CSR In The Pharmaceutical Industry

"Communities and countries, and ultimately the world, are only as strong as the health of their women," Michelle Obama once said. The impact of women’s health on our societies cannot be overlooked. The theme of today’s International Women’s Day is #InspireInclusion. To me, closing the gender health gap is an important requirement to create a truly inclusive society. Women are still underdiagnosed, undertreated, and underserved – often due to a historical lack of women-centric research and female health data. They face serious health risks because of this. Last year, a study suggested that women may be twice as likely to experience a fatal heart attack because of unrecognized unique risk factors. In its recent report on women’s health, the World Economic Forum revealed that women are diagnosed later than men: 4.5 years later for diabetes. 2.5 years for cancer. These years can cost lives. Genetics and environmental factors might be at play here, but gender bias is also an important factor. The latest #WEF report suggests that addressing this bias and closing the women’s health gap would allow 3.9 billion women to live healthier and higher-quality lives. As the gender health gap really is essentially a female data gap, AI and digitalization offer huge opportunities to transform women’s health. Health apps can facilitate access to services and empower women with technology that is tailored to their needs and lifestyles, for instance. AI can help process huge amounts of anonymized data that may help close the gap. I strongly believe that health equity and inclusion are about overcoming disparities, about looking for what unites us. However, in order to tackle the gender health gap, we must first acknowledge the differences. Male bodies have represented humanity for too long, with women treated as “small men”. The COVID-19 pandemic, for instance, proved just how untrue that is. It revealed the fundamental gender differences in the immune system – just one instance where a human organism’s gender matters. One interesting fact: Women account for almost 80 percent of people with autoimmune diseases. Immunologist Akiko Iwasaki, who was honored with the Else Kröner Fresenius Prize for Medical Research in 2023, has devoted herself to teasing apart the differences between the immune responses of men and women to COVID-19 and other viral infections. Incidentally, our very own female leader, Else Kröner, was an early advocate of better healthcare for women. In 1973, she joined the international women's association #Zonta and became one of its most active German leaders. To this day, Zonta remains committed to improving health access for women and among others to equal rights issues. From Else Kröner to Akiko Iwasaki, countless remarkable women have made tremendous contributions to improving women’s health. Kudos to their commitment! Let’s take this day as an opportunity to raise further awareness and to commit to advancing this important topic. #IWD2024

As a physician and advocate, I've seen the stark realities of healthcare inequality up close. It's a multifaceted challenge, deeply rooted in socioeconomic disparities, systemic barriers, and historical injustices. Yet, it's not insurmountable. We have the tools, the knowledge, and the collective will to forge a more equitable future in healthcare. The path forward involves a holistic approach: 1️⃣Embrace Preventative Care: Early intervention can prevent conditions from escalating into serious diseases. Community-based health education and accessible preventative services are key. 2️⃣Expand Telehealth: Telehealth can transcend geographic and transportation barriers, making healthcare accessible for all, but we must ensure it's equitably deployed. 3️⃣Diversify the Healthcare Workforce: A workforce that reflects the diversity of the population it serves can improve patient outcomes and trust. 4️⃣Advocate for Policy Change: Systemic change is essential. We need policies that ensure universal healthcare access and tackle the social determinants of health. Change won't happen overnight, but each step brings us closer to a healthcare system defined by its inclusivity and equity. Let's work together to make healthcare a right, not a privilege. #HealthcareEquity #SystemicChange #PreventativeCare #Telehealth #DiversityInMedicine #PolicyChange

The “One Big Beautiful Bill” is shaping up to be one of the most sweeping overhauls to federal healthcare policy in more than a decade—and we must be clear about what’s at stake.    While the bill sidesteps a full repeal of the ACA, it introduces deep structural changes that could jeopardize access to care for millions, especially those served by Medicaid, safety-net hospitals, and rural providers. With more than $1 trillion in projected cuts to Medicaid and related programs, we’re looking at stricter eligibility rules, work requirements, and state-level funding limitations that could destabilize the very providers communities rely on most.    For those of us who believe in the promise of value-based care, this shift is troubling. VBC thrives on stable infrastructure, cross-sector collaboration, and a strong foundation of access. When coverage erodes, so does the ability to deliver consistent, preventive, and equitable care—particularly in underserved areas.    There are some bright spots, such as expanded rural hospital payments and continued telehealth flexibility. But without additional investment—especially in broadband infrastructure—these advances might not reach the communities that need them most.  We can’t afford to go backward. It’s vital that policymakers, healthcare leaders and advocates speak up to protect coverage, strengthen delivery systems, and support sustainable care models that improve outcomes for all.  #healthcare #OneBigBeautifulBill #ValueBasedCare 

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

Data privacy is a leadership responsibility. In healthcare, trust is built long before a patient interacts with a product, a clinician, or a digital experience. It’s built in how we govern data, how we secure it, and how intentionally we decide when and how it’s used. As analytics and AI unlock powerful new ways to advance care, the obligation to protect information only grows. A few principles I believe matter most right now:  1️⃣ Privacy by design, not by retrofit. Governance and security must be embedded from the start.  2️⃣ Use data with purpose. Patient benefit should lead every decision.  3️⃣ Security is a shared responsibility. Cyber resilience relies on a culture that values continuous learning and accountability across the enterprise.  4️⃣ Transparency builds trust. Clear communication about how data is protected matters. At #JNJ, protecting patient and customer data goes hand in hand with using analytics responsibly to improve outcomes. This work is made possible by strong partnership across our technology and security teams, including leadership from Gary Harbison, our CISO at Johnson & Johnson. As our industry continues to evolve, strong data stewardship will remain one of the clear-cut indicators of trustworthy leadership. #DataPrivacyWeek #DataPrivacyDay

In my latest Forbes column, I propose five practical reforms that could enhance the Medicare Advantage program, making it more transparent, equitable, and effective for the seniors and communities it serves: 1) Pilot Mult-Year Enrollment in Plans. No matter what anyone says about their value-based care results, value is ill-produced in healthcare in one year increments. 2) Standardize Plan Benefits. Plans are competing on nonsense and unsustainable benefits—often leading to a bait and switch for seniors. Standardizing benefits will improve plan competition and ensure every plan delivers consistent and meaningful value. 3) Reform broker incentives, rewarding year-round member support—not just sign-ups. Effectively deployed, brokers can be the missing glue we need t improve American healthcare. 4) Vary maximum broker commissions with star ratings. Star ratings should be an accelerant to sales. Fact: Today, a good number of brokers will tell you they don’t even know plan star ratings when they sell them. 5) Drive adoption of capitated payments to align provider incentives toward coordinated, value-based care. Health systems have been snookered by health plans who non-transparently push off the costs of supplemental benefits and other expenses. This explains why so many health systems complain of underpayments while MEDPAC claims Medicare Advantage overpayments. There is a better way. Collectively, these reforms aim to improve the quality of the Medicare Advantage marketplace. As we collectively work toward building a more sustainable healthcare system, these are actionable steps that lawmakers, payers, providers, and advocates can take now. I invite you to read the full article and share your thoughts: how might these recommendations reshape Medicare Advantage—and what additional levers are we missing? https://lnkd.in/gQmevX5K

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

Seven years ago, The Rockefeller Foundation made a bet: that a small amount of patient, risk-tolerant capital could unlock investment that private markets weren't yet ready to make on their own. The Rockefeller Foundation’s Zero Gap Fund's 2025 State of the Portfolio report shows the results. $30 million in charitable capital has helped mobilize $1.05 billion in private investment, a 35x return reaching people in underserved communities through food security, climate adaptation, healthcare, and U.S. jobs. Behind those numbers are real people. A growth equity fund has reached 362 million consumers across Asia and Africa through financial services and healthcare access. An employee-ownership model has converted six companies into worker-owned businesses, creating more than 1,500 new employee owners. And in Ukraine, a technology investment fund is supporting more than 5,100 jobs even as the country's economy absorbs the shock of war. As wealthy nations pull back, cutting more than $40 billion in aid last year alone, the UN estimates the world now needs $4 trillion a year to achieve its Sustainable Development Goals. Philanthropy alone can't fill that gap. But it can invest courageous capital, prove what works, and build the kind of partnerships that get private capital moving toward the world's pressing challenges. Read the full report: https://lnkd.in/e4H7zjXk

🎤 New Publication Alert 🎉 I used a nationally representative survey to examine how medical (dis)trust and experiences of discrimination affect shared decision-making across racial and ethnic groups. 💡 What did I find? Regardless of race and ethnicity, medical mistrust reduces the odds of shared decision-making, and high-quality care increases the likelihood of shared decision-making 🚨 But when teasing out the data in the adjusted predicted models, we see a clear pattern: Experience of discrimination and medical mistrust, particularly among Black and Hispanic women, significantly reduced the predicted probability of shared decision-making. This group saw the steepest decline in collaborative care. 🔦 Big takeaways: 1. While Black women demonstrate high levels of patient advocacy, they still encounter systemic obstacles that threaten the quality of their clinical interactions. 2. Hispanic women face the most dramatic declines in collaborative care when experiencing discrimination. 3. Systemic failures have deeply eroded trust among women, particularly those from marginalized communities. 4. I argue that achieving health equity requires the elimination of discriminatory practices and the fostering of woman-centered care to rebuild institutional trust. Take a read and share your thoughts. https://lnkd.in/gnA6Yc4a #healthequity #shareddecisionmaking #womenshealth

Medical algorithms have long scaled bias under the guise of objectivity. If we don't explicitly test AI for equity, we risk automating the very inequities we're trying to dismantle. Take dermatology: AI models routinely fail to describe how conditions like psoriasis present on darker skin — violaceous, dark brown, or gray plaques. Without intentional design, these diagnostic blind spots get replicated at scale. 📋 So we built a Health Equity evaluation into Doximity Ask. Our Health Equity & Inclusion team partnered with clinical fellows to develop 30 specialized prompts and 150 criteria — auditing the model for racial bias, skin tone representation, and safety for marginalized populations. All grounded in a physician-governed evidence library with citation-based generation. As Louis Mullie, MD writes: "The appropriate response is not to assume errors can be eliminated, but to measure them, make them visible, and continuously evaluate whether they are becoming less frequent and less clinically significant over time." Technology is never neutral. Designing for a single "default" user is a choice — and one we can no longer afford to make. When we build with historically excluded groups, we don't just improve equity. We improve accuracy for everyone. That's the curb-cut effect in action. The future of clinical AI can't be a black box. The tools that earn clinician trust will be the ones built with transparency, reliability, and equity by design. 🔗 https://lnkd.in/eYhna8yG