Search

OBJECTIVE: To assess the association between the onset of the COVID-19 pandemic and change in low-value cancer services. STUDY DESIGN: In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March 31, 2021. METHODS: We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non--guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. RESULTS: Among 117,116 members (median [IQR] age, 60 [53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862 (27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95% CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non--guideline-based antiemetics, and 2.71 (95% CI, -0.59 to 6.02) for aggressive end-of-life care. CONCLUSIONS: Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To describe trends in US health care spending in a large, national, and commercially insured population during the COVID-19 pandemic. STUDY DESIGN: Cross-sectional study of commercially insured members enrolled between May 1, 2018, and December 31, 2021. METHODS: The study utilized a population-based sample of continuously enrolled members in a geographically diverse federation of Blue Cross Blue Shield plans across the United States. Our sample excluded Medicare and Medicare Advantage beneficiaries. The COVID-19 exposure period was defined as 2020-2021; 2018-2019 were pre--COVID-19 years. We defined 4 post--COVID-19 periods: March 1 to April 30, 2020; May 1 to December 31, 2020; January 1 to March 31, 2021; and April 1 to December 31, 2021. The primary outcome was inflation-adjusted overall per-member per-month (PMPM) medical spending adjusted for age, sex, Elixhauser comorbidities, area-level racial composition, income, and education. RESULTS: Our sample included 97,319,130 individuals. Mean PMPM medical spending decreased from $370.92 in January-February 2020 to $281.00 in March-April 2020. Between May and December 2020, mean PMPM medical spending recovered to--but did not exceed--prepandemic levels. Mean PMPM medical spending stayed below prepandemic levels between January and March 2021, rose above prepandemic baselines between April and June 2021, and decreased below baseline between July and December 2021. CONCLUSIONS: The COVID-19 pandemic induced a spending shock in 2020, and health care spending did not recover to near baseline until mid-2021, with some emerging evidence of pent-up demand. The observed spending below baseline through the end of 2021 will pose challenges to setting spending benchmarks for alternative payment and shared savings models. [ABSTRACT FROM AUTHOR]

The Oncology Care Model (OCM), launched in 2016 by the Centers for Medicare and Medicaid Services, was the first demonstration of value-based payment in oncology. Although the OCM delivered mixed results in terms of quality of care and total episode costs, the model had no statistically significant impact on remediating racial, ethnic, and socioeconomic disparities among beneficiaries. These deficits have been prominent in other aspects of US healthcare, and as a result, the Institute for Healthcare Improvement has advocated for stakeholders to leverage improvement science, an applied science that focuses on implementing rapid cycles for change, to identify and overcome barriers to health equity. With the announcement of the new Enhancing Oncology Model, a continuation of the OCM's efforts in introducing value to cancer care for episodes surrounding chemotherapy administration, both policymakers and providers must apply tenets of improvement science and make eliminating disparities in alternative payment models a forefront objective. In this commentary, we discuss previous inequities in alternative payment models, the role that improvement science plays in addressing health-care disparities, and steps that stakeholders can take to maximize equitable outcomes in the Enhancing Oncology Model. [ABSTRACT FROM AUTHOR]

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period. [ABSTRACT FROM AUTHOR]

Background The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic's impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. Methods We used an electronic health record–derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. Results The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = −2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = −2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age. Conclusions Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers. [ABSTRACT FROM AUTHOR]

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024.

Importance: Serious illness conversations (SICs) that elicit patients' values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion., Objective: To test the independent and combined effects of clinician and patient nudges on SIC completion., Design, Setting, and Participants: A 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality)., Interventions: Clinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges., Main Outcomes and Measures: The primary outcome was a documented SIC in the electronic health record within 6 months of a participant's first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level., Results: The study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not., Conclusions and Relevance: In this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care., Trial Registration: ClinicalTrials.gov Identifier: NCT04867850.