Merritt DeGraw

This is fascinating. Clearly clinicians won’t be able to review the tsunami of data that could flow in. This is why nurses and doctors must have AI and data analytics literacy as new clinical decision support tools are developed to analyze this data.

5

AI technology is set to streamline the process of clinical trials in medical technology. Here's a look at its transformative impact: 1. Participant Selection: AI algorithms can efficiently sift through patient databases to identify ideal candidates for trials based on genetic markers and health history. 2. Real-time Data Analysis: AI enhances the monitoring of trial data, providing real-time insights that can accelerate development timelines and improve safety monitoring. 3. Outcome Prediction: AI can predict potential trial outcomes based on interim data, allowing researchers to make quicker decisions about the efficacy of a treatment. Navigating AI in clinical trials requires careful consideration: -Ethical considerations in algorithm design to prevent bias. -Compliance with regulatory standards for AI in healthcare. -Integration of AI tools with existing clinical trial management systems. As AI continues to evolve, it offers promising enhancements to the efficiency and effectiveness of clinical trials. How is your organization preparing to harness the power of AI in research? Let's explore the future of clinical trials together.

4

LSI, BioscienceLA, #LAMedtechweek, and HLTH's #VIVE2024 event resulted in the latest GTM Pulse Check offering which has successfully taken off! As a result, I am continuing on last week's series of go-to-market tactics. Crafting a robust marketing plan is essential for medical device companies to thrive in today's world. By leveraging these outlined sections, organizations can optimize their marketing strategies and drive meaningful impact.This post takes a step back to outline the sections of a marketing plan. Read on 👇🏼 Looking for support in creating your marketing plan?  Let’s chat! Book here 👉🏼 https://lnkd.in/g92YA9WK _____ P.S. Enjoying my content? ⚕️Follow me for more tips on medical device commercialization and repost to share with your network. #medtech #gotomarket #medicaldevices #healthcareinnovation

27

2 Comments

A good article about the need to build patient trust in Generative AI. One method not mentioned is allowing for the patient to "opt-out" for their own exposure to the tool. Dynamic Global Healthcare/Rev Cycle Staffing What Hospitals Need to Know about Patient Trust in GenAI - MedCity News https://buff.ly/3XhiDDT #healthcare #dallasfortworth

1

Very well put Brian Casey. We are at a complex time in the business of AI. Like so many transformative technologies, it takes longer to find the sustainable business model than forecast. Overall, the innovation economy has been experiencing a very rough patch since the peek of 2021. Combining these factors is creating challenges. On the other side, we are seeing green shoots at Intelerad Medical Systems. * we and our partners at Hg are doubling down on AI and are support of customers who are undertaking innovative integrations that are driving real benefits * specific conversations revolve around both radiology practice innovation (breast imaging is an area of focus - maybe in part because it feels like a logical extension of more established CAD workflows) as well as hospital- based innovation to improve the speed of triage image interpretation for non-radiologists (fracture detection in the ER is an example) * we also continue to see a pace of change in technology that is unprecedented. By the way, that in and of itself has been a paradoxical headwind because it has made it difficult to double down on a specific technology when the field is changing so fast. I remain super optimistic and grateful for the fine work Anthony Chang, MD, MBA, MPH, MS and his team are doing!

12

1 Comment

This! 💯 “Nurses are among the most important constituents to guide AI-driven workflow change and shape Responsible AI principles. Period.” -Tom Lawry from Microsoft Success = having #nurses at the table #nursesonlinkedin

5

1 Comment

The challenge of determining appropriate payment models for AI is not unique to payers and providers – it also affects pharma companies. Example: in clinical trials, pharma companies often hire expert human reviewers known as "central readers" to assess clinical trial endpoints. An expert gastroenterologist might review colonoscopy videos for a Crohn's Disease trial – a manual process that can take months. These reviewers are commonly paid per-read, hourly, or on a fixed fee basis. Interestingly, when a computer vision algorithm is used instead to perform these reviews (or as one of many reviewers), the payment model differs. Many pharma companies choose to pay based on the number of successful patient randomizations in the trial, rather than per read. This approach ties AI compensation to a downstream event that is actually influenced by multiple human factors, rather than the AI's direct performance. This phenomenon is interesting because (assuming performance equivalence) the AI delivers the same value as a human reader in less time. the output (i.e., determining whether a patient met the trial endpoint) is the same. However, the AI is compensated based on a broader metric of trial success, which may not solely reflect its efficiency or accuracy on the determined task, or factors that the AI can meaningfully influence. This example poses the question: should AI be paid for via the same structures as humans for performing similar tasks? My short answer is no. In many cases, traditional healthcare payment models (e.g., time-based billing) don't make sense for AI; partially because we are using AI to redesign task constructs altogether, but also because the payment models must be aligned with the actual value the AI is generating. Non-sensical (and at times, misaligned) business models seem to emerge when we hold AI to a higher, magical bar (above status quo human performance), or allow for AI scope creep (i.e., the assumption that AI will impact/fix everything in a workflow). In healthcare, what we seek to demonstrate first is human performance equivalence, robustness, value creation, and importantly, safety. The business models that support these demonstrations should reflect this.

40

9 Comments

Big healthcare AI news yesterday as Tempus AI received the first-ever CPT code for an algorithm-only analysis of previously-sequenced, laboratory developed test transcriptomic data. TLDR: AI molecular phenotyping has a new business model. The PurISTSM test uses RNA profiling to identify molecular subtypes in patients with advanced pancreatic ductal adenocarcinoma, which may inform first-line therapy selection and management. While I'm skeptical that payer balance sheets are going to solve the "who pays for healthcare AI" problem, the reimbursement pipes for clinical AI are maturing. I am surprised to see more progress for usage-based vs. performance-based models given the many challenges with FFS economics. It will be interesting to see how payers assess the "medical necessity" and therefore reimbursability of clinical AI as these models come to market. https://t.co/eLe18SmihV

420

8 Comments

70% of hospitals participate in healthcare interoperability. Healthcare #interoperability is growing across hospitals, but gaps remain, especially in data exchange with long-term care and behavioral health providers. CapMinds offered health information exchange(#hie) and health interoperability (#fhir, #hl7, #mirthconnect) to its various clients across the specilaity including #behavioralhealth #mentalhealth

12

1 Comment

As clinicians experience unprecedented levels of administrative and cognitive overload (and burnout), conversational and ambient AI technologies are transforming healthcare delivery, allowing providers to focus on patient care instead of data entry and documentation. https://lnkd.in/eRX2FKbS

8

Right #ehr with right features & pricing Vozo

5

Distilled from discussions with numerous healthcare C-level executives, sharing here today the HEART Framework that Health systems and IDNs can use for evaluating and embracing AI-driven solutions in Healthcare. Your insights are invaluable—please share your thoughts on what resonates or what can be added! 🔹 Healthcare Value: Prioritizing and solving key pain points swiftly—can you see tangible benefits within months? 🔹 Economic Analysis: Is the ROI justified? How do you evaluate the impact of AI ? 🔹 Adoptability: How do you ensure sustained adoption and engagement across your organization? For example: Deep workflow integrations 🔹 Regulatory Compliance: Does the solution uphold the highest standards of security and compliance? 🔹 Transformation and Risk: Ready to balance risks and manage change by starting in low-risk, high-value areas? Full article here: https://lnkd.in/dthy5_yw Your feedback is crucial as we navigate the integration of AI in healthcare. Let's discuss how we can drive transformation effectively. #AI #HealthcareInnovation #DigitalHealth #Leadership #HEARTFramework

46

1 Comment

New AI tool could revolutionize clinical trial efficiency and cost https://buff.ly/3y7aM1s #clinicaltrials #clinicaltrialtechnology #AI #ArtificialIntelligence AIinClinicalTrials

4

Everyone seems to want to emulate Kaiser as the go-to model for how healthcare works But almost no one has actually managed to pull off the payer provider combination that Kaiser has. Even Kaiser itself struggled in areas outside of California. I'd be curious what others think is the main reason, but the two big ones IMO 1) You need to command a critical mass of patient lives to be able to pull off exclusive contracts with other physician practices/medical groups in a given geography. Kaiser pulled this off by getting basically all the shipyard workers in California in WWII and used it to its advantage. Other companies have not quite been able to do that. 2) You need to build your internal workflows around the idea that you're taking on financial risk of everyone that comes through your doors. That means way more protocolized care and also getting patients to try cheaper options when medically appropriate. Most hospitals do not operate like this and try to tack on an insurance plan without changing their workflows - in the healthcare 101 course (starts in 1 week), we talk a bit about how Kaiser works and why it's different from other health systems You can sign up for it here: https://lnkd.in/eKycDuTV

89

23 Comments

Recently, I sat down with Vishal Iyengar from Deloitte Consulting LLP, along with Matt Rhenish, COO, Health Plan Operations at enGen, to talk about how our partnership between technology and operations is revolutionizing health care. Check out the article in the Wall Street Journal’s CIO Journal to see how our human-centered approach enhances adoption and improves patient outcomes. https://lnkd.in/ejHH-QNr

140

2 Comments

Argonaut's 2024 "FHIR Write" initiative launches today, focused on continuous glucose monitoring! We're streamlining standards-based integration of CGM data into EHRs to support collaborative diabetes management. Huge thanks to Redox, Rimidi, Rhapsody, Sensotrend, and Tidepool for helping me and Brett Marquard come up to speed ahead of the project launch. I also want to share my personal pre-launch exploration to learn about CGM: 1. Got a CGM prescription and began tracking my blood sugar. 2. Mapped my CGM readings to FHIR Observation and Device resources. 3. Calculated key metrics like "time in range" as FHIR Observations 4. Created a visualization PDF based on the Ambulatory Glucose Profile, and wrapped this in a FHIR DiagnosticReport 5. Published same visualization as an interactive web view 6. Packaged all this up in a SMART Health Link for secure, structured data sharing + built-in visualization This SMART Health Link represents my past 120 days of glucose history, updated automatically and covering all core data elements described in the Diabetes Technology Society (DTS)'s iCoDE data set: ** Josh's glucose SHLink: https://lnkd.in/g2TQb5tN ** Anyone with the SHLink can access the structured FHIR resources behind the visualization. In fact, the viewer itself is a SHLink client that knows how to render the data it finds. For a behind-the-scenes tour of the data set, see https://lnkd.in/gYpGuGEQ This proof of concept shows how FHIR can streamline CGM data flows. It can give clinicians structured data for analysis AND summary reports for record-keeping. I should note that this consumer-mediated direction is not necessarily the approach we'll focus on in this year's Argonaut project -- but it has been a great learning exercise for me. Check out the project and code: https://lnkd.in/gY2nwv2h #healthcare #diabetes #data #FHIR #ArgonautProject A final thought: Even with an "all green" glucose report, there's still much to learn from analyzing specific responses like post-meal glucose spikes. Right now, such subtle signals may be outside the standard clinical focus, but it highlights the wealth of potential insights hidden within raw data.

348

49 Comments

Is your software platform a medical device according to FDA? If so, what does that mean for your business? These are essential questions for developers in #DTx, #HealthcareXR, and #remotetherapeuticmonitoring. Even if your software is a medical device, you might be subject to enforcement discretion - a more affordable path to market. But, there is a thin line between enforcement discretion and active regulation, which Michael Schellhous breaks down in the article below. Check it out! https://lnkd.in/edDfNkgc

23

Q1 2024 was big for Digital Health, with $1.1B in VC funding. A large portion came from major deals like $126M for Transcarent and $108.8M for Rightway. Despite some challenges in digital therapeutics and telehealth, investments in general care ($213.4M) and health benefits navigation ($140.7M) are promising. Read what’s driving the shift from direct-to-consumer to enterprise telehealth models here: https://ow.ly/uNYs50SpXGh

27

1 Comment