INTRODUCTION:Hospital quality leaders and clinicians in urology receive conflicting signals from hospital rankings, such as the US News & World Report (USNWR), which reports prostatectomy patients to target for quality improvement. Our objective was to develop a clinically coherent cohort definition for radical prostatectomy spanning inpatient and outpatient settings and to assess overlap with USNWR prostate cancer surgery cohorts. This is a cross-sectional, multisite study of all consecutive radical prostatectomies performed within a large, integrated US health system in 2024. METHODS:We created an institutional definition of prostate cancer surgery inclusive of inpatient and outpatient procedures across all payers, requiring an ICD (International Classification of Diseases)-10 diagnosis code for prostate cancer (C61) plus a procedural code for radical prostatectomy (Common Procedural Terminology 55810, 55812, 55815, 55840, 55842, 55845, or 55866, or ICD-10 procedure codes with clinical confirmation). We then reconstructed the 3 USNWR prostate cancer surgery cohorts: (1) outpatient volume, (2) outpatient potentially preventable complications, and (3) inpatient measures. Our main outcome was percent agreement between the institutional prostatectomy cohort and each USNWR comparator cohort. RESULTS:Only 716/1591 (45%) radical prostatectomies performed at our institution were captured in at least 1 USNWR definition, 613/1591 (39%) were captured by the USNWR potentially preventable outpatient complications definition, and 97 (6.1%) were captured by the inpatient outcomes definition. CONCLUSIONS:USNWR prostatectomy cohorts capture less than half of clinically relevant cases. More inclusive, clinically coherent definitions-such as our institutional cohort-are needed to reliably benchmark hospital performance in radical prostatectomy.

BACKGROUND:Publicly reported 30-day acute myocardial infarction (AMI) mortality rates are widely used to benchmark hospital performance. However, these measures may lack validity when they include deaths among patients with do-not-resuscitate (DNR) orders or those receiving palliative care-groups for whom mortality may be expected and goal-concordant. METHODS:We analyzed the national 100% Medicare Inpatient Standard Analytic File and Medicare Beneficiary Summary file to assess 30-day AMI mortality rates stratified by DNR status (absent, present-on-admission, and postadmission) and palliative care involvement. We then conducted a retrospective chart review of all 30-day AMI mortalities at a single academic medical center (2019-2025), applying Global Registry of Acute Coronary Events and a Palliative Performance Scale scores to identify unpreventable deaths, which we defined as a Global Registry of Acute Coronary Events >190.5 or Palliative Performance Scale ≤40%. Global Registry of Acute Coronary Events is a risk-stratification tool using clinical and laboratory variables whereas the Palliative Performance Scale is a bedside functional status and palliative care assessment quantifying patient's self-care ability. RESULTS:Across n=467 259 total AMI encounters, mean (SD) age was 75.1 (10.5), and the population was 44% female. Thirty-day mortality was 10.4% (39 112/376 062) among patients without DNRs, 43.1% (26 181/60 719) in those with present-on-admission DNR, and 72.1% (21 984/30 478) in those with postadmission DNR. Among 9.2% (43 090/467 259) of encounters with palliative care, there was a 76.9% mortality rate (33 151/43 090). Hazard ratios for mortality were 5.25 (95% CI, 5.17-5.33) for present-on-admission DNR and 10.73 (95% CI, 10.55-10.91) for postadmission DNR as compared with encounters without DNRs. Among n=24 AMI mortalities at our institution, 71% (17/24) had DNRs, and 33% (8/24) met the unpreventable death criteria. CONCLUSIONS:The validity of 30-day AMI mortality metrics may be undermined by their inclusion of high-acuity, end-of-life patients, which may disincentivize hospitals from delivering appropriate, patient-centered care. Removal of DNR or palliative cases from performance measures should be explored to better identify opportunities for reducing preventable deaths.

OBJECTIVE:The Centers for Medicare and Medicaid Services (CMS) inpatient claims data include up to 25 International Classification of Diseases Clinical Modification (ICD-10-CM) diagnosis codes per encounter, which are relied upon for comparing hospitals' risk-adjusted outcomes. Because hospital billing frequently includes more than 25 codes, we aimed to assess the extent of underestimation of Elixhauser comorbidities in risk-adjusted hospital outcomes. METHODS:A retrospective cohort study of all Medicare inpatient encounters across our health system from January 2019 to December 2025. We calculated number of codes per encounter and prevalence of Elixhauser comorbidities. We compared Elixhauser prevalence when limiting to 25 codes versus collecting all codes using chi-squared and Kruskal-Wallis tests and calculated the percent of Elixhausers that go uncaptured by the 25-code restriction. We assessed variation ICD-10 capture across all US hospitals using the 2024 CMS inpatient claims data, using codes in the 25th position as a proxy for percentage of encounters potentially having more than 25 codes. RESULTS:148,929/397,301 (37.5%) inpatient encounters had more than 25 ICD-10-CM codes in our health system. 16/38 (42.1%) Elixhausers were significantly more prevalent when using all codes. There were 25 ICD-10 codes documented in 1,470,064/8,096,044 (18.2%) Medicare inpatient encounters across all US hospitals. CONCLUSION:More than 1/3 of inpatient encounters at our institution, and as many as 1/6 across the United States, have ICD-10-CM codes which are not included in CMS risk-adjustment models due to the limit of 25 ICD-10-CM codes per claim. CMS should expand claims data to include all ICD-10-CM codes.

Importance:Hospital ratings including the US News & World Report's Best Hospitals rankings and the Centers for Medicare & Medicaid Services' (CMS') Overall Hospital Quality Star Rating (Overall Star Rating) measure different outcomes and are weakly correlated. Therefore, methods for defining and measuring reliable excellence, defined as consistently great performance across all quality measures, are needed. Objective:To assess a measure of reliable excellence using the 45 quality measures reported in the Overall Star Ratings. Design, Setting, and Participants:This cross-sectional study used hospital-level data from the 2023 and 2024 CMS Overall Star Ratings at all US hospitals with a 2023 and 2024 CMS Overall Star Rating. Exposures:The exposure was the CMS Overall Star Rating summary score, a continuous variable calculated from the weighted z scores of 45 quality measures used in the Overall Star Rating. A total of 100 000 simulations were run in which all US hospitals' CMS Overall Star Rating summary scores were calculated through summation of z scores from randomly generated measure weights, as opposed to the existing weights used in the Overall Star Rating method. Main Outcomes and Measures:Reliable excellence, defined as achieving a 90th percentile (or better) CMS Overall Star Rating summary score on at least 50 000 of 100 000 simulations. The percentage of hospitals achieving reliable excellence was calculated both overall and stratified by CMS Ovearll Star Ratings. Results:There were 2700 hospitals in the analysis, with 335 5-star hospitals (12.4%), 727 4-star hospitals (26.9%), 799 3-star hospitals (29.6%), 572 2-star hospitals (21.2%), and 267 1-star hospitals (9.9%) in the 2024 CMS Overall Star Rating. A total of 244 of 2700 hospitals (9.0%) met the study definition of reliable excellence, whereas 1287 of 2700 hospitals (47.7%) achieved excellence in at least 1 simulation. Conclusions and Relevance:This cross-sectional study of 2700 US hospitals found that only 244 hospitals (9.0%), including less than two-thirds of the CMS 5-star rated hospitals, were reliably excellent across 100 000 CMS Overall Star Rating scoring simulations using random measure weightings. These findings lend credence to the ubiquity of inconsistent greatness in health care quality and illuminate the need for methods to distinguish hospitals that provide reliably excellent care.

In this article, the authors discuss the creation of HealthLocator, a public, digital platform designed to address the confusion and inconsistencies between hospital rating systems. HealthLocator aims to empower patients with transparent, data-driven insights on more than 5,000 U.S. hospitals, helping them make informed decisions based on reliable metrics. Developed by the Mayo Clinic's Kern Center, HealthLocator integrates three core domains - quality outcomes, patient experience, and patient safety - into a single composite score. These domains draw from publicly available U.S. Centers for Medicare & Medicaid Services (CMS) and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) data, spanning measures of readmissions, mortality, timeliness of care, patient experience, and hospital-acquired conditions, subsequently transformed into percentiles to more clearly present and interpret performance. An unique complementary feature is the reliable excellence indicator, which identifies top-tier hospitals consistently scoring highly across all CMS measures through robust simulations. In addition, HealthLocator includes high-volume and high-6-month-survival indicators for surgeries and specialized care, using CMS claims data. The novel 6-month mortality model adjusts for patient risk while avoiding the potential for methodological manipulation through inappropriate use of observation stays that are excluded from mortality measurement in other rating and ranking systems. The Mayo Clinic plans to continually refine HealthLocator based on user feedback, with future updates focusing on personalization, outpatient and long-term care quality measures, expanded data sources, and broader stakeholder input via an external advisory board. The platform's goal is to become the definitive, patient-centered resource for hospital performance - advancing transparency, trust, and clinical quality across the U.S. health care system. In essence, HealthLocator is not just another ranking system - it is a public service initiative rooted in peer-reviewed methods and designed to help patients find high-quality care while promoting accountability and improvement in health care delivery.

Background: The US News & World Report's Best Hospitals Procedures and Conditions ratings aim to assess hospital performance for routine inpatient care. Aortitis is a complicating factor for abdominal aortic aneurysm (AAA) repair, but aortitis diagnoses are not currently an exclusion criteria for the AAA repair rating. We assessed 30-day mortality among patients with aortitis during AAA repair to determine whether aortitis should be an exclusion criterion. Methods: We used the Medicare Beneficiary Summary File and Inpatient Limited Data Sets from January 1, 2019, to December 1, 2022. We included all encounters for AAA repair with a diagnosis code for unruptured AAA. We excluded encounters with a diagnosis code for ruptured AAA. We calculated the prevalence of aortitis (defined using International Classification of Diseases, 10th edition, codes) in this population, and used log-linear regression to compare the age-and sex-adjusted risk of 30-day mortality in patients with aortitis vs those without aortitis. We reported the adjusted risk ratio and 95% confidence interval. Results: There were 51,508 AAA repair encounters. The prevalence of aortitis was 2.3% (1167/51,508); 30-day mortality occurred in 37/1167 (3.2%) encounters with an aortitis diagnosis vs 998/50,341 (2.0%) without aortitis (adjusted risk ratio, 1.50; 95% confidence interval,1.09-2.07; P 1/4 .01). Conclusions: AAA repair with concurrent aortitis should be excluded from quality outcome measures. (JVS-Vascular Insights 2025;3:100178.)

BACKGROUND:The COVID-19 pandemic introduced unique challenges to healthcare systems, particularly in relation to patient safety and adverse events during hospitalization. There is limited understanding of COVID-19's association with some patient safety indicators (PSIs). OBJECTIVES:This study aimed to investigate the impact of COVID-19 infection on the rate of PSI-3 events and its implications on quality metrics. We compared PSI-3 event rates between COVID-19-infected and uninfected patients and examined the clinical characteristics of COVID-19 patients experiencing PSI-3 events. METHODS:This is a retrospective study at Mayo Clinic hospitals between January 2020 and February 2022, analyzing patients meeting PSI-3 denominator eligibility criteria. PSI-3 events were identified using AHRQ WinQI software. Patients were categorized based on COVID-19 status. Patient demographics, characteristics, and PSI-3 rates were compared. A case series analysis described clinical details of COVID-19 patients with PSI-3 events. RESULTS:Of 126,781 encounters meeting PSI-3 criteria, 8674 (6.8%) had acute COVID-19 infection. COVID-19-infected patients were older, more likely to be male, non-white, and had private insurance. PSI-3 rates were significantly higher in COVID-19 patients (0.21% versus 0.06%, P < 0.0001), even after risk adjustment (adjusted risk ratio, 3.24, P < 0.0001). The case series of 17 COVID-19 patients with PSI-3 events showed distinctive clinical characteristics, including higher medical device-related pressure injuries, and greater predisposition for head, face, and neck region. CONCLUSIONS:Acute COVID-19 infection correlates with higher PSI-3 event rates. Current quality indicators may require adaptation to address the pandemic's complexities and impact on patient safety. Further research is needed to comprehensively understand the intricate relationship between COVID-19 and patient outcomes.

Background: The Joint Commission uses nulliparous, term, singleton, vertex, cesarean delivery (NTSV-CD) rates to assess hospitals' perinatal care quality through the Cesarean Birth measurement (PC-02). However, these rates are not riskadjusted for maternal health factors, putting this measure at odds with the risk adjustment paradigm of most publicly reported hospital quality measures. Here, the authors tested whether risk adjustment for readily documented maternal risk factors affected hospital-level NTSV-CD rates in a large health system. Methods: Included were all consecutive NTSV pregnancies from January 2019 to April 2023 across 10 hospitals in one health system. Logistic regression, adjusting for age, obesity, diabetes, and hypertensive disorders. was used to calculate hospital-level risk-adjusted NTSV-CD rates by multiplying observed vs. expected ratios for each hospital by the systemwide unadjusted NTSV-CD rate. The authors calculated intrahospital risk differences between unadjusted and risk-adjusted rates and calculated the percentage of hospitals qualifying for different reporting status after risk adjustment using the 30% Joint Results: Of 23,866 pregnancies, 6,550 (27.4%) had cesarean deliveries. Across 10 hospitals, the number of deliveries ranged from 393 to 7,671, with unadjusted NTSV-CD rates ranging from 21.0% to 30.5%. Risk-adjusted NTSV-CD rates ranged from 21.5% to 30.4%, with absolute intrahospital differences in risk-adjusted vs. unadjusted rates ranging from -1.33% (indicating lower rate after risk adjustment) to 3.37% (indicating higher rate after risk adjustment). Three of 10 (30.0%) hospitals qualified for different reporting statuses after risk adjustment. Conclusion: Risk adjustment for age, obesity, diabetes, and hypertensive disorders is feasible and resulted in meaningful changes in hospital-level NTSV-CD rates with potentially impactful consequences for hospitals near The Joint Commission reporting threshold.

BACKGROUND:Perioperative blood transfusion is associated with adverse outcomes and higher costs after coronary artery bypass graft (CABG) surgery. We developed risk assessments for patients' probability of perioperative transfusion and the expected transfusion volume to improve clinical management and resource use. METHODS:Among 1,266,545 consecutive (2008-2016) isolated CABG operations in The Society of Thoracic Surgeons Adult Cardiac Surgery Database, 657,821 (51.9%) received perioperative transfusions of red blood cells (RBC), fresh frozen plasma (FFP), cryoprecipitate, and/or platelets. We developed "full" models to predict perioperative transfusion of any blood product, and of RBC, FFP, or platelets. Using least absolute shrinkage and selection operator model selection, we built a rapid risk score based on 5 variables (age, body surface area, sex, preoperative hematocrit, and use of intra-aortic balloon pump). RESULTS:C statistics for the full model were 0.785, 0.815, 0.707, and 0.699 for any blood product, RBC, FFP, and platelets, respectively. C statistics for rapid risk assessments were 0.752, 0.785, 0.670, and 0.661 for any blood product, RBC, FFP, and platelets, respectively. The observed vs expected risk plots showed strong calibration for full models and risk assessment tools; absolute differences between observed and expected risks of transfusion were <10.8% in each percentile of expected risk. Risk assessment-predicted probabilities of transfusion were strongly and nonlinearly associated (P < .0001) with total units transfused. CONCLUSIONS:These robust and well-calibrated risk assessment tools for perioperative transfusion in CABG can inform surgeons regarding patients' risks and the number of RBC, FFP, and platelets units they can expect to need. This can aid in optimizing outcomes and increasing efficient use of blood products.

This study investigated the accuracy of mortality attributions assigned by the US News and World Report (USNWR) to the diabetes and endocrinology specialty. We reviewed medical records of all consecutive Medicare fee-for-service inpatients at Mayo Clinic, Florida (Jacksonville, Florida) with a Medicare Severity Diagnosis Related Group included in the USNWR Diabetes & Endocrinology specialty cohort admitted from November 2018 to April 2022, with documented mortality in our institution’s electronic health record within 30 days of the index admission. A clinician adjudicated the primary cause of death, categorizing it as diabetes or endocrine, cancer, failure to thrive, or other. Among 49 deceased patients, only 7 (14.3%) had diabetes or an endocrine-related cause of death. Cancer (49.0%) and failure to thrive (30.6%) were the leading causes. This substantial discrepancy (86% misattribution) suggests USNWR’s methodology might not precisely reflect the quality of care, potentially misleading patients and impacting hospital rankings.

Objective To assess whether the US News and World Report (USNWR) Urology specialty ranking methodology accurately captures and classifies complications following elective outpatient urology procedures. Methods We conducted electronic health record chart review of n = 80 elective, outpatient urology procedures with complications from 2019-2023 across 4 hospitals in our integrated US health system. We used the Solventum AM-PPC software and USNWR methodology to determine eligibility and measure complications. For each complication identified by the software, we assessed: (1) whether the procedure was performed by a urologist; (2) whether the adjudicator agreed with the complication type; and (3) whether the complication was a clinically related sequelae of the index procedure. We reported Clavien-Dindo severity of each complication. Results Our adjudication agreed on complication type in 62/80 (78%) complications, and 64/80 (80%) complications were clinically related to the index urology procedure. Combined, 57/80 (71%) complications were concordant on both complication type and clinical relatedness. However, 38/80 (48%) index procedures were conducted by interventional radiologists, not urologists. Furthermore, 11/80 (13.8%) complications were false positive urinary tract infections (UTIs). Conclusion The USNWR methodology for elective outpatient urology procedural complications showed reasonable clinical validity but detected several false positive UTIs. Further, USNWR should clarify the extent to which procedures performed by interventional radiologists belong in urology rankings.

Using the Statistical Analysis System (SAS) program shared here, all US hospitals can calculate hospital-specific hospital-acquired infection threshold counts for achieving a pre-specified benchmarked Standardized Infection Ratio performance percentile.

PURPOSE:By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS:We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS:Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS:US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Objectives In the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals’ pre-pandemic Centers for Medicare and Medicaid Services (CMS) Overall Hospital Star ratings and early pandemic 30-day mortality among both patients with COVID and non-COVID to understand whether pre-existing structures, processes and outcomes related to quality enabled greater pandemic resiliency.Design and data source A retrospective, claim-based data study using the 100% Inpatient Standard Analytic File and Medicare Beneficiary Summary File including all US Medicare Fee-for-Service inpatient encounters from 1 April 2020 to 30 November 2020 linked with the CMS Hospital Star Ratings using six-digit CMS provider IDs.Outcome measure The outcome was risk-adjusted 30-day mortality. We used multivariate logistic regression adjusting for age, sex, Elixhauser mortality index, US Census Region, month, hospital-specific January 2020 CMS Star rating (1–5 stars), COVID diagnosis (U07.1) and COVID diagnosis×CMS Star Rating interaction.Results We included 4 473 390 Medicare encounters from 2533 hospitals, with 92 896 (28.2%) mortalities among COVID-19 encounters and 387 029 (9.3%) mortalities among non-COVID encounters. There was significantly greater odds of mortality as CMS Star Ratings decreased, with 18% (95% CI 15% to 22%; p<0.0001), 33% (95% CI 30% to 37%; p<0.0001), 38% (95% CI 34% to 42%; p<0.0001) and 60% (95% CI 55% to 66%; p<0.0001), greater odds of COVID mortality comparing 4-star, 3-star, 2-star and 1-star hospitals (respectively) to 5-star hospitals. Among non-COVID encounters, there were 17% (95% CI 16% to 19%; p<0.0001), 24% (95% CI 23% to 26%; p<0.0001), 32% (95% CI 30% to 33%; p<0.0001) and 40% (95% CI 38% to 42%; p<0.0001) greater odds of mortality at 4-star, 3-star, 2-star and 1-star hospitals (respectively) as compared with 5-star hospitals.Conclusion Our results support a need to further understand how quality outcomes were maintained during the pandemic. Valuable insights can be gained by including the reporting of risk-adjusted pandemic era hospital quality outcomes for high and low performing hospitals.

Background Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. Methods Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. Results A total of 15,615 people were observed to have at least one serology test 14–90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79–96%) compared to non-Hispanic (60–89%) patients; in those presenting with at least one COVID-19 related symptom (69–93%) as compared to no such symptoms (63–91%); and in inpatient (70–97%) and emergency department (93–99%) compared to outpatient (63–92%) settings across datasets. PPA was highest in those with diabetes (75–94%) and kidney disease (83–95%); and lowest in those with auto-immune conditions or who are immunocompromised (56–93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59–3.86), patients with diabetes (1.49–1.56), and obesity (1.63–2.23); and lower in those with immunocompromised or autoimmune conditions (0.25–0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. Conclusion Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

BackgroundAs diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. MethodsSix health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. ResultsAcross datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. ConclusionOur results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.