Brainforged Healthcare Operations 🏥

Hi there.

Recording this video to talk through how brainforged works with healthcare providers.

we've worked with a wide range of healthcare organizations but we feel like our core expertise has been best leveraged the, within provider operations.

just to give a overview of what we'll cover.

first I'll kind of chat through from our perspective and the projects that we've worked on what the most common areas for how AI really impacts the provider's practice.

and then we'll kind of just position how does brainforce differentiate from what else is in the market and then ultimately kind of how do we approach some sort of partnership design partnership where we're able to build a custom solution for our clients.

so I'll lead off by saying that we've worked with a wide range of healthcare providers.

One side of the spectrum completely digital native, kind of telehealth, providers such as Talkspace, Headspace, that don't have a physical presence and all of the appointments are conducted completely virtually.

we've also worked with somewhere in between le Mental health digital native platform but then work with a lot of network of physical, physical clinics that they don't manage and then even specialty clinics that have a mix of healthcare providing healthcare services as well as selling consumer products.

In the GLP1 category, we're talking about med spas and then all the way to just being purely physical kind of traditional clinic but maybe focus on a specialty.

So we've worked with single site clinics in New York City, multi site regional clinics on the east Coast.

And so we've just seen a wide range of how operations is run differently across the entire spectrum of digital native to being completely a physical operation.

And you know it's boiled down into four modules.

One is around patient access and intake.

And so this is really about do clinics, how do providers identify qualified patients, bring them through triage and get them booked properly?

right, we're trying to minimize the amount of back and forth, reduce friction also you know collect enough compliant data upfront so that we can do better targeting and go and find more patients within the target audience.

Then the second is around care delivery documentation.

This is really where the sausage is made.

And a lot of what we found is that in healthcare operations these protocols and being able to adhere to them to a T is so important which is why there are so many specialists that and the handoffs are very very careful There, there's risk of audits.

Obviously, you want patient outcomes to be consistent.

And so these protocols are all, and there could be multiple protocols running on one site because most clinics are not providing just a single service line.

And so to be able to codify SOPs protocols, whatever operating procedures you want to call it, codify them, make them rules based logic and allow tech systems, the technology systems to be able to streamline them and to enforce those standards.

That's really what this is about, about streamlining patient care.

Then there's around quality and outcomes review.

So many of the clients that we're working with, you know, they're not, not traditional legacy providers.

And so a lot of what they're doing is wanting to feed their results back into research and they're dealing with multiple kind of stakeholders, whether it's you know, payers and insurance providers, maybe sponsors who care about kind of development of the patient outcomes and being able to tell a very coherent story.

you know, with all the advancements in data and AI with we have a lot of tools at our disposal to help in developing a measurement that actually proves the outcomes that clinics are delivering.

And then fourth and finally is around creating this like you know, what we would call patient 360 or patient arc, end to end from when we first engage with the patient to the end of their life cycle, making sure that they are completely taken care of, that the communications are personalized, that if there are changes to policy that are, that impact patients because of the way that they can pay or the types of treatments they're eligible for, all of that is part, patients don't keep track of all of that's going on in the market.

And providers have kind of the expectation, even if it's not a fiduciary responsibility, to be thought partners for patients to make sure that they have the clearest understanding of what they're getting themselves into.

and patient transparency is a huge part of development in healthcare now across the universe of where all the different processes that healthcare providers have to have to keep up.

We've kind of mapped out like an opportunity map of all the different processes that we've seen.

this is supposed to be an exhaustive list but maybe there are more and kind of where we think that the, that the prime areas for disruption are.

So I mean first I'll call off that like you know, clinical documentation.

There are a lot of AI notetakers kind of dictation apps, medical encoding kind of encoders, you know that using machine learning and other data science best practices.

You know there are plenty of products out there already and this is where all of the biggest systems of all the systems of record are working, are working on productizing that.

there's also around revenue cycle management and just pay around payments.

Right, Medical payments, huge volumes of data and money being transacted.

And there are a lot of kind of products around this as well and patient comms too where you know you don't really need to develop another patient comms platform.

There are so many bespoke solutions that fit you know your, your specific use case.

And so we're not in the business of trying to recreate these.

We understand kind of the inner workings of it and we've actually built out modules of this to kind of bolt on to existing pieces of software.

But this isn't actually where brainforge is going to be the most effective.

Where we think the biggest opportunities are around, yeah referrals and scheduling, making sure that we're able to, clinics are able to generate highly qualified leads.

Not from the marketing perspective.

You can go work with a marketing agency, run paid ads, do cold calls, you know, activate all the different channels to bring, dial up the volume of like being able to drive, being able to bring patients through your funnel.

But what's that Martech operations you got to look like in order to make sure that there is a good mix of human and AI, kind of handoffs and to streamline highly qualified leads getting into the booking system.

Right.

So I think there's plenty of work to be done there.

And a lot of these are bespoke solutions.

It's not one size fits all.

Every patient experience, every clinic's patient provider's patient experience is going to be different.

then there's also really aligning on core definitions and questions.

so you know, this is really why you can't just you know plug in a generalized LLM.

Whether you're using like a co pilot or a cloud or an OpenAI, it doesn't know the specific definitions that the operators have.

And so being able to build up what's known as the semantic layer or context layer, or you know, organizational brain that basically takes dumps the brain out of the coo, the cmo, the chief Medical officer, the chief Information officer, whoever is running the practice and knows kind of the very nuanced nitty gritty we need to be able to codify that because that is going to be really rich context that then informs the rest of the decisions that ultimately need to be made.

So we think that those are really the two areas that we typically start with when we're working with healthcare providers and then beyond that where there's a few other core competencies really being able to know, codify protocols and turn it into a rules based terminus deterministic logic and then being able to kind of configure like bespoke systems.

Whether we're jumping into an emr, an ehr, PRM or whatever, whatever it may be.

you know we have a team of engineers that's able to go and orchestrate that without having to go and pay for very expensive specialists.

then there's obviously quality control and this is really more later stage.

You know, what are the types of analysis and views and cuts of information that help leaders make decisions.

And you know that's usually not the first thing that we can get to right away.

but you know, once data is accessible and you know it's handled compliantly, we have all the right definitions, then we can start to trust the answers that are being produced and we can assemble that last mile of analysis, reporting, dashboards, decks, whatever it needs to be so that leadership can disseminate information to everyone internally and also externally.

so that's really kind of our vision for how we work with providers today.

And so put another way, there's a few kind of ways to break down into very concrete initiatives per, per domain that we would help with.

all of it really stems from clinics not having a single source of truth that's accessible for all their data.

So once you're able to build that then you'll be able to really match your bar of operational excellence from a technical systems perspective.

And then there are a lot of really creative AI applications that we can build on top of that that helps streamline operations there.

yeah ultimately the goal is not to take the decisions out of human hands, it's just to make sure that you know, the biggest risk factor is actually human input and human error.

And so as much as we can turn best practices into kind of basically code that the models and the agents are actually more reliable than humans if you can configure it properly.

And so that, that's really our job of making sure that we do hit the highest bar of compliance.

We are handling data efficiently and we're able to really keep the back office kind of functions of these operations, very tight and so that providers can focus on delivering the best quality care that they can.

from a design perspective, how I would model this out is we're typically working with a COO or like a VP of Clinical Ops.

we're kind of defining kind of what are the different lines of ownership.

So there is some governor of systems internally.

Typically, where we come in is we really manage all that context.

We're designing the package agents, we're helping design evals so that we can codify what does good look like.

and then we're helping build out the clinical brain.

and then having a champion on the inside that really is, is able to say give us, has good taste for what does good actually look like.

That's the most important person that works with us.

we would limit to a few kind of initiatives at first and we can be very opinionated about what typically works.

and yeah, I think that's typically how we would get started.

the rest is really just kind of, more kind of how to.

For what brainforge is and how we work.

yeah.

Let me know if you have any questions.