Increasing Postpartum Primary Care Engagement through Default Scheduling and Tailored Messaging: A Randomized Clinical Trial.

Importance: Over 30% of pregnant people have at least one chronic medical condition, and nearly 20% develop gestational diabetes or pregnancy-related hypertension, increasing the risk of future chronic disease. While these individuals are often monitored closely during pregnancy, they face significant barriers when transitioning to primary care following delivery, due in part to a lack of health care support for this transition. Objective: To evaluate the impact of an intervention designed to improve postpartum primary care engagement by reducing patient administrative burden and information gaps. Design: Individual-level randomized controlled trial conducted from November 3, 2022 to October 11, 2023. Setting: One hospital-based and five community-based outpatient obstetric clinics affiliated with a large academic medical center. Participants: Participants included English- and Spanish-speaking pregnant or recently postpartum adults with obesity, anxiety, depression, diabetes mellitus, chronic hypertension, gestational diabetes, or pregnancy-related hypertension, and a primary care practitioner (PCP) listed in their electronic health record (EHR). Intervention: A behavioral economics-informed intervention bundle, including default scheduling of postpartum PCP appointments and tailored messages. Main Outcome: Completion of a PCP visit for routine or chronic condition care within 4 months of delivery. Results: 360 patients were randomized (Control: N=176, Intervention: N=184). Individuals had mean (SD) age 34.1 (4.9) years and median gestational age of 36.3 weeks (interquartile range (IQR) 34.0-38.6 weeks) at enrollment. The distribution of self-reported races was 7.4% Asian, 6.8% Black, 15.0% multiple races or "Other," and 68.6% White. Most (75.8%) participants had anxiety or depression, 15.9% had a chronic or pregnancy-related hypertensive disorder, 19.8% had pre-existing or gestational diabetes, and 40.4% had a pre-pregnancy BMI ≥30 kg/m2. Medicaid was the primary payer for 21.9% of patients. PCP visit completion within 4 months occurred in 22.0% in the control group and 40.0% in the intervention group. In regression models accounting for randomization strata, the intervention increased PCP visit completion by 18.7 percentage points (95%CI 10.7-29.1). Intervention participants also had fewer postpartum readmissions (1.7 vs. 5.8%) and increased receipt of the following services by a PCP: blood pressure screening (42.8 vs. 28.3%), weight assessment (42.8 vs. 27.7%), and depression screening (32.8 vs. 16.8%). Conclusions and Relevance: In this randomized trial of pregnant individuals with or at risk for chronic health conditions, default PCP visit scheduling, tailored messages, and reminders substantially improved postpartum primary care engagement. The current lack of support for postpartum transitions to primary care is a missed opportunity to improve recently pregnant individual's short- and long-term health. Reducing patient administrative burdens may represent relatively low-resource, high-impact approaches to improving postpartum health and wellbeing. Trial Registration: NCT05543265.

Increased Rates of Hypertensive Disorders of Pregnancy during the COVID-19 Pandemic.

OBJECTIVE: Hypertensive disorders of pregnancy (HDP) are common complications associated with severe maternal and neonatal morbidity. One goal of prenatal care, especially at term, is to screen for HDP. As treatment of HDP centers on delivery when appropriate, timely diagnosis is crucial. We postulated that reduced in-person visits during the coronavirus disease 2019 (COVID-19) pandemic may have resulted in delayed diagnosis of HDP with concomitant higher rates of maternal morbidity. We sought to investigate the prevalence of HDP during the COVID-19 pandemic, as well as median gestational age at time of delivery as compared with the prepandemic median. STUDY DESIGN: This was a retrospective cohort analysis comparing singleton deliveries at four large-volume hospitals during the COVID-19 pandemic (April-July 2020 during a statewide "stay-at-home" order) to those in a pre-COVID era (April-July 2019). Deliveries complicated by HDP were identified by International Classification of Disease, Tenth Revision codes. Rates of HDP and markers of severe disease were the primary outcomes compared between the groups; multivariate regression was used to calculate the odds ratio of severe disease among women with any diagnosis of HDP. RESULTS: The cohort included 9,974 deliveries: 5,011 in 2020 and 4,963 in 2019. Patient characteristics (age, body mass index, race, ethnicity, and insurance type) did not differ significantly between the groups. There was an increase in HDP during the COVID era (9.0 vs. 6.9%; p < 0.01), which was significant even when controlling for patient parity (odds ratio 1.41, 95% CI 1.20-1.66). Among women with HDP, gestational age at delivery did not differ between the cohorts, nor did the proportion of patients with severe disease. CONCLUSION: We found a statistically significant increase in the rate of HDP during the COVID-19 pandemic. However, there was no change in the proportion of severe disease, suggesting that this increase did not significantly impact clinical morbidity. KEY POINTS: · Rates of HDP increased during the COVID-19 pandemic.. · There was no change in the proportion of severe HDP.. · HDP-related maternal/neonatal morbidity was unchanged..

Performance of a Maternal Risk Stratification System for Predicting Low Apgar Scores.

OBJECTIVE: Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. STUDY DESIGN: Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. RESULTS: A total of 14,497 maternal-neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. CONCLUSION: A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. KEY POINTS: · EOCS had moderate discrimination for Apgar <7.. · Predictive performance declined when limited to low-risk term singletons.. · Relationship between EOCS and Apgar <7 was likely driven by prematurity..

Regional Variation in Antenatal Late Preterm Steroid Use Following the ALPS Trial.

Importance: The publication of the Antenatal Late Preterm Steroids (ALPS) trial in February 2016 demonstrated that antenatal administration of betamethasone in the late preterm period (between 34 to 36 weeks of gestation) for individuals with a high risk of delivery decreased neonatal respiratory morbidity. National estimates have suggested the trial did change obstetric practice, but little is known if the evidence was adopted uniformly or equitably. Objective: To assess regional variation in the use of late preterm steroids after the publication of the Antenatal Late Preterm Steroids (ALPS) Trial and to understand factors associated with a region's pace of adoption. Design, Setting, and Participants: This cross-sectional study used US natality data from February 2015 to October 2017 from hospital referral regions (HRRs) within the US. Inclusion criteria included live-born, nonanomalous, singleton, late preterm (34 to 36 completed weeks of gestation) neonates born to individuals without pregestational diabetes. This study was conducted from November 15, 2022, to January 13, 2023. Main Outcome and Measures: HRRs were categorized as either a slower adopter or faster adopter of antenatal late preterm steroids based on the observed vs expected pace of antenatal steroid adoption in a 1-year period after the trial's dissemination. Patient and regional factors hypothesized a priori to be associated with the uptake of late preterm steroids were compared between faster and slower adopters. Comparisons were made using Student t test or Wilcoxon rank-sum test, as appropriate. A multivariable logistic regression was constructed to identify factors associated with faster adopter status in the postperiod. Results: There were 666 097 late preterm births in 282 HRRs. The mean (SD) maternal age in HRRs was 27.9 (1.2) years. The median (IQR) percentage of births by race categories in HRRs for patients identifying as American Indian or Alaskan Native was 0.5% (0.2%-1.3%); Asian or Pacific Islander, 3.0% (1.7%-5.3%); Black, 12.9% (5.1%-29.1%); and White, 78.6% (66.6%-87.0%). The median percentage of births in HRRs to patients of Hispanic ethnicity was 11.2% (6.3%-27.4%). In this study, 136 HRRs (48.2%) were classified as faster adopters and 146 (51.8%) were classified as slower adopters. Faster adopters increased their steroid use by 12.1 percentage points (from 5.9% to 18.0%) compared with a 5.5 percentage point increase (from 3.7% to 9.2%) among slower adopters (P < .001). Most examined patient and regional factors were not associated with a region's pace of adoption, with the exception of the regional prevalence of prior preterm birth (adjusted odds ratio [aOR], 2.04 [95% CI, 1.48-2.82]) and the percentage of deliveries at 34 to 35 weeks of gestation (aOR, 0.68 [95% CI, 0.47-0.99]) compared with 36 weeks. Conclusions and Relevance: In this cross-sectional study, there was widespread geographic variation in the adoption of antenatal steroid administration for late preterm births that largely remained unexplained by population factors. These findings should prompt further investigations to barriers to timely or equitable access to new evidence-based practices and guide future dissemination strategies with the goal of more uniform adoption.

External Validation of a Prediction Model for External Cephalic Version Success.

External cephalic version (ECV) success correlates with numerous maternal and pregnancy factors. A prior study developed an ECV success prediction model based on body mass index, parity, placental location, and fetal presentation. We performed external validation of this model using a retrospective cohort of ECV procedures from a separate institution between July 2016 and December 2021. Four hundred thirty-four ECV procedures were performed, with a 44.4% success rate (95% CI 39.8-49.2%), which was similar to the derivation cohort (40.6%, 95% CI 37.7-43.5%, P =.16). There were significant differences in patients and practices between cohorts, including the rate of neuraxial anesthesia (83.5% derivation cohort vs 10.4% our cohort, P <.001). The area under the receiver operating characteristic curve (AUROC) was 0.70 (95% CI 0.65-0.75), which was similar to that in the derivation cohort (AUROC 0.67, 95% CI 0.63-0.70). These results suggest the published ECV prediction model's performance is generalizable outside the original study institution.

Disparities from bedside to "webside": barriers to achieving equity in telemedicine in obstetrics.

Given the urgency of the COVID-19 pandemic, telehealth was swiftly implemented in the United States and shifted from an optional to an essential modality of care. This was important in both maintaining continuity of care, especially for those with chronic illnesses, and in evaluating individuals presenting with illness or acute needs. One of the primary benefits of telemedicine is convenience; however, this is juxtaposed with the various downsides that result from a lack of physical appointments and accessible technology. Notably, there are large barriers to achieving equity for certain communities, of which the most apparent is the "digital divide" that results from a lack of adequate broadband infrastructure and lower technology literacy among members of certain communities and those in rural areas. Without attention to factors that can improve availability, uptake, and experience, disparities will increase with telemedicine. As this technology becomes common practice, it will be important to provide equal reimbursement for in-person and telehealth visits, center patient design and cultural competency in telemedicine programs, and improve broadband and technology access, ensuring that the highest-quality care is delivered to all patients.

The Application of a Standard Risk Threshold for the Stratification of Maternal Morbidity among Population Subgroups.

OBJECTIVE: The aim of this study was to determine if a universally applied risk score threshold for severe maternal morbidity (SMM) resulted in different performance characteristics among subgroups of the population. STUDY DESIGN: This is a retrospective cohort study of deliveries that occurred between July 1, 2016, and June 30, 2020, in a single health system. We examined the performance of a validated comorbidity score to stratify SMM risk in our cohort. We considered the risk score that was associated with the highest decile of predicted risk as a "screen positive" for morbidity. We then used this same threshold to calculate the sensitivity and positive predictive value (PPV) of this "highest risk" designation among subgroups of the overall cohort based on the following characteristics: age, race/ethnicity, parity, gestational age, and planned mode of delivery. RESULTS: In the overall cohort of 53,982 women, the C-statistic was 0.755 (95% confidence interval [CI], 0.741-0.769) and calibration plot demonstrated that the risk score was well calibrated. The model performed less well in the following groups: non-White or Hispanic (C-statistic, 0.734; 95% CI, 0.712-0.755), nulliparas (C-statistic, 0.735; 95% CI, 0.716-0.754), term deliveries (C-statistic, 0.712; 95% CI, 0.694-0.729), and planned vaginal delivery (C-statistic, 0.728; 95% CI, 0.709-0.747). There were differences in the PPVs by gestational age (7.8% term and 29.7% preterm) and by planned mode of delivery (8.7% vaginal and 17.7% cesarean delivery). Sensitivities were lower in women who were <35 years (36.6%), non-White or Hispanic (40.7%), nulliparous (38.9%), and those having a planned vaginal delivery (40.9%) than their counterparts. CONCLUSION: The performance of a risk score for SMM can vary by population subgroups when using standard thresholds derived from the overall cohort. If applied without such considerations, such thresholds may be less likely to identify certain subgroups of the population that may be at increased risk of SMM. KEY POINTS: · Predictive risk models are helpful at condensing complex information into an interpretable output.. · Model performance may vary among different population subgroups.. · Prediction models should be examined for their potential to exacerbate underlying disparities..

Change in Emergency Medical Services-Attended Out-of-Hospital Deliveries during COVID-19 in the United States.

BACKGROUND: The COVID-19 pandemic disrupted access to routine in-person prenatal care, potentially leading to higher risk of out-of-hospital deliveries. Unplanned out-of-hospital deliveries pose a substantial risk of morbidity and mortality for pregnant patients and newborns. Our objective was to determine the change in rate of emergency medical services (EMS)-attended out-of-hospital deliveries during the COVID-19 pandemic. We hypothesized that COVID-19-related stay-at-home orders were associated with a higher rate of out-of-hospital deliveries during the initial wave of COVID-19. METHODS: We conducted an interrupted time series analysis using the 2019 and 2020 National EMS Information System datasets. We included 9-1-1 scene activations for patients 12-50 years old with out-of-hospital deliveries who were treated and transported by EMS. We calculated the weekly rate of deliveries per 100,000 EMS emergency activations each year overall, and for each census division. The interruption modeled was the enactment of stay-at-home orders, with March 25-31 selected as when most orders had been enacted. We fit ordinary least squares regression models with Newey-West standard errors to adjust for autocorrelation, testing for a change in level and slope overall and by census division. RESULTS: A total of 10,778 out-of-hospital deliveries were included, 58% (n = 6,254) in 2020. The mean weekly rate of out-of-hospital deliveries in 2019 was 29.4 per 100,000 activations (95% CI: 28.4 to 30.4) versus 33.0 (95% CI: 31.8 to 34.1) in 2020. There was an immediate increase of 6.3 deliveries per 100,000 activations (95% CI: 3.3 to 9.3) after the week of March 25-31, with a subsequent decrease of 0.3 deliveries per 100,000 per week after (95% CI: -0.4 to -0.2). There were also statistically significant immediate increases in out-of-hospital deliveries after March 25-31 in the New England, East North Central, West South Central, and Mountain divisions. CONCLUSION: EMS-attended out-of-hospital deliveries remained rare during the COVID-19 pandemic, but there was an immediate increase during the initial wave of the pandemic with evidence of geographic variation. Large-scale disruptions in the health care system may result in increases in uncommon patient presentations to EMS.

Comparison of Maternal and Neonatal Subspecialty Care Provision by Hospital.

OBJECTIVE: The aim of the study is to determine the relationship between a hospital's provision of subspecialty neonatal and maternal care. Specifically, we sought to understand where women with high-risk maternal conditions received intrapartum care and estimate the potential transfer burden for those with maternal high-risk conditions delivering at hospitals without subspecialty maternal care. STUDY DESIGN: This is a descriptive study using data from 2015 State Inpatient Databases and the American Hospital Association Annual Survey. Characteristics were compared between hospitals based on the concordance of their maternal and neonatal care. The incidences of high-risk maternal conditions (pre-eclampsia with severe features, placenta previa with prior cesarean delivery, cardiac disease, pulmonary edema, and acute liver failure) were compared. To determine the potential referral burden, the percent of women with high-risk conditions delivering at a hospital without subspecialty maternal care but delivering in a county with a hospital with subspecialty maternal care was calculated. RESULTS: The analysis included 486,398 women who delivered at 544 hospitals, of which 104 (19%) and 182 (33%) had subspecialty maternal and neonatal care, respectively. Ninety-eight hospitals provided both subspecialty maternal and neonatal care; however, 84 hospitals provided only subspecialty neonatal care but no subspecialty maternal care. Among high-risk maternal conditions examined, approximately 65% of women delivered at a hospital with subspecialty maternal care. Of the remainder who delivered at a hospital without subspecialty maternal care, one-third were in a county where subspecialty care was present. For women with high-risk conditions who delivered in a county without subspecialty maternal care, the median distance to the closest county with subspecialty care was 52.8 miles (IQR: 34.3-87.7 miles). CONCLUSION: Approximately 50% of hospitals with subspecialty neonatal care do not provide subspecialty maternal care. This discordance may present a challenge when both high-risk maternal and neonatal conditions are present. KEY POINTS: · High-risk women who deliver at hospitals without subspecialty care are in more rural areas.. · Approximately 50% of hospitals with subspecialty neonatal care do not provide subspecialty maternal care.. · This discordance may present a challenge when both high-risk maternal and neonatal conditions are present..

Obstetric and Neonatal Outcomes 1 or More Years After a Diagnosis of Breast Cancer.

OBJECTIVE: To evaluate obstetric and neonatal outcomes of the first live birth conceived 1 or more years after breast cancer diagnosis. METHODS: We performed a population-based study to compare live births between women with a history of breast cancer (case group) and matched women with no cancer history (control group). Individuals in the case and control groups were identified using linked data from the California Cancer Registry and California Office of Statewide Health Planning and Development data sets. Individuals in the case group were diagnosed with stage I-III breast cancer at age 18-45 years between January 1, 2000, and December 31, 2012, and conceived 12 or more months after breast cancer diagnosis. Individuals in the control group were covariate-matched women without a history of breast cancer who delivered during 2000-2012. The primary outcome was preterm birth at less than 37 weeks of gestation. Secondary outcomes were preterm birth at less than 32 weeks of gestation, small for gestational age (SGA), cesarean delivery, severe maternal morbidity, and neonatal morbidity. Subgroup analyses were used to assess the effect of time from initial treatment to fertilization and receipt of additional adjuvant therapy before pregnancy on outcomes of interest. RESULTS: Of 30,021 women aged 18-45 years diagnosed with stage I-III breast cancer during 2000-2012, 553 met the study inclusion criteria. Those with a history of breast cancer and matched women in the control group had similar odds of preterm birth at less than 37 weeks of gestation (odds ratio [OR], 1.29; 95% CI 0.95-1.74), preterm birth at less than 32 weeks of gestation (OR 0.77; 95% CI 0.34-1.79), delivering an SGA neonate (less than the 5th percentile: OR 0.60; 95% CI 0.35-1.03; less than the 10th percentile: OR 0.94; 95% CI 0.68-1.30), and experiencing severe maternal morbidity (OR 1.61; 95% CI 0.74-3.50). Patients with a history of breast cancer had higher odds of undergoing cesarean delivery (OR 1.25; 95% CI 1.03-1.53); however, their offspring did not have increased odds of neonatal morbidity compared with women in the control group (OR 1.15; 95% CI 0.81-1.62). CONCLUSION: Breast cancer 1 or more years before fertilization was not strongly associated with obstetric and neonatal complications.

Comparison of Natural Language Processing of Clinical Notes With a Validated Risk-Stratification Tool to Predict Severe Maternal Morbidity.

Importance: Risk-stratification tools are routinely used in obstetrics to assist care teams in assessing and communicating risk associated with delivery. Electronic health record data and machine learning methods may offer a novel opportunity to improve and automate risk assessment. Objective: To compare the predictive performance of natural language processing (NLP) of clinician documentation with that of a previously validated tool to identify individuals at high risk for maternal morbidity. Design, Setting, and Participants: This retrospective diagnostic study was conducted at Brigham and Women's Hospital and Massachusetts General Hospital, Boston, Massachusetts, and included individuals admitted for delivery at the former institution from July 1, 2016, to February 29, 2020. A subset of these encounters (admissions from February to December 2018) was part of a previous prospective validation study of the Obstetric Comorbidity Index (OB-CMI), a comorbidity-weighted score to stratify risk of severe maternal morbidity (SMM). Exposures: Natural language processing of clinician documentation and OB-CMI scores. Main Outcomes and Measures: Natural language processing of clinician-authored admission notes was used to predict SMM in individuals delivering at the same institution but not included in the prospective OB-CMI study. The NLP model was then compared with the OB-CMI in the subset with a known OB-CMI score. Model discrimination between the 2 approaches was compared using the DeLong test. Sensitivity and positive predictive value for the identification of individuals at highest risk were prioritized as the characteristics of interest. Results: This study included 19 794 individuals; 4034 (20.4%) were included in the original prospective validation study of the OB-CMI (testing set), and the remaining 15 760 (79.6%) composed the training set. Mean (SD) age was 32.3 (5.2) years in the testing cohort and 32.2 (5.2) years in the training cohort. A total of 115 individuals in the testing cohort (2.9%) and 468 in the training cohort (3.0%) experienced SMM. The NLP model was built from a pruned vocabulary of 2783 unique words that occurred within the 15 760 admission notes from individuals in the training set. The area under the receiver operating characteristic curve of the NLP-based model for the prediction of SMM was 0.76 (95% CI, 0.72-0.81) and was comparable with that of the OB-CMI model (0.74; 95% CI, 0.70-0.79) in the testing set (P = .53). Sensitivity (NLP, 28.7%; OB-CMI, 24.4%) and positive predictive value (NLP, 19.4%; OB-CMI, 17.6%) were comparable between the NLP and OB-CMI high-risk designations for the prediction of SMM. Conclusions and Relevance: In this study, the NLP method and a validated risk-stratification tool had a similar ability to identify patients at high risk of SMM. Future prospective research is needed to validate the NLP approach in clinical practice and determine whether it could augment or replace tools requiring manual user input.

Antenatal Steroid Exposure Among Term Newborns.

This cross-sectional study evaluates the association between dissemination of the Antenatal Late Preterm Steroid trial and changes in steroid exposure among term newborns.

Physician Practice Pattern Variations in Common Clinical Scenarios Within 5 US Metropolitan Areas.

Importance: While variations in quality of care have been described between US regions, physician-level practice pattern variations within regions remain poorly understood, notably among specialists. Objective: To examine within-area physician-level variations in decision-making in common clinical scenarios where guidelines specifying appropriateness or quality of care exist. Design Setting and Participants: This cross-sectional study used 2016 through 2019 data from a large nationwide network of commercial insurers, provided by Health Intelligence Company, LLC, within 5 metropolitan statistical areas (MSAs). Physician-level variations in appropriateness and quality of care were measured using 14 common clinical scenarios involving 7 specialties. The measures were constructed using public quality measure definitions, clinical guidelines, and appropriateness criteria from the clinical literature. Physician performance was calculated using a multilevel model adjusted for patient age, sex, risk score, and socioeconomic status with physician random effects. Measure reliability for each physician was calculated using the signal-to-noise approach. Within-MSA variation was calculated between physician quintiles adjusted for patient attributes, with the first quintile denoting highest quality or appropriateness and the fifth quintile reflecting the opposite. Data were analyzed March through October 2021. Main Outcomes and Measures: Fourteen measures of quality or appropriateness of care, with 2 measures each in the domains of cardiology, endocrinology, gastroenterology, pulmonology, obstetrics, orthopedics, and neurosurgery. Results: A total of 8788 physicians were included across the 5 MSAs, and about 2.5 million unique patient-physician pairs were included in the measures. Within the 5 MSAs, on average, patients in the measures were 34.7 to 40.7 years old, 49.1% to 52.3% female, had a mean risk score of 0.8 to 1.0, and more likely to have an employer-sponsored insurance plan that was either self-insured or fully insured (59.8% to 97.6%). Within MSAs, physician-level variations were qualitatively similar across measures. For example, statin therapy in patients with coronary artery disease ranged from 54.3% to 70.9% in the first quintile of cardiologists to 30.5% to 42.6% in the fifth quintile. Upper endoscopy in patients with gastroesophageal reflux disease without alarm symptoms spanned 14.6% to 16.9% in the first quintile of gastroenterologists to 28.2% to 33.8% in the fifth quintile. Among patients with new knee or hip osteoarthritis, 2.1% to 3.4% received arthroscopy in the first quintile of orthopedic surgeons, whereas 25.5% to 30.7% did in the fifth quintile. Appropriate prenatal screening among pregnant patients ranged from 82.6% to 93.6% in the first quintile of obstetricians to 30.9% to 65.7% in the fifth quintile. Within MSAs, adjusted differences between quintiles approximated unadjusted differences. Measure reliability, which can reflect consistency and reproducibility, exceeded 70.0% across nearly all measures in all MSAs. Conclusions and Relevance: In this cross-sectional study of 5 US metropolitan areas, sizeable physician-level practice variations were found across common clinical scenarios and specialties. Understanding the sources of these variations may inform efforts to improve the value of care.