Equity in the Setting of Heart Failure Diagnosis: An Analysis of Differences Between and Within Clinician Practices.

BACKGROUND: Timely heart failure (HF) diagnosis can lead to earlier intervention and reduced morbidity. Among historically marginalized patients, new-onset HF diagnosis is more likely to occur in acute care settings (emergency department or inpatient hospitalization) than outpatient settings. Whether inequity within outpatient clinician practices affects diagnosis settings is unknown. METHODS: We determined the setting of incident HF diagnosis among Medicare fee-for-service beneficiaries between 2013 and 2017. We identified sociodemographic and medical characteristics associated with HF diagnosis in the acute care setting. Within each outpatient clinician practice, we compared acute care diagnosis rates across sociodemographic characteristics: female versus male sex, non-Hispanic White versus other racial and ethnic groups, and dual Medicare-Medicaid eligible (a surrogate for low income) versus nondual-eligible patients. Based on within-practice differences in acute diagnosis rates, we stratified clinician practices by equity (high, intermediate, and low) and compared clinician practice characteristics. RESULTS: Among 315 439 Medicare patients with incident HF, 173 121 (54.9%) were first diagnosed in acute care settings. Higher adjusted acute care diagnosis rates were associated with female sex (6.4% [95% CI, 6.1%-6.8%]), American Indian (3.6% [95% CI, 1.1%-6.1%]) race, and dual eligibility (4.1% [95% CI, 3.7%-4.5%]). These differences persisted within clinician practices. With clinician practice adjustment, dual-eligible patients had a 4.9% (95% CI, 4.5%-5.4%) greater acute care diagnosis rate than nondual-eligible patients. Clinician practices with greater equity across dual eligibility also had greater equity across sex and race and ethnicity and were more likely to be composed of predominantly primary care clinicians. CONCLUSIONS: Differences in HF diagnosis rates in the acute care setting between and within clinician practices highlight an opportunity to improve equity in diagnosing historically marginalized patients.

The Next Era of Assessment: Building a Trustworthy Assessment System.

Assessment in medical education has evolved through a sequence of eras each centering on distinct views and values. These eras include measurement (e.g., knowledge exams, objective structured clinical examinations), then judgments (e.g., workplace-based assessments, entrustable professional activities), and most recently systems or programmatic assessment, where over time multiple types and sources of data are collected and combined by competency committees to ensure individual learners are ready to progress to the next stage in their training. Significantly less attention has been paid to the social context of assessment, which has led to an overall erosion of trust in assessment by a variety of stakeholders including learners and frontline assessors. To meaningfully move forward, the authors assert that the reestablishment of trust should be foundational to the next era of assessment. In our actions and interventions, it is imperative that medical education leaders address and build trust in assessment at a systems level. To that end, the authors first review tenets on the social contextualization of assessment and its linkage to trust and discuss consequences should the current state of low trust continue. The authors then posit that trusting and trustworthy relationships can exist at individual as well as organizational and systems levels. Finally, the authors propose a framework to build trust at multiple levels in a future assessment system; one that invites and supports professional and human growth and has the potential to position assessment as a fundamental component of renegotiating the social contract between medical education and the health of the public.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Promoting Equity In Clinical Decision Making: Dismantling Race-Based Medicine.

As the use of artificial intelligence has spread rapidly throughout the US health care system, concerns have been raised about racial and ethnic biases built into the algorithms that often guide clinical decision making. Race-based medicine, which relies on algorithms that use race as a proxy for biological differences, has led to treatment patterns that are inappropriate, unjust, and harmful to minoritized racial and ethnic groups. These patterns have contributed to persistent disparities in health and health care. To reduce these disparities, we recommend a race-aware approach to clinical decision support that considers social and environmental factors such as structural racism and social determinants of health. Recent policy changes in medical specialty societies and innovations in algorithm development represent progress on the path to dismantling race-based medicine. Success will require continued commitment and sustained efforts among stakeholders in the health care, research, and technology sectors. Increasing the diversity of clinical trial populations, broadening the focus of precision medicine, improving education about the complex factors shaping health outcomes, and developing new guidelines and policies to enable culturally responsive care are important next steps.

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.

BACKGROUND: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. METHODS: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. RESULTS: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. CONCLUSIONS: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

Natural language processing to identify reasons for sex disparity in statin prescriptions.

Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity. Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets. Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003). Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD.

Picture a data scientist: a call to action for increasing diversity, equity, and inclusion in the age of AI.

The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought. These recommendations take into consideration mitigation at several levels, including outreach programs at the local level, diversity statements at the academic level, and regulatory steps at the federal level.

Cost-Effectiveness Analysis and Microsimulation of Serial Multiparametric Magnetic Resonance Imaging in Active Surveillance of Localized Prostate Cancer.

PURPOSE: Many localized prostate cancers will follow an indolent course. Management has shifted toward active surveillance (AS), yet an optimal regimen remains controversial especially regarding expensive multiparametric magnetic resonance imaging (MRI). We aimed to assess cost-effectiveness of MRI in AS protocols. MATERIALS AND METHODS: A probabilistic microsimulation modeled individual patient trajectories for men diagnosed with low-risk cancer. We assessed no surveillance, up-front treatment (surgery or radiation), and scheduled AS protocols incorporating transrectal ultrasound-guided (TRUS) biopsy or MRI based regimens at serial intervals. Lifetime quality-adjusted life-years and costs adjusted to 2020 US$ were used to calculate expected net monetary benefit at $50,000/quality-adjusted life-year and incremental cost-effectiveness ratios. Uncertainty was assessed with probabilistic sensitivity analysis and linear regression metamodeling. RESULTS: Conservative management with AS outperformed up-front definitive treatment in a modeled cohort reflecting characteristics from a multi-institutional trial. Biopsy decision conditional on positive imaging (MRI triage) at 2-year intervals provided the highest expected net monetary benefit (incremental cost-effectiveness ratio $44,576). Biopsy after both positive and negative imaging (MRI pathway) and TRUS biopsy based regimens were not cost-effective. MRI triage resulted in fewer biopsies while reducing metastatic disease or cancer death. Results were sensitive to test performance and cost. MRI triage was the most likely cost-effective strategy on probabilistic sensitivity analysis. CONCLUSIONS: For men with low-risk prostate cancer, our modeling demonstrated that AS with sequential MRI triage is more cost-effective than biopsy regardless of imaging, TRUS biopsy alone or immediate treatment. AS guidelines should specify the role of imaging, and prospective studies should be encouraged.

Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model.

As artificial intelligence (AI) makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities faced by minority populations through dangerous feedback loops. The aim of this study is to raise broad awareness of the pervasive challenges around bias and fairness in risk prediction models. We performed a case study on a MIMIC-trained benchmarking model using a broadly applicable fairness and generalizability assessment framework. While open-science benchmarks are crucial to overcome many study limitations today, this case study revealed a strong class imbalance problem as well as fairness concerns for Black and publicly insured ICU patients. Therefore, we advocate for the widespread use of comprehensive fairness and performance assessment frameworks to effectively monitor and validate benchmark pipelines built on open data resources.

Gaps in standardized postoperative pain management quality measures: A systematic review.

BACKGROUND: The goal of this study was an assessment of availability postoperative pain management quality measures and National Quality Forum-endorsed measures. Postoperative pain is an important clinical timepoint because poor pain control can lead to patient suffering, chronic opiate use, and/or chronic pain. Quality measures can guide best practices, but it is unclear whether there are measures for managing pain after surgery. METHODS: The National Quality Forum Quality Positioning System, Agency for Healthcare Research and Quality Indicators, and Centers for Medicare and Medicaid Services Measures Inventory Tool databases were searched in November 2019. We conducted a systematic literature review to further identify quality measures in research publications, clinical practice guidelines, and gray literature for the period between March 11, 2015 and March 11, 2020. RESULTS: Our systematic review yielded 1,328 publications, of which 206 were pertinent. Nineteen pain management quality measures were identified from the quality measure databases, and 5 were endorsed by National Quality Forum. The National Quality Forum measures were not specific to postoperative pain management. Three of the non-endorsed measures were specific to postoperative pain. CONCLUSION: The dearth of published postoperative pain management quality measures, especially National Quality Forum-endorsed measures, highlights the need for more rigorous evidence and widely endorsed postoperative pain quality measures to guide best practices.

Changes in Medicaid enrollment during the COVID-19 pandemic across 6 states.

The coronavirus disease 2019 public health emergency (PHE) caused extensive job loss and loss of employer-sponsored insurance. State Medicaid programs experienced a related increase in enrollment during the PHE. However, the composition of enrollment and enrollee changes during the pandemic is unknown. This study examined changes in Medicaid enrollment and population characteristics during the PHE. A retrospective study documenting changes in Medicaid new enrollment and disenrollment, and enrollee characteristics between March and October 2020 compared to the same time in 2019 using full-state Medicaid populations from 6 states of a wide geographical region. The primary outcomes were Medicaid enrollment and disenrollment during the PHE. New enrollment included persons enrolled in Medicaid between March and October 2020 who were not enrolled in January or February, 2020. Disenrollment included persons who were enrolled in March of 2020 but not enrolled in October 2020. The study included 8.50 million Medicaid enrollees in 2020 and 8.46 million in 2019. Overall, enrollment increased by 13.0% (1.19 million) in the selected states during the PHE compared to 2019. New enrollment accounted for 24.9% of the relative increase, while the remaining 75.1% was due to disenrollment. A larger proportion of new enrollment in 2020 was among adults aged 27 to 44 (28.3% vs 23.6%), Hispanics (34.3% vs 32.5%) and in the financial needy (44.0% vs 39.0%) category compared to 2019. Disenrollment included a larger proportion of older adults (26.1% vs 8.1%) and non-Hispanics (70.3% vs 66.4%) than in 2019. Medicaid enrollment grew considerably during the PHE, and most enrollment growth was attributed to decreases in disenrollment rather than increases in new enrollment. Our results highlight the impact of coronavirus disease 2019 on state health programs and can guide federal and state budgetary planning once the PHE ends.

Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions.

PURPOSE: Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS: Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS: Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION: Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

Health management via telemedicine: Learning from the COVID-19 experience.

At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.

Disparity in the Setting of Incident Heart Failure Diagnosis.

BACKGROUND: Early heart failure (HF) recognition can reduce morbidity, yet HF is often initially diagnosed only after a patient clinically worsens. We sought to identify characteristics that predict diagnosis in the acute care setting versus the outpatient setting. METHODS: We estimated the proportion of incident HF diagnosed in the acute care setting (inpatient hospital or emergency department) versus outpatient setting based on diagnostic codes from a claims database covering commercial insurance and Medicare Advantage between 2003 and 2019. After excluding new-onset HF potentially caused by a concurrent acute cause (eg, acute myocardial infarction), we identified demographic, clinical, and socioeconomic predictors of diagnosis setting. Patients were linked to their primary care clinicians to evaluate diagnosis setting variation across clinicians. RESULTS: Of 959 438 patients with new HF, 38% were diagnosed in acute care. Of these, 46% had potential HF symptoms in the prior 6 months. Over time, the relative odds of acute care diagnosis increased by 3.2% annually after adjustment for patient characteristics (95% CI, 3.1%-3.3%). Acute care diagnosis setting was more likely for women compared with men (adjusted odds ratio, 1.11 [95% CI, 1.10-1.12]) and for Black patients compared with White patients (adjusted odds ratio, 1.18 [95% CI, 1.16-1.19]). The proportion of acute care diagnosis varied substantially (interquartile range: 24%-39%) among clinicians after adjusting for patient-level risk factors. CONCLUSIONS: A large proportion of first HF diagnoses occur in the acute care setting, particularly among women and Black patients, yet many had potential HF symptoms in the months before acute care visits. These results raise concerns that many HF diagnoses are missed in the outpatient setting. Earlier diagnosis could allow for timelier high-value interventions, addressing disparities and reducing the progression of HF.

Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer.

Importance: Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings. Objective: To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs). Design, Setting, and Participants: The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020. Exposures: Patients who received treatment for metastatic CRPC. Main Outcomes and Measures: The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics. Results: Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort (mean [SD] iAUC, 0.722 [0.118] and 0.762 [0.106], respectively); model optimization was associated with improved performance of the best-performing EHR model (mean [SD] iAUC, 0.792 [0.097]). The EHR-trained model classified 256 patients as having a high risk of mortality and 256 patients as having a low risk of mortality (hazard ratio, 2.7; 95% CI, 2.0-3.7; log-rank P < .001). Conclusions and Relevance: In this study, although the RCT-trained models did not perform well when applied to real-world EHR data, retraining the models using real-world EHR data and optimizing variable selection was beneficial for model performance. As clinical evidence evolves to include more real-world data, both industry and academia will likely search for ways to balance model optimization with generalizability. This study provides a pragmatic approach to applying RCT-trained models to real-world data.

Real-world Evidence to Estimate Prostate Cancer Costs for First-line Treatment or Active Surveillance.

BACKGROUND: Prostate cancer is the most common cancer in men and second leading cause of cancer-related deaths. Changes in screening guidelines, adoption of active surveillance (AS), and implementation of high-cost technologies have changed treatment costs. Traditional cost-effectiveness studies rely on clinical trial protocols unlikely to capture actual practice behavior, and existing studies use data predating new technologies. Real-world evidence reflecting these changes is lacking. OBJECTIVE: To assess real-world costs of first-line prostate cancer management. DESIGN SETTING AND PARTICIPANTS: We used clinical electronic health records for 2008-2018 linked with the California Cancer Registry and the Medicare Fee Schedule to assess costs over 24 or 60 mo following diagnosis. We identified surgery or radiation treatments with structured methods, while we used both structured data and natural language processing to identify AS. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Our results are risk-stratified calculated cost per day (CCPD) for first-line management, which are independent of treatment duration. We used the Kruskal-Wallis test to compare unadjusted CCPD while analysis of covariance log-linear models adjusted estimates for age and Charlson comorbidity. RESULTS AND LIMITATIONS: In 3433 patients, surgery (54.6%) was more common than radiation (22.3%) or AS (23.0%). Two years following diagnosis, AS ($2.97/d) was cheaper than surgery ($5.67/d) or radiation ($9.34/d) in favorable disease, while surgery ($7.17/d) was cheaper than radiation ($16.34/d) for unfavorable disease. At 5 yr, AS ($2.71/d) remained slightly cheaper than surgery ($2.87/d) and radiation ($4.36/d) in favorable disease, while for unfavorable disease surgery ($4.15/d) remained cheaper than radiation ($10.32/d). Study limitations include information derived from a single healthcare system and costs based on benchmark Medicare estimates rather than actual payment exchanges. PATIENT SUMMARY: Active surveillance was cheaper than surgery (-47.6%) and radiation (-68.2%) at 2 yr for favorable-risk disease, which decreased by 5 yr (-5.6% and -37.8%, respectively). Surgery was less costly than radiation for unfavorable risk for both intervals (-56.1% and -59.8%, respectively).

Contemporary Practices and Complications of Surgery for Thoracic Outlet Syndrome in the United States.

BACKGROUND: Thoracic outlet syndrome (TOS) surgery is relatively rare and controversial, given the challenges in diagnosis as well as wide variation in symptomatic and functional recovery. Our aims were to measure trends in utilization of TOS surgery, complications, and mortality rates in a nationally representative cohort and compare higher versus lower volume centers. METHODS: The National Inpatient Sample was queried using International Classification of Diseases, Ninth Revision, codes for rib resection and scalenectomy paired with axillo-subclavian aneurysm (arterial [aTOS]), subclavian deep vein thrombosis (venous [vTOS]), or brachial plexus lesions (neurogenic [nTOS]). Basic descriptive statistics, nonparametric tests for trend, and multivariable hierarchical regression models with random intercept for center were used to compare outcomes for TOS types, trends over time, and higher and lower volume hospitals, respectively. RESULTS: There were 3,547 TOS operations (for an estimated 18,210 TOS operations nationally) performed between 2010 and 2015 (89.2% nTOS, 9.9% vTOS, and 0.9% aTOS) with annual case volume increasing significantly over time (P = 0.03). Higher volume centers (≥10 cases per year) represented 5.2% of hospitals and 37.0% of cases, and these centers achieved significantly lower overall major complication (defined as neurologic injury, arterial or venous injury, vascular graft complication, pneumothorax, hemorrhage/hematoma, or lymphatic leak) rates (adjusted odds ratio [OR] 0.71 [95% confidence interval 0.52-0.98]; P = 0.04], but no difference in neurologic complications such as brachial plexus injury (aOR 0.69 [0.20-2.43]; P = 0.56) or vascular injuries/graft complications (aOR 0.71 [0.0.33-1.54]; P = 0.39). Overall mortality was 0.6%, neurologic injury was rare (0.3%), and the proportion of patients experiencing complications decreased over time (P = 0.03). However, vTOS and aTOS had >2.5 times the odds of major complication compared with nTOS (OR 2.68 [1.88-3.82] and aOR 4.26 [1.78-10.17]; P < 0.001), and ∼10 times the odds of a vascular complication (aOR 10.37 [5.33-20.19] and aOR 12.93 [3.54-47.37]; P < 0.001], respectively. As the number of complications decreased, average hospital charges also significantly decreased over time (P < 0.001). Total hospital charges were on average higher when surgery was performed in lower volume centers (<10 cases per year) compared with higher volume centers (mean $65,634 [standard deviation 98,796] vs. $45,850 [59,285]; P < 0.001). CONCLUSIONS: The annual number of TOS operations has increased in the United States from 2010 to 2015, whereas complications and average hospital charges have decreased. Mortality and neurologic injury remain rare. Higher volume centers delivered higher value care: less or similar operative morbidity with lower total hospital charges.

Improvement in Patient Safety May Precede Policy Changes: Trends in Patient Safety Indicators in the United States, 2000-2013.

OBJECTIVES: Quality and safety improvement are global priorities. In the last two decades, the United States has introduced several payment reforms to improve patient safety. The Agency for Healthcare Research and Quality (AHRQ) developed tools to identify preventable inpatient adverse events using administrative data, patient safety indicators (PSIs). The aim of this study was to assess changes in national patient safety trends that corresponded to U.S. pay-for-performance reforms. METHODS: This is a retrospective, longitudinal analysis to estimate temporal changes in 13 AHRQ's PSIs. National inpatient sample from the AHRQ and estimates were weighted to represent a national sample. We analyzed PSI trends, Center for Medicaid and Medicare Services payment policy changes, and Inpatient Prospective Payment System regulations and notices between 2000 and 2013. RESULTS: Of the 13 PSIs studied, 10 had an overall decrease in rates and 3 had an increase. Joinpoint analysis showed that 12 of 13 PSIs had decreasing or stable trends in the last 5 years of the study. Central-line blood stream infections had the greatest annual decrease (-31.1 annual percent change between 2006 and 2013), whereas postoperative respiratory failure had the smallest decrease (-3.5 annual percent change between 2005 and 2013). With the exception of postoperative hip fracture, significant decreases in trends preceded federal payment reform initiatives. CONCLUSIONS: National in-hospital patient safety has significantly improved between 2000 and 2015, as measured by PSIs. In this study, improvements in PSI trends often proceeded policies targeting patient safety events, suggesting that intense public discourses targeting patient safety may drive national policy reforms and that these improved trends may be sustained by the Center for Medicare and Medicaid Services policies that followed.

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.

Patient Electronic Health Records Score for Preoperative Risk Assessment Before Total Knee Arthroplasty.

BACKGROUND: Current preoperative risk assessment tools are often cumbersome, have limited accuracy, and are poorly adopted. The Care Assessment Need (CAN) score, an existing tool developed for primary care providers in the U.S. Veterans Administration health-care system (VA), is automatically calculated for individual patients using electronic health record data. Therefore, it could present an efficient preoperative risk assessment tool. The aim of this project was to determine if the CAN score can be repurposed as a preoperative risk assessment tool for patients undergoing total knee arthroplasty (TKA). METHODS: A multicenter retrospective observational study was conducted using national VA data from 2013 to 2016. The cohort included veterans who underwent TKA identified through ICD-9 (International Classification of Diseases, Ninth Revision), ICD-10, and CPT (Current Procedural Terminology) codes. The focus of the study was the preoperative patient CAN score, a single numerical value ranging from 0 to 99 (with a higher score representing greater risk) that is automatically calculated each week using multiple data points in the VA electronic health record. Study outcomes of interest were 90-day readmission, prolonged hospital stay (>5 days), 1-year mortality, and non-routine patient discharge. RESULTS: The study included 17,210 veterans. Their median preoperative CAN score was 75, although there was substantial variability in patient CAN scores among different facilities. A preoperative CAN score of >75 was significantly associated with mortality (odds ratio [OR] = 3.54), prolonged length of stay (OR = 1.97), 90-day readmission (OR = 1.65), and non-routine discharge (OR = 1.57). The CAN score had good accuracy with a receiver operating characteristic (ROC) curve value of >0.7 for all outcomes except 90-day readmission. CONCLUSIONS: The CAN score can be leveraged as an extremely efficient way to risk-stratify patients before TKA, with results that surpass other commonly available and labor-intensive alternatives. As a result, this simple and efficient solution is well positioned for broad adoption as a standardized decision support tool. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.