Nancy Colton

Great insight by my colleague David Devine 🚀 Transforming Healthcare with Data Analytics 🚀 The North American healthcare analytics market is projected to soar to $11.4 billion by 2023, up from $3.13 billion in 2018. Despite this growth, many healthcare providers are still catching up with the advancements seen in other sectors, such as medical devices and payers. Key Highlights: Real-Time Decision Making: Leveraging basic patient data to make immediate care decisions. Predictive Analytics: Using machine learning to predict and reduce hospital readmissions, saving billions annually. Combating the Opioid Crisis: Data tools are crucial in monitoring e-prescribing practices and integrating with state prescription drug monitoring programs. Future Strategies: Moving beyond traditional data sets to include less structured data, such as genomic and social data, for a comprehensive analysis. Healthcare leaders must think, plan, and act differently to harness the full potential of data analytics. By advancing data strategies and focusing on both structured and unstructured data, we can transform patient care and outcomes. Let’s embrace the power of data to create a more efficient, patient-centric healthcare system! 🌟 https://lnkd.in/ebu35PAh #Healthcare #DataAnalytics #MachineLearning #AI #BigData #HealthTech

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great analysis by Defacto Health about the state of provider directory APIs. good to pair with Flexpa's state of patient access APIs

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The shift towards value-based care is transforming how patient care is delivered and CMS is continuing to increase the the adoption of these APMs (alternative payment models). Your Health is continuously leveraging data from multiple sources, to give us the insights to deliver the most precise, timely and valuable care to our patients. Are you a payor looking to reduce overall costs while improving quality outcomes and people's experience of care? #healthcare #primarycare #yourhealth #VBC

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In case you missed it last week, we discussed "TEFCA Operations (aka TEFCA for Payers)". Key Topics Covered - Payer Use Cases: Exploration of data exchange use cases and the regulatory landscape (CMS-9115, CMS-0057, HTI-2). - TEFCA Operations Overview: How payers can access data for care coordination, HEDIS reporting, and quality measures. - Comparative Analysis: TEFCA Operations vs. alternatives like Individual Access Services (IAS) and secondary use patterns. - Data Reciprocity: The importance of avoiding asymmetric relationships in health information networks. - Regulatory Integration: Potential integration of TEFCA with CMS-0057 requirements. -Adoption Timeline: Overview of upcoming deadlines and the current state of TEFCA adoption. We had great discussion after the overview presentation, tackling: - Challenges in managing multi-line business organizations and data usage rules - The February 2026 deadline for certain healthcare operations exchange purposes - Industry participation in real-world testing initiatives (e.g., NCQA's Bulk FHIR Quality Coalition) - Integration of TEFCA and CMS-0057 requirements, particularly for provider access API and payer-to-payer API - TEFCA's potential as a foundation for payer-to-payer exchange - The significance of the 10x10 initiative in shaping healthcare interoperability - Proposal for an "11x11" initiative to include health plans working with pharmacies/pharmacists in TEFCA operations Check it out here! https://lnkd.in/gP4ys37W

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The Secret to Driving AI Impact Isn't in the Algorithms – It's in the Execution. Over my 10 years in the AI/ML industry, leading teams at IBM Watson, Centene Corporation, and Datavant, I've seen one common thread that separates successful AI initiatives from failures: It’s not just about having the smartest data scientists/MLEs or the most cutting-edge algorithms – it's about bridging the gap between AI potential and business value. Here’s what I've learned about translating AI into real business impact: 1. Start with the Problem, Not the Tech Too often, I see companies enamored with the latest AI trends without a clear understanding of the business problem they aim to solve. The most successful projects I’ve led started with a well-defined pain point. AI is only effective when it’s solving a real, high-priority issue. 2. Build a Cross-Functional Team AI isn't just for data scientists. You need domain experts, product managers, and operational leaders who understand the intricacies of your industry. When I led a project that saved a healthcare company millions, it wasn’t just AI magic – it was a collaborative effort between our tech team and healthcare professionals. 3. Establish a Robust MLOps Framework Early Deploying a model isn't the finish line – it's just the beginning. One of the proudest achievements in my career was building an MLOps platform that allowed my team to monitor, iterate, and scale models efficiently. This ensured our solutions adapted as the business evolved, rather than becoming outdated and irrelevant. 4. AI Governance is Non-Negotiable Data privacy, ethical considerations, and transparency are not just buzzwords. Establishing an AI governance framework should be a top priority. When I implemented AI governance at scale, it not only safeguarded us from potential risks but also built trust with stakeholders, accelerating AI adoption across the organization. 5. Communicate the Value to Non-Technical Stakeholders One of the biggest challenges in AI leadership is getting buy-in from non-technical stakeholders. Early in my career, I struggled with this, but now I focus on framing AI outcomes in terms of revenue, cost savings, or customer experience improvements. This shift in communication led to multiple successful AI initiatives gaining executive sponsorship. 💡 Pro tip: Stop obsessing over “explainability” in purely technical terms. Instead, illustrate how the model’s insights translate into actionable business strategies. I’d love to hear your experiences – what’s the biggest challenge you’ve faced in implementing AI? How did you overcome it? #AILeadership #AIExecution #MLOps #BusinessImpact #AIProductManagement

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Interesting perspective on #TechnologicalDebt in health care by Dave Lamar focused on the provider market, but we seem the same issues within state depts and payers. While good data governance practices are key, there must be a better way to solve this common - if not universal - challenge in health care, if only by offloading some of the administrative responsibilities that require the individual management and administration of no-value-add administrative functions. #healthdata

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I work with so many great minds. They uncover so many great data insights. For example, a recent dive into the data by Press Ganey uncovered something fascinating: a compelling link between patient experience and safety perceptions. Patients who felt their clinical safety was a priority were far more likely to give the hospital a top score on recommending the hospital. On the flip side, those who had doubts about safety were less inclined to recommend. The data revealed the following: Among patients who reported an optimal safety experience, approximately 85% indicated they would "definitely recommend" the hospital. In contrast, among patients who did not report an optimal safety experience, only 34% stated they would "definitely recommend" the hospital.   This significant disparity—a difference of 51 percentage points—underscores the critical importance of clinical safety in shaping patient perceptions and influencing their likelihood to recommend the healthcare facility.   Key takeaway:   This finding underscores the critical importance of safety in shaping overall patient satisfaction. By acknowledging this crucial relationship, progressive safety and experience experts can develop more comprehensive strategies.

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We’re excited to share Optum Rx is the first to develop a new capability that helps address a major provider pain point: prior authorizations. Prior authorization is an important checkpoint helping patients receive coverage for safe, effective care supported by the most up-to-date clinical guidelines. But the process can add manual, time-consuming tasks to providers’ already heavy workloads. Together with our partners, Surescripts and Cleveland Clinic, we developed Optum Rx PreCheck Prior Authorization, an innovative solution that helps automate the prior authorization process for select drugs, including GLP-1s – from 8.5 hours to under 30 seconds. Learn more about Optum’s commitment to providing a better provider and patient experience: http://spr.ly/6045S4vph

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Leigh McCormack shares her insights on AI model drift in this Data Nerds in the OR episode! Her perspective aligns with how we at Caresyntax focus on maintaining data integrity across the surgical journey. It’s a great reminder that AI models need constant monitoring to remain safe and effective, especially as their role expands in clinical settings.   Learn more here: https://lnkd.in/gDSKJXfk   #AIinHealthcare #DataScience #Caresyntax

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Looking to enhance your understanding of digital health care tools? Explore the debate surrounding EHRs, AI, and more in this informative blog post. Learn skills to set a foundation that will make it easier to adapt to this fast-paced (and increasingly AI-enabled) digital environment and support your various initiatives over the long term.

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🚨New #JAMA Viewpoint 🚨 “Misaligned Pharmacy Incentives in Value-Based Care” The authors discuss how rising #prescription drug spending has been particularly challenging in the context of value-based contracts between payers and providers. This is partly due to confidential #rebates, which create misaligned incentives between payers/PBMs and hospital administrators. Read more below: 🔗 https://lnkd.in/dwzBT4WD #marketaccess #pbm #access #pricing #payer #valuebased #valuebasedcare #patientjourney #patientaccess #pharmacy #managedcare #pharma

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The cloud is no longer just a destination – it is an operating model for the future. Redox helps you unlock Snowflake's promise and mission to improve healthcare and life science outcomes. Learn more about our new technology partnership all around interoperability and real-time, actionable data. https://lnkd.in/e4ttrsU3

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"𝗗𝗶𝘀𝗽𝗮𝗿𝗮𝘁𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗼𝗳 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮, 𝘄𝗵𝗶𝗰𝗵 𝗮𝗯𝗼𝘂𝗻𝗱 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗮𝘀𝘀𝗲𝘁𝘀 𝘁𝗼 𝗽𝗼𝘄𝗲𝗿 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜." This article is all about the honeymoon fit of unstructured healthcare data & generative AI. Data entry done manually by providers (especially into EMRs & payer portals) is becoming a thing of the past. Prior authorization & claims processing are still in the lead as the two top areas that providers are implementing automation & AI for within the back-end of the revenue cycle. 𝘗.𝘚. 𝘍𝘦𝘦𝘭𝘪𝘯𝘨 𝘨𝘪𝘥𝘥𝘺 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦 𝘶𝘱𝘤𝘰𝘮𝘪𝘯𝘨 𝘩𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘦𝘷𝘦𝘯𝘵 𝘪𝘯 𝘦𝘹𝘢𝘤𝘵𝘭𝘺 𝘵𝘸𝘰 𝘥𝘢𝘺𝘴. 𝘖𝘷𝘦𝘳 450+ 𝘩𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦 𝘭𝘦𝘢𝘥𝘦𝘳𝘴 (𝘢𝘯𝘥 𝘤𝘰𝘶𝘯𝘵𝘪𝘯𝘨) 𝘢𝘳𝘦 𝘫𝘰𝘪𝘯𝘪𝘯𝘨 𝘪𝘯. 𝘊𝘩𝘦𝘤𝘬 𝘰𝘶𝘵 𝘵𝘩𝘦 𝘥𝘦𝘵𝘢𝘪𝘭𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘣𝘦𝘭𝘰𝘸. 𝘕𝘰𝘵 𝘰𝘯𝘦 𝘺𝘰𝘶 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘮𝘪𝘴𝘴! #mckinsey #ai #healthcare McKinsey & Company

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Somewhat predictably, the headlines of Epic's UGM were mostly focused on AI. In my mind, the ones to focus on got buried as footnotes. Products leveraging Epic's position as the dominant inpatient EHR and selling differentiated products as a wedge into new markets (mostly under Health Grid) should frighten other healthcare vendors more than the AI arms race (which they'll largely win as the default option): - Payer Platform: A Health Grid product for payers. This was decently well covered, but that's because it's a mature product at this point. A lot of outlets covered it as if it was net new, but it was launched in 2019. The most noteworthy piece is Judy's quote saying they’re investigating submitting claims directly through Epic so you don’t need a clearinghouse - this represents a threat to clearinghouses and could represent the expansion from Payer Platform as a beachhead to attack incumbent SaaS, claims processing systems. - Epic Aura (labs): This is a Health Grid product for labs. This was launched previously. I don't see any announcements, but this is a beachhead for Epic to sell to labs that have or built their own LIS, allowing them to eventually replace it over time (or at least have all major labs as customers). This threatens existing LISes even more than Epic's LIS product, Beaker. They also announced a planned Blood Bank module, which increases their appeal as an all-encompassing LIS in this push. - Epic Aura (medical devices): A new sub-category of Aura (and Health Grid) for expanding into medical devices. I was waiting to see if they'd launch a separate category for medical devices or reuse Aura. First devices are cardiac arrhythmia and CGM. This threatens businesses like Redox to some degree. - Per Healthcare IT today, "Judy said they’re working on better surgical device manufacture planning leveraging the Epic schedule." This sounds like further expansion of Aura/Health Grid to surgical supply manufacturers. - I was surprised to not hear any updates about Discovery, Epic's Health Grid program for pharma. I'm curious what shape that will take. - I thought the use of the state of Washington + the Garden Plot model is a smart distribution play. Garden Plot has existed since at least 2022, but using a state for distribution to get to new markets that Epic struggles to sell to (small clinics, behavioral health clinics, correctional medical facilities and tribal facilities) is again another example of their expansionary mode. Health Grid products will be more impactful than the Vendor Services rat races or AI wars. It's worth paying attention to and taking advantage of: https://lnkd.in/gimisDzZ

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𝐓𝐡𝐢𝐬 𝐢𝐬 𝐬𝐨 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐋𝐓𝐏𝐀𝐂 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲. But the ruling could also have broader implications for data privacy and particularly around the use case of analytics companies having broad access to patient health information. I can imagine that this might also have implications for LLM training data, as AI continues to march throughout the healthcare ecosystem. Real Time Medical Systems alleges that PointClickCare (PCC) is blocking its access to EHR data by using security ciphers. Real Time claims this is an anti-competitive move following PCC's expansion into post-acute analytics. A judge appears likely to block PCC's actions, emphasizing the need for data access to improve patient care as mandated by the 21st Century Cures Act. This may enhance interoperability, but there is a lot to consider. Thank you Brendan Keeler for bringing this important topic to light.

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Don't let poor data quality derail your new EHR system. 😬 Embrace Lean principles before project kickoff to create a rock-solid data migration plan. 💪 Remember, it's your data, not the vendor's. Take ownership and ensure a seamless transition that empowers clinicians, boosts satisfaction, and fuels innovation. At Spectralink, we're committed to empowering healthcare organizations with the tools they need to thrive. We understand the critical role data quality plays in achieving success. https://lnkd.in/e76j6PyP #Healthcare #DataExchange #EHRs

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🌟 Informed Decisions, Better Outcomes 🌟 In today's complex healthcare landscape, access to the correct data isn't just a nice-to-have—it's essential. Data insights can illuminate inefficiencies, drive innovation, and improve patient care and operational outcomes. At Salient Health, we're passionate about turning data into actionable intelligence. It's about more than just numbers—uncovering the stories they tell and empowering organizations to make informed, impactful decisions. From addressing price transparency to enhancing value-based care, data insights can break down barriers, foster trust, and ensure resources are aligned where they matter most: improving health outcomes. The future of healthcare lies in collaboration, clarity, and a commitment to continuous improvement. Let's build that future together. 💡 #HealthData #Innovation #ContinuousImprovement #Salient Health

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Does your data foundation support your AI strategy? Join our discussion next week!

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