Josh Mandel, MD’s Post

Chief Architect at Microsoft Healthcare

2mo Edited

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.

49 Comments

Andrea Pitkus, PhD, MLS(ASCP)CM, FAMIA

Laboratory Informatics Expert

2mo

Great work, Josh! I first checked to see which LOINCs you used. Grahame Grieve had a post not too long ago to look for LOINCs for these types of values/devices. Love, love, love that you have the glucose from insterstitial fluid. Keeps these values distinct from lab performed (serum, plasma) Glucs, as well as point of care testing such as capillary blood or self monitoring (SMB) glucoses from meters. Super important so these different glucose values aren't comingled in the EHR, HIT, algorithms, etc. LOINC has been discussing LOINCs for these types of devices, "specimens/sources", etc. for use cases like this. Pamela Banning, MLS(ASCP)cm, PMP Would be great if all device vendors would ensure LOINCs are available (whether lab, smartwatches, etc.) Next question is IVD device info. Looks like Abbott Freestyle Libre3? Do you have the UDI (from FDA's GUDID) for the IVD device(s) involved in generating the values? Does the app allow you to capture a point in time value or only the average range/values? Julia Skapik, how'd you integrate these into the EHR so they are available discretely, encoded, and computer processable/usable, but distinct from the other types of lab results above? Brian Fung FHIRed up?

3 Reactions

Michelle Currie MS, RN, CPHQ, CPHIMS

Founder & Principal Business Solution Architect | Healthcare Quality Improvement/Informatics SME

2mo

Super cool! I'd love to work together on something sometime. A few questions if you don't mind - What "frequency" did you choose to capture continuous readings and why? Why did you choose to capture "time in range" versus "time out of range? Is one or the other more indicative of HgA1C results? Thanks for posting the code!

2 Reactions

Alexander Walley

Software Engineer at NCQA. Opera Singer.

2mo

This is awesome work! I have been thinking about how FHIR could be created as a primary medium for embedded computing. If embedded devices outputted FHIR data directly, it would eliminate yet another layer of complexity / the need to map data formats.

Sherri Douville

2mo

Great work Joshua Mandel. How do we make sure the technical capabilities advance while minimizing inconsequential noise to doctors?

James Tannahill, MBA

President of Private Equity, Plocamium Holdings

1mo

The involvement of industry leaders like Redox, Rimidi, Rhapsody, Sensotrend, and Tidepool underscores the collaborative nature of this endeavor, bringing together expertise and resources to drive meaningful progress. The personal exploration undertaken by the project lead, including obtaining a CGM prescription, mapping data to FHIR resources, and creating interactive visualizations, demonstrates a deep understanding of the challenges and opportunities inherent in this space. The creation of a SMART Health Link, which securely packages the individual's glucose history, represents a significant step forward in patient-centered care. This innovation not only facilitates seamless data sharing but also enhances the ability of healthcare providers to deliver personalized and timely interventions. Awesome update, Josh Mandel Microsoft

2 Reactions

Gilbert Ramirez

Faculty Director of Health Information Technology at University of California, San Francisco - School of Medicine

2mo

Eventually everyone will realize we should never have adopted the EHR in mass, vendors have been lack to support any true innovation here. We never needed an EHR, we needed common data threads linking your healthcare record longitudinally along any point of access. Ultimately owned by you and your care team. The electronic medical record should essentially be a set of applications that interact with these threads in the way best seen fit by the end user, each free to market as such. We would be in a much better healthcare dataspace if this was the present.

1 Reaction

Susanne A.

Data -> information -> knowledge -> wisdom

2mo

Constructing what's "normal" or "good" for you, and what impacts this, is essential for developing the person centred care that we are all going to need if we are going to live lives that are good quality as well as long. I get annual blood tests that surfaced an emerging issue, which is usually only caught when it's serious and options are limited. Knowing this now means that I can be careful of things that could aggravate the condition, such as prolonged physical stress/dehydration; and signs that further deterioration is happening. (Happily this isn't the case presently).

Vincent Keunen

Andaman7 founder and CEO | Healthcare and medical research change maker | Patient advocate | Digital influencer at HIMSS

1mo

This is excellent. Now, to generalize this: 1. let's have all patients manage their health data in full autonomy (with a tool like the one we built: http://bit.ly/a7vkblogen as a patients initiative) 2. let's push for hospitals / EHR vendors to ACCEPT the data provided by tools like Andaman7 into their EHR (of course, they need to manage traceability to see where the data comes from; on traceability, see chapter "Extreme traceability of http://bit.ly/a7TechInno) 3. let's use AI to help manage the large amount of information, see https://bit.ly/a7nwai ... we are getting there! Dave deBronkart Josh Mandel, MD

1 Reaction

Vijayaditya Ayyagari

2mo

Josh Mandel your initiative with the FHIR Write project is commendable, particularly in advancing CGM data integration for diabetes management. Your work is always cutting edge and inspiring to many. On this day of Argonaut 2024 FHIR Write launch, I am glad to share Carefluence has been actively supporting FHIR Write initiatives integral to our platform. Like your CGM report, we have done spirometry integration and vitals from Apple health app using FHIR. As part of ONC’s real-world test plan, we've been reporting spirometry integration and vitals from the Apple Health app data. It's exciting to see how you used SHLink and I am looking forward to review it as an option to share data from our platform. Keep up the excellent work!

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Karthikeyan Kameswaran

Digital Health Innovator -Patent Holder | Digital Transformation & Medical Imaging/ Health IT delivery professional passionate about driving efficiency in the Patient (Consumer) care continuum | STEAM Advocate
2mo
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Great work on showcasing the potential of FHIR in streamlining CGM data flows. The insights from this structured data can be incredibly valuable across various care settings: 1. Primary Care: PCPs can leverage the glucose metrics and visualizations to identify patterns, optimize medication plans, and provide targeted lifestyle recommendations during routine visits. 2. Endocrinology: Specialists can dive deep into the raw data to fine-tune diabetes management, spot subtle glucose fluctuations, and personalize treatment plans based on individual responses. 3. Inpatient Care: Having real-time, interoperable CGM data can help hospitals maintain tighter glycemic control, reduce complications, and streamline transitions of care. 4. Remote Monitoring: Care teams can proactively monitor patients' glucose trends, receive alerts for out-of-range values, and intervene early to prevent adverse events. 5. Population Health: Aggregated, de-identified CGM data can inform risk stratification, resource allocation, and quality improvement initiatives at the health system level. The possibilities are endless when we harness the power of structured, standardized data. Precision medication powered by FHIR, position Patient at the centre of care continuum. Kate Ebrill #FHIR https://lnkd.in/g74uaaad
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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.
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Dave deBronkart

"e-Patient Dave" - Patient Empowerment evangelist. #PatientsUseAI. No pitches please.
2mo
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PEOPLE, THIS IS LEGITIMATELY HUGE BREAKTHROUGH NEWS. The first project I’ve heard where patients PUT DATA IN THE EMR. The transformational breakthrough is that this alters the landscape: we are already in the era where I have more data on my health than my doctor (from my ŌURA ring, Withings scale, iCare eye pressure device, etc). THIS BREAKTHROUGH BEGINS THE ERA WHERE IT CAN BE COMBINED. Massive work will be needed to USE that data meaningfully. But now that work can BEGIN.
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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.
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Eliot Levitt
1mo Edited
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From the technical perspective, there is nothing inherently more difficult about writing to an #API than reading from one. Most engineers and architects will quickly dispute this statement. Accepting inbound data into your #EMR, they will argue, is inherently more complex and risky compared to simply reading from it: When you allow a #FHIR #client to retrieve patient from your #endpoint, you are simply presenting structured data back to the calling application. When you allow the same client to write data to your endpoint, by contrast, you risk introducing horribly mangled, malformed, mistaken, or misinterpreted information that can choke the sunlight from the carefully-curated data seedlings in your #PHI garden. That, of course, is true. And not true. 🚨 Unless you have an #monolithic code base, you are already using #APIs to read and write your underlying data with all the appropriate protections in place, in what interface experts colorfully call "eating your own dog food." If you are overly concerned about accepting structured data from the outside, you are either not following this paradigm, or your internal APIs lack a comprehensive set of data governance protections. Time to shore them up. 🚨 The fact that an inbound API might originate from any number of systems does not increase the technical complexity for you. You don't care that there might be ten thousand different ways to represent a specific kind of data across source systems: you are accepting only one. 🚨 You probably already have any number of inbound, carbon-based interfaces, of a wide range of unpredictability, all of whom suffer from confusion, distraction, mistakes, and misinterpretations to at least the same degree as their silicon-based counterparts, and often more so. What's different for #FHIR #Write? Provenance. Although the #FHIRWrite standard leaves the ultimate use of the specific Provenance object unaddressed, a key thing this project does is tackle the tracability, reliability, and ultimately the believability of inbound data in a reasonably rigorous and auditable manner. Honestly, this is something that EMR vendors should have built into their data models a long time ago. Increased expectations from #interoperability may make this question more urgent, but there has always been a difference between data that is measured and data that is observed, information directly entered and information transcribed, and information that is exact and information that is estimated. Agreeing on guidelines for storing and tracking provenance is the first step toward EMRs implementing a more rigorous review and data validation process than "I have seen this and am accepting it into the record," which is really all that most EMRs do today. Scales can be wrong. Data can be mistaken. Patients can be unreliable narrators of their own health. Capturing the nuances of truth in an EMR will be the new #frontier of #data #confidence. This is an encouraging first step.
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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.
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Victor Chai

Head of Engineering Capabilities
1mo Edited
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This is amazing, truly revoluationary and transform how EHR product shall be designed and implemented. This is level 3 of the design which it not only pre-empty the problems, also unlock the potential of the numerous possibilities which I shared in my previous repost, whereas current EHR products are only at level 1 which is to chase after the problems to solve
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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 this 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.
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Jung Hoon Son, M.D.

Knowledge Architect @ Cerevel // informaticist // clinical data expert // knowledge mapper
2mo Edited
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FHIR’s hope is that it can effectively replace EHRs if given the permissions for a “trustless” storage location. Currently most of FHIR’s write capabilities are mostly disabled for most EHRs which are owned by hospital systems and other entities, usually citing the following: "CYA (urban dictionary) medicine". “it’s our database and the information is now a medical record - anything missed becomes our liability” type excuse. FHIR specs make data completely interoperable and potentially portable without needing a traditional schema, as much as it can be unwieldy as a centralized store for health systems. Generally I watch its development with skeptical optimism that it’ll eventually become the standard. Imagine a world where patients are given full access to their medical record - read and write (like git, suggest changes).
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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.
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Rob Fraser

Nurse | Digital Wound Care | Research | Data
2mo
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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.
Josh Mandel, MD

Chief Architect at Microsoft Healthcare
2mo Edited

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.
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SweetSpot™

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6mo
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Introducing the SweetSpot Dashboard: Your All-in-One Platform Diabetes Management and CGM/Insulin Pump Data Access Managing patients with continuous glucose monitors (CGMs) and pumps can be complex and time-consuming. Feeling overwhelmed by individual platforms and siloed information? SweetSpot is your life raft, bringing clarity, efficiency, and easy insights to your fingertips. Imagine: - One unified platform: All your CGM and pump patients, regardless of device, in one easy to use, organized dashboard. Goodbye portal-hopping! - Instant patient prioritization: See who needs your attention most with clear visual cues for out-of-range metrics, missing data, and upcoming reviews. No more sifting, just focused care. - Granular data at your fingertips: Filter and sort by any criteria - device, time in, below, or above range, glucose levels - to find the specific information you need, instantly. Knowledge is power. - Meaningful insights, actionable outcomes: Gain deep understanding of your entire patient population on CGMs and/or pumps with trends, averages, and variability metrics. Make data-driven decisions that improve patient outcomes. - Effortless billing: Forget CPT code confusion. SweetSpot flags eligible patients with a simple "Ready" icon and automatically generates the right codes for review activities. Save time and ensure accurate billing. Experience the SweetSpot difference! Learn more and schedule a non-committal demo at our website: https://lnkd.in/e83frghU P.S. Don't just take our word for it. See the power of SweetSpot in action with the image below. #diabetescare #CGM #populationhealth #diabetestechnology #doctorlife #simplifydiabetes
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Karin Hoelzer

Senior Director, Policy and Regulatory Affairs at NORD
1mo
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Incorporating the patient perspective in #medicalproduct #development is key to building products that actually meet patient needs. If #patientperspectives are forgotten, #patients can be at risk - just think pulse oximeters that only work on some skin tones, CPAP machines that deteriorate to potentially release toxic substances into patient's lungs, and many other #medicalproducts that lack a smooth user experience, in particular for patients whose medical conditions impact mobility or sight as common in #rarediseases. In the #medicaldevice field, the Medical Device Innovation Consortium (MDIC) has in large parts written the playbook on how to do this correctly. Excited to see this new publication, it will be a big benefit to patients everywhere.

Medical Device Innovation Consortium (MDIC)

13,885 followers
1mo

How can #patientpreferences shape the future of #clinicaltrials? Discover the latest Executive Summary from the SPI Heart Failure (HF) Working Group of MDIC. This study underscores the importance of incorporating patient input into trial designs and offers insight into the risk-benefit trade-off for HF patients. Dive into the research and discover how it can lead to more patient-centered health technologies: https://lnkd.in/etmKkjsm

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https://mdic.org
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Peter Keeling

Founder at Diaceutics PLC
3mo
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Love this article :10 high-value use cases for predictive analytics in healthcare For those who need case studies to believe in the convergence of precision medicine, data analytics to power value based care ... this article is for you.. https://lnkd.in/eKkCUVFm Precision medicine is the GenAI of Value Based care .

10 high-value use cases for predictive analytics in healthcare

healthitanalytics.com
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Chris Carswell
9mo
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The latest issue of #ThePatient is now avilable. Link in the comments below. IAHPR Office · In an opinion piece Agnes Grudniewicz et al. suggest the need to strenghten the chronic care model with a goal orientated approach. Authors suggest this will allow the development of care systems that are more patient centered · Davene R. Wright, PhD et al. systematically review quantitative patient preference studies around diagnostic imaging. For quality assessment they use PREFS – a commonly used simple quality tool. They found an increasing number of studies over time, a reasonably high quality of articles, and highlight the limitations of the PREFS tool. · Shunping Li et al. report the qualitative development of a discrete choice experiment to investigate non-monetary incentives for blood donation within China. They highlight the importance of thorough survey development work including cognitive interviews and pilot testing of survey instruments. · Juan Marcos González Sepúlveda et al. quantify patient preferences for attributes of chemohormonal therapies in patients with metastatic #ProstateCancer. Not unsuprisingly patients preferred treatments with the highest efficacy and ones without risks of serious infections. Interestingly patients valued treatment convenience as much as avoiding safety issues. · Dawn Swancutt et al. explored patient and staff experiences of UK specialist weight management services. Service capacity and patient anxiety were seen as barriers to accessing services. · Jorien Veldwijk et al. use two techniques to estimate maximum acceptable risk of treatments for #RheumatoidArthritis. They suggest discrete choice experiments may be easier for respondents to understand than probabilistic threshold designs, · Lidia Engel et al. report the face validity of preference-weighted QOL measures in residential aged care using think aloud interviews. They found issues with all four measures (EQ-5D. EQ-HWB, ASCOT and QOL-ACC) and suggest some needed modifications.
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