Association Between Medicare's Sepsis Reporting Policy (SEP-1) and the Documentation of a Sepsis Diagnosis in the Clinical Record.

STUDY DESIGN: Interrupted time series analysis of a retrospective, electronic health record cohort. OBJECTIVE: To determine the association between the implementation of Medicare's sepsis reporting measure (SEP-1) and sepsis diagnosis rates as assessed in clinical documentation. BACKGROUND: The role of health policy in the effort to improve sepsis diagnosis remains unclear. PATIENTS AND METHODS: Adult patients hospitalized with suspected infection and organ dysfunction within 6 hours of presentation to the emergency department, admitted to one of 11 hospitals in a multi-hospital health system from January 2013 to December 2017. Clinician-diagnosed sepsis, as reflected by the inclusion of the terms "sepsis" or "septic" in the text of clinical notes in the first two calendar days following presentation. RESULTS: Among 44,074 adult patients with sepsis admitted to 11 hospitals over 5 years, the proportion with sepsis documentation was 32.2% just before the implementation of SEP-1 in the third quarter of 2015 and increased to 37.3% by the fourth quarter of 2017. Of the 9 post-SEP-1 quarters, 8 had odds ratios for a sepsis diagnosis >1 (overall range: 0.98-1.26; P value for a joint test of statistical significance = 0.005). The effects were clinically modest, with a maximum effect of an absolute increase of 4.2% (95% CI: 0.9-7.8) at the end of the study period. The effect was greater in patients who did not require vasopressors compared with patients who required vasopressors ( P value for test of interaction = 0.02). CONCLUSIONS: SEP-1 implementation was associated with modest increases in sepsis diagnosis rates, primarily among patients who did not require vasoactive medications.

Parent Perceptions of Telemedicine for Acute Pediatric Respiratory Tract Infections: Sequential Mixed Methods Study.

BACKGROUND: Since 2020, parents have had increasing opportunities to use telemedicine for their children, but how parents decide whether to use telemedicine for acute pediatric care relative to alternative sites of care is not clear. One of the most common reasons parents seek acute care for their children is for acute respiratory tract infections (ARTIs). OBJECTIVE: This study aims to examine parental expectations of care via telemedicine for pediatric ARTIs, contrasting expectations of care delivered via primary care telemedicine and direct-to-consumer (DTC) telemedicine. METHODS: We performed a sequential mixed methods analysis to examine how parents assess telemedicine for their children's acute care. We used ARTIs as a case study for examining parent perceptions of telemedicine. First, we analyzed semistructured interviews focused on parent responses about the use of telemedicine. Each factor discussed by parents was coded to reflect whether parents indicated it incentivized or disincentivized their preferences for telemedicine versus in-person care. Results were organized by a 7-dimension framework of parental health care seeking that was generated previously, which included dimensions related to care sites (expected access, affordability, clinical quality, and site quality) and dimensions related to child or family factors (perceived illness severity, perceived child susceptibility, and parent self-efficacy). Second, we analyzed responses to a national survey, which inquired about parental expectations of primary care telemedicine, commercial DTC telemedicine, and 3 in-person sites of care (primary care, urgent care, and emergency department) across 21 factors identified through prior qualitative work. To assess whether parents had different expectations of different telemedicine models, we compared survey responses for primary care telemedicine and commercial DTC telemedicine using weighted logistic regression. RESULTS: Interview participants (n=40) described factors affecting their perceptions of telemedicine as a care modality for pediatric ARTIs. Generally, factors aligned with access and affordability (eg, decreased wait time and lower out-of-pocket cost) were discussed as potential incentives for telemedicine use, while factors aligned with perceived illness severity, child susceptibility, and clinician quality (eg, trustworthiness) were discussed as potential disincentives for telemedicine use. In survey responses (n=1206), primary care and commercial DTC telemedicine were rated similarly on items related to expected accessibility and affordability. In contrast, on items related to expected quality of care, primary care telemedicine was viewed similarly to in-person primary care, while commercial DTC telemedicine was rated lower. For example, 69.7% (weighted; 842/1197) of respondents anticipated their children would be comfortable and cooperative with primary care telemedicine versus 49.7% (weighted; 584/1193) with commercial DTC telemedicine (P<.001). CONCLUSIONS: In a mixed methods analysis focused on telemedicine for ARTIs, parents expressed more concerns about telemedicine quality in commercial DTC models compared with primary care-based telemedicine. These results could help health systems better design telemedicine initiatives to support family-centered care.

Association Between Nurse Copatient Illness Severity and Mortality in the ICU.

OBJECTIVES: In the context of traditional nurse-to-patient ratios, ICU patients are typically paired with one or more copatients, creating interdependencies that may affect clinical outcomes. We aimed to examine the effect of copatient illness severity on ICU mortality. DESIGN: We conducted a retrospective cohort study using electronic health records from a multihospital health system from 2018 to 2020. We identified nurse-to-patient assignments for each 12-hour shift using a validated algorithm. We defined copatient illness severity as whether the index patient's copatient received mechanical ventilation or vasoactive support during the shift. We used proportional hazards regression with time-varying covariates to assess the relationship between copatient illness severity and 28-day ICU mortality. SETTING: Twenty-four ICUs in eight hospitals. PATIENTS: Patients hospitalized in the ICU between January 1, 2018, and August 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main analysis included 20,650 patients and 84,544 patient-shifts. Regression analyses showed a patient's risk of death increased when their copatient received both mechanical ventilation and vasoactive support (hazard ratio [HR]: 1.30; 95% CI, 1.05-1.61; p = 0.02) or vasoactive support alone (HR: 1.82; 95% CI, 1.39-2.38; p < 0.001), compared with situations in which the copatient received neither treatment. However, if the copatient was solely on mechanical ventilation, there was no significant increase in the risk of death (HR: 1.03; 95% CI, 0.86-1.23; p = 0.78). Sensitivity analyses conducted on cohorts with varying numbers of copatients consistently showed an increased risk of death when a copatient received vasoactive support. CONCLUSIONS: Our findings suggest that considering copatient illness severity, alongside the existing practice of considering individual patient conditions, during the nurse-to-patient assignment process may be an opportunity to improve ICU outcomes.

Machine learning-based prediction of low-value care for hospitalized patients.

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period; and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system.

Patient Outcomes After Long-Term Acute Care Hospital Closures.

Importance: Long-term acute care hospitals (LTCHs) are common sites of postacute care for patients recovering from severe respiratory failure requiring mechanical ventilation (MV). However, federal payment reform led to the closure of many LTCHs in the US, and it is unclear how closure of LTCHs may have affected upstream care patterns at short-stay hospitals and overall patient outcomes. Objective: To estimate the association between LTCH closures and short-stay hospital care patterns and patient outcomes. Design, Setting, and Participants: This retrospective, national, matched cohort study used difference-in-differences analysis to compare outcomes at short-stay hospitals reliant on LTCHs that closed during 2012 to 2018 with outcomes at control hospitals. Data were obtained from the Medicare Provider Analysis and Review File, 2011 to 2019. Participants included Medicare fee-for-service beneficiaries aged 66 years and older receiving MV for at least 96 hours in an intensive care unit (ie, patients at-risk for prolonged MV) and the subgroup also receiving a tracheostomy (ie, receiving prolonged MV). Data were analyzed from October 2022 to June 2023. Exposure: Admission to closure-affected hospitals, defined as those discharging at least 60% of patients receiving a tracheostomy to LTCHs that subsequently closed, vs control hospitals. Main Outcomes and Measures: Upstream hospital care pattern outcomes were short-stay hospital do-not-resuscitate orders, palliative care delivery, tracheostomy placement, and discharge disposition. Patient outcomes included hospital length of stay, days alive and institution free within 90 days, spending per days alive within 90 days, and 90-day mortality. Results: Between 2011 and 2019, 99 454 patients receiving MV for at least 96 hours at 1261 hospitals were discharged to 459 LTCHs; 84 LTCHs closed. Difference-in-differences analysis included 8404 patients (mean age, 76.2 [7.2] years; 4419 [52.6%] men) admitted to 45 closure-affected hospitals and 45 matched-control hospitals. LTCH closure was associated with decreased LTCH transfer rates (difference, -5.1 [95% CI -8.2 to -2.0] percentage points) and decreased spending-per-days-alive (difference, -$8701.58 [95% CI, -$13 323.56 to -$4079.60]). In the subgroup of patients receiving a tracheostomy, there was additionally an increase in do-not-resuscitate rates (difference, 10.3 [95% CI, 4.2 to 16.3] percentage points) and transfer to skilled nursing facilities (difference, 10.0 [95% CI, 4.2 to 15.8] percentage points). There was no significant association of closure with 90-day mortality. Conclusions and Relevance: In this cohort study, LTCH closure was associated with changes in discharge patterns in patients receiving mechanical ventilation for at least 96 hours and advanced directive decisions in the subgroup receiving a tracheostomy, without change in mortality. Further studies are needed to understand how LTCH availability may be associated with other important outcomes, including functional outcomes and patient and family satisfaction.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds.

OBJECTIVE: To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions. METHODS: We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices. RESULTS: The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision. CONCLUSION: A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

The Role of Data Science in Closing the Implementation Gap.

Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.

Association Between Hospital Consolidation and Loss of Pediatric Inpatient Services.

This study examines the association between hospital consolidation and loss of pediatric inpatient services.

Intensivist physician-to-patient ratios and mortality in the intensive care unit.

PURPOSE: A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients. METHODS: We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days. RESULTS: The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14). CONCLUSIONS: Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.

Parent Care-Seeking Decisions for Pediatric Acute Respiratory Tract Infections in the United States: A Mental Models Approach.

OBJECTIVE: To understand US parent health care-seeking decisions in the context of multiple in-person and telehealth care options. As the health care landscape evolves, new research is needed to explain how parents now decide when and where to seek acute pediatric health care. METHODS: We applied a mental models approach, focusing on the archetypal example of care-seeking for pediatric acute respiratory tract infections (ARTIs), by first reviewing pediatric ARTI guidelines with 16 health care professionals to inform 40 subsequent semi-structured interviews with parents of young children in 2021. Interviews were qualitatively coded using thematic analysis, with code frequency and co-occurrence informing the final influence model of parent health care-seeking decisions. RESULTS: Parent interviewees identified 33 decisional factors which were synthesized into seven dimensions influencing care-seeking decisions: perceived illness severity, perceived child susceptibility, parental self-efficacy, expected accessibility of care, expected affordability of care, expected quality of clinician, and expected quality of site. The first three dimensions (perceived severity, perceived susceptibility, parental self-efficacy) influenced an initial decision about whether to seek care, while all seven factors influenced a subsequent decision about where to seek care (eg, in-person primary care, primary care-based telehealth, urgent care, direct-to-consumer telehealth). Uncertainty was present within many dimensions (eg, severity, access, quality) indicating potential targets to support parent decision-making processes and optimize care-seeking behaviors. CONCLUSIONS: A mental models approach identified dimensions influencing parent choice to seek care and choice of care site for children with ARTIs, suggesting targets to advance family-centered practice and policy.

Measuring Performance on the ABCDEF Bundle During Interprofessional Rounds via a Nurse-Based Assessment Tool.

BACKGROUND: Nurse-led rounding checklists are a common strategy for facilitating evidence-based practice in the intensive care unit (ICU). To streamline checklist workflow, some ICUs have the nurse or another individual listen to the conversation and customize the checklist for each patient. Such customizations assume that individuals can reliably assess whether checklist items have been addressed. OBJECTIVE: To evaluate whether 1 critical care nurse can reliably assess checklist items on rounds. METHODS: Two nurses performed in-person observation of multidisciplinary ICU rounds. Using a standardized paper-based assessment tool, each nurse indicated whether 17 items related to the ABCDEF bundle were discussed during rounds. For each item, generalizability coefficients were used as a measure of reliability, with a single-rater value of 0.70 or greater considered sufficient to support its assessment by 1 nurse. RESULTS: The nurse observers assessed 118 patient discussions across 15 observation days. For 11 of 17 items (65%), the generalizability coefficient for a single rater met or exceeded the 0.70 threshold. The generalizability coefficients (95% CIs) of a single rater for key items were as follows: pain, 0.86 (0.74-0.97); delirium score, 0.74 (0.64-0.83); agitation score, 0.72 (0.33-1.00); spontaneous awakening trial, 0.67 (0.49-0.83); spontaneous breathing trial, 0.80 (0.70-0.89); mobility, 0.79 (0.69-0.87); and family (future/past) engagement, 0.82 (0.73-0.90). CONCLUSION: Using a paper-based assessment tool, a single trained critical care nurse can reliably assess the discussion of elements of the ABCDEF bundle during multidisciplinary rounds.

The political polarization of COVID-19 treatments among physicians and laypeople in the United States.

In the United States, liberals and conservatives disagree about facts. To what extent does expertise attenuate these disagreements? To study this question, we compare the polarization of beliefs about COVID-19 treatments among laypeople and critical care physicians. We find that political ideology predicts both groups' beliefs about a range of COVID-19 treatments. These associations persist after controlling for a rich set of covariates, including local politics. We study two potential explanations: a) that partisans are exposed to different information and b) that they interpret the same information in different ways, finding evidence for both. Polarization is driven by preferences for partisan cable news but not by exposure to scientific research. Using a set of embedded experiments, we demonstrate that partisans perceive scientific evidence differently when it pertains to a politicized treatment (ivermectin), relative to when the treatment is not identified. These results highlight the extent to which political ideology is increasingly relevant for understanding beliefs, even among expert decision makers such as physicians.

Patient and Family Factors Associated with Use of Telemedicine Visits for Pediatric Acute Respiratory Tract Infections, 2018-2019.

Background: Pediatric acute respiratory tract infections (ARTIs) were a common reason for commercial direct-to-consumer (DTC) telemedicine use before the COVID-19 pandemic, but the factors associated with this use are unknown. Objective: To identify child and family factors associated with use of commercial DTC telemedicine for ARTIs in 2018-2019. Methods: We performed a retrospective cohort analysis of claims data from the Optum Clinformatics® Data Mart Database. Among children with ARTI visits, we fitted logit models to examine child and family characteristics associated with DTC telemedicine use. Results: Of 660,725 children with ARTI visits, 12,944 (2.0%) had ≥1 commercial DTC telemedicine encounter. The odds of DTC telemedicine use were higher for children with age ≥12 years, lower parent educational attainment, higher household income, white non-Hispanic race/ethnicity, and residency in the West South Central census division. Conclusion: In 2018-2019, commercial DTC telemedicine use varied with child age, child race/ethnicity parent educational attainment, household income, and geography.

The Use of Electronic Health Record Metadata to Identify Nurse-Patient Assignments in the Intensive Care Unit: Algorithm Development and Validation.

BACKGROUND: Nursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse's unique skills are matched to each patient's needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale. OBJECTIVE: This study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU. METHODS: We used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts. RESULTS: The analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions. CONCLUSIONS: Metadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Characterizing intensive care unit rounding teams using meta-data from the electronic health record.

PURPOSE: Teamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams. METHODS: We analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization. RESULTS: We identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0-706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0-99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07-0.23), degree centralization (0.50, 0.35-0.65) and closeness centralization (0.45, 0.11-0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics. CONCLUSIONS: EHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.

Helicopter versus ground ambulance transport for interfacility transfer of critically ill children.

BACKGROUND: Following initial stabilization, critically ill children often require transfer to a specialized pediatric hospital. While the use of specialized pediatric transport teams has been associated with improved outcomes for these patients, the additional influence of transfer mode (helicopter or ground ambulance) on clinical outcomes remains unknown. METHODS: We investigated the association between transport mode and outcomes among critically ill children transferred to a single pediatric hospital via a specialized pediatric transport team. We designed a retrospective cohort study to reduce indication bias by limiting analysis to patients for whom a helicopter transport was initially requested. We compared outcomes for those who ultimately traveled via helicopter, and for those who ultimately traveled via ground ambulance due to non-clinical factors. RESULTS: We compared transport times, in-hospital mortality, and hospital length of stay by transport mode. Transport time in minutes was shorter for helicopter transports (median = 143, interquartile range [IQR]: 118-184) compared to ground ambulance transports (median = 289, IQR: 213-258; difference in medians = 146, 95% CI: 12 to 168, p < 0.001). In unadjusted analysis, helicopter transport was not associated with a difference in in-hospital mortality (helicopter = 6.0%, ground ambulance = 7.0%; 95% CI for difference: -6.6% to 3.3%; p = 0.64) but was associated with a statistically significant reduction in median hospital days (helicopter = 4, ground ambulance = 5; 95% CI -3 to 0; p = 0.04). In adjusted analyses, there were no statistically significant associations. These results were consistent across sensitivity analyses. CONCLUSIONS: Among critically ill pediatric patients without traumatic injuries transported by a specialty team, those patients who would have been transferred by helicopter if available but were instead transferred by ground ambulance reached their site of definitive care approximately 2.5 h later. Helicopter transport for these patients was not associated with in-hospital mortality, but was potentially associated with reduced hospital length of stay.

Adoption and Deadoption of Medications to Treat Hospitalized Patients With COVID-19.

OBJECTIVES: The COVID-19 pandemic was characterized by rapidly evolving evidence regarding the efficacy of different therapies, as well as rapidly evolving health policies in response to that evidence. Data on adoption and deadoption are essential as we learn from this pandemic and prepare for future public health emergencies. DESIGN: We conducted an observational cohort study in which we determined patterns in the use of multiple medications to treat COVID-19: remdesivir, hydroxychloroquine, IV corticosteroids, tocilizumab, heparin-based anticoagulants, and ivermectin. We analyzed changes both overall and within subgroups of critically ill versus Noncritically ill patients. SETTING: Data from Optum's deidentified Claims-Clinical Dataset, which contains multicenter electronic health record data from U.S. hospitals. PATIENTS: Adults hospitalized with COVID-19 from January 2020 to June 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 141,533 eligible patients, 34,515 (24.4%) required admission to an ICU, 14,754 (10.4%) required mechanical ventilation, and 18,998 (13.4%) died during their hospitalization. Averaged over the entire time period, corticosteroid use was most common (47.0%), followed by remdesivir (33.2%), anticoagulants (19.3%), hydroxychloroquine (7.3%), and tocilizumab (3.4%). Usage patterns varied substantially across treatments. For example, hydroxychloroquine use peaked in March 2020 and leveled off to near zero by June 2020, whereas the use of remdesivir, corticosteroids, and tocilizumab all increased following press releases announcing positive results of large international trials. Ivermectin use increased slightly over the study period but was extremely rare overall (0.4%). CONCLUSIONS: During the COVID-19 pandemic, medication treatment patterns evolved reliably in response to emerging evidence and changes in policy. These findings may inform efforts to promote optimal adoption and deadoption of treatments for acute care conditions.