About

Rachel Kohn, MD, MSCE, is an Assistant Professor of Medicine in the Department of Medicine at the Perelman School of Medicine, University of Pennsylvania. She specializes in pulmonary, allergy, and critical care medicine, with clinical interests including inpatient pulmonology and critical care. Dr. Kohn serves as an attending physician on the Pulmonology Consultation Services, the Pulmonary Diagnostic Lab, and the Medical Intensive Care Unit at the Hospital of the University of Pennsylvania and Penn Presbyterian Medical Center. Her research focuses on health services, healthcare operations, and healthcare delivery equity, particularly regarding representativeness in clinical trials and prospective research, as well as inpatient care delivery. She completed a Master of Science in Clinical Epidemiology at the University of Pennsylvania, emphasizing patient-centered outcomes research. Dr. Kohn's work aims to improve understanding and address disparities in healthcare, contributing to the fields of clinical epidemiology and health services research.

Research topics

• Medicine
• Emergency medicine
• Intensive care medicine
• Internal medicine
• Computer science

Selected publications

• Letter to the Editor Regarding “Palliative Care in Acute Cardiovascular Hospitalizations: A National Inpatient Sample Analysis”

  Journal of Pain and Symptom Management · 2026-04-09

  article
  PublisherDOI

• An external, contemporary evaluation of the Epic End of Life Care Index among hospitalized patients across two large health systems: A retrospective cohort study

  medRxiv · 2026-01-03

  articleOpen access1st authorCorresponding

  ABSTRACT Background The Epic End of Life Care Index (EOLCI) predicts one-year mortality and was developed to improve serious illness care. However, prior external EOLCI evaluations had limited sample sizes, populations, and equity evaluations. In preparation for a multi-system pragmatic clinical trial, we sought to evaluate the EOLCI performance and equity in the trial’s two participating health systems. Objective Evaluate EOLCI model performance overall and across key subgroups. Design/Setting/Patients Retrospective cohort study of patients hospitalized for ≥36 hours in 2022 to 39 hospitals in the Trinity Health and Kaiser Permanente Southern California (KPSC) health systems. Measurements We predicted one-year mortality risk stratified by health system using the EOLCI, a logistic regression model including age, sex, race/ethnicity, ethnicity, insurance, and diagnoses. We evaluated model performance using Scaled Brier Scores (SBS; range -1 to 1; composite measures of calibration and discrimination), calibration plots, and c-statistics. Results Among 116,749 Trinity patients with 154,063 encounters, 12,054 (10.3%) patients died within one year. Among 94,489 KPSC patients with 133,043 encounters, 16,872 (17.9%) died within one year. The SBS was -0.007 at Trinity and 0.178 at KPSC. Calibration was poor for both. Trinity’s discrimination was acceptable/good (c-statistic 0.76, 95% CI 0.76-0.77), and KPSC’s was good/very good (c-statistic 0.81, 95% CI 0.81-0.81). Model performance across subgroups was similar to the overall cohort. Limitations Death data were collected exclusively within Trinity and KPSC, risking outcome misclassification; several subgroup evaluations were limited by small sample sizes. Conclusions An external evaluation of the widely available Epic EOLCI demonstrated adequate to very good discrimination, poor calibration, and equitable performance across sociodemographic characteristics and diagnoses in two of the nation’s largest health systems. Primary funding source PCORI PLACER-2022C3-30553.

  PublisherOA PDFDOI

• Top Ten Tips Palliative Care Clinicians Should Know About Caring for People with Postintensive Care Syndrome

  Journal of Palliative Medicine · 2026-03-18

  article

  Intensive care unit (ICU) mortality rates have substantially declined over the past few decades. Accordingly, there has been an increase in the number of ICU survivors, who are often burdened by long-term sequelae and high morbidity following their discharge. The term postintensive care syndrome (PICS) was first coined in 2012 to describe this constellation of physical, psychological, and cognitive sequelae, which may persist long after acute care hospitalization and may also affect family members. In this context, the timely integration and delivery of palliative care has the potential to alleviate the suffering experienced by both ICU survivors and their families. In this article, an interdisciplinary team presents ten tips to describe PICS and enhance the quality of care for palliative care clinicians caring for people with PICS.

  PublisherDOI

• Patient Dignity in Long-Term Recovery among Survivors of Acute Respiratory Failure: A Prospective Cohort Study

  American Journal of Respiratory and Critical Care Medicine · 2025-03-05 · 1 citations

  letter1st authorCorresponding
  PublisherDOI

• Comparison of inpatient subspecialty care delivery models: Clinical outcomes and racial disparities in dedicated versus consultative pulmonary care

  Journal of Hospital Medicine · 2025-05-23

  articleOpen accessSenior author

  BACKGROUND: Subspecialty inpatient care is associated with improved outcomes in various clinical settings. However, clinical outcomes and racial disparities between dedicated inpatient pulmonary care and general medicine services with pulmonary consultation remain unknown. OBJECTIVE: To compare clinical outcomes between dedicated and consultative inpatient pulmonary care and evaluate whether racial disparities in outcomes differ by care model. METHODS: Retrospective cohort study of 1072 self-identified Black and White adults admitted to dedicated pulmonary or general medicine services with pulmonary consultation (April 2017-February 2020) at an academic medical center. Exposures included the care model, race, and the interaction between the two. Outcomes included hospital length of stay (LOS; modeled as risk of discharge alive using competing risk models), hospital readmissions, and outpatient pulmonary follow-up. We performed multivariable regression models with interaction terms adjusted for demographics, comorbidities, clinical severity, and pulmonary diagnosis. RESULTS: Dedicated pulmonary service patients had shorter LOS (subdistribution hazard ratio [SHR]: 1.38, 95% confidence interval [CI]: 1.14-1.67, p = .001) and improved 90-day outpatient follow-up (odds ratio [OR]: 1.63, 95% CI: 1.07-2.49, p = .023). The interaction between care model and race demonstrated significantly lower odds of 30-day follow-up among Black patients admitted to the dedicated service versus those with consultations; no other significant racial disparities in outcomes were demonstrated. CONCLUSIONS: Dedicated pulmonary inpatient care was associated with shorter hospital LOS and higher 90-day outpatient follow-up without significant racial disparities in most outcomes. Hospitals could consider pilot-testing dedicated inpatient pulmonary care models, as more work is needed to validate these findings in broader settings.

  PublisherOA PDFDOI

• Measuring Representativeness in Clinical Trials

  Circulation · 2025-02-03 · 13 citations

  reviewOpen access

  Representativeness in randomized clinical trials remains a critical concern, affecting the external validity of trial results, equitable access to the risks and benefits of research participation, and public trust in clinical research. Although representative participation by members of groups traditionally underrepresented in clinical trials is just a surrogate for true diversity, equity, inclusion, and belonging in clinical trials, it can be quantified, allowing stakeholders to add empirical rigor to diversity, equity, inclusion, and belonging efforts. Multiple ways to measure representativeness have been proposed, including the participation-to-prevalence ratio, raw participation proportions or numbers for relevant subgroups, and enrollment fraction for relevant subgroups. These methods have strengths and weaknesses and may be appropriate to report in certain circumstances, depending on why stakeholders seek to assess representativeness. Stakeholders-including regulatory agencies, journal editors, clinical trial investigators, and trial sponsors-may use quantitative measures of representativeness to establish trial enrollment standards, monitor equitable participation in ongoing trials, and condition funding or drug or device approval on achieving specific representativeness targets. However, using quantitative measures of representativeness in this way could have unintended consequences, including researchers "gaming" recruitment strategies to meet target numbers, overlooking nuanced variations within communities, and potentially incentivizing problematic and exploitative recruitment strategies. Although no single method of measuring representativeness offers a comprehensive solution for increasing diversity, equity, inclusion, and belonging in all randomized clinical trials, a carefully designed, multifaceted approach to measuring representativeness may provide stakeholders with useful perspectives for measuring progress in increasing the diversity of clinical trial participation. For stakeholders seeking a single number to assess the representativeness of a trial enrolling patients with a disease state with well-delineated demographics, the participation-to-prevalence ratio is ideal; however, for a more nuanced view of representativeness, the combination of enrollment fraction in subgroups of relevance plus a full report of the demographics of patients approached for enrollment may be more appropriate.

  PublisherOA PDFDOI

• Critical Care Fellows’ Training Experiences with Obstetric Critical Care: A Cross-Sectional Survey

  ATS Scholar · 2025-07-23

  articleOpen access
  PublisherDOI

• Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome

  Critical Care Medicine · 2025-04-08 · 4 citations

  articleOpen access

  OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute respiratory distress syndrome (acute respiratory distress syndrome [ARDS]) using electronic health record (EHR) data. DESIGN: In this retrospective cohort study, ARDS was identified via physician-adjudication in three cohorts of patients with hypoxemic respiratory failure (training, internal validation, and external validation). Machine-learning models were trained to classify ARDS using vital signs, respiratory support, laboratory data, medications, chest radiology reports, and clinical notes. The best-performing models were assessed and internally and externally validated using the area under receiver-operating curve (AUROC), area under precision-recall curve, integrated calibration index (ICI), sensitivity, specificity, positive predictive value (PPV), and ARDS timing. PATIENTS: Patients with hypoxemic respiratory failure undergoing mechanical ventilation within two distinct health systems. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 1,845 patients in the training cohort, 556 in the internal validation cohort, and 199 in the external validation cohort. ARDS prevalence was 19%, 17%, and 31%, respectively. Regularized logistic regression models analyzing structured data (EHR model) and structured data and radiology reports (EHR-radiology model) had the best performance. During internal and external validation, the EHR-radiology model had AUROC of 0.91 (95% CI, 0.88-0.93) and 0.88 (95% CI, 0.87-0.93), respectively. Externally, the ICI was 0.13 (95% CI, 0.08-0.18). At a specified model threshold, sensitivity and specificity were 80% (95% CI, 75%-98%), PPV was 64% (95% CI, 58%-71%), and the model identified patients with a median of 2.2 hours (interquartile range 0.2-18.6) after meeting Berlin ARDS criteria. CONCLUSIONS: Machine-learning models analyzing EHR data can retrospectively identify patients with ARDS across different institutions.

  PublisherOA PDFDOI

• Interhospital Transfers in Critical Illness

  CHEST Critical Care · 2025-09-23

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Behavioral Economic Strategies to Promote Cardiovascular Clinical Trial Enrollment Among Underrepresented Populations: A Discrete Choice Experiment

  American Journal of Respiratory and Critical Care Medicine · 2025-05-01

  article1st authorCorresponding

  Abstract Rationale: Black, Hispanic/Latinx, female, and rural US populations face disparities in cardiovascular disease risk factors, care, outcomes, and interventions, in part from underrepresentation in clinical trials. Behavioral economics (BE), principles of economics and psychology to understand decision-making, may improve trial enrollment and retention, but has not been evaluated among underrepresented populations. Therefore, we aimed to quantify preferences for BE strategies to optimize enrollment representation. Methods: We conducted a discrete choice experiment (DCE), a quantitative method used to elicit preferences without directly asking, to determine BE strategies’ influence on trial enrollment among adult participants who identified as Black, Hispanic/Latinx, female, and/or living in rural areas. Each participant completed 12 forced-choice tasks in random order. For each task, participants were presented with two hypothetical cardiovascular trials and selected which trial they would be more likely to enroll in. Study conditions were identical except for: enrollment method (opt-in, opt-out), recruiter role (physician they followed with longitudinally, physician researcher, research staff), and incentive (none, $150 cash, $150 gift card, $150 debit card). We performed logistic mixed effects models to determine associations of BE strategies with enrollment consent, using crossed random effects for participant and task number, overall and by subgroup. Results: We surveyed 247 eligible participants 1/3/24-6/18/24 (2,964 surveys). 147 (60%) identified as Black, 37 (15%) Hispanic/Latinx, 177 (72%) female, and 88 (36%) living in rural areas. Median age was 35 (IQR 28-50). Participants overall revealed preferences for participating in studies when an opt-in recruitment strategy was employed vs opt-out (OR=0.55, 95% CI=0.49-0.62 vs OR=0.45, 95% CI=0.38-0.52, p<0.001); the recruiter was a physician they followed with longitudinally vs a physician researcher or research staff (OR=0.63, 95% CI=0.56-0.69 vs OR=0.48, 95% CI=0.41-0.55, p<0.001; OR=0.41, 95% CI=0.33-0.48, p<0.001); and the incentive was $150 cash vs none, $150 gift card, and $150 debit card (OR=0.75, 95% CI=0.68-0.82 vs OR=0.09, 95% CI=0.06-0.13, p<0.001; OR=0.54, 95% CI=0.45-0.63, p<0.001; OR=0.71, 95% CI=0.64-0.79, p=0.09). Effect estimates were similar across subgroups (Table). Conclusions: Leveraging DCE methodology, Black, Hispanic/Latinx, female, and/or rural areas participants revealed preferences for participating in studies that utilized opt-in recruitment performed by a physician they saw longitudinally, with $150 cash incentive, with similar findings across subgroups. Future work is needed to confirm our findings, and test our findings in larger sample sizes of underrepresented US populations and in the context of actual prospective studies, to ultimately improve enrollment representativeness.

  PublisherDOI

Recent grants

• The influence of ward capacity strain on outcomes among survivors of acute respiratory failure

  NIH · $1.0M · 2019–2025

• Defining measures of strain on hospital wards and their influence on survivors of acute respiratory failure

  NIH · $70k · 2018–2018

Frequent coauthors

• Scott D. Halpern

  University of Pennsylvania

  66 shared

• George L. Anesi

  University of Pennsylvania

  39 shared

• Gary E. Weissman

  California University of Pennsylvania

  38 shared

• Meeta Prasad Kerlin

  University of Pennsylvania

  36 shared

• M. Kit Delgado

  University of Pennsylvania

  14 shared

• Brian Bayes

  University of Pennsylvania

  14 shared

• Jennifer C. Ginestra

  Hospital of the University of Pennsylvania

  13 shared

• Robert Wise

  University of Oxford

  12 shared