIR includes constructs based on the original Promoting Action Research into Health Services (PARiHS) framework which highlights the key roles of strength of evidence for a specific evidence-based intervention (EBI); favorability of organizational context for change; and capacities to facilitate implementation of the EBI. Although the CFIR is among the most frequently used implementation frameworks, it does not include quantitative measures. The Organizational Resource and Context Assessment (ORCA) is a quantitative measurement instrument that was developed based on PARiHS, assessing its three domains. Factors within these three domains are conceptually similar to constructs in the CFIR but do not match directly. The aim of this work was to map ORCA survey items to CFIR constructs to enable direct comparisons and syntheses of findings across studies using the CFIR and/or ORCA. Methods: Two distinct, independent research teams, each used rigorous constant comparative techniques with deliberation and consensus to map individual items from the ORCA to the five domains and 39 constructs of CFIR. Results: ORCA items were mapped primarily to three of five CFIR domains: Inner Setting, Process and Intervention Characteristics. The two research teams agreed on 88% of mappings at the higher domain level; at the lower construct level, their mappings aligned for 62.2% of the ORCA items. Conclusions: Mapping results reveal that the ORCA focuses measurement prominently on Inner Setting, Process, and Intervention Characteristics. This mapping guide can help improve consistency in measurement and reporting, enabling more efficient comparison and synthesis of findings that use either the ORCA instrument or the CFIR framework. The guide helps advance implementation science by providing CFIR users with quantitative measures for selected constructs and enables ORCA users to map their findings to CFIR constructs.

Background Risk assessment is essential to informed decision making in surgery. Preoperative use of the Surgical Risk Preoperative Assessment System (SURPAS) providing individualized risk assessment, may enhance informed consent. We assessed patient and provider perceptions of SURPAS as a risk assessment tool. Methods A convergent mixed-methods study assessed SURPAS’s trial implementation, concurrently collecting quantitative and qualitative data, separately analyzing it, and integrating the results. Patients and providers were surveyed and interviewed on their opinion of how SURPAS impacted the preoperative encounter. Relationships between patient risk and patient and provider assessment of SURPAS were examined. Results A total of 197 patients were provided their SURPAS postoperative risk estimates in nine surgeon’s clinics. Of the total patients, 98.8% reported they understood their surgical risks very or quite well after exposure to SURPAS; 92.7% reported SURPAS was very helpful or helpful. Providers shared that 83.4% of the time they reported SURPAS was very or somewhat helpful; 44.7% of the time the providers reported it changed their interaction with the patient and this change was beneficial 94.3% of the time. As patient risk increased, providers reported that SURPAS was increasingly helpful (p Conclusions Patients and providers reported the use of SURPAS helpful and informative during the preoperative risk assessment of patients, thus improving the surgical decision making process. Patients thought that SURPAS was helpful regardless of their risk level, whereas providers thought that SURPAS was more helpful in higher risk patients.

Background Risk assessment is essential to informed decision making in surgery. Preoperative use of the Surgical Risk Preoperative Assessment System (SURPAS) providing individualized risk assessment, may enhance informed consent. We assessed patient and provider perceptions of SURPAS as a risk assessment tool. Methods A convergent mixed-methods study assessed SURPAS’s trial implementation, concurrently collecting quantitative and qualitative data, separately analyzing it, and integrating the results. Patients and providers were surveyed and interviewed on their opinion of how SURPAS impacted the preoperative encounter. Relationships between patient risk and patient and provider assessment of SURPAS were examined. Results 197 patients were provided their SURPAS postoperative risk estimates in nine surgeon’s clinics. 98.8% of patients reported they understood their surgical risks very or quite well after exposure to SURPAS; 92.7% reported SURPAS was very helpful or helpful. 83.4% of the time providers reported SURPAS was very or somewhat helpful; 44.7% of the time the providers reported it changed their interaction with the patient and this change was beneficial 94.3% of the time. As patient risk increased, providers reported that SURPAS was increasingly helpful (pConclusions Patients and providers reported the use of SURPAS helpful and informative during the preoperative risk assessment of patients, thus improving the surgical decision making process. Patients thought that SURPAS was helpful regardless of their risk level, whereas providers thought that SURPAS was more helpful in higher risk patients.

Background The Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in elective surgery using only 8 preoperative variables, but its performance in emergency surgery has not been evaluated. We hypothesized that the Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in emergency surgery and compared its performance to that of the American College of Surgeons Surgical Risk Calculator. Methods We calculated the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator risk estimates in a random sample of 1,010 emergency surgery cases from the American College of Surgeons National Surgical Quality Improvement Program 2014 to 2017 database. Risk estimates were compared with known outcomes. Analyses included the Hosmer-Lemeshow goodness of fit graphs and P values, c-indexes, and Brier scores. Results Results were similar between the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator for mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications. The American College of Surgeons Surgical Risk Calculator underestimated morbidity compared with the Surgical Risk Preoperative Assessment System (16.04% American College of Surgeons Surgical Risk Calculator vs 24.88% Surgical Risk Preoperative Assessment System vs 24.3% observed). Both calculators overestimated readmission (7.67% American College of Surgeons Surgical Risk Calculator vs 5.18% Surgical Risk Preoperative Assessment System vs 4.1% observed). Conclusion Both calculators predicted mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications well, but readmissions relatively poorly. The Surgical Risk Preoperative Assessment System estimated overall morbidity accurately, while the American College of Surgeons Surgical Risk Calculator underestimated this risk.

Background Risk assessment in surgery is essential to guide treatment decisions but is highly variable in practice. Providing formal preoperative risk assessment to surgical teams and patients may optimize understanding of risk. Implementation of the Surgical Risk Preoperative Assessment System (SURPAS), an innovative real time, universal, preoperative tool providing individualized risk assessment, may enhance informed consent and reduce adverse outcomes. To ensure optimal development and implementation of SURPAS we performed an in-depth pre-implementation evaluation of SURPAS at an academic tertiary referral center in Colorado. Methods Four focus groups with 24 patients, three focus groups with 29 surgical providers and clinic administrators, and five individual interviews with administrative officials were conducted to elicit their perspectives about the development and implementation of SURPAS. Qualitative data collection and analyses, utilizing a Matrix Analysis approach were used to explore insights regarding SURPAS. Results Participants were positive about SURPAS and provided suggestions to improve and address concerns regarding it. For healthcare personnel three major themes emerged: 1) The SURPAS tool - Important work especially for high risk patients, yet not a substitute for clinical judgment; 2) Benefits of SURPAS to the risk assessment process - Improves the processes, enhances patients’ participation in shared decision-making process, and creates a permanent record; and 3) Facilitators and barriers of implementation of SURPAS - Easy to incorporate into clinical practice in spite of surgical providers’ resistance to adoption of new technology. For patients three major themes emerged: 1) Past experience of preoperative risk assessment discussions – Patients were not made aware of possible complications that occurred; 2) The SURPAS tool - All patients liked SURPAS and believed having printed material would be useful to guide discussions and facilitate remembering conversations with the providers; and 3) Potential concerns with having risk assessment information – Patients were mixed in deciding to have an operation with high risks. Conclusions Systematically capturing data from the beginning of the implementation process from key stakeholders (patients, surgical providers, clinical staff, and administrators) that includes adaptations to the tool and implementation process will help to inform pragmatic approaches for implementing the SURPAS tool in various settings, scaling-up, and sustaining it.

Objectives Strategies were explored to improve patient adherence to cardioprotective medications by borrowing from a motivational framework used in psychology, regulatory focus theory. The current study is part of a larger randomized control trial and was aimed at understanding what written educational messages, based on patients’ regulatory focus tendency, resonated with each individual as a potential reminder to take medications. This study was also aimed at understanding why messages resonated with the patients. Methods Twenty veterans were tested for regulatory fitand presented with messages dependent on focus tendency. In-person semi-structured interviews were conducted to collect feedback of messages. An iterative analysis drawing primarily on matrix and reflexive team analyses was conducted. Result Six promotion and six prevention messages emerged, such as “team up with your provider to create a combination of medications to prevent illness” and “Live your best life – Take your medications”. Five themes related to types of health messages that spoke to patients’ regulatory fit were discovered: relatability; empowerment and control; philosophy on life; relationship with provider and medications; and vocabulary effect on the impact of messages. Discussion Motivational messages based on regulatory fit may be useful in improving patient medication adherence, leading to improved cardiovascular outcomes.

Objective The Veterans Health Administration (VHA) is adapting to meet the changing needs of our Veterans. VHA leaders are promoting quality improvement strategies including Lean Six Sigma (LSS). This study used LSS tools to evaluate the Veterans Choice Program (VCP), a program that aims to improve access to health care services for eligible Veterans by expanding health care options to non-VHA providers. Research design LSS was utilized to assess the current process and efficiency patterns of the VCP at 3 VHA Medical Centers. LSS techniques were used to assess data obtained through semistructured interviews with Veterans, staff, and providers to describe and evaluate the VCP process by identifying wastes and defects. Results The LSS methodology facilitated the process of targeting priorities for improvement and constructing suggestions to close identified gaps and inefficiencies. Identified key process wastes included inefficient exchange of clinical information between stakeholders in and outside of the VHA; poor dissemination of VCP programmatic information; shortages of VCP-participating providers; duplication of appointments; declines in care coordination; and lack of program adaptability to local processes. Recommendations for improvement were formulated using LSS. Conclusions This evaluation illustrates how LSS can be utilized to assess a nationally mandated health care program. By focusing on stakeholder, staff, and Veteran perspectives, process defects in the VCP were identified and improvement recommendations were made. However, the current LSS language used is not intuitive in health care and similar applications of LSS may consider using new language and goals adapted specifically for health care.

Veteran's Affairs Office of Specialty Care (OSC) launched four national initiatives (Electronic-Consults [e-Consults], Specialty Care Access Networks-Extension for Community Healthcare Outcomes [SCAN-ECHO], Mini-Residencies, and Specialty Care Neighborhood) to improve specialty care delivery and funded a center to evaluate the initiatives.The evaluation, guided by two implementation frameworks, provides formative (administrator/provider interviews and surveys) and summative data (quantitative data on patterns of use) about the initiatives to OSC.Evaluation of initiative implementation is assessed through CFIR (Consolidated Framework for Implementation Research)-grounded qualitative interviews to identify barriers/facilitators. Depending on high or low implementation, factors such as receiving workload credit, protected time, existing workflow/systems compatibility, leadership engagement, and access to information/resources were considered implementation barriers or facilitators. Findings were shared with OSC and used to further refine implementation at additional sites. Evaluation of other initiatives is ongoing.The mixed-methods approach has provided timely information to OSC about initiative effect and impacted OSC policies on implementation at additional sites.

Background Innovations and improvements in care delivery are often not spread across all settings that would benefit from their uptake. Scale-up and spread efforts are deliberate efforts to increase the impact of innovations successfully tested in pilot projects so as to benefit more people. The final stages of scale-up and spread initiatives must contend with reaching hard-to-engage sites. Objective To describe the process of scale-up and spread initiatives, with a focus on hard-to-engage sites and strategies to approach them. Design Qualitative content analysis of systematically identified literature and key informant interviews. Participants Leads from large magnitude scale-up and spread projects. Approach We conducted a systematic literature search on large magnitude scale-up and spread and interviews with eight project leads, who shared their perspectives on strategies to scale-up and spread clinical and administrative practices across healthcare systems, focusing on hard-to-engage sites. We synthesized these data using content analysis. Key Results Searches identified 1919 titles, of which 52 articles were included. Thirty-four discussed general scale-up and spread strategies, 11 described hard-to-engage sites, and 7 discussed strategies for hard-to-engage sites. These included publications were combined with interview findings to describe a fourth phase of the national scale-up and spread process, common challenges for spreading to hard-to-engage sites, and potential benefits of working with hard-to-engage sites, as well as useful strategies for working with hard-to-engage sites. Conclusions We identified scant published evidence that describes strategies for reaching hard-to-engage sites. The sparse data we identified aligned with key informant accounts. Future work could focus on better documentation of the later stages of spread efforts, including specific tailoring of approaches and strategies used with hard-to-engage sites. Spread efforts should include a “flexible, tailored approach” for this highly variable group, especially as implementation science is looking to expand its impact in routine care settings.

Background: Improving surgical outcomes is important to patients, providers, and healthcare systems. Understanding best methods to ensure evidence based practices are successfully implemented and sustained in clinical practices leads to improved care. Dissemination and implementation (D&I) science facilitates the successful pathway from clinical trials to sustained implementation. Methods: We describe D&I science, introduce the consolidated framework for implementation research (CFIR), a D&I framework, and provide an example of how CFIR was utilized to facilitate the translational process from design adaptations to implementation, broad utilization by clinicians, and sustainability of the SUrgical Risk Preoperative Assessment System (SURPAS) tool into regular clinical practice. SURPAS creates data-driven individualized risk assessments of common adverse postoperative outcomes to enhance the informed consent process, shared decision making, and consequently improved surgical outcomes. The CFIR provided a structured systematic way to identify constructs influencing the D&I of SURPAS, including adaptations for the process and tool. Results: We identified three domains, each with specific constructs, that participants believed would strongly influence effectiveness of SURPAS implementation efforts: the importance of patients’ perspectives (outer setting); the quality of SURPAS (intervention characteristic); and integration of SURPAS into the electronic health record (inner setting). Additionally, providers’ positive attitudes toward and support of SURPAS (characteristics of individuals); and the ease of integration of SURPAS into the workflow (process), were also identified. Tension emerged between patients’ preference of the provision of risk information and providers’ concern about additional clinic time required for formal risk discussion with low-risk patients. Conclusions: Systematically identifying constructs from the beginning of the design through the implementation process can guide design of a multi-component strategy for future large-scale implementation by assessing the relative impact of factors on implementation using the CFIR framework. In the example studied, this allows key stakeholders to ensure success of D&I of SURPAS at multiple levels and times, continuously optimizing the process.

As VHA expands the use of e-consults, this study suggests that they often are more timely than are face-to-face consultations.

The Veterans Health Administration (VHA) is proposing full-practice authority for advanced practice registered nurses (APRNs) to improve access, care delivery, and patient choice, as well as reduce costs. The authors performed a mixed-methods assessment to obtain the perspectives of administrators and APRNs on the characterization of the APRN workforce and their present practice in the VHA.

The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process.Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test.One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients.The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process.

Background The use of inappropriate elective Percutaneous Coronary Intervention (PCI) has decreased over time, but hospital-level variation in the use of inappropriate PCI persists. Understanding the barriers and facilitators to the implementation of Appropriate Use Criteria (AUC) guidelines may inform efforts to improve elective PCI appropriateness. Methods All hospitals performing PCI in Washington State were categorized by their use of inappropriate elective PCI in 2010 to 2013. Semi-structured, qualitative telephone interviews were then conducted with 17 individual interviews at 13 sites in Washington State to identify barriers and facilitators to the implementation of the AUC guidelines. An inductive and deductive, team-based analytical approach, drawing primarily on Matrix analysis was performed to identify factors affecting implementation of the AUC. Results Specific facilitators were identified that supported successful implementation of the AUC. These included collaborative catheterization laboratory environments that allow all staff to participate with questions and opinions; ongoing AUC education with catheterization laboratory teams and referring providers; internal AUC peer review processes; interventional cardiologist be directly involved with the pre-procedural review process; checklist-based algorithms for pre-procedural documentation; systems redesign to include insurance companies; and AUC educational information with patients. Barriers to implementation of the AUC included external pressures, such as competition for patients, and the lack of shared medical records with sites that referred patients for coronary angiography. Conclusions The identified facilitators enabled sites to successfully implement the AUC. Catheterization laboratories struggling to successfully implement the AUC may consider utilizing these strategies to improve their processes to improve patient selection for elective PCI.