Tag: digital health

A new viewpoint in the Harvard Business Review made the case that health systems and retailers are only scratching the surface of their partnership potential.

The authors – a trio of professors out of Harvard and UNC – outline four actions they believe health systems and retailers should take to better coordinate their complementary services.

Move Beyond Convenience. Retailers like CVS and Walmart are beginning to add services such as primary care, mental health counseling, and home care, yet even more robust solutions like Amazon Clinic still fall short of integrated care. 

• Things like cancer treatments and surgeries remain well outside the realm of retail health, yet a close partnership between a retailer and a health system could help integrate the many elements involved in treating more-serious conditions.

Move Care Into the Home. Although retail clinics are more convenient and accessible than hospitals, patient homes have them beat on both metrics. Hospitals have begun offering more care in the home, but often lack the logistical prowess to supply patients with the monitoring tech needed for larger programs.

• Efficiently equipping patients’ homes with RPM devices is right in the retailer wheelhouse, and a partnership could fill the gap. Look no further than Best Buy and Geisinger for proof.

Leverage Data to Improve Care. The data held by retailers and health systems largely remains in separate databases, with some notable exceptions like Target-Kaiser Permanente.

• The authors point out that better integration could help with everything from flu outbreak prediction (grocery carts filled with tissues = sick people) to food-as-medicine programs (well-timed nudges and incentives).

Change Who Delivers Care. Labor shortages are one of healthcare’s biggest immediate obstacles, and few employers have a larger workforce than retailers. The article gives the example of Walmart, which subsidizes education for its employees to train for roles like pharmacy technician and medical assistant.

• Health systems could ensure these training programs meet quality standards and help graduates find jobs, creating a model where retailers attract more ambitious candidates and providers have a new talent pool to tap into.

The Takeaway

One way or the other, retailers are moving past the Retail Care 1.0 era, and it’s hard to argue against the case for tighter retailer-provider partnerships. Even if consumers might not jump at the idea of sharing their grocery list with their physician, the ideas outlined in this article are good food-for-thought for combining the complementary strengths of retailers and providers to improve the system as a whole.

Andreessen Horowitz partner Julie Yoo and Bassett Healthcare CDO Paul Uhrig recently shared their playbook for entrepreneurs looking to partner with health systems, which included plenty of insider tips to stand out in a crowded field.

Getting in the (right) door is the first step to any pitch, but an academic medical center with a healthy mix of payor contracts will have a different lens than a rural hospital serving mostly Medicaid patients.

• Advice: Research target health systems and make sure they align with your product’s value proposition. Make sure you’re reaching the right person, which usually involves multiple stakeholders across clinical, operational, and financial leadership. The value proposition needs to hold up to each.

Making the pitch will vary by health system (an asterisk that could probably be added to every tip), so it’s important to tailor all information and supporting data to individual priorities. This section stresses that it’s “imperative” to illustrate a positive financial impact. 

• Advice: Don’t be afraid to ask about budget and clarify your revenue model. Even if stakeholders like the solution, it’s moot if they aren’t able to find the funds for it. 

The evaluation process can run the gamut from informal discussion to formalized diligence, but health systems aren’t usually opposed to giving visibility into the evaluation checklist.

• Advice: Upfront qualification work is intended to de-risk the implementation process and identify potential blockers early. Be prepared with case studies and references from other customers to support the evaluation process.

Pilot programs are a health system favorite, but clearly defined success criteria and a commitment to move forward if those are met are two key ways to avoid “death by pilots.” 

• Advice: Try not to get hung up on IT integration, and if possible steer toward an implementation scope that requires minimal integration before phasing into a full-blown integration to ramp up to your product’s full value.

The Takeaway

As Yoo and Uhrig describe it, partnering with providers is a bit like “making an emulsion from oil and water,” especially at a time when many of them are grappling with rising labor costs and slim margins. Health systems see a daily flurry of startups offering to solve these problems, and if this playbook makes one thing crystal clear, it’s that the only way to get a pitch to land is to make it hit squarely in the center of their individual needs.

With Clement Goehrs, MD
CEO and Co-founder of Synapse Medicine

In this Digital Health Wire Q&A, we sat down with Synapse Medicine CEO and Co-Founder Clement Goehrs, MD to discuss the challenge of accessing up-to-date drug information and how that data can be used to improve care delivery.

With more medications hitting the market every week, providers face the impossible task of tracking countless new interactions, and software developers don’t have it any easier as they look to equip providers with the right tools for writing safe prescriptions. Dr. Goehrs co-founded Synapse in 2017 to help them do just that, with easy-to-implement UI components making real-time drug data and decision support more accessible than ever.

Can you give the audience a quick introduction to Synapse Medicine and the story arch that brought us to your current solution set?

To put it simply, our mission is to make it as easy as possible to access medication information – wherever it is – and to help providers make the best clinical decisions using that data.

During my time as a physician, I saw first hand how difficult it was to find information for optimizing a prescription, and that we’re also facing a huge public health problem due to people dying from avoidable medication errors. Those are the problems we’re aiming to solve.

And when I say that we’re trying to make drug information easy to find, I mean for our end users (providers, pharmacists, nurse physicians), as well as our clients (usually EHRs, ePrecribers, or telemedicine companies – any type of software company building for clinicians that wants to add a drug information component).

Can you give us a deeper dive into Synapse Medicine’s clinical decision support solution, and walk us through what that user experience looks like?

One of the things that we understood very quickly was that providers don’t want to have multiple tools. That means that if you’re providing some kind of clinical decision support, and you want your end user to have the best experience, you also need to have a seamless integration inside the EHR.

You also have to be able to provide a wide range of tools without making the solution overly complex, and we do that with what we call components. Our components leverage our APIs with a UI layered on top for specific use cases, like drug interactions or side effects. That makes them easy to integrate and customize, but the user doesn’t even notice it’s a third party feature.

To give you an example, if a physician starts a prescription within the EHR, as they begin entering drugs they’ll start to see safety notifications on the side effects, potential interactions, and dosages. All of that is communicated in a way that’s easy to understand. 

No physician wants to see a pile of alerts, but they do want to have info on how to help their patients. So instead of just saying “there’s a severe interaction” like most tools do, we take it a step further by saying “you might want to divide that dose in half to avoid these side effects, and here are the publications supporting that decision.” That explanation is super important.

What are some of the big trends that you’re keeping your eye on, and how do you see them continuing to unfold going forward?

One of the main trends that I’m seeing is that there are a lot of legacy players, very big EHRs and software companies, that aren’t able to innovate at the pace they would like to because they have so much on their plate.

These companies face so many regulatory hurdles and have so many things they need to build, so with all the innovations and AI progress happening every day, more of them have been saying “we can’t keep building every single use case, and we are going to start working with companies that are focused on this specific problem.”

As more EHRs and telehealth companies start to integrate with other players to get the best of both worlds – breadth and depth of features – there’s a big opportunity for the startups working on those use cases.

Has there been anything that surprised you about the recent AI momentum, or as you implemented any new technology into your own platform?

AI is a fundamental part of a lot of what we do, and of course we continue to learn more about it every day. One project that we did with France’s equivalent of the FDA, and probably one of the first healthcare AI projects deployed at that scale, was to help monitor vaccine side effects and other drug interactions on a national level.

As we developed that algorithm, every time we made a major jump in performance, it was never because of the mathematical model. It was always because we ended up back at the data.

We understood the data very well from a medical point of view, and also had physicians on our team that could point out things like why we shouldn’t train the model on certain data. That’s what really enabled most of the fine tuning.

So for the short answer to your question: I’ve been surprised by how much truly understanding your field, and truly understanding your specific use case, is actually what makes AI smarter. It’s not just more data and a bigger model. I think the real progress wiIl come from the AI teams that have physicians and data scientists working together on performance.

What advice would you give to providers or startups that are thinking about improving their own prescribing strategy?

Number one, I would say to think deeply about the end user, about the physicians and the prescribers. That may seem trivial, but that experience is so important, and it’s surprising how often it gets overlooked.

Number two, think about scalability, particularly around adding secure data. What worked for 10 physicians in the beginning might not work as you scale. You’ll probably need better data, and you’ll probably want to do something with that data. If that data isn’t secure from the beginning, you’re going to lose a lot of time if you have to rebuild everything.

That brings me to number three: I wouldn’t recommend doing that by yourself. There are new drugs every week. There’s drug information all over the place, a lot of terminology, and few standards tying it all together. It’s a complex mess, and it would be a mistake to only consider the resources you would need to build it, because it’s maintaining it over time that gets really painful. You can probably avoid a lot of pain by having someone else do it.

For more on Synapse Medicine’s clinical decision support for medication success, head over to their website or reach out to [email protected].

Since Medicare coverage first took effect almost six decades ago, the program’s runaway spending has played a leading role in the story of the federal budget. Now, the end of that growth is stealing the spotlight.

An excellent piece in The New York Times highlighted how Medicare’s unsustainable climb reached a turning point in 2011, and for reasons that aren’t exactly clear.

In 2011, Medicare spending per beneficiary (MSPB) reached $13,159, nearly double the level it was at near the turn of the century.

• If historical growth had sustained beyond that point, we’d currently be sitting at roughly $22,006 MSPB. Luckily, that’s not what happened.

• Spending leveled out, and we now find ourselves at $12,459 MSPB, a nearly $4 trillion gap compared to previous projections… yet the underlying cause remains a mystery.

The trillion dollar question: what changed? The authors call out obvious shifts in Medicare policy, namely the Affordable Care Act in 2010, and its reduced Medicare payments to hospitals and payors with private Medicare Advantage plans.

• While ACA was certainly a contributor, most of the reductions are attributed to a category that the budget office calls “technical adjustment,” which describe changes to a wide base of topics such as the expansion of cholesterol and blood pressure medicines.

The NY Times concludes that the true reason for the change is a hard problem that remains unsolved, but the smart folks on social media were quick to pick up where they left off, floating possibilities such as:

• As MA lives increased, the types of MA plans also improved due to the phasing out of inefficient plan designs
• Age of death increases stopped around this time, so US citizens aren’t living to older ages with increasingly complicated health issues 
• The rise of ACOs started in 2012, although we just covered why that factor probably doesn’t account for a huge share of cost reductions

The Takeaway

Savings attribution has always been a fundamental challenge for the healthcare industry, underpinning many of the issues with value-based care and other alternative models. Now that we’ve found ourselves at an inflection point where Medicare spending is slowing but still outpacing the federal budget, the solution to that savings attribution problem will also be what lets us identify the levers that will keep the trend heading in the right direction.