An Institutional Curriculum for Opioid Prescribing Education: Outcomes From 2017 to 2022.

INTRODUCTION: Prescription opioids, including those prescribed after surgery, have greatly contributed to the US opioid epidemic. Educating opioid prescribers is a crucial component of ensuring the safe use of opioids among surgical patients. METHODS: An annual opioid prescribing education curriculum was implemented among new surgical prescribers at our institution between 2017 and 2022. The curriculum includes a single 75-min session which is comprised of several components: pain medications (dosing, indications, and contraindications); patients at high risk for uncontrolled pain and/or opioid misuse or abuse; patient monitoring and care plans; and state and federal regulations. Participants were asked to complete an opioid knowledge assessment before and after the didactic session. RESULTS: Presession and postsession assessments were completed by 197 (89.6%) prescribers. Across the five studied years, the median presession score was 54.5%. This increased to 63.6% after completion of the curriculum, representing a median relative knowledge increase of 18.2%. The median relative improvement was greatest for preinterns and interns (18.2% for both groups); smaller improvements were observed for postgraduate year 2-5 residents (9.1%) and advanced practice providers (9.1%). On a scale of 1 to 10 (with 5 being comfortable), median (interquartile range) self-reported comfort in prescribing opioids increased from 3 (2-5) before education to 5 (4-6) after education (P < 0.001). CONCLUSIONS: Each year, the curriculum substantially improved provider knowledge of and comfort in opioid prescribing. Despite increased national awareness of the opioid epidemic and increasing institutional initiatives to improve opioid prescribing practices, there was a sustained knowledge and comfort gap among new surgical prescribers. The observed effects of our opioid education curriculum highlight the value of a simple and efficient educational initiative.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

OBJECTIVE: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. BACKGROUND: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. METHODS: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. CONCLUSIONS: Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Comparing Clinician Consensus Recommendations to Patient-reported Opioid Use Across Multiple Hospital Systems.

OBJECTIVE: We compare consensus recommendations for 5 surgical procedures to prospectively collected patient consumption data. To address local variation, we combined data from multiple hospitals across the country. SUMMARY OF BACKGROUND DATA: One approach to address the opioid epidemic has been to create prescribing consensus reports for common surgical procedures. However, it is unclear how these guidelines compare to patient-reported data from multiple hospital systems. METHODS: Prospective observational studies of surgery patients were completed between 3/2017 and 12/2018. Data were collected utilizing post-discharge surveys and chart reviews from 5 hospitals (representing 3 hospital systems) in 5 states across the USA. Prescribing recommendations for 5 common surgical procedures identified in 2 recent consensus reports were compared to the prospectively collected aggregated data. Surgeries included: laparoscopic cholecystectomy, open inguinal hernia repair, laparoscopic inguinal hernia repair, partial mastectomy without sentinel lymph node biopsy, and partial mastectomy with sentinel lymph node biopsy. RESULTS: Eight hundred forty-seven opioid-naïve patients who underwent 1 of the 5 studied procedures reported counts of unused opioid pills after discharge. Forty-one percent did not take any opioid medications, and across all surgeries, the median consumption was 3 5 mg oxycodone pills or less. Generally, consensus reports recommended opioid quantities that were greater than the 75th percentile of consumption, and for 2 procedures, recommendations exceeded the 90th percentile of consumption. CONCLUSIONS: Although consensus recommendations were an important first step to address opioid prescribing, our data suggests that following these recommendations would result in 47%-56% of pills prescribed remaining unused. Future multi-institutional efforts should be directed toward refining and personalizing prescribing recommendations.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Evaluation of U.S. state opioid prescribing restrictions using patient opioid consumption patterns from a single, urban, academic institution.

Background: Since 2017, states, insurers, and pharmacies have placed blanket limits on the duration and quantity of opioid prescriptions. In many states, overlapping duration and daily dose limits yield maximum prescription limits of 150-350 morphine milligram equivalents (MMEs). There is limited knowledge of how these restrictions compare with actual patient opioid consumption; while changes in prescription patterns and opioid misuse rates have been studied, these are, at best, weak proxies for actual pain control consumption. We sought to determine how patients undergoing surgery would be affected by opioid prescribing restrictions using actual patient opioid consumption data. Methods: We constructed a prospective database of post-discharge opioid consumption: patients undergoing surgery at one institution were called after discharge to collect opioid consumption data. Patients whose opioid consumption exceeded 150 and 350 MME were identified. Results: Two thousand nine hundred and seventy-one patients undergoing 54 common surgical procedures were included in our study. Twenty-one percent of patients consumed more than the 150 MME limit. Only 7% of patients consumed above the 350 MME limit. Typical (non-outlier) opioid consumption, defined as less than the 75th percentile of consumption for any given procedure, exceeded the 150 MME and 350 MME limits for 41 and 7% of procedures, respectively. Orthopedic, spinal/neurosurgical, and complex abdominal procedures most commonly exceeded these limits. Conclusions: While most patients undergoing surgery are unaffected by recent blanket prescribing limits, those undergoing a specific subset of procedures are likely to require more opioids than the restrictions permit; providers should be aware that these patients may require a refill to adequately control post-surgical pain. Real consumption data should be used to guide these restrictions and inform future interventions so the risk of worsened pain control (and its troublesome effects on opioid misuse) is minimized. Procedure-specific prescribing limits may be one approach to prevent misuse, while also optimizing post-operative pain control.

Evolving phenotypes of non-hospitalized patients that indicate long COVID.

BACKGROUND: For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. METHODS: In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. RESULTS: We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients' medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94-3.46]), alopecia (OR 3.09, 95% CI [2.53-3.76]), chest pain (OR 1.27, 95% CI [1.09-1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22-2.10]), shortness of breath (OR 1.41, 95% CI [1.22-1.64]), pneumonia (OR 1.66, 95% CI [1.28-2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22-1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. CONCLUSIONS: The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Effects of laparoscopic vs open abdominal surgery on costs and hospital readmission rate and its effect modification by surgeons' case volume.

BACKGROUND: Laparoscopy provides a minimally invasive alternative to open abdominal surgery. Current data describing its association with hospital readmission and costs in relation to surgeon laparoscopic case volume is limited to smaller databases and subsets of operations. METHODS: This retrospective cohort study of 23,285 adult abdominal operations from 2007 to 2015 compares 30-day readmission rate and costs between laparoscopic and open abdominal operations and examines effect modification by surgeon laparoscopic case volume. Outcomes were all-cause hospital readmission within 30 days after discharge and index hospital admission cost. RESULTS: All-cause hospital readmission rates were significantly lower after laparoscopic abdominal operations compared with open operations (adjusted odds ratio [aOR] 0.56, 95% CI 0.46-0.69, p < 0.001) with a difference in readmission risk attributable to laparoscopic approach of - 4.0% (95% CI - 5.4 to - 2.6%) in complete-case analysis. Among surgeons with a high laparoscopic case volume, the estimated difference in readmission risk through laparoscopy was magnified (- 5.8%, 95% CI - 7.5 to - 4.1%) compared to low surgeon laparoscopic case volume (- 2.9%, 95% CI - 4.8 to -1.1%, p for interaction = 0.005). The estimated difference in costs of the index hospital admission attributable to laparoscopic approach was - $3869 (95% CI - $4200 to - $3538; adjusted incidence rate ratio 0.77, 95% CI 0.75-0.79, p < 0.001). Laparoscopy was followed by significantly lower rates of readmissions related to gastrointestinal (aOR 0.68, 95% CI 0.55-0.85, p = 0.001), wound complications (infection: aOR 0.33, 95% CI 0.23-0.47, p < 0.001; non-infectious: aOR 0.47, 95% CI 0.30-0.74, p = 0.001), and malignancy (aOR 0.68, 95% CI 0.55-0.85, p < 0.001). The findings remain robust after multiple imputation and sensitivity analyses. CONCLUSIONS: Laparoscopy versus open abdominal surgery is associated with reduced hospital readmissions related to malignancy, gastrointestinal, and wound complications. Effect modification by higher laparoscopy case volume argues for continued proliferation of laparoscopy in abdominal surgeries.

Incidence, Prediction, and Causes of Unplanned 30-Day Hospital Admission After Ambulatory Procedures.

BACKGROUND: Unanticipated hospital admission is regarded as a measure of adverse perioperative patient care. However, previously published studies for risk prediction after ambulatory procedures are sparse compared to those examining readmission after inpatient surgery. We aimed to evaluate the incidence and reasons for unplanned admission after ambulatory surgery and develop a prediction tool for preoperative risk assessment. METHODS: This retrospective cohort study included adult patients undergoing ambulatory, noncardiac procedures under anesthesia care at 2 tertiary care centers in Massachusetts, United States, between 2007 and 2017 as well as all hospitals and ambulatory surgery centers in New York State, United States, in 2014. The primary outcome was unplanned hospital admission within 30 days after discharge. We created a prediction tool (the PREdicting admission after Outpatient Procedures [PREOP] score) using stepwise backward regression analysis to predict unplanned hospital admission, based on criteria used by the Centers for Medicare & Medicaid Services, within 30 days after surgery in the Massachusetts hospital network registry. Model predictors included patient demographics, comorbidities, and procedural factors. We validated the score externally in the New York state registry. Reasons for unplanned admission were assessed. RESULTS: A total of 170,983 patients were included in the Massachusetts hospital network registry and 1,232,788 in the New York state registry. Among those, the observed rate of unplanned admission was 2.0% (3504) and 1.7% (20,622), respectively. The prediction model showed good discrimination in the training set with C-statistic of 0.77 (95% confidence interval [CI], 0.77-0.78) and satisfactory discrimination in the validation set with C-statistic of 0.71 (95% CI, 0.70-0.71). The risk of unplanned admission varied widely from 0.4% (95% CI, 0.3-0.4) among patients whose calculated PREOP scores were in the first percentile to 21.3% (95% CI, 20.0-22.5) among patients whose scores were in the 99th percentile. Predictions were well calibrated with an overall ratio of observed-to-expected events of 99.97% (95% CI, 96.3-103.6) in the training and 92.6% (95% CI, 88.8-96.4) in the external validation set. Unplanned admissions were most often related to malignancy, nonsurgical site infections, and surgical complications. CONCLUSIONS: We present an instrument for prediction of unplanned 30-day admission after ambulatory procedures under anesthesia care validated in a statewide cohort comprising academic and nonacademic hospitals as well as ambulatory surgery centers. The instrument may be useful in identifying patients at high risk for 30-day unplanned hospital admission and may be used for benchmarking hospitals, ambulatory surgery centers, and practitioners.

Effect of Surgery-Specific Opioid-Prescribing Education in a Safety-Net Hospital.

BACKGROUND: As the nation works to improve the opioid epidemic, safer opioid prescribing is needed. Prescriber education is one method to assist with this aim. To gauge current surgical residents' opioid prescribing practices at a safety-net hospital, an evaluation was completed before a general surgery-specific opioid prescribing education (OPE) session. The effectiveness of this OPE was measured through a postparticipation evaluation. METHODS: A voluntary, anonymous evaluation immediately before and after a one-hour OPE session was performed at an urban safety-net hospital. Descriptive statistics and Student's t-test comparisons of means were performed to analyze the results. RESULTS: Twenty-three residents completed the surveys. Eleven (47.8%) completed prior OPE with the most common modality being online (7, 63.6%). No participant performed an opioid risk assessment before prescribing opioids. More than half of the residents (14, 60.9%) never used the prescription drug monitoring program. Less than 1/3 (7, 30.4%) used preoperative gabinoids (gabapentin or pregabalin) for elective surgeries. Only two residents provided information on unused opioid disposal. After the OPE, the participants were more likely to prescribe preoperative gabinoids (7 versus 21, P < 0.001). The mean opioid pills prescribed for laparoscopic cholecystectomy, open inguinal hernia repair, laparoscopic ventral hernia repair, and laparoscopic appendectomy were reduced by 2.6 (14.2%, P = 0.23), 3.7 (18.9%, P = 0.07), 2.6 (13.1%, P = 0.23), and 1.1 (7.3%, P = 0.60) pills, respectively. CONCLUSION: A short OPE delivered to surgical residents at a safety-net hospital significantly improved the use of preoperative gabinoids. Although the pill count reductions after the OPE were not statistically significant, there was a consistent reduction in amount of opiates prescribed after the OPE. However, clinical significance is important, as a reduction in any amount of opioid medication can help deter misuse and diversion. This suggests resident surgeons could participate in a specialty-specific OPE to improve opioid prescribing.

Phenotyping the Immune Response to Trauma: A Multiparametric Systems Immunology Approach.

OBJECTIVE: Trauma induces a complex immune response that requires a systems biology research approach. Here, we used a novel technology, mass cytometry by time-of-flight, to comprehensively characterize the multicellular response to trauma. DESIGN: Peripheral blood mononuclear cells samples were stained with a 38-marker immunophenotyping cytometry by time-of-flight panel. Separately, matched peripheral blood mononuclear cells were stimulated in vitro with heat-killed Streptococcus pneumoniae or CD3/CD28 antibodies and stained with a 38-marker cytokine panel. Monocytes were studied for phagocytosis and oxidative burst. SETTING: Single-institution level 1 trauma center. PATIENTS OR SUBJECTS: Trauma patients with injury severity scores greater than 20 (n = 10) at days 1, 3, and 5 after injury, and age- and gender-matched controls. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Trauma-induced expansion of Th17-type CD4 T cells was seen with increased expression of interleukin-17 and interleukin-22 by day 5 after injury. Natural killer cells showed reduced T-bet expression at day 1 with an associated decrease in tumor necrosis factor-ß, interferon-γ, and monocyte chemoattractant protein-1. Monocytes showed robust expansion following trauma but displayed decreased stimulated proinflammatory cytokine production and significantly reduced human leukocyte antigen - antigen D related expression. Further analysis of trauma-induced monocytes indicated that phagocytosis was no different from controls. However, monocyte oxidative burst after stimulation increased significantly after injury. CONCLUSIONS: Using cytometry by time-of-flight, we were able to identify several major time-dependent phenotypic changes in blood immune cell subsets that occur following trauma, including induction of Th17-type CD4 T cells, reduced T-bet expression by natural killer cells, and expansion of blood monocytes with less proinflammatory cytokine response to bacterial stimulation and less human leukocyte antigen - antigen D related. We hypothesized that monocyte function might be suppressed after injury. However, monocyte phagocytosis was normal and oxidative burst was augmented, suggesting that their innate antimicrobial functions were preserved. Future studies will better characterize the cell subsets identified as being significantly altered by trauma using cytometry by time-of-flight, RNAseq technology, and functional studies.

Ultrasonography training and utilization in surgical critical care fellowships: a program director's survey.

BACKGROUND: Intensivist-performed ultrasound (IPUS) is an adjunctive tool used to assist in resuscitation and management of critically ill patients. It allows clinicians real-time information through noninvasive methods. We aimed to evaluate the types of IPUS performed and the methods surgical critical care (SCC) fellows are trained along with challenges in training. METHODS: One hundred SCC fellowship directors were successfully sent an email inviting them to participate in a short Web-based survey. We inquired about program characteristics including hospital type, fellowship size, faculty size and training, dedicated surgical critical care beds, and ultrasound equipment availability. The survey contained questions regarding the program directors' perception on importance on cost effectiveness of IPUS, types of IPUS examinations performed, fellows experience with IPUS, challenges to training, and presence and methods of quality assurance (QA) programs. RESULTS: A total of 38 (38.0%) program directors completed the survey. Using a 100-point Likert scale, the majority of the respondents indicated that IPUS is important to patient care in the SICU and is cost-effective (mean score 85.5 and 84.6, respectively). Most (34, 89.5%) utilize IPUS and conduct a mean of 5.1 different examination types with FAST being the most prevalent examination (33, 86.8%). Thirty-three (86.8%) programs include IPUS in their SCC training with varying amounts of time spent training. Of these programs, 19 (57.6%) have a specific curriculum. The most frequently used modalities for training fellows were informal bedside teaching (28, 84.8%), hands-on lectures (20, 60.6%) and formal lectures (19, 57.6%). The top three challenges program directors cited for IPUS education was time (23, 69.7%), followed by concerns for ongoing QA (19, 57.6%) and lack of faculty trained in IPUS (18, 53.9%). Only 20 (60.6%) programs review images as a part of QA/quality improvement. CONCLUSIONS: Utilization and training of IPUS is common in SCC fellowships. There is varied education type and training time devoted to IPUS which could lead to gaps in knowledge and care. Development of a standard curriculum for SCC fellowships could assist surgical intensivists in achieving a base of knowledge in IPUS to create a more homogenously trained workforce and standards of care.

An operational guide to translational clinical machine learning in academic medical centers.

Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation. Many information technology companies have created Machine Learning Operations (MLOps) teams to help with such tasks at scale, but the low penetration of home-grown data science tools in regular clinical practice precludes the formation of such teams in healthcare organizations. Based on experiences deploying data science tools at two large academic medical centers (Beth Israel Deaconess Medical Center, Boston, MA; Mayo Clinic, Rochester, MN), we propose a strategy to facilitate this transition from academic product to operational tool, defining the responsibilities of the principal investigator, data scientist, machine learning engineer, health system IT administrator, and clinician end-user throughout the process. We first enumerate the technical resources and stakeholders needed to prepare for model deployment. We then propose an approach to planning how the final product will work from data extraction and analysis to visualization of model outputs. Finally, we describe how the team should execute on this plan. We hope to guide health systems aiming to deploy minimum viable data science tools and realize their value in clinical practice.

Barriers to Postdischarge Smartphone App Use Among Patients With Traumatic Rib Fractures.

Rib fractures commonly result from traumatic injury and often require hospitalization for pain control and supportive pulmonary care. Although the use of mobile health technology to share patient-generated health data has increased, it remains limited in patients with traumatic injuries. We sought to assess the feasibility of mobile health tracking in patients with rib fractures by using a smartphone app to monitor postdischarge recovery. We encountered patient, institutional, and process-related obstacles that limited app use. The success of future work requires the acknowledgment of these limitations and the use of an implementation science framework to effectively integrate technological tools for personalized trauma care.

Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data.

The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues. The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses. Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.

Postoperative Opioid Prescribing via Rule-Based Guidelines Derived from In-Hospital Consumption: An Assessment of Efficacy Based on Postdischarge Opioid Use.

BACKGROUND: Many institutions have developed operation-specific guidelines for opioid prescribing. These guidelines rarely incorporate in-hospital opioid consumption, which is highly correlated with consumption. We compare outcomes of several patient-centered approaches to prescribing that are derived from in-hospital consumption, including several experimental, rule-based prescribing guidelines and our current institutional guideline. STUDY DESIGN: We performed a retrospective, cohort study of all adults undergoing surgery at a single-academic medical center. Several rule-based guidelines, derived from in-hospital consumption (quantity of opioids consumed within 24 hours of discharge), were used to specify the theoretical quantity of opioid prescribed on discharge. The efficacy of the experimental guidelines was compared with 3 references: an approximation of our institution's tailored prescribing guideline; prescribing all patients the typical quantity of opioids consumed for patients undergoing the same operation; and a representative rule-based, tiered framework. For each scenario, we calculated the penalized residual sum of squares (reflecting the composite deviation from actual patient consumption, with 15% penalty for overprescribing) and the proportion of opioids consumed relative to prescribed. RESULTS: A total of 1,048 patients met inclusion criteria. Mean (SD) and median (interquartile range [IQR]) quantity of opioids consumed within 24 hours of discharge were 11.2 (26.9) morphine milligram equivalents and 0 (0 to 15) morphine milligram equivalents. Median (IQR) postdischarge consumption was 16 (0 to 150) morphine milligram equivalents. Our institutional guideline and the previously validated rule-based guideline outperform alternate approaches, with median (IQR) differences in prescribed vs consumed opioids of 0 (-60 to 27.25) and 37.5 (-37.5 to 37.5), respectively, corresponding to penalized residual sum of squares of 39,817,602 and 38,336,895, respectively. CONCLUSIONS: Rather than relying on fixed quantities for defined operations, rule-based guidelines offer a simple yet effective method for tailoring opioid prescribing to in-hospital consumption.