Digital Health Tools

This paper delves into the potential of digital transformation in reshaping the delivery of efficient, high-quality, and secure #Healthcare. The authors highlight the immense promise digital transformation holds for the development and deployment of new care models. By integrating information, computing, communication, and connectivity technologies, digital transformation can revolutionize clinical care processes. The paper also emphasizes the potential disruptions traditional medicine might face with the entry of digital health care companies. However, it underscores the significant opportunities that arise from innovative partnerships between traditional and digital providers. 1️⃣ Digital transformation's role in healthcare: The paper emphasizes how digital transformation can significantly enhance organizational efficiencies. By leveraging technology, healthcare institutions can transform patient care models, emphasizing patient empowerment and active participation in their health journey. 2️⃣ Potential disruption in traditional #Medicine: With the rise of digital healthcare companies, traditional medical practices are at a crossroads. These digital entities are reshaping consumer expectations and putting pressure on conventional healthcare models to innovate. 3️⃣ Emerging technologies in digital healthcare: Companies in the digital healthcare space are harnessing the power of #ArtificialIntelligence, telemedicine, and blockchain electronic health records. These technologies streamline workflows, reduce errors, and ultimately lead to improved patient outcomes. 4️⃣ The promise of collaborative models: The paper suggests that there's immense potential in collaborative models between traditional and digital healthcare providers. These collaborations can span across various clinical care value-chain activities, offering a more holistic approach to patient care. 5️⃣ Use cases - Diabetes and IBD: The authors present diabetes and Inflammatory Bowel Disease (IBD) as practical examples to demonstrate the potential of digital-traditional collaborations. For instance, in diabetes care, digital tools can provide continuous feedback, medication tracking, and provider recommendations, while traditional practices offer diagnostics and routine screenings. The paper offers a comprehensive insight into the transformative potential of digital healthcare. It not only highlights the challenges faced by traditional medical practices but also presents actionable solutions through collaborative models. For anyone keen on understanding the future trajectory of healthcare, this paper provides a roadmap for harnessing the power of digital transformation. 🌐⇢ https://lnkd.in/epr_q3YS ✍🏻 Jon O. Ebbert, MD, Rita G. Khan, MBA, Bradley C. Leibovich, MD. Mayo Clinic Proceedings: Digital Health. Published:March 25, 2023. DOI: 10.1016/j.mcpdig.2023.02.006

💡 AI in healthcare has a data problem—EHRs weren’t built for it. Electronic Health Records (EHRs) are often seen as a goldmine for AI, particularly in clinical decision support. ❗ But there’s a fundamental issue: EHRs were never designed for AI. As Louis Agha-Mir-Salim and colleagues recently pointed out [1], these systems impose a rigid structure optimized for billing and administration, often at the expense of medical usefulness, research, and learning. The data they collect is incidental, shaped primarily by workflow constraints—not by medical needs and certainly not by the requirements for building accurate AI models. The “Invisible Success” Problem A recent piece by Hugh Logan Ellis and colleagues [2] highlights this perfectly: Take non-AI Early Warning Scores (EWS) in the ICU. These scores are designed to predict patient deterioration, and when they work—when doctors intervene early—the event effectively disappears from the data. For AI models trained on EHRs, these cases where early action made a difference are invisible, distorting the dataset and reinforcing bias. This isn’t just an isolated issue—it applies to every EHR dataset where an effective intervention already exists. And that’s just the beginning. Other Critical Problems with EHR Data for AI ⚠️ Messy and incomplete data – Human entry errors, missing values, and inconsistencies degrade model performance. ⚠️ Interoperability issues – Different hospitals and vendors use non-standardized formats, making data integration and harmonization difficult. ⚠️ Temporal biases – Data isn’t collected at consistent intervals, making patient trajectory modeling unreliable. ⚠️ Ethical and privacy risks – AI models must balance data utility with patient rights and transparency. We Need to Get Rid of the “Kaggle Mentality” Too often, AI in healthcare is driven by data availability rather than clinical need. This “Kaggle mentality” leads to models being built simply because a dataset exists—not because it actually solves a well-defined clinical problem. We Need a New Mindset 🔹 AI development should start with a clearly defined clinical need and a priori data characteristics—not just whatever data happens to be available. 🔹 Datasets should be characterized for specific use cases and labeled accordingly. Not all data is suitable for all AI applications, and pretending otherwise leads to misleading claims. 🔹 Hot take: It’s a waste of resources to let individual researchers and startups decide which data is useful—especially when their work is often backed by unscrutinized claims to funders like VCs who lack the expertise to evaluate these complexities. We need to move beyond opportunistic, dataset-driven development and instead align data strategy with real clinical needs. #AIinHealthcare #MachineLearning #EHR #DataBias #ClinicalAI #HealthTech 🔗 References: [1] https://lnkd.in/dTTCWJnM [2] https://lnkd.in/dzKTSxtA

The biggest barrier to AI transforming healthcare? It’s not the tech. Before we can fully benefit from AI in healthcare, we have to confront these issues. Because AI in HC is not without challenges. And understanding the challenges is critical to unlocking AI’s full impact in HC. Here are some of the challenges that need to be addressed before we can fully benefit. (And yes, there are more than these). 𝗘𝘁𝗵𝗶𝗰𝗮𝗹: - Bias AI may perpetuate or amplify biases in HC data, leading to unequal care across demographics. - Impact on Patient-Provider Relationship The use of AI may reduce human interaction, empathy, and personalized care, potentially dehumanizing HC. - Environmental and Social Implications AI consume significant resources and energy, raising ethical questions about environmental sustainability and the social consequences of workforce displacement. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹: - Integration with Legacy Systems Difficulty in connecting AI tools with outdated IT infrastructure. - Handling Unstructured Data Large volumes of data are unstructured and hard for AI to analyze. - AI Hallucinations and Reliability Issues AI models sometimes generate incorrect or fabricated outputs that can mislead clinical decisions. 𝗠𝗲𝗱𝗶𝗰𝗮𝗹: - Managing Increased Demand from AI-Driven Diagnostics AI-enhanced disease detection may increase demand for follow-up tests and interventions, potentially overwhelming HC capacity. - Clinical Scope and Generalizability AI models may have limited applicability outside the specific clinical contexts or patient populations they were trained on. - Alignment with Local Care Practices AI systems need to be adapted to the unique workflows, protocols, and standards of care specific to each HC setting or region. 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆: - Need for Adaptive and Forward-Looking Regulation Current regulations lag behind AI innovation, creating gaps in oversight and compliance. - Governance of AI Use by Clinicians and Patients GenAI tools like ChatGPT are increasingly used by clinicians and patients without clear policies or training. - Liability and Accountability in AI-Driven Care Who is legally responsible when AI systems cause patient harm remains unclear and complex. 𝗧𝗿𝘂𝘀𝘁:  - Workforce Resistance Distrust in AI due to fears of job displacement or lack of transparency. - Transparency and Explainability Many AI tools make it difficult for clinicians and patients to understand how decisions are made. - Reliability and Performance Over Time AI models may become less accurate over time. 𝗗𝗮𝘁𝗮: - Limited Digitalization of HC Data A lot of HC data is not digitized, limiting AI’s access to comprehensive information. - Data Quality and Accuracy HC data often contains errors, inconsistencies, missing values, and outdated information. - Data Privacy and Security HC data is highly sensitive, raising concerns about unauthorized access and breaches. What challenges would you add to the list? 

I have always been interested in Geography, and working across several continents keep that passion burning. Anyone who is interested in Geography will naturally be drawn to maps. And, I have always had a fascination with the shape of Chile as a country. It is unique. It is the longest country north to south, extending across 39 degrees of latitude. But that uniqueness also presents unique challenges when it comes to healthcare delivery. Chile has long struggled with connectivity between regions, patients, and health institutions. The National Cancer Plan aims to address these issues by enhancing infrastructure and equipment, ensuring timely and quality care for all cancer patients. However, the implementation of advanced technologies like Digital Pathology has faced hurdles, including sub-optimal experience in pathology labs and a lack of trained personnel. Roche Chile has partnered with the Chilean health system to advance cancer care through the incorporation of Digital Pathology into the National Cancer Network. This initiative is a significant milestone, marking the recognition of digital pathology's importance in the National Cancer Plan 2024. Roche has developed a health system modelling strategy to accelerate the adoption of digital pathology in Chile. The strategy includes a pilot implementation in a leading public institution for breast cancer, generating local economic evidence on productivity and diagnostic outcomes, and hosting a co-creation workshop with key stakeholders, including the Ministry of Health, academia, and digital hospitals. The potential impact of this initiative and others like it is substantial. Digital pathology will, I believe, enable seamless connectivity between pathology labs across public hospitals, fostering a national network for expert inter-consultations. This means faster, more accurate diagnoses for patients, significantly reducing waiting lists and improving survival rates and quality of life for approximately 60,000 oncology patients in the public system. The inclusion of digital pathology in the National Cancer Plan 2024, backed by a dedicated budget, is a monumental step forward. It underscores the commitment to leveraging innovative solutions to enhance cancer care, ensuring that more patients benefit from prompt and precise diagnoses without incurring additional costs.

We’re excited to share research from our health team at Microsoft AI: a proof-of-concept showing that AI can master medicine’s most intricate diagnostic challenges by following the same step-by-step reasoning expert physicians use. There's more detail in our pre print paper & blog Paper-> https://lnkd.in/egDiNsqR Blog-> https://lnkd.in/esGFhSeB Sharing what I'm most excited about from this work. 1. Benchmarks Traditional medical benchmarks like the USMLEs condense clinical cases into neat multiple-choice questions—far from the real clinical workflow. We’ve approached things in a different way: Sequential Diagnosis Benchmark (SDBench) deconstructs 304 of the most diagnostically complex and demanding cases in medicine published in the New England Journal of Medicine. SD Bench requires models—and physician—to begin with an initial presentation, ask follow-up questions, order tests, and converge on the confirmed diagnosis—just as in routine clinical practice. You can see how this works in a video with Xiao Liu on our blog. 2. Performance With this new benchmark we tested a suite of the best known generative AI models against the 304 NEJM cases with impressive out-the-box performance. Beyond this we developed the Microsoft AI Diagnostic Orchestrator (MAI-DxO). By emulating a virtual panel of physicians with diverse thinking styles, MAI-DxO boosts raw model accuracy and solves a remarkable 85.5% of NEJM cases. For comparison we evaluated 21 practicing UK/US physicians and on the same tasks, these experts achieved a mean accuracy of 20%. 3. Costs One of our concerns was that AI would default to ordering every investigation to arrive at the correct diagnosis. So we set the system up such that each requested investigation also incurred a cost. This allowed us to evaluate performance against both diagnostic accuracy and resource expenditure. As MAI-DxO is configurable it is seen to operate along a Pareto frontier of accuracy versus resource use. What’s next: While for now exciting research, we believe this kind of superhuman clinical reasoning will in future reshape medicine. A particular focus for our group is on consumer health. Today, Bing and Copilot answer over 50 million health queries daily—from a first-time knee-pain search to finding a late-night pharmacy. We’re committed to bringing rigorous and reliable AI support into these journeys, backed by clinical evidence and robust commitments to quality, safety and trust. A huge shout-out to everyone on our new team who contributed, our partners across Microsoft, and particularly to Mustafa Suleyman for his vision. He saw the opportunity for AI to improve healthcare more than a decade ago and it now feels like this is the right time to deliver on the opportunity. Harsha Nori Mayank Daswani Christopher Kelly Scott Lundberg Marco Túlio Ribeiro Marc Wilson Xiao Liu Viknesh Sounderajah Bay Gross Peter Hames Eric Horvitz Charlotte Cooper Simpson, PhD

In healthcare, progress only matters when it reaches patients. Digital pathology is helping make that happen. For decades, pathology relied on tissue samples on fragile glass slides and manual workflows. Getting a second opinion could take time and, in some cases, physical samples had to be shipped, with the risk of delay or damage. Digital pathology is changing that. High-resolution imaging and AI can help pathologists see more, faster and with greater precision. They can also help identify patterns beyond what the human eye can detect alone. And once slides are digitised, cases can be shared instantly, anywhere. What inspires me most? How this innovation breaks down barriers. Digital pathology enables collaboration across institutions and borders, helping specialists connect and bringing answers to patients faster. In 2021, Roche launched the Digital Pathology Open Environment, which encourages collaboration among software developers to improve patient outcomes and expand personalised healthcare through innovative image analysis. Today, together with partners such as Dr Dolores Lozano Escario and the team at the Clínica Universidad de Navarra in Pamplona, Spain, we are helping redefine how we study and detect disease. When technology supports human expertise, the impact is real: better insights, greater confidence and ultimately better outcomes for patients.

HealthTech AI is no longer exciting. It’s expensive. And the market has re-priced itself for performance. The first half of 2025 solidified a new reality in digital health. US-based digital health startups secured $6.4 billion across 245 deals (Rock Health). While total funding is up from H1 2024, the trend of fewer, larger checks persists. Rock Health pegs the average deal size at a robust $26.1 million, a significant increase from $20.4 million in 2024, signaling a concentrated investment in more mature, impactful companies. Investors are no longer buying potential. They're buying precision and demonstrable value. They care if your AI: Saves hours, not just clicks: The focus is on quantifiable time savings for clinicians and administrative staff, directly addressing burnout and efficiency gaps. Cuts costs, not just code: Real-world cost reduction is paramount, whether through optimized operations, reduced errors, or improved resource allocation. Embeds in real workflows, not pitch decks: Solutions need to be seamlessly integrated into existing healthcare systems, proving their utility in daily practice. McKinsey calls this the "productivity premium," and it has become the new funding filter. A significant portion of VC dollars continues to flow into AI-enabled startups, not because they're novel, but because they perform and deliver tangible returns. Abridge: This AI note-taking startup for doctors raised a staggering $316 million in June 2025 (Series E), bringing its total funding to over $770 million. Its value proposition is clear: giving clinicians hours back by automating documentation. Innovaccer: Secured $275 million in Series F funding in January 2025 to expand its AI and cloud capabilities, aiming to be a "one-stop shop" for healthcare AI solutions. They focus on data aggregation and intelligence to optimize value-based care programs and reduce administrative burden. Truveta: Raised $320 million in Series C funding in January 2025, solidifying its position in health data and analytics. Their mission revolves around leveraging data to drive insights and improve care. Hippocratic AI: Completed a $141 million Series B financing round in February 2025, valuing the company at $1.64 billion. Their focus is on developing safe, patient-facing AI for non-diagnostic tasks, addressing healthcare staffing shortages. These companies optimize operations, not optics. The delta? Execution. This is not a hype cycle. It’s a competency correction. The end of vision-only founders. The rise of operator-founders who understand: Unit economics: The true cost and value generated by each patient interaction or service delivered. Integration latency: The speed and ease with which new technologies can be embedded into complex, often legacy, healthcare IT infrastructure. Reimbursement drag: Navigating the intricate and often slow process of getting innovative solutions covered by payers. What part of this feels uncomfortably true?

💎 What a gem! Stark’s Web Accessibility Library (https://lnkd.in/eistWYpN), with handpicked design patterns, guides and collections on accessibility — from articles and books to checklists and tools. Just in time for European Accessibility Act — for designers and engineers. One for the bookmarks! To many companies, accessibility is still a highly specialized, technical, and confusing term. They often relate it to technical implementation details and optimizations for specialized tools such as screen readers — rather than designing a resilient and clear experience that everybody can benefit from. I always try to make accessibility more relatable to people who might have a wrong perception of it. For example, I explain that glasses and magnification are assistive technologies. That accessibility isn’t an on/off condition but a spectrum. I show that it can be temporary or situational — when you are holding a baby, or get stuck in a noisy airport. And I show how real people who happen to colorblind, deaf or neurodivergent use real products in real situations. Products are rarely accessible by accident. There must be an intent that captures and drives accessibility efforts in a product. And the best way to do that is by involving people with temporary, situational and permanent disabilities into the design process. Accessibility doesn’t have to be challenging if it’s considered early. No digital product is neutral. Accessibility is a deliberate decision, and a commitment. Not only does it help everyone; it also shows what a company believes in and values. And once you do have a commitment, it will be so much easier to retain accessibility, rather than adding it last minute as a crutch — because that’s where it’s way too late to do it right, and way too expensive to make it well. And yet again, a kind word of support to everyone speaking for and supporting accessibility work, often with a lot of resistance, with very little budget and with a lot of care and persistence — to help people who often need help most, and add benefits for everybody else. You are my heroes. 👏🏼👏🏽👏🏾 #ux #accessibility #design

Empathy-powered. Digitally enabled. Patient connected In today’s fast-evolving healthcare landscape, connected care isn’t just about tech—it’s about enhancing human connection at every touchpoint. Key insights from Deloitte ’s 2025 Global Health Care Executive Outlook show how we can harmonize digital transformation with the human-centric care our patients deserve: 1. Prioritize integrated digital platforms • ~70% of global C‑suite leaders are investing in digital tools and services to enable seamless patient journeys . • This connectivity supports continuous care—whether in-hospital, remote, or at home. 2. Modernize core systems while keeping the human anchor • 60% are upgrading EMRs and ERP systems . • When clinicians can access integrated data swiftly, they spend less time documenting and more time connecting with patients. 3. Embed empathy into every digital interaction • Cybersecurity (78% prioritize) builds trust—patients feel cared for when their data is protected . • A secure, respectful environment is the foundation for truly human-centered care. 4. Enhance clinician well-being to improve connectedness • 80% of leaders recognize workforce strain; digital tools can reduce burnout and foster deeper patient engagement . • When staff feel supported, they show up both professionally and emotionally. 5. Expand virtual and hybrid care with a personal touch • 65% of consumers find virtual care more convenient —but scaling it successfully means integrating empathy and follow-up. • Reimagining care pathways ensures consistent human connection, whether digital or face-to-face. ⸻ 🎯 Managing connected care with humanity means: • Leveraging interoperable systems that share real-time insights across care teams. • Training clinicians in digital empathy—listening through the screen, addressing emotional cues. • Designing secure, intuitive platforms that empower patients without overwhelming them. • Supporting staff with AI-driven admin relief, enabling them to focus on people. • Creating holistic care pathways that blend telehealth, in-clinic, and home-based services under one cohesive plan. By weaving technology into our care systems thoughtfully, we can create a healthcare experience that’s efficient, personalized, and emotionally resonant. Looking forward to your thoughts: how is your organization balancing connectivity with compassion? Sara Siegel Link to the report: https://lnkd.in/etDPEc3a #connectedcare

Some digital health companies go through such an interesting evolution! Empatica used to focus on developing a wearable sensor that could help detect epilepsy seizures and alert caregivers. Then they got into the business of developing wearables that can be used during clinical trials. Now they have acquired a company and plan to integrate its movement disorder algorithms into Empatica's wearables to better monitor Parkinson’s disease. The acquisition positions the company deeper into neurology beyond just vitals. It seems that the wearables + smart algorithms combination highlights the next frontier in chronic neuro care. And those startups that can mature with the whole field of digital health and are ready to reposition themselves might get a competitive advantage. Source: https://lnkd.in/eiJXZi8A