Europe’s economy is “geared towards a world that is gradually disappearing”, according to a warning from Christine Lagarde that the EU needs reforms to spur growth.
The president of the European Central Bank (ECB) said the EU’s dependence on international trade had left it vulnerable, as major partners had turned away from the trade that made the bloc’s exporters wealthy.
Donald Trump has led a global turn towards protectionism and against globalisation, with steep tariffs imposed on almost every trading partner. At the same time, China has used its dominance of production of certain critical materials and products to exert pressure.
Lagarde argued that Europe was vulnerable because of a “dependency on third countries for our security and the supply of critical raw materials”. She cited China’s control of the supply of rare earth metals that are crucial in electric motors and wind turbines, as well as the “choke point” of power chips made by Nexperia in China that threatened to shut down production across the global car industry.
Speaking at the European Banking Congress in Frankfurt, Germany, Lagarde said Europe had failed to address its own problems. Policymakers had instead allowed its weaknesses to “erode growth quietly, as each new shock nudges us onto a slightly lower trajectory”.
“Our internal market has stood still, especially in the areas that will shape future growth, like digital technology and artificial intelligence, as well as the areas that will finance it, such as capital markets,” she said.
Europe also faced a “vicious circle” of its own savers allocating money to US stocks, helping the American economy to advance faster than the EU and leaving “stagnating productivity at home and growing dependence on others”, she said.
Lagarde did highlight some European strengths as well, including a resilient labour market, increasing digital investment, and government spending, particularly on defence in response to Russia’s invasion of Ukraine, that has counteracted economic slowdown.
Part of Lagarde’s prescription for recovery was lowering barriers to services and goods trade between EU countries. Those barriers are equivalent to a 100% tariff on services and 65% on goods, according to ECB analysis. Lowering those barriers to the same level as the Netherlands – a relatively open economy – would fully make up for the hit from US tariffs, she said.
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She called for mutual recognition of regulated companies, allowing them to sell across Europe when authorised by any one country. She also said the EU should adopt qualified majority voting on tax, preventing any single member state from vetoing changes.
She argued that benefits would include allowing the harmonisation of VAT, making it easier for smaller European companies to gain access to the whole EU market without having to comply with 27 different tax regimes.
Leading edge artificial intelligence and machine learning platform supports enhanced salesforce targeting and engagement for cutaneous T-cell lymphoma immunotherapy launch
CRANFORD, N.J., Nov. 21, 2025 /PRNewswire/ — Citius Oncology, Inc. (“Citius Oncology”) (Nasdaq: CTOR), the oncology-focused subsidiary of Citius Pharmaceuticals, Inc. (“Citius Pharma”) (Nasdaq: CTXR), a late-stage biopharmaceutical company developing and commercializing first-in-class critical care products, today announced a deeper collaboration with Verix, a leader in AI-powered commercial optimization technology for the life sciences sector. Citius Oncology’s commercial team intends to further leverage Verix’s innovative Tovana platform to support the anticipated fourth quarter 2025 U.S. commercialization of LYMPHIR™ (denileukin diftitox-cxdl), a novel immunotherapy approved by the U.S. Food and Drug Administration (FDA) for the treatment of adult patients with relapsed or refractory Stage I-III cutaneous T-cell lymphoma (CTCL) after at least one prior systemic therapy.
Verix’s Tovana platform integrates advanced analytics, real-world claims data, and machine learning to help inform Citius Oncology’s commercial strategy and enable real-time field execution. The platform refines targeting over time and supports data-driven decisions, allowing the Citius Oncology commercial team to embed predictive intelligence into sales and marketing plans, and to prioritize engagement with high-impact healthcare providers (HCPs). Using company-defined criteria, the system identifies patterns in treatment and diagnosis, enabling Citius Oncology’s commercial team to promptly engage prescribers whose patients may benefit from LYMPHIR.
“We are committed to leveraging leading-edge technologies to maximize the commercial impact of LYMPHIR and look forward to using this innovative AI platform to amplify the precision and impact of our experienced commercial and marketing teams,” stated Leonard Mazur, Chairman and CEO of Citius Oncology and Citius Pharma. “Through our collaboration with Verix, we are able to enhance our salesforce’s experience with the ability to identify key treatment patterns, personalize provider engagement, and allocate commercial resources efficiently. With LYMPHIR poised to become a meaningful new option for patients with relapsed or refractory CTCL, it’s critical that our commercial organization has precision tools that accelerate physician engagement and product uptake. This data-driven approach positions us to execute a focused and impactful launch while maintaining a lean infrastructure,” added Mazur.
“We are proud to partner with Citius Oncology at this critical stage in their commercialization journey,” said Doron Aspitz, CEO of Verix. “The Tovana platform is designed to empower life sciences commercial organizations to move from retrospective analysis to real-time action. With Citius Oncology, we see a strong alignment in using AI to bridge data with execution, and we are excited to help deliver meaningful insights that can accelerate the reach of an innovative therapy like LYMPHIR.”
This initiative builds upon foundational launch activities including the establishment of distribution partnerships, permanent reimbursement codes (J-code: J9161), and inclusion in the National Comprehensive Cancer Network (NCCN) Guidelines. Commercial availability of LYMPHIR in the U.S. is expected in the fourth quarter of 2025.
Citius Oncology believes that integrating Verix’s AI capabilities will not only enable faster market penetration but also deliver durable advantages as the company builds its oncology franchise. This initiative reflects Citius Oncology’s disciplined focus on capital efficiency, executional excellence, and sustainable value creation for shareholders. Most importantly, it reflects the company’s commitment to facilitating access to care for patients living with cutaneous T-cell lymphoma.
About LYMPHIR™ (denileukin diftitox-cxdl)
LYMPHIR is a targeted immune therapy for relapsed or refractory cutaneous T-cell lymphoma (CTCL) indicated for use in Stage I-III disease after at least one prior systemic therapy. It is a recombinant fusion protein that combines the IL-2 receptor binding domain with diphtheria toxin (DT) fragments. The agent specifically binds to IL-2 receptors on the cell surface, causing diphtheria toxin fragments that have entered cells to inhibit protein synthesis. After uptake into the cell, the DT fragment is cleaved and the free DT fragments inhibit protein synthesis, resulting in cell death. Denileukin diftitox-cxdl demonstrated the ability to deplete immunosuppressive regulatory T lymphocytes (Tregs) and antitumor activity through a direct cytocidal action on IL-2R-expressing tumors.
In 2021, denileukin diftitox received regulatory approval in Japan for the treatment of relapsed or refractory CTCL and peripheral T-cell lymphoma (PTCL). Subsequently, in 2021, Citius acquired an exclusive license with rights to develop and commercialize denileukin diftitox in all markets except for India, Japan and certain parts of Asia. LYMPHIR (denileukin diftitox-cxdl) was approved by the FDA in August 2024.
About Cutaneous T-cell Lymphoma
Cutaneous T-cell lymphoma is a type of cutaneous non-Hodgkin lymphoma (NHL) that comes in a variety of forms and is the most common type of cutaneous lymphoma. In CTCL, T-cells, a type of lymphocyte that plays a role in the immune system, become cancerous and develop into skin lesions, leading to a decrease in the quality of life of patients with this disease due to severe pain and pruritus. Mycosis Fungoides (MF) and Sézary Syndrome (SS) comprise the majority of CTCL cases. Depending on the type of CTCL, the disease may progress slowly and can take anywhere from several years to upwards of ten to potentially reach tumor stage. However, once the disease reaches this stage, the cancer is highly malignant and can spread to the lymph nodes and internal organs, resulting in a poor prognosis. Given the duration of the disease, patients typically cycle through multiple agents to control disease progression. CTCL affects men twice as often as women and is typically first diagnosed in patients between the ages of 50 and 60 years of age. Other than allogeneic stem cell transplantation, for which only a small fraction of patients qualify, there is currently no curative therapy for advanced CTCL.
INDICATION
LYMPHIR is an IL2-receptor-directed cytotoxin indicated for the treatment of adult patients with r/r Stage I-III cutaneous T-cell lymphoma (CTCL) after at least one prior systemic therapy.
IMPORTANT SAFETY INFORMATION
BOXED WARNING: CAPILLARY LEAK SYNDROME
Capillary leak syndrome (CLS), including life-threatening or fatal reactions, can occur in patients receiving LYMPHIR. Monitor patients for signs and symptoms of CLS during treatment. Withhold LYMPHIR until CLS resolves, or permanently discontinue based on severity.
WARNINGS AND PRECAUTIONS
Capillary Leak Syndrome
LYMPHIR can cause capillary leak syndrome (CLS), including life-threatening or fatal reactions. CLS was defined in the clinical trials as the occurrence of at least 2 of the following symptoms at any time during LYMPHIR therapy: hypotension, edema, and serum albumin <3 g/dL. These symptoms were not required to occur simultaneously to be characterized as capillary leak syndrome.
As defined, CLS occurred in 27% of patients in the pooled population across 3 clinical trials, including 8% with Grade 3. There was one (0.8%) fatal occurrence of CLS. Of the patients with CLS, 22% had recurrence. The majority of CLS events (81%) occurred within the first 2 cycles of treatment. The median time to onset from Cycle 1, Day 1 was 6.5 days (range: 1 to 77), the median duration of CLS was 14 days (range: 2 to 40), and 75% of patients had resolution. The most common symptoms included edema, hypoalbuminemia, and hypotension. Pleural effusion, pericardial effusion, and dehydration also occurred.
Regularly assess patients for weight gain, new onset or worsening of edema, dyspnea, and hypotension (including orthostatic changes). Monitor serum albumin levels prior to the initiation of each cycle of therapy and more often as clinically indicated.
Withhold, reduce dose, or permanently discontinue based on severity. If LYMPHIR is withheld, resume LYMPHIR following resolution of CLS and when serum albumin is greater than or equal to 3 g/dL.
Visual Impairment
LYMPHIR can cause serious visual impairment, including changes in visual acuity and color vision. In the pooled population across 3 clinical trials, visual impairment occurred in 9%, with Grade 1 in 8% and Grade 2 in 1%. The most commonly reported symptom was blurred vision. Of the patients with visual impairment, 67% had resolution of their visual impairment.
Perform baseline ophthalmic examination and monitor as clinically indicated. If patients experience symptoms of visual impairment, such as changes in visual acuity, changes in color vision, or blurred vision, refer for ophthalmologic evaluation.
Withhold LYMPHIR until visual impairment resolves or permanently discontinue based on severity.
Infusion-Related Reactions
LYMPHIR can cause serious infusion-related reactions. Infusion-related reactions were reported in 69% of patients in the pooled population across 3 clinical trials of patients who received LYMPHIR, with Grade 3 infusion-related reactions in 3.4%. Eighty-three percent of infusion-related reactions occurred in Cycles 1 and 2. The most common symptoms included nausea, fatigue, chills, musculoskeletal pain, vomiting, fever, and arthralgia.
Premedicate patients for the first three cycles prior to starting a LYMPHIR infusion. Monitor patients frequently during infusion. For Grade 2 or higher infusion reactions, premedicate at least 30 minutes prior to each subsequent infusion with a systemic steroid for at least 3 cycles.
Interrupt or discontinue LYMPHIR based on severity. Institute appropriate medical management.
Hepatotoxicity
LYMPHIR can cause hepatotoxicity. In the pooled safety population, elevated ALT occurred in 70% of patients, with Grade 3 ALT occurring in 22%; elevated AST occurred in 64% of patients, with Grade 3 AST elevation occurring in 9%. For Grade 3 events, median time to onset was 8 days (range: 1 to 15 days); median time to resolution was 15 days (range: 7 to 50 days); all cases of Grade 3 ALT or AST elevations resolved. Elevated total bilirubin occurred in 5% of patients, with Grade 3 occurring in 0.9%.
Monitor liver enzymes and bilirubin at baseline and during treatment as clinically indicated. Withhold, reduce dose, or permanently discontinue LYMPHIR based on severity.
Embryo-Fetal Toxicity
Based on its mechanism of action, LYMPHIR can cause fetal harm when administered to a pregnant woman. Verify the pregnancy status of females of reproductive potential prior to the initiation of LYMPHIR. Advise pregnant women of the potential risk to the fetus. Advise females of reproductive potential to use effective contraception during treatment and for 7 days following the last dose of LYMPHIR.
ADVERSE REACTIONS
The most common adverse reactions (≥20%), including laboratory abnormalities, are increased transaminases, albumin decreased, nausea, edema, hemoglobin decreased, fatigue, musculoskeletal pain, rash, chills, constipation, pyrexia, and capillary leak syndrome.
USE IN SPECIFIC POPULATIONS
Pregnancy
Risk Summary Based on its mechanism of action, LYMPHIR can cause fetal harm when administered to a pregnant woman. There are no available data on the use of LYMPHIR in pregnant women to evaluate for a drug-associated risk. No animal reproductive and developmental toxicity studies have been conducted with denileukin diftitox.
Denileukin diftitox-cxdl causes depletion of regulatory T lymphocytes (Treg), immune activation, and capillary leak syndrome, compromising pregnancy maintenance. Advise pregnant women of the potential risk to a fetus.
In the U.S. general population, the estimated background risk of major birth defects and miscarriage in clinically recognized pregnancies are 2-4% and 15-20%, respectively.
Lactation
Risk Summary No data are available regarding the presence of denileukin diftitox-cxdl in human milk, the effects on the breastfed child, or on milk production. Because of the potential for serious adverse reactions in breastfed children, advise women not to breastfeed during treatment with LYMPHIR and for 7 days after the last dose.
Females and Males of Reproductive Potential
Based on its mechanism of action, LYMPHIR can cause fetal harm when administered to a pregnant woman.
Pregnancy Testing Verify the pregnancy status of females of reproductive potential prior to initiating LYMPHIR.
Contraception
Females Advise females of reproductive potential to use effective contraception during treatment with LYMPHIR and for 7 days after the last dose.
Infertility
Males Based on findings in rats, male fertility may be compromised by treatment with LYMPHIR. The reversibility of the effect on fertility is unknown.
Pediatric Use Safety and effectiveness of LYMPHIR in pediatric patients have not been established.
Geriatric Use Of the 69 patients with Stage I-III r/r CTCL who received LYMPHIR, 34 patients (49%) were 65 years of age and older and 10 patients (14%) were 75 years of age and older. Clinical studies of LYMPHIR did not include sufficient numbers of patients 65 years of age and older to determine whether they respond differently from younger adult patients.
You may report side effects to the FDA at 1-800-FDA-1088 or www.fda.gov/medwatch. You may also report side effects to Citius Oncology at 1-844-459-6744.
Please read Important Safety Information and full Prescribing Information, including Boxed WARNING, for LYMPHIR.
About Verix
Verix is a leading provider of advanced commercial optimization solutions for the life sciences industry. Its flagship product, Tovana™, is a patented, AI-powered SaaS platform that enables life science companies to optimize commercial strategy and execution. Tovana integrates cutting-edge technology, advanced machine learning and deep domain expertise into a single, intuitive platform, to transform how life science companies deploy data-driven strategies. The platform analyzes large volumes of data, enabling companies to continuously and consistently automate key commercial functions such as healthcare provider (HCP) targeting, precision forecasting, omnichannel engagement, and patient identification.
Trusted by Fortune 500 pharmaceutical companies around the world, Verix’s innovative Tovana platform helps teams turn complex data into clear, actionable insights that drive brand performance. Tovana’s agile and easy-to-use platform accelerates decision-making, improves launch execution, and enhances commercial outcomes. For more information, visit www.verix.com.
About Citius Oncology, Inc.
Citius Oncology, Inc. (Nasdaq: CTOR) is a platform to develop and commercialize novel targeted oncology therapies. In August 2024, its primary asset, LYMPHIR, was approved by the FDA for the treatment of adults with relapsed or refractory Stage I–III CTCL who had had at least one prior systemic therapy. Management estimates the initial market for LYMPHIR currently exceeds $400 million, is growing, and is underserved by existing therapies. Robust intellectual property protections that span orphan drug designation, complex technology, trade secrets and pending patents for immuno-oncology use as a combination therapy with checkpoint inhibitors would further support Citius Oncology’s competitive positioning. For more information, please visit www.citiusonc.com.
About Citius Pharmaceuticals, Inc.
Citius Pharmaceuticals, Inc. (Nasdaq: CTXR) is a biopharmaceutical company dedicated to the development and commercialization of first-in-class critical care products. In August 2024, the FDA approved LYMPHIR, a targeted immunotherapy for an initial indication in the treatment of adults with relapsed or refractory Stage I–III CTCL who had had at least one prior systemic therapy. Citius Pharma’s late-stage pipeline also includes Mino-Lok®, a catheter lock solution to salvage catheters in patients with catheter-related bloodstream infections, and CITI-002 (Halo-Lido), a topical formulation for the relief of hemorrhoids. A pivotal Phase 3 trial for Mino-Lok and a Phase 2b trial for Halo-Lido were completed in 2023. Mino-Lok met primary and secondary endpoints of its Phase 3 trial. Citius is actively engaged with the FDA to outline next steps for both programs. Citius Pharmaceuticals owns 79% of Citius Oncology. For more information, please visit www.citiuspharma.com.
Forward-Looking Statements
This press release may contain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. Such statements are made based on our expectations and beliefs concerning future events impacting Citius Pharma or Citius Oncology. You can identify these statements by the fact that they use words such as “will,” “anticipate,” “estimate,” “expect,” “plan,” “should,” and “may” and other words and terms of similar meaning or use of future dates. Forward-looking statements are based on management’s current expectations and are subject to risks and uncertainties that could negatively affect our business, operating results, financial condition and stock price. Factors that could cause actual results to differ materially from those currently anticipated, and, unless noted otherwise, that apply to Citius Pharma and Citius Oncology, are: our need for substantial additional funds and our ability to raise additional money to fund our operations for at least the next 12 months as a going concern; our ability to commercialize LYMPHIR and any of our other product candidates that may be approved by the FDA; our ability to use the latest technology to support our commercialization efforts; our ability to maintain Nasdaq’s continued listing standards; our ability to successfully implement and maintain distribution agreements with current or other future distribution partners; potential disruptions or performance issues involving third-party logistics providers; the estimated markets for our product candidates and the acceptance thereof by any market; the ability of our product candidates to impact the quality of life of our target patient populations; risks relating to the results of research and development activities, including those from our existing and any new pipeline assets; our dependence on third-party suppliers; our ability to procure cGMP commercial-scale supply; our ability to obtain, perform under and maintain financing and strategic agreements and relationships; uncertainties relating to preclinical and clinical testing; the early stage of products under development; market and other conditions; risks related to our growth strategy; patent and intellectual property matters; our ability to identify, acquire, close and integrate product candidates and companies successfully and on a timely basis; government regulation; competition; as well as other risks described in our Securities and Exchange Commission (“SEC”) filings. These risks have been and may be further impacted by any future public health risks. Accordingly, these forward-looking statements do not constitute guarantees of future performance, and you are cautioned not to place undue reliance on these forward-looking statements. Risks regarding our business are described in detail in our SEC filings which are available on the SEC’s website at www.sec.gov, including in Citius Oncology’s and Citius Pharma’s Annual Reports on Forms 10-K for the year ended September 30, 2024, filed with the SEC on December 27, 2024, each as amended on January 27, 2025, as updated by our subsequent filings with the SEC. These forward-looking statements speak only as of the date hereof, and we expressly disclaim any obligation or undertaking to release publicly any updates or revisions to any forward-looking statements contained herein to reflect any change in our expectations or any changes in events, conditions or circumstances on which any such statement is based, except as required by law.
By 2031, health care expenditures in the United States are expected to approach 20% of the gross domestic product, surpassing other high-income nations with comparable clinical resources []. A significant driver of these costs is the fee-for-service payment model, which incentivizes the use of costly health care resources and services, even though they may not improve patient outcomes or quality of life. The Medicare Access and Children’s Health Insurance Program Reauthorization Act of 2015 marked a pivotal transition from a volume-oriented to value-based payment model for health care services [-]. Concurrently, the Centers for Medicare and Medicaid Services have prioritized development of patient-centered measures, incorporating patient-reported outcomes (PROs), such as the Patient-Reported Outcomes Measurement Information System (PROMIS), into quality evaluation and pay-for-performance programs [].
These changes are important because patients and clinicians increasingly grapple with rising health care costs while striving to maintain high-quality care. The burden of health care expenses disproportionately affects patients with chronic, complex diseases such as advanced cancer and chronic kidney disease (CKD) [-]. These patients often experience distressing symptoms like fatigue, pain, anxiety, and depression [-]. These symptoms frequently go unnoticed during routine visits, leading to unmanaged symptoms and a greater likelihood of potentially avoidable health care resource use [-]. Financial strain has also been linked to increased patient debt and bankruptcy [-].
Given this financial strain, patients may be more willing to engage in shared decision-making (SDM) with clinicians, collaboratively weighing the benefits and risks of treatment options to align health care decisions with their preferences and values [-]. The growing engagement with digital health care presents an opportunity for innovative health IT solutions, such as PRO-based clinical dashboards that provide decision-making information in a format that benefits both patients and clinicians. These dashboards track clinical and health outcome trends over time, potentially reducing the risk of unplanned or low-benefit health services use by enabling early intervention in symptom management and fostering SDM discussions about bothersome side effects and treatment alternatives [,,]. Prior studies have suggested that early symptom management may reduce acute care use and improve quality of life [,,]. We hypothesized that PRO-based clinical dashboards could empower patients with complex diseases to visualize the relationship between interventions and outcomes, thereby reducing unnecessary spending while delivering high-value care more effectively [].
Although PROs and SDM have been shown to be effective in symptom monitoring for patients with cancer and CKD, their influence on reducing the use of potentially unnecessary health services of limited benefit is less understood [,,,,]. Our previous work showed that a PRO-based dashboard enhanced SDM and reduced patient anxiety [], yet the specific role of SDM in reducing health care use remains unclear. While some studies suggest that SDM tools may reduce health care use, a Cochrane review [,] found mixed results, with inconsistent effects on use, outcomes, and costs, and no consistent reduction in invasive or expensive treatments [].
In this study, we focus on the potential for PRO-based dashboards to influence the use of potentially unnecessary, expensive, and low-benefit health care services, improving SDM, enhancing symptom management, and engaging patients in care optimization. We hypothesize that patients using the dashboard will show reduced use of these services compared to a matched cohort not exposed to the dashboard [].
Methods
Dashboard Design, Content, and Integration With Clinical Workflow
The development and integration of the dashboard, including its co-design process, visual elements, included assessments, and the ways physicians used it, have been comprehensively described elsewhere [,]. Briefly, the dashboard was co-designed with the collective effort of 20 diverse stakeholders, including patients, clinicians, care partners, investigators, and health IT professionals. The goal was to support symptom management and facilitate SDM during health care visits for patients with advanced cancer or CKD. Integrated into Northwestern Medicine’s electronic health record system, the dashboard displays PROs along with other clinical data. Clinicians were encouraged to use the dashboard, updated in real time, during clinical encounters with patients who met the study’s inclusion criteria.
Three days before a scheduled visit, patients were prompted to complete a PRO questionnaire (), which assessed symptoms and supportive care needs. The questionnaire included PROMIS measures to assess anxiety, depression, pain, fatigue, and physical functioning. In addition, patients responded to five open-ended questions in the “symptoms and goals” section, which populated the dashboard. These questions focused on (1) top concerns for discussion, (2) the most troubling side effects, (3) overall goals for their cancer or CKD treatment, (4) personal goals and values, and (5) potential ways to collaborate with their care team to achieve these goals. Patient responses were automatically scored, recorded in the electronic health record, and generated alerts to their care team if any clinically significant symptoms or needs were identified. Clinicians then used this information, along with the patient’s clinical data from the dashboard, to facilitate SDM and improve communication between patients and care teams.
Study Design and Location
We conducted a propensity score–weighted, difference-in-differences (DiD) analysis [] to determine the association between dashboard use and high-cost health services use. The study protocol has previously been published []. This manuscript adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cohort studies.
Ethical Considerations
All study activities were reviewed and approved by the Northwestern University Institutional Review Board (protocols STU00210091, STU00211654, and STU00212634). Health services use data were extracted from the Northwestern Medicine Enterprise Data Warehouse by trained Northwestern Medicine data analysts and entered into the study’s REDCap database by the study coordinator (AC), as approved by the ethics committee. All study procedures were considered low risk by the Northwestern University Institutional Review Board, and the ethics review concluded that the benefits outweighed any minimal risks. Participants provided informed consent to complete a follow-up survey at 3 and 6 months. Participants did not receive any incentives for enrollment or survey completion.
All data were aggregated by an Enterprise Data Warehouse (EDW) analyst prior to analysis, and no protected health information (PHI) was visible to the research team. Limited variables—such as medical record number (MRN), race/ethnicity, and age—were retained solely to enable patient-level linkage and inclusion as covariates in multivariable analyses. All data were stored on secure, access-controlled institutional servers, and no identifiers were shared outside the research environment. All study procedures adhered to institutional privacy and confidentiality standards and complied with the Health Insurance Portability and Accountability Act (HIPAA) regulations.
Participants and Eligibility Criteria
The intervention group consisted of Northwestern Medicine patients in Chicago, Illinois, diagnosed with advanced cancer or CKD between June 2020 and January 2022 who had previously received care from at least one clinician participating in the study (). These clinicians at Northwestern Memorial Health Care included 2 oncologists, a nephrologist, a nephrology physician assistant, and 2 primary care physicians. Patients also consented to follow-up surveys at 3- and 6-month intervals. Patients with advanced cancer were defined as having either stage IV gastrointestinal cancer receiving intravenous chemotherapy for at least 3 months or stage III or IV lung cancer undergoing first- or second-line chemotherapy for at least 3 months. Patients with CKD required a confirmed diagnosis of at least stage III CKD or an estimated glomerular filtration rate (eGFR) below 60. For the intervention group, the baseline date was defined individually as the date on which each participant completed the initial dashboard questionnaire from June 8, 2020, to November 1, 2022. For the comparison group, we identified patients with advanced cancer and CKD not exposed to the dashboard. The inclusion criteria were patients who received care from Northwestern Medicine clinicians who participated in the dashboard study but chose not to enroll in it or patients who received care during the same time period from Northwestern Medicine clinicians who were not involved in the dashboard study.
For patients with advanced cancer in the comparison group, the baseline date was determined as the visit closest to the intervention patient’s baseline date (within 30 days) during the established time frame for the dashboard baseline (June 8, 2020, to November 1, 2022). For CKD comparison patients, the baseline was similarly defined as the first instance where their eGFR dropped below 60, adjusted to align with the intervention patients’ baseline date within the same time frame. To account for variability in eGFR, the mean baseline eGFR for each patient was calculated using eGFR values within 30 days of the baseline date for both groups. Patients with a mean eGFR above 60, indicating CKD stage II or lower, were excluded to ensure comparability in disease severity. Because this was a pragmatic, real-world evaluation, no power calculation was performed a priori. Instead, we included every patient who met the above eligibility criteria during the June 2020 to January 2022 accrual window.
Outcome Variables
Data on the use of potentially avoidable, high-cost, or low-value health services and select metrics of appropriate care were extracted from the clinical records for the period spanning 6 months before and 6 months after each patient’s baseline date. Specific indicators included unplanned all-cause hospital admissions, potentially avoidable all-cause emergency department use, all-cause excess days in acute care (EDAC) within 30 days following hospital discharge, and 7-day readmissions. Among patients with advanced cancer, the following disease-specific indicators were also assessed: hospital admissions and emergency department visits for patients receiving outpatient chemotherapy, use of a triage clinic, completion of an advance directive, and any hospice use. Among patients with CKD, we also assessed the following additional indicators: CKD-related emergency department or hospital inpatient use and progression from CKD stage III to stage IV, stage IV to stage V, or stage III to stage V.
Statistical Analyses
DiD Framework
We used a DiD approach to assess changes in the use of high-cost health services and select metrics of appropriate care between patients in the dashboard groups and those in the comparison groups. All analyses were run separately for each disease cohort (advanced cancer and CKD) to account for their distinct clinical trajectories and use patterns. Health services use in the 6 months before the dashboard intervention were compared with use during the 6 months after the intervention, with the following equation:
An interaction term for intervention group (“treated”) and time period (“time”) was included to test whether changes from the preintervention to postintervention periods differed between the dashboard and comparison groups. A statistically significant effect for the time period × intervention group interaction term (β3) would suggest that the dashboard intervention is associated with differential health services use. The DiD models were estimated with Huber-White cluster-robust SEs that correct for heteroskedasticity, account for paired observations within patients, and cluster at the health care provider level to absorb unmeasured health care provider–level confounding [].
Propensity Scores and Inverse-Propensity Weighting
A valid DiD estimate rests on the parallel trends assumption—that in the absence of the intervention, average outcomes in the dashboard and comparison groups would have evolved similarly over time. Because our dataset included a single preintervention measurement for each outcome, we could not empirically test for parallel preperiod slopes. Instead, we increased the plausibility of a conditional parallel trends assumption by ensuring that the two groups were closely matched on observed baseline characteristics. Specifically, we estimated propensity scores [,] using a logistic regression model that included race, ethnicity, age, sex, insurance category, Charlson Comorbidity Index, baseline encounter date, median household income, and baseline health services use (number of observation admissions, number of outpatient encounters, and number of immediate urgent care encounters). Median household income, used as a proxy for socioeconomic status, was obtained from 5-digit residential zip codes in the Social Determinants of Health Database from the Agency for Healthcare Research and Quality []. Cancer type was added as a covariate to the advanced cancer propensity score model, and CKD stage was included in the CKD propensity score model. Dashboard patients received a weight equal to 1/(propensity score), whereas comparison patients were weighted as 1/(1 – propensity score). Weights greater than 10 were truncated to reduce the influence of outliers. The weighted analysis, therefore, estimated the average treatment effect on the treated (ATT).
Primary Model: Weighted Linear Probability DiD
We chose a linear probability model for ease of interpretation. The resulting β coefficients reflect absolute percentage point changes (eg, β=–0.05 implies a 5–percentage point reduction in risk for the dashboard group relative to the comparison group). Cancer type (advanced cancer cohort) or CKD stage (CKD cohort) was included as a fixed effect. Hospice use and CKD progression were observed only after the intervention; these outcomes were analyzed with single period–weighted linear probability models that omitted the period variable. Unadjusted models are reported in and .
Robustness for Low-Prevalence Outcomes
Some outcomes (eg, 7-day admission) have prevalences less than 20%. We, therefore, reestimated each model with the same inverse propensity weights using a logistic DiD model; the exponentiated interaction coefficient is reported as the ATT ratio-in–odds ratios (ROR). Linear ATT and logit ATT RORs are presented in the Results section, and the two specifications produce directionally and statistically consistent results. For very low sample prevalence (<10%), we additionally applied the Firth penalized likelihood method to mitigate small sample bias () []. For readers interested in population-level effects, a set of inverse propensity–weighted linear probability models estimating the average treatment effect in the entire study population is also provided in and .
Software and Packages
All statistical analyses were conducted in R (version 4.2.1; R Foundation for Statistical Computing) using sandwich 3.0.2 (robust variance) and WeightIt 1.4.0 (inverse propensity score weighting).
Hypothesis
We hypothesized that patients in the dashboard group would have fewer unplanned all-cause hospital admissions, fewer EDACs, and lower 7-day readmission rates than patients in the comparison cohort. For the cancer dashboard group, we anticipated higher rates of oncology triage clinic visits, advance directive completion, and hospice use, with a reduced likelihood of chemotherapy within the last 14 days of life. For the CKD dashboard group, we expected a decrease in emergency-start dialysis, CKD-related emergency department or inpatient use, and slower progression of CKD stages.
Results
Study Population
Of the 748 patients enrolled in the dashboard study, 284 patients with advanced cancer and 365 patients with CKD completed the baseline questionnaires and composed the dashboard cohorts (). The comparison cohorts consisted of 917 patients with advanced cancer and 2137 patients with CKD who met the eligibility criteria but were not exposed to the dashboard. Before weighting, several baseline characteristics differed statistically between the dashboard and comparison groups ( and ). For example, the patients with advanced cancer in the dashboard group had more comorbidities (mean Charlson Comorbidity Index 8.62, SD 3.22, vs 6.93, SD 3.34) and were more often treated for lung cancer (191/284, 67.3% vs 176/917, 19.2%). Among patients with CKD, there were significant differences between the dashboard and comparison groups in CKD stage (P=0.005) and in the number of inpatient (P<0.001) and outpatient encounters (P<0.001).
Figure 1. Study flowchart of dashboard and comparison group selection. CKD: chronic kidney disease.
Table 1. Cancer cohort: comparisons of demographic characteristics, previous health care use, and comorbidities between dashboard and comparison groups.
Unweighted
Weighteda
Dashboard (n=284)
Comparison (n=917)
SMDb,c
P value
Dashboard
Comparison
SMDc
P value
Race, n (%)
0.130
.05
0.067
.79
White
189 (66.5)
628 (68.5)
68.8
68.2
Black
46 (16.2)
109 (11.9)
13.5
12.6
Declined to answer
17 (6.0)
61 (6.7)
6.9
6.5
Other
32 (11.3)
119 (13.0)
10.7
12.7
Ethnicity, n (%)
0.186
.002
0.090
.50
Non-Hispanic
253 (89.1)
774 (84.4)
82.2
85.3
Declined to answer
23 (8.1)
82 (8.9)
10.2
9.0
Hispanic
8 (2.8)
61 (6.7)
7.6
5.7
Charlson Comorbidity Index, mean (SD)
8.62 (3.22)
6.93 (3.34)
0.52
<.001
7.59 (3.23)
7.40 (3.54)
0.057
.38
Age (years), mean (SD)
63.12 (12.84)
62.04 (12.66)
0.085
.08
63.37 (12.49)
62.48 (12.38)
0.072
.25
Insurance, n (%)
0.194
.002
0.111
.34
Commercial
135 (47.5)
480 (52.3)
51.5
51.5
Medicaid
15 (5.3)
74 (8.1)
5.1
7.1
Medicare
134 (47.2)
360 (39.3)
43.4
41.2
Uninsured
0 (0.0)
3 (0.3)
0.0
0.2
Male, n (%)
130 (45.8)
476 (51.9)
0.123
.01
48.1
50.4
0.046
.49
Lung cancer, n (%)
191 (67.3)
176 (19.2)
1.109
<.001
32.9
31.4
0.032
.58
Emergency encounters (n), mean (SD)d
1.15 (1.72)
0.83 (1.48)
0.203
<.001
1.15 (1.68)
0.92 (1.54)
0.146
.03
Observation hospital admissions (n), mean (SD)d
0.20 (0.55)
0.18 (0.54)
0.038
.42
0.27 (0.72)
0.19 (0.55)
0.134
.10
Inpatient encounters (n), mean (SD)d
0.78 (1.26)
0.59 (1.04)
0.167
<.001
0.71 (1.22)
0.62 (1.08)
0.074
.27
Immediate urgent care encounters (n), mean (SD)d
0.54 (1.12)
0.37 (0.98)
0.153
.001
0.53 (1.15)
0.43 (1.07)
0.094
.17
Outpatient encounters (n), mean (SD)d
25.44 (18.69)
14.98 (15.41)
0.611
<.001
20.42 (14.64)
17.72 (17.32)
0.168
.002
Median household income (US $), mean (SD)
85,720 (32,862)
86,292 (31,095)
0.179
.71
85,285 (32,921)
85,873 (30,875)
0.018
.79
aThe sum of weights for the dashboard group was 1135. The sum of weights for the comparison group was 1212.45. Previous health care use indicates health care services used during the 12 months prior to the baseline assessment.
bSMD: standardized mean difference.
cSMD quantifies the difference in a covariate’s mean values between groups, scaled by the pooled SD. While SMD values closer to 0 suggest better balance, values over 0.1 may indicate potential differences. In this study, most covariates demonstrated good balance after weighting, although a small number retained statistical significance at P<.05. To mitigate potential residual differences, the difference-in-differences outcome model included unit and time fixed effects and controlled for all baseline covariates regardless of their P values.
dEncounters that occurred 12 months prior to baseline were included.
Table 2. Chronic kidney disease (CKD) cohort: comparisons of demographic characteristics, previous health care use, and comorbidities between the dashboard and comparison groups.
Unweighted
Weighteda
Dashboard (n=365)
Comparison (n=2137)
SMDb,c
P value
Dashboard
Comparison
SMDc
P value
Race, n (%)
0.092
.19
0.050
.75
White
183 (50.1)
1036 (48.5)
46.6
48.6
Black
120 (32.9)
674 (31.5)
33.8
31.8
Declined to answer
15 (4.1)
125 (5.8)
6.0
5.6
Other
47 (12.9)
302 (14.1)
13.6
13.9
Ethnicity, n (%)
0.101
.05
0.024
.87
Non-Hispanic
312 (85.5)
1753 (82.0)
81.6
82.5
Declined to answer
19 (5.2)
121 (5.7)
6.0
5.6
Hispanic
34 (9.3)
263 (12.3)
12.4
11.9
Charlson Comorbidity Index, mean (SD)
8.38 (3.79)
7.73 (3.69)
0.173
<.001
7.85 (3.65)
7.82 (3.81)
0.008
.85
Age (years), mean (SD)
64.31 (14.10)
63.46 (15.97)
0.056
.18
64.08 (14.42)
63.54 (15.90)
0.036
.40
Insurance, n (%)
0.058
.72
0.055
.55
Commercial
131 (35.9)
744 (34.8)
34.2
35.0
Medicaid
33 (9.0)
192 (9.0)
9.6
9.0
Medicare
201 (55.1)
1198 (56.1)
56.2
56.0
Uninsured
0 (0.0)
3 (0.1)
0.1
0.0
Male, n (%)
209 (57.3)
1066 (49.9)
0.148
<.001
51.7
50.9
0.017
.70
Emergency encounters (n), mean (SD)d
1.58 (2.23)
1.37 (2.60)
0.086
.04
1.62 (2.48)
1.40 (2.63)
0.087
.15
Observational encounters (n), mean (SD)d
0.38 (0.71)
0.29 (0.70)
0.126
.002
0.32 (0.66)
0.31 (0.71)
0.025
.55
Inpatient encounters (n), mean (SD)d
1.05 (1.51)
0.72 (1.35)
0.229
<.001
0.80 (1.25)
0.77 (1.41)
0.021
.62
Immediate urgent care encounters (n), mean (SD)d
0.73 (1.63)
0.64 (2.03)
0.046
.28
0.69 (1.60)
0.66 (2.07)
0.019
.51
Outpatient hospital admissions (n), mean (SD)d
20.69 (15.39)
15.78 (14.31)
0.331
<.001
17.10 (13.08)
16.73 (16.44)
0.025
.51
Median household income (US $), mean (SD)
77,925 (30,283)
77,432 (31,131)
0.016
.69
77,401.71 (30,239)
77,505.09 (31,146)
0.034
.94
CKD stage, n (%)
0.129
.005
0.037
.68
Stage III
54.8
61.1
58.3
60.1
Stage IV
30.1
26.2
27.7
26.7
Stage V
15.1
12.7
14.0
13.2
aThe sum of weights for the dashboard group was 2504.1. The sum of weights for the comparison group was 2504.85. Previous health care use indicates health care services used during the 12 months prior to the baseline assessment.
bSMD: standardized mean difference.
cSMD quantifies the difference in a covariate’s mean values between groups, scaled by the pooled SD. While SMD values closer to 0 suggest better balance, values over 0.1 may indicate potential differences. In this study, P values were also assessed, demonstrating minimal statistical differences between groups, even for covariates with SMD values close to 0.1. To mitigate potential residual differences, the difference-in-differences outcome model included unit and time fixed effects and controlled for all baseline covariates regardless of their P values.
dEncounters that occurred 12 months prior to baseline were included.
After applying inverse propensity weighting, the standardized mean difference was less than 0.10 for every variable except outpatient encounter volume in the cancer cohort. Accordingly, the covariates were included as fixed effects in the weighted DiD models to further control for potential confounding and improve comparability ( and ) [,].
Association Between Dashboard Use and High-Cost Services Use
Advanced Cancer Cohort
In weighted DiD models, dashboard exposure was not associated with changes in unplanned all-cause admission rates (ATT: β=–0.017, 95% CI –0.107 to 0.072; ROR 0.89, 95% CI 0.46-1.72; ). In contrast, dashboard exposure was associated with a 4–percentage point increase in EDACs relative to the comparison group (ATT: β=0.040, 95% CI –0.001 to 0.089), although the corresponding odds ratio was not significant (ROR 5.84, 95% CI 0.89-38.42). Dashboard users also experienced a 3.7–percentage point rise in the 7-day readmission rate (ATT: β=0.037, 95% CI 0.008-0.066), with a significant increase in odds (Firth-adjusted ROR 8.58, 95% CI 2.28-32.32). Chart review confirmed that 93% of these early readmissions were scheduled by clinicians, suggesting intentional proactive care rather than unplanned deterioration.
Hospital admissions and EDe visits for patients receiving outpatient chemotherapy
30 (10.6)
54 (19.0)
43 (4.7)
161 (17.6)
–0.014 (–0.102 to 0.074)
0.351 (0.163 to 0.753)
Oncology triage clinic use
40 (14.1)
56 (19.7)
32 (3.4)
80 (8.7)
0.047 (–0.031 to 0.125)
0.674 (0.322 to 1.412)
Completion of an advanced directive
5 (1.7)
5 (1.7)
11 (1.2)
33 (3.6)
–0.009 (–0.039 to 0.020)
0.245 (0.048 to 1.247)
Hospice usef
—g
11/27 (40.7)
—
32/65 (49.2)
0.203 (–0.049 to 0.454)
2.837 (0.755 to 10.662)
aAll coefficients are the ATT (b) obtained with inverse propensity-weighted difference-in-differences modes. Linear ATT is the treatment-effect coefficient from the weighted linear probability DiD; logit ATT ROR is the corresponding ratio-in–odds ratios from a weighted logistic regression fit to the identical analytic sample. Both models adjust for all baseline covariates included in the propensity score specification to minimize residual confounding: race, ethnicity, age, sex, insurance category, Charlson Comorbidity Index, cancer type, baseline use encounter date, median household income (zip code level), and baseline use counts (emergency, observation, inpatient, immediate/urgent care, and outpatient encounters). Time and time × treated interaction terms were excluded from the regression analyses.
bATT: average treatment effect on the treated.
cROR: ratio-in–odds ratios.
dEDAC: excess days in acute care.
eED: emergency department.
fFor hospice use, the denominator is restricted to patients who died during the study period who received care from a participating study physician.
gNot applicable.
Notably, the pattern reversed among patients receiving outpatient chemotherapy. The dashboard intervention was associated with a significant 65% reduction in the odds of acute care use (hospital admission or emergency department visit) in this subgroup (ROR 0.35, 95% CI 0.16-0.75), although the linear DiD estimate did not reach statistical significance. Conversely, completion of advance directives declined among dashboard users (Firth-adjusted ROR 0.25, 95% CI 0.10-0.67). No statistically significant associations were observed for oncology triage visits or hospice use.
CKD Cohort
In the CKD cohort, dashboard exposure was not associated with any of the prespecified outcomes, including unplanned admissions, EDACs, 7-day readmissions, CKD-related acute use, or CKD progression, under either the linear ATT β or Firth-adjusted logit ATT ROR specification ().
Progression from CKD stage III to IV, stage IV to V, and stage III to stage V
—f
31 (8.4)
—
334 (15.6)
–0.002 (–0.034 to 0.029)
1.073 (0.695 to 1.655)
aAll coefficients are the ATT obtained with inverse propensity weighted difference-in-differences modes. Linear ATT (b) is the treatment effect coefficient from the weighted linear probability difference in differences; logit ATT ROR is the corresponding ratio-in–odds ratios from a weighted logistic regression fit to the identical analytic sample. Both models adjust for all baseline covariates included in the propensity score specification to minimize residual confounding: race, ethnicity, age, sex, insurance category, Charlson Comorbidity Index, CKD stage, baseline encounter date, median household income (zip code level), and baseline use counts (emergency, observation, inpatient, immediate/urgent care, and outpatient encounters). Time and time × treated interaction terms were excluded from the regression analyses.
bATT: average treatment effect on the treated.
cROR: ratio-in–odds ratios.
dEDAC: excess days in acute care.
eED: emergency department.
fNot applicable.
Discussion
Principal Findings
Using a propensity score–weighted DiD design, we found that the co-designed PRO dashboard had disease-specific and mixed effects on use of high-cost services. For patients with advanced cancer, dashboard use was associated with fewer acute encounters during outpatient chemotherapy (a 65% reduction in odds), yet dashboard use also coincided with increased planned 7-day readmissions and a modest rise in excess days in acute care. No other use outcomes changed, and no effects were detected in the CKD cohort.
Comparison With Prior Work
Previous randomized symptom monitoring trials in oncology have reported 30% to 50% reductions in emergency department visits and hospitalizations when PRO alerts triggered nurse triage or oncologist feedback [,]. Our observed 65% reduction in odds of acute care encounters related to outpatient chemotherapy aligns with these findings, suggesting that PRO-informed dashboards can be effective in real-world clinical settings, not just trials. An increase in 7-day readmission rates mirrors patterns seen in heart failure programs, where early, proactive readmissions are reframed as planned care. This suggests that the dashboard may support clinician-directed early symptom management.
We also observed a significant decrease in advance directive completion among dashboard users in the advanced cancer cohort. This may reflect a shift of clinical attention toward pressing symptoms, similar to a US Department of Veterans Affairs trial where increased planning discussions did not improve alignment with patient preferences []. This highlights how even well-designed dashboards can induce unintended consequences, underscoring the need for thoughtful integration into care processes.
For CKD, our null findings align with a systematic review indicating that stand-alone SDM tools rarely impact high-cost use metrics []. Without concurrent disease management supports or stronger engagement by clinicians and patients, dashboards may be insufficient to change care trajectories in slowly progressive conditions. Current evidence, therefore, remains mixed on whether SDM interventions alone can consistently curb costly service use.
Implications for SDM Tools
Patient engagement in SDM is shaped by multiple forces. While financial strain can sharpen patients’ desire to weigh costs against benefits, the general shift toward patient-centered care, greater transparency in outcomes, and heightened emphasis on quality of life also motivate participatory decisions []. Our dashboard, which pairs PRO trends with open-ended prompts on the goals of care, may have resonated most strongly with oncology patients because acute toxicity and quality of life trade-offs are immediate and visible during chemotherapy. CKD trajectories are typically slower; without real-time laboratory or symptom triggers, the dashboard information may have seemed less actionable to nephrology teams, resulting in lower engagement and impact in that setting.
Cancer-Type Heterogeneity
The advanced cancer cohort comprised patients with lung and gastrointestinal malignancies. Although both subgroups face heavy symptom burdens, needs can diverge: patients with lung cancer report dyspnea and cough as dominant concerns, whereas patients with gastrointestinal cancer often prioritize nausea, appetite, and bowel symptoms [-]. The dashboard displayed all PROMIS domains identically, which may have diluted its relevance for patients whose dominant symptoms were not being addressed in the clinic. Future iterations could include cancer-specific symptom widgets or algorithmic highlighting of domain scores most relevant to each cancer type to increase salience for both patients and health care providers.
Strengths and Limitations
The study’s key strengths are its evaluation in routine clinical practice across two distinct specialties (oncology and nephrology), use of rigorous inverse propensity weighting that achieved robust covariate balance, and parallel reporting of additive (percentage point) and multiplicative (ROR) effects, which allows findings to be interpreted consistently across outcomes with different prevalence rates.
There are several limitations. First, all use data were drawn from a single US academic health network; encounters that occurred elsewhere were unseen, and results may not generalize to community or non-US settings. Second, patients were not randomly assigned to the dashboard intervention, which limits our ability to make causal claims about the effect of dashboard use. Although our weighting approach improved the balance of patient characteristics between the intervention and comparison groups, residual confounding from unmeasured factors (eg, digital literacy) could remain. Third, dashboard patients exhibited higher baseline use, suggesting higher initial health care needs; the analytical approach mitigated but may not have fully eliminated this imbalance, potentially biasing postintervention contrasts.
Conceptual Implications for Dashboard Design
Our findings reinforce a fundamental principle of user-centered, participatory design: a dashboard’s value depends on how well it integrates with the downstream clinical workflow. In our advanced cancer cohort, the dashboard was associated with fewer chemotherapy-related acute care encounters, suggesting that when clinicians have a clear, rapid way to respond to symptom data, visualizing those data in a dashboard may avert costly health services use. In contrast, the absence of any effect among patients with CKD may imply that, without an equally responsive care pathway, a stand-alone dashboard cannot produce meaningful change.
This observation aligns with evidence from a recent participatory design intervention in surgical ward rounds, where stakeholders confirmed that successful implementation hinged on both tool usability and system readiness, including routines, coordination, and technology infrastructure []. Just as that study revealed the importance of aligning design with contextual factors and cultural norms, our dashboard’s impact depended on having disease-specific support pathways in place.
Future versions should, therefore, be co-designed not only around what information is displayed but also around disease-specific response workflows (eg, automated alerts routed to the appropriate clinician, or prompts that trigger standing orders) [].
Conclusions
While a co-designed dashboard may help reduce high-cost health services use and improve select care metrics for patients with advanced cancer, the dashboard appears to be less effective for patients with CKD. As the first study on an SDM intervention and its impact on health services use for these groups, the results were mixed. More research is needed to fully understand the impact of co-designed dashboards on improving emotional, clinical, and use outcomes.
We would like to extend our gratitude to the patients and clinicians involved in this study for their contribution and collaboration toward improving person-centered care. Their insights and expertise were instrumental to our research. OpenAI’s GPT-4o was used to support code analysis and grammatical editing of the manuscript. All artificial intelligence–generated content was carefully reviewed and verified by the authors to ensure accuracy and originality.
We gratefully acknowledge the financial support provided by the Peterson Center on Healthcare (principal investigator DC).
The data supporting these study findings were derived from electronic health records within the participating health care system and are not publicly available due to privacy and confidentiality restrictions.
SA and NJ conducted the statistical analyses and drafted the initial manuscript. All authors conceived and designed the study. All authors reviewed and approved the manuscript.
None declared.
Edited by N Cahill; submitted 21.Dec.2024; peer-reviewed by T Brown, LR Guo; comments to author 05.May.2025; revised version received 25.Jul.2025; accepted 29.Sep.2025; published 21.Nov.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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Here are five key things investors need to know to start the trading day:
1. Hero to zero
Stock investors didn’t end up getting the post-Nvidia earnings market bounce they hoped for. After opening yesterday’s trading session higher, stocks took a dramatic midday tumble, once again casting doubt on the artificial intelligence trade.
Here’s what to know:
Nvidia shares gave up their 5% post-earnings gain, ending the session down more than 3% despite the chipmaker’s blockbuster quarterly results and guidance. The AI darling’s stock is on track to finish the week down 5%.
The Dow Jones Industrial Average swung more than 1,100 between its session highs and lows. All three major averages closed solidly in the red, with the tech-heavy Nasdaq Composite ending the day down 2.15%.
Meanwhile, the CBOE Volatility Index — better known as Wall Street’s fear gauge — ended the session at a level not seen since April.
Bitcoin fell to lows going back to April, further illustrating the shift away from risk assets.
Before stocks’ midday reversal, Bridgewater founder Ray Dalio told CNBC that “we are in that territory of a bubble,” but that you don’t need to sell stocks because of it.
The three major indexes are all on track to end the week in the red.
Follow live markets updates here.
2. Prediction market
A ‘Now Hiring’ sign is posted outside of a business on Oct. 3, 2025 in Miami, Florida.
Joe Raedle | Getty Images
The belated September jobs report was finally released yesterday, and the headline number was much hotter than economists expected with an increase of 119,000 jobs. On the other hand, the unemployment rate ticked up to 4.4%, its highest level since 2021.
The chance of a rate cut at the Federal Reserve’s next meeting remained low after the report, according to the CME FedWatch Tool. But the odds flipped this morning after New York Fed President John Williams said he sees “room for a further adjustment” in interest rates, reviving hopes of a December cut.
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3. Better than yours
Merchandise on display in a Gap store on November 21, 2024 in Miami Beach, Florida.
Joe Raedle | Getty Images
Gap‘s “Milkshake” ad brought all the shoppers to the store. The retailer’s viral “Better in Denim” campaign with girl group Katseye helped drive comparable sales up 5% in its third quarter, beating analyst expectations.
The Old Navy and Banana Republic parent also surpassed Wall Street’s estimates on both the top and bottom lines, sending shares rising 4.5% in overnight trading. Athleta was the notable outlier, with the athleisure brand’s sales falling 11%.
Gap’s report comes at the end of a busy week for retail earnings. As CNBC’s Melissa Repko reports, one key theme of this quarter’s results has been that value-oriented retailers are winning favor with shoppers across income brackets.
4. AI in D.C.
U.S. President Donald Trump speaks in the Oval Office at the White House on Oct. 6, 2025 in Washington, DC.
Anna Moneymaker | Getty Images
The White House is putting together an executive order that would thwart states’ individual AI laws. A draft obtained by CNBC shows the order would focus on staging legal challenges and blocking federal funding for states to ensure their compliance.
The draft would work to the advantage of many AI industry leaders who have pushed back on a state-by-state approach to the technology’s regulation. A White House official told CNBC that any discussion around the draft is speculation until an official announcement.
Click here to read the full draft.
5. Flight fight
Courtesy: Archer Aviation
Joby Aviation is taking air taxi competitor Archer Aviation to court. In a lawsuit filed Wednesday, Joby accused Archer of using information stolen by a former employee to “one-up” a deal with a real estate developer.
Joby alleges that George Kivork, its former U.S. state and local policy lead, took files and information before jumping to the competitor in an act of “corporate espionage.” Archer called the case “baseless litigation” and said it’s “entirely without merit.”
The Daily Dividend
Here are our recommendations for stories to circle back to this weekend:
— CNBC’s Liz Napolitano, Tasmin Lockwood, Melissa Repko, Jeff Cox, Sarah Min, Emily Wilkins, Mary Catherine Wellons and Samantha Subin contributed to this report. Josephine Rozzelle edited this edition.
AI relies on rare-earth elements to grow its infrastructure – and the U.S. relies on AI to grow GDP
Capital spending on AI has been a key driver of U.S. stock market returns and continues to exceed expectations, comprising a large portion of S&P 500 SPX capital expenditures.
Jason Furman, a Harvard University economics professor, calculated that 92% of total U.S. GDP growth for the first half of 2025 could be attributed to AI spending. Without AI-related data-center construction, he reported, GDP growth would have been an anemic 0.1% on an annualized basis.
Given so much riding on the AI capex boom, it’s important to consider what could derail U.S. economic growth and the U.S. stock market
One major risk is access to rare earth elements. Limited rare-earth access could present the U.S. with challenges similar to what it faced in the 1970s from its dependence on oil.
Rare-earth elements are used extensively in artificial intelligence, including disk drives, cooling servers and especially semiconductor fabrication. Artificial intelligence has enormous computational and memory demands, which is why high-capacity, high-performance semiconductors are the linchpin of the AI build-out. Rare earths are also integral for national security – used in radar, lasers and satellite systems.
From the 1960s to the 1990s, the U.S. was the leader in rare-earth elements production. In 1995, two decisions were made that had far-ranging consequences, dramatically changing the trajectory of U.S. leadership in rare earth elements.
First, the U.S. approved China’s purchase of U.S. rare-earth magnet company Magnequench from General Motors, thereby acquiring a highly advanced technology that arguably would have taken many years to develop.
Second, China applied to join the World Trade Organization, ultimately enabling it to sell its rare-earth elements to a global market. China was able to sell at a lower cost than the U.S., contributing to the closure of the U.S. mining company that produced rare earth elements, MP Materials Corp. (MP), in 2002.
MP Materials was reopened for national defense use in 2017. U.S. production has since ramped up, with rare-earth production reaching 45,000 tons in 2024 – yet that’s still less than one-sixth of China’s production.
Yet the U.S. Department of Defense’s lofty goal of meeting defense-related demand for light- and heavy rare earths by 2027 may not be achieved, given America’s rare-earth mining and processing limitations. Even if it is, significant commercial demand, including the enormous AI build-out, will not be met.
China controls the supply
China controls around 70% of the world’s rare earth resource output and about 90% of the world’s rare earth processing capabilities. Access to rare-earth elements has been a key bargaining chip in U.S. trade negotiations with China.
As a result, the U.S. has been increasing efforts to diversify its rare-earths supply and gain reliable and adequate exposure to these elements through its allies. Australia and Canada, for instance, have significant rare-earth resources that can help support America’s rare-earth element needs.
New technologies may also lessen or eliminate the need for rare-earth elements in various uses and make rare-earth element recycling more efficient (currently, just 1% of rare-earth elements are recycled). In addition, U.S. government policies can discourage or at least disincentivize demand for rare earth element-intensive products such as electric vehicles, as the Trump administration has done by eliminating EV tax credits.
Rare earth element independence should be as high a priority for the U.S. as energy independence was 50 years ago. Until there’s a viable alternative to the China-dominated rare-earth supply chain, AI capital spending – and both the U.S. economy and stock market – are vulnerable. Accordingly, stock investors should pay attention to trade deals and policymakers’ comments, and consider supply-chain risks when evaluating AI-related investments.
Kristina Hooper is chief market strategist at Man Group, which manages alternative investments. The opinions expressed are her own.
More: Big Tech is spending on power for AI – whether Washington functions or not
Also read: AI has real problems. The smart money is investing in the companies solving them now.
-Kristina Hooper
This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.
BBVA has set itself some ambitious financial targets for the 2025–2028 horizon: €48 billion in cumulative attributable net profit, an average RoTE of 22%, and a compound annual growth rate in tangible book value per share plus dividends of around 15%. The bank is also looking to improve its cost-to-income ratio to around 35%. The outstanding results for the first nine months of the year—with record profit of nearly €8 billion, a RoTE of 19.7%, and year-on-year growth in tangible book value per share plus dividends of 17% as of September—go to show that the Group is already on the right path.
Gómez Bravo outlined several structural factors that set BBVA apart and which will continue to cement its leadership in profitability and growth moving forward. First of all, the Group’s geographic diversification is a key competitive strength. It operates in a number of attractive markets with low leverage levels and compelling growth potential.
Second, and following on from this, BBVA holds leading positions in its main markets. Achieving local scale and operating as one of the top banks in each country leads to higher profitability when compared with its peers.
Third, the bank is one step ahead of its peers in the digital realm and also in terms of sustainability. BBVA invested in digital transformation earlier and with greater determination than most of its competitors, and today those investments are paying off. As Gómez Bravo explained, the bank’s strategy is to grow the business by expanding its customer base, with its digital offering being the key enabler in this regard. Over the past three years, BBVA has succeeded in adding around 11 million new customers per year, of which almost two-thirds arrived through digital channels. And the value these new customers bring increases significantly over time. For all these reasons, “we are very optimistic about the future,” she remarked.
Gómez Bravo also discussed the growth drivers of the Group’s main franchises. Looking at Spain, she noted that “BBVA is the best bank in the country, being the most profitable and also the most efficient.” She also cited the bank’s ability to continue expanding its customer base in Spain: “Since 2022, we’ve added more than three million customers in Spain. So far this year, around 730,000, of which around 100,000 are SMEs. And what we’re seeing is that when we bring in a new customer, that customer becomes ‘engaged’ within the following 12 months, all thanks to our end-to-end digital experience.” She added that the bank is “highly disciplined” on pricing and to drive further growth in Spain it relies on customer growth and engagement, and also on its distribution and risk models.
BBVA aims to continue outpacing its competitors in terms of loan book growth (at around +5% annually through 2028), focusing on those segments that offer the best risk-adjusted returns, mainly corporate and consumer lending, which will help shift the business mix toward more profitable segments.
In Mexico, BBVA’s Global Head of Finance sees a very positive backdrop for the banking sector thanks to the resilience of the economy and low leverage levels, which have been consistently driving credit growth above nominal GDP for the past 20 years.
BBVA fully expects its loan book in Mexico to grow at a high single-digit compound annual rate between 2024 and 2028, on the back of consumer and corporate lending. For this year, the bank projects around 10% loan growth and expects that increased activity to feed directly into earnings, with the interest rate-cutting cycle now nearing its end: “BBVA Mexico will make a strong contribution to the Group’s positive forward momentum,” she added.
As for Türkiye, Gómez Bravo pointed to a steady improvement in the macroeconomic outlook, which will boost the local franchise’s contribution to the Group’s earnings. This positive outlook is supported by two factors: Garanti BBVA benefits from positive sensitivity to lower interest rates, and lower inflation is easing the strain on the Turkish economy and is already having a less negative impact on the franchise’s attributable profit. “Our strategy in Türkiye is very clear: to maintain a high-quality franchise that stands to benefit as the economy steadily normalizes,” she remarked.
Against this positive backdrop of growth and value creation, Gómez Bravo also addressed the Group’s capital outlook. As of the end of September, the CET1 capital ratio stood at 13.42%. The bank’s goal is to pay out capital above 12%, something that will happen “in a matter of months, not years,” she added. BBVA plans to allocate up to €36 billion to its shareholders through 2028.¹
On the subject of BBVA’s CET1 target, she noted that the bank happens to have one of the largest buffers above minimum regulatory capital requirements among all European banks: “We are one of the banks with the best relative position compared with what the supervisor requires.”
¹Pending approval from the governing bodies and subject to mandatory regulatory approvals.
Good morning. It’s a pleasure be here to celebrate the 100th anniversary of the Central Bank of Chile. The topic of my remarks today is inflation targeting, which is both an important part of Chile’s central banking history and a core foundation of successful monetary policy.
Most central banks around the world have adopted inflation targeting regimes over the past 35 years, and Chile was among those leading the way. Although specifics vary across jurisdictions, these strategies share three principles: independence and accountability, transparency and the clear communication of an inflation target, and well-anchored inflation expectations, gained from the credibility that central banks build over time.1
Today I will discuss the success of inflation targeting strategies in helping central banks achieve price stability and better economic outcomes. I’ll also talk about how these strategies were critically important in managing uncertainty after the onset of the COVID-19 pandemic—and how they helped countries bring inflation down while minimizing disruptions to financial markets and economies.
But first, I must give the standard Fed disclaimer that the views I express today are mine alone and do not necessarily reflect those of the Federal Open Market Committee (FOMC) or others in the Federal Reserve System.
Autonomy, Transparency, and Confidence
Inflation targeting is like guiding an excursion through the Andes. Independence gives the central bank the ability to choose the best path to meet its objectives. Transparency ensures that people understand where they are headed and why the route may change. And a central bank earns public confidence by consistently reaching its goals, even amid sudden detours and a jagged course, laying the groundwork for maintaining well-anchored inflation expectations.
Inflation targeting strategies were introduced and have evolved as central banks sought to avoid repeating prior mistakes. Too often in the past, some central banks behaved as if they were powerless to control inflation in the face of shocks. Over time, they found they could be more successful at delivering price stability when they owned the responsibility for that goal and had the independence of action and tools to achieve it.
Transparency—including clear communication of an explicit numerical inflation target—reinforces public accountability for price stability and focuses the internal policy debate on how best to attain that objective. The Central Bank of Chile established a 3 percent inflation target when it formally adopted its current framework in 1999.2 And the Federal Reserve set an inflation goal of 2 percent over the longer run, which it announced in an FOMC statement in January 2012.3
By communicating an explicit inflation target—and then delivering inflation consistent with that goal—central banks establish and reinforce trust with the public. But transparency does not stop with declaring a destination. It also means describing the road ahead to reach that goal. Many inflation-targeting central banks, including the Central Bank of Chile, provide detailed analyses of their economic situations, outlooks, and risks.4
Transparency about goals, strategy, and what that means for policy helps to anchor inflation expectations, which, in turn, contributes to low and stable inflation.5,6 The feedback loop between effective policy actions and communications, well-anchored expectations, and price stability is now a core tenet of modern central banking. It short-circuits so-called second-round effects in wage and price setting that exacerbate and prolong the effects of shocks.
Put to the Test
Inflation targeting regimes were instrumental in bringing about a prolonged period of price stability in many countries through 2020. But it wasn’t until the onset of the COVID-19 pandemic that they were truly put to the test.
The pandemic, followed by Russia’s war on Ukraine, dealt the most dramatic supply shocks to the world’s economy in generations. Starting in 2021, global supply-chain disruptions, along with acute imbalances between supply and demand, led to inflation skyrocketing around the world. Inflation peaked at over 7 percent in the U.S.7 and at over 14 percent in Chile.8 Other Latin American countries, as well as Canada and Europe, followed similar patterns.
While the sources of inflation were comparable across countries, they affected countries differently. For example, supply-chain bottlenecks and higher commodity prices hit Chile and other countries especially hard. In addition, inflation accelerated earlier and with greater force in Latin American countries than in advanced economies.9
In response, central banks leaned into their inflation targeting strategies to guide their economies to bring inflation down. Many benefited from the public trust built from years of low and stable inflation. As a result, longer-term inflation expectations remained well anchored in the U.S., Chile, and other Latin American countries.10 This was a key difference between this episode and bouts of high inflation in the past, boding well for disinflation to occur.
The connections between policy communications and actions, inflation outcomes, and expectations are at the core of policy strategies that are robust to extreme uncertainty, a topic that Athanasios Orphanides and I studied in a sequence of research papers.11 If a central bank has credibility in achieving price stability, longer-term expectations should remain anchored at levels consistent with its inflation target.
Carving Their Own Paths
During my tenure at the Fed, the comment I’ve heard most often from economists and central bankers in emerging market countries is that the Fed’s actions have meaningful effects on the capital flows and exchange rate movements in their countries. We saw this in action during the so-called taper tantrum in mid-2013. After Fed Chairman Ben Bernanke indicated that the FOMC might start tapering the pace of its asset purchases later that year, U.S. Treasury yields rose sharply, causing significant volatility in global financial markets—particularly for emerging market economies.
What is striking to me is that after the onset of the pandemic, those concerns were not nearly so top of mind. Until COVID-19, central banks in emerging economies, including many in Latin America, typically had followed the lead of the Fed when responding to shocks. This time, because they did not want to risk a new episode of very high inflation and thus potentially lose their hard-earned credibility, they moved first.12 Latin American central banks acted quickly and decisively to tame high inflation by raising interest rates, starting with Brazil in the spring of 2021, followed by Chile, Colombia, Mexico, and Peru later that summer.13 In contrast, central banks of many advanced economies—including the Bank of England, the Federal Reserve, the Bank of Canada, and the European Central Bank—raised rates later and by smaller amounts.
Around the world, inflation—and the responses of central banks—largely rose and fell along the same path. Following central banks’ actions, inflation declined across the board, and economies weathered the disinflation much better than anticipated. And despite big movements in interest rates across countries, disinflation occurred without dramatic disruptions to capital flows, exchange rates, or financial markets. It’s a true testament to the success of inflation targeting.
The Current Situation
This brings me to the current situation. As I have emphasized, inflation targeting is a strategic framework that provides the foundation for effective policy decisions and communication. The decisions and actions themselves depend on the circumstances that policymakers face.
Here in Chile, in the United States, and across the globe, strong actions have proven effective at restoring price stability. In some cases, inflation has returned comfortably back to target levels, while in others, including the United States, the job of bringing inflation sustainably back to target is not yet complete.
I’ll comment briefly on the current economic situation in the U.S. and what it means for monetary policy. Economic growth has slowed from its pace last year, and the labor market has gradually cooled. In particular, indicators of the balance between labor demand and supply, including the unemployment rate, have gradually softened over the past year, reaching levels seen prior to the pandemic when the labor market was not overheated. I would emphasize that this has been an ongoing, gradual process, without signs of a significant rise in layoffs or other indications of a sharp deterioration in the labor market.
Inflation declined from a peak of 7-1/4 percent in mid-2022 to 2-3/4 percent in 2024. Looking back at FOMC participants’ projections in December of last year, the median expectation was for inflation to slow to 2-1/2 percent this year and approach 2 percent next year. Since then, the effects of trade policies and other developments have boosted U.S. inflation somewhat, offsetting the expected downward trajectory. As a result, progress toward our 2 percent goal has temporarily stalled, with the latest available data indicating that inflation remains around 2-3/4 percent.
It is not possible to measure the effects of trade policy actions on inflation with precision. My estimate is that increased tariffs have contributed about one half to three quarters of a percentage point to the current inflation rate. I do not see any signs of tariffs contributing to second-round or other spillover effects on inflation. In particular, inflation expectations are very well anchored, no broad-based supply chain bottlenecks have emerged, labor markets are not creating inflationary pressures, and wage growth has moderated. As a result, I expect the effects of tariffs on inflation will play out over the rest of this year and the first half of next year. Inflation should thereafter get back on track to 2 percent in 2027.
Given this backdrop, monetary policy is very focused on balancing the downside risks to our maximum employment goal and the upside risks to price stability. My assessment is that the downside risks to employment have increased as the labor market has cooled, while the upside risks to inflation have lessened somewhat. Underlying inflation continues to trend downward, absent any evidence of second-round effects emanating from tariffs. For these reasons, I fully supported the FOMC’s decisions to reduce the target range for the federal funds rate by 25 basis points at each of its past two meetings.
Looking ahead, it is imperative to restore inflation to our 2 percent longer-run goal on a sustained basis. It is equally important to do so without creating undue risks to our maximum employment goal. I view monetary policy as being modestly restrictive, although somewhat less so than before our recent actions. Therefore, I still see room for a further adjustment in the near term to the target range for the federal funds rate to move the stance of policy closer to the range of neutral, thereby maintaining the balance between the achievement of our two goals. My policy views will, as always, be based on the evolution of the totality of the data, the economic outlook, and the balance of risks to the achievement of our maximum employment and price stability goals.
Conclusion
In conclusion, central bank independence and accountability, clear communication and an explicit inflation target, and well-anchored inflation expectations have proven to be invaluable in ensuring price stability in the face of unexpected shocks and extreme uncertainty.
Sharp turns and unpredictable terrain have been an unavoidable part of our journey, and we must accept that shocks and uncertainty will continue to define our future. I am confident that inflation targeting strategies will continue to serve us well against any challenges we may face ahead.
Sustainability continues to hold a prominent place on the C-suite agenda. Deloitte Global’s 2025 survey of over 2,100 executives across 27 countries—now in its fourth year—shows sustainability ranking among the top three matters for global business leaders to focus on over the next year alongside technology adoption and innovation, and economic outlook. This year’s findings reveal both progress and complexity, with executives demonstrating continued investment while adopting a more selective, strategic approach to their sustainability initiatives.
Investment continues
Eighty-three percent of respondents reported increasing their sustainability investments in the last year, with 69% increasing somewhat (6–19%) and 14% increasing significantly (≥20%).
Technology adoption, particularly artificial intelligence (AI), appears to be playing an expanding role. Eighty-one percent of respondents indicated they are already using AI to further their company’s sustainability efforts, with applications spanning monitoring and reporting, scenario analysis, product innovation, and operational efficiency.
Leaders identify revenue generation as a key business benefit
Revenue generation emerged as the most frequently cited benefit across sustainability actions, followed by compliance-related outcomes, brand and reputation, and risk and resiliency. Very few respondents (10% or less) reported negative impacts on business outcomes from their sustainability initiatives.
A pragmatic path companies can follow
Based on multiple years of survey data, a set of sustainability actions is emerging as a de facto roadmap for leaders, including:
Implementing technology solutions
Using more sustainable materials
Developing more sustainable products and services
Implementing operational efficiency measures
Tracking and disclosing sustainability metrics
Some actions show a slight decrease after years of advancement
Compared to last year, the survey reveals a slight decrease in the percentage of respondents who say they have undertaken certain sustainability actions including:
Tying senior leaders’ compensation to sustainability performance: 36% vs. 43% (2025 vs. 2024)
Requiring suppliers to meet specific sustainability standards: 38% vs. 47%
Decreasing emissions by purchasing renewable energy: 42% vs. 49%.
These shifts may reflect a more selective and strategic approach rather than a retreat from sustainability commitments.
Shifting dynamics
The survey indicates changing dynamics in pressure. Across nearly every major stakeholder group, fewer respondents say they are feeling pressure to act on sustainability compared to 2022—for example, shareholders (71% in 2022 to 58% in 2025), boards (75% to 60%), governments (77% to 58%), and customers (75% to 57%). Respondents also indicate that climate change is now viewed as less disruptive to their business strategy in the near term than in past years.
Key questions for leaders
Today’s dynamic conditions provide an opportunity for organizations to reevaluate their sustainability ambition, strategy, investments, initiatives, and execution to help ensure they both meet their sustainability goals and further build resilience into their organizations. To guide that effort, leaders can consider:
Which sustainability matters are material for their business and stakeholders?
What resources is their organization willing and able to commit?
How patient is their organization? How patient are their key stakeholders?
What level of risk and uncertainty can their business tolerate?
What are the dependencies? What would this action require?
The findings from Deloitte Global’s 2025 survey reflect a sustainability landscape that is both advancing and evolving. From AI adoption to increased investments, organizations are building resilience today to help shape the next wave of business value. Many leaders have an opportunity to assess whether their sustainability strategy and investments are integrated with key performance drivers, material risks, and strategic priorities—helping ensure they continue delivering value and operational resilience into the future.
