Rise and shine, everyone, another busy day is on the way. Already, the Pharmalot campus has been bustling as the official mascots have been busy chasing assorted creatures in search of their breakfast. As for us, yes, we are firing up the trusty kettle once again and brewing another cuppa stimulation. Our choice today is Earl Gray. Now, though, the time has come to get still busier. On that note, here are some tidbits. Have a lovely day and do keep in touch. …
As links between pharmaceutical companies and telehealth providers grow, health policy experts and legislators are raising concerns over the large fees that telehealth companies can receive from drugmakers each year, STAT explains. Critics have questioned whether those partnerships break federal laws prohibiting financial kickbacks to induce prescribing, highlighting their potential to promote uncoordinated care and overprescriping of unnecessary, and often expensive, branded medications. The same questions apply to coupons extended for a drug-specific telehealth visit. Drugmakers have long used discount coupons to encourage patients to use their high-cost medications. But coupons can influence not just the out-of-pocket price for the drug, but the cost of consulting with a clinician who can prescribe it.
U.S. Health and Human Services Secretary Robert F. Kennedy Jr. told a Senate hearing on Wednesday that he had nothing to do with a U.S. Food and Drug Administration decision to not approve Replimune’s advanced skin cancer drug, saying it was in the hands of FDA Commissioner Marty Makary,Reuters writes. Earlier this month, the FDA declined to approve the drug after taking issue with the company’s reliance on a single‑arm study for the drug without a control group. In its letter, the FDA said the company must provide data from a well-controlled trial demonstrating adequate evidence of effectiveness. “This decision comes out of FDA, and we trust the process there. And I’ve been told by Marty Makary that every panel that looked at that drug unanimously voted against it … because it does not appear to work,” Kennedy said. An op-ed in The Wall Street Journal maintained Kennedy’s comments were not true and cited cancer doctors who have worked on trials of the drug and said it was effective.
San Francisco, CA – On April 20, the Child Mind Institute and SoFi held its fifth annual Golf Invitational at the Olympic Club in San Francisco. Participants included legendary athletes Marcus Allen (Los Angeles Raiders), Barry Bonds (San Francisco Giants), Royce Clayton (San Francisco Giants), Vince Coleman (St. Louis Cardinals), Al Joyner (Olympic gold medalist), Gary Payton (Miami Heat), and Sterling Sharpe (Green Bay Packers). The event raised $630,000 to support the organization’s mission to transform the lives of children and families struggling with mental health and learning disorders.
The day’s programming began with a round of golf where participants enjoyed time on the course alongside fellow supporters. Following the tournament, guests gathered for an evening reception and seated dinner highlighted by a live auction featuring exclusive experiences, and an awards presentation for tournament winners. The event featured remarks from Harold S. Koplewicz, MD, president of the Child Mind Institute, and Brian Boitano, Olympic gold medalist skater, who talked candidly about the mental pressures of performing on a global stage.
Raj Mathai, 12-time Emmy Award winner and NBC Bay Area weeknight news anchor, hosted the event and served as the dinner program emcee and auctioneer.
During the reception, the Child Mind Institute announced it is now seeing patients in a new San Francisco location, in addition to their San Mateo clinic, making it easier for families across the city, Marin County, and the northern East Bay to access care.
“Even as we grow our presence here in California, we know this challenge is bigger than any one location,” said Dr. Koplewicz. “If we’re going to meet the need, we have to reach children earlier in spaces where they already are: at home, in schools, in their communities, and increasingly, in the digital spaces where they spend so much of their time. Technology is already shaping young people’s lives. Our responsibility is to make sure it also supports them.”
“Supporting mental health is fundamental to building stronger families and more resilient communities,” said Anthony Noto, CEO of SoFi. “We’re proud to partner with the Child Mind Institute to expand access to critical mental health resources for children and families, helping empower the next generation to realize their ambitions and reach their full potential.”
Additional sponsors include Prologis, the Silk Family, GingerBread Capital, and Platform Golf, as well as product and vendor support from Bay Golf Club, Dryvebox, Drops of Dough, Goated Golf, Moretz Marketing, Sightglass Coffee, and Supergoop. Tracy Toyota served as the event’s Hole-in-One Sponsor.
The SoFi | Child Mind Institute Golf Invitational event committee included Stacy Denman, Ronnie Lott, Kristin Noto, and Linnea Roberts.
Photos are available upon request.
About the Child Mind Institute The Child Mind Institute is dedicated to transforming the lives of children and families struggling with mental health and learning disorders by giving them the help they need. We’ve become the leading independent nonprofit in children’s mental health by providing gold-standard, evidence-based care, delivering educational resources to millions of families each year, training educators in underserved communities, and developing tomorrow’s breakthrough treatments.
About SoFi SoFi Technologies (NASDAQ: SOFI) is a one-stop shop for digital financial services on a mission to help people achieve financial independence to realize their ambitions. 13.7 million members trust SoFi to borrow, save, spend, invest, and protect their money and buy, sell and hold their crypto – all in one app – and get access to financial planners, exclusive experiences, and a thriving community. Fintechs, financial institutions, and brands use SoFi’s technology platform Galileo to build and manage innovative financial solutions across 128 million global accounts. For more information, visit www.sofi.com or download our iOS and Android apps.
BackgroundThe detrimental effect of stigma on healthcare for individuals with alcohol use disorders (AUDs) is well-established, often resulting in social distance and diminished helping behavior tendencies. However, contemporary neuroscience reconceptualizes addiction as a brain disease, potentially altering emotional responses to stigma. This study examines a seemingly paradoxical possibility: that under specific conditions, perceived stigma is primarily associated with sympathy (rather than anger or fear), which in turn is linked to helping behavior tendencies among nurses.MethodsA cross-sectional survey was administered to 348 clinical nurses from tertiary hospitals in China. Participants completed standardized scales assessing perceived stigma of patients with AUDs, causal attributions, emotional responses (including sympathy, anger, and fear), and helping behavior tendencies. Data were analyzed using Pearson correlation and mediation analysis (PROCESS macro, Model 4) with 5,000 bootstrap samples to test the mediating role of sympathy.ResultsPerceived stigma showed a significant positive correlation with sympathy (r= .160, p<.05), which was in turn positively correlated with helping behavior tendencies (r= .269, p<.05). Critically, mediation analysis revealed that sympathy fully mediated the relationship between perceived stigma and helping behavior tendencies. The standardized indirect effect was significant (β= 0.15, 95% CI [0.08, 0.23]), accounting for the total observed relationship, as the direct effect was non-significant. Additionally, compared to non-psychiatric nurses, psychiatric nurses perceived patients as significantly less dangerous and reported lower levels of fear and anger, along with a stronger intention to help and a lower tendency to avoid patients.ConclusionChallenging conventional perspectives, this study supports a dual-pathway model in which perceived stigma can indirectly associated with professional helping behavior tendencies through the elicitation of sympathy. While other emotions like anger and fear were also measured, the findings highlight the pivotal role of cognitive-affective processes, shaped by neurobiological understandings of addiction, in determining nursing care. Specifically, sympathy, but not anger or fear, was found to mediate the stigma-helping relationship. Enhancing neuroscience-informed education and targeted empathy training, particularly for general nurses, could transform stigmatizing attitudes into supportive care, ultimately improving outcomes for patients with AUDs.
Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey.
But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data — they must understand the business context behind it.
Without that context, AI can generate answers quickly but still make the wrong decision, says Irfan Khan, president and chief product officer of SAP Data & Analytics.
“AI is incredibly good at producing results,” he says. “It moves fast, but without context it can’t exercise good judgment, and good judgment is what creates a return on investment for the business. Speed without judgment doesn’t help. It can actually hurt us.”
In the emerging era of autonomous systems and intelligent applications, that context layer is becoming essential. To provide context, companies need a well-designed data fabric that does more than just integrate data, Khan says. The right data fabric allows organizations to scale AI safely, coordinate decisions across systems and agents, and ensure that automation reflects real business priorities rather than making decisions in isolation.
Recognizing this, many organizations are rethinking their data architecture. Instead of simply moving data into a single repository, they are looking for ways to connect information across applications, clouds, and operational systems while preserving the semantics that describe how the business works. That shift is driving growing interest in data fabric as a foundation for AI infrastructure.
Losing context is a critical AI problem
Traditional data strategies have largely focused on aggregation. Over the past two decades, organizations have invested heavily in extracting information from operational systems and loading it into centralized warehouses, lakes, and dashboards. This approach makes it easier to run reports, monitor performance, and generate insights across the business, but in the process, much of the meaning attached to that data — how it relates to policies, processes, and real-world decisions — is lost.
Take two companies using AI to manage supply-chain disruptions. If one uses raw signals such as inventory levels, lead times, and supply scores, while the other adds context across business processes, policies, and metadata, both systems will rapidly analyze the data but likely come up with different conclusions.
Information such as which customers are strategic accounts, what tradeoffs are acceptable during shortages, and the status of extended supply chains will allow one AI system to make strategic decisions, while the other will not have the proper context, Khan says.
“Both systems move very quickly, but only one moves in the right direction,” he says. “This is the context premium and the advantage you gain when your data foundation preserves context across processes, policies and data by design.”
In the past, companies implicitly managed a lack of context because human experts provided the missing information, but with AI, there is a shortfall and that creates serious limitations. AI systems do not just display information; they act on it. If a system does not explain why data matters, an AI model may optimize for the wrong outcome. Inventory numbers, payment histories, or demand signals might be accurate, but they do not necessarily reveal which customers must be prioritized, which contractual obligations apply, or which products are strategically important. As a result, the system can produce answers that are technically correct but operationally flawed.
This realization is changing how companies think about AI readiness. Most acknowledge that they do not have the mature data processes and infrastructure in place to trust their data and their AI systems. Only one in five organizations consider their approach to data to be highly mature, and only 9% feel fully prepared to integrate and interoperate with their data systems.
Don’t consolidate, integrate
The emerging solution is a data fabric: An abstraction layer that spans infrastructure, architecture, and logical organization. For agentic AI, the fabric becomes the primary interface, allowing agents to interact with business knowledge rather than raw storage systems. Knowledge graphs play a central role, enabling agents to query enterprise data using natural language and business logic.
The value of the data fabric relies on three components: Intelligent compute to provide speed, a knowledge pool to provide business understanding and context, and agents to provide autonomous action are grounded in that understanding. What makes this powerful is how these capabilities work together, says Khan.
The technology provides the architecture — a foundation that makes agent-to-agent communication and coordination possible. The process will define how businesses and IT share ownership, and establish governance and a culture in which people trust enough to adopt it. Now all three things must work together for a business data fabric to truly be successful.
“It empowers confident, consistent decisions, and when these elements all come together, AI just doesn’t analyze and interpret the data — it drives smarter, faster decisions that really create business impact,” he says. “This is the promise of a thoughtfully designed business data fabric, where every part reinforces the other, and every insight is grounded in trust and clarity.”
Technically, building a data-fabric layer requires several capabilities. Data must be accessible across multiple environments through federation rather than forced consolidation. A semantic or knowledge layer is needed to harmonize meaning across systems, often supported by knowledge graphs and catalog-driven metadata. Governance and policy enforcement must also operate across the fabric so that AI systems can access data securely and consistently.
Together, these elements create a foundation where AI interacts with business knowledge instead of raw storage systems — an essential step for moving from experimentation to real enterprise automation.
Beyond data isolation and dashboards
In the emerging era of agentic AI, the responsibility for monitoring, analyzing, and making decisions based on data increasingly shifts to software. AI agents can monitor events, trigger workflows, and make decisions in real time, often without direct human intervention. That speed creates new opportunities, but it also raises the stakes. When multiple agents operate across finance, supply chain, procurement, or customer operations, they must be guided by the same understanding of business priorities.
Without a common knowledge layer connecting disparate data together, coordination between systems quickly breaks down. One system might optimize for margin, another for liquidity, and another for compliance, each working from a different slice of data.
Importantly, most enterprises already possess much of the knowledge needed to make this work, says Khan. Years of operational data, master data, workflows, and policy logic already exist across business applications — companies just need to make it accessible. Companies that deploy data fabrics gain greater trust in their data, with more than two thirds of enterprises seeing improved data accessibility, data visibility, and exerting more control over their data.
“The opportunity isn’t just inventing context from scratch, it’s activating and connecting the context across your business that already exists,” he continues, adding that a data fabric is the “architecture that ensures data semantics, business processes and policies are connected as a unified system across all the clouds.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Background: Text generation approaches in health care communication have evolved along 2 major paths. The first path involves generative adversarial networks, progressing from basic architectures to specialized variants like Text-to-Text Generative Adversarial Network (TT-GAN) and Time and Frequency Domain-Based Generative Adversarial Network (TF-GAN), which address challenges in discrete text generation through techniques such as Gumbel-Softmax and reinforcement learning. The second path emerges from transformer-based architectures, particularly Generative Pretrained Transformer-2 (GPT-2), which uses extensive pretraining and self-attention mechanisms to generate contextually appropriate text. GPT-2’s transformer architecture enhances persuasive health communication by generating personalized messages using various strategies like task support, dialogue support, and social support for effective health interventions. Objective: This study aimed to use GPT-2 as a generative method to construct persuasive text in a dataset and compare the performance of sentiment analysis and emotion detection analysis. Methods: We combined sentiment analysis tools (VADER [Valence Aware Dictionary and Sentiment Reasoner] and TextBlob) with emotion detection methods (Text2Emotion and NRCLex [National Research Council Lexicon]) to analyze health coaching messages across different persuasive types: reminder, reward, suggestion, and praise. Results: TextBlob and VADER achieved accuracies of 57% and 69%, respectively, while RoBERTa (robustly optimized BERT approach)-sentiment outperformed them with an accuracy of 88%. Emotion detection showed a high prevalence of “joy” and “happy” labels (93.69% positive skew). While transformers excel in accuracy, lexicon-based models like VADER offer a better performance-efficiency balance for real-time health communication systems. For emotion detection, all categories showed perfect accuracy (1.0), while trust showed mixed results, with precision, recall, and -score values ranging from 0.81 to 0.96. The emotion detection analysis revealed varying success rates across different emotions, with some categories, such as anger and neutral, showing reasonable performance and others, such as trust, showing mixed performance. Conclusions: This research contributes to understanding the emotional dynamics of persuasive health communication and highlights both the capabilities and limitations of current natural language processing tools in analyzing health-related persuasive messaging. This proof-of-concept study using synthetically generated data establishes a methodological framework for multimodal sentiment and emotion analysis. The findings require validation with real-world health coaching messages before clinical deployment.
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Background: Youth experiencing early psychosis in West Africa often face delays in accessing evidence-based treatment. Digital mental health interventions may offer an acceptable and scalable approach to improve access to early psychosis care in West Africa; however, few data exist on the experiences and perspectives of patients with early psychosis and their caregivers to inform digital intervention development. Objective: This study aims to explore current experiences of early psychosis care, identify barriers and facilitators to in-person early psychosis care within health facilities, and identify opportunities for digital interventions to support patients with early psychosis and caregivers in Ghana. Methods: We conducted qualitative focus group discussions among patients with early psychosis, their caregivers, and their mental health care providers recruited at Accra Psychiatric Hospital in Accra, Ghana. Trained qualitative researchers facilitated discussions using a structured qualitative interview guide, exploring current care practices for early psychosis in Ghana, barriers and facilitators to facility-based care, and perceptions of digital mental health interventions. Transcripts were translated, transcribed, and analyzed thematically using a hybrid inductive and deductive approach grounded in the theoretical framework of acceptability. Results: Overall, we conducted 4 focus group discussions (N=31) among 7 patients with early psychosis (median age 28, IQR 21‐41 years), 6 caregivers (median age 58, IQR 29‐34 years), and 18 clinicians (median age 30, IQR 29‐34 years). Participants described current early psychosis care practices in Ghana, including seeking spiritual and traditional healing, the dearth of information and resources about psychosis, and the integral role of caregivers in facilitating treatment engagement and continuation (often at the cost of caregiver mental distress and burnout). Common barriers to facility-based mental health care included stigma associated with mental illness, lack of prior knowledge about early psychosis and treatment options, and practical constraints (eg, financial, logistical, and health care system limitations). Motivating factors for facility-based care included success stories from community members and strong rapport and trust in mental health clinicians. Technology (eg, mobile phones, laptops, radio, and television) was commonly used among participants in typical daily tasks, health information seeking, and stress reduction. Participants expressed support for digital tools that could deliver psychoeducation about early psychosis, support treatment adherence, and extend patient-provider communication between clinic visits. Conclusions: Digital mental health interventions have the potential to complement facility-based early psychosis services in Ghana by addressing misinformation, reducing access barriers, and supporting caregiver roles. These qualitative findings inform potential integration points, content, attributes, and strengths of digital modalities that could be leveraged to support patients with early psychosis and their caregivers in Ghana.
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