What really acts as catalyst for creating good health-tech companies

1. Change in technology

2. Change in regulatory landscape

3. Change in underlying biology

4. Change in information density (due to 1, 2, or 3)

Notes on Immunotherapies (for non-clinical readers)

 Why is it important to know about immunotherapies?

• One of the largest non-governmental entities in healthcare space are Pharma and Biotech companies 
• Most of the top Pharma and Biotech companies (certainly top 10) generate bulk of their revenue from oncology (largest therapeutic area) and invest heavily in the same space
• In clinical trials the largest therapeutic area is oncology (number or trials or pipeline or investment)
• The therapies in oncology (loosely speaking) has evolved from surgery, chemotherapy, radiation therapy, hormonal therapy, stem cell transplant to immunotherapy
• Immunotherapies offer significant benefits in cancer patients and are increasingly becoming part of standard of care (NCCN guidelines)
• Thus immunotherapies are focal point for many stakeholders - patients, regulators, drug developers, payers etc.
• As a result if you work for a company building solutions for life-science organisations, it is unlikely to miss Immunotherapies
• Note: Although Immunotherapies are primarily used to treat cancer, they can be used to treat other conditions as well.

What is immunotherapy?

A type of therapy that uses substances to stimulate or suppress the immune system to help the body fight cancer, infection, and other diseases. Some types of immunotherapy only target certain cells of the immune system. Others affect the immune system in a general way. - NCI

What kind of therapies are considered as immunotherapies?

A bit outdated source but still useful. The landscape has evolved significantly since this.

source: Cancer Immunotherapy

source: cancersupportcommunity.org

The National Cancer Institute has a clarification system which is uptodate but not that different.

• Immune checkpoint inhibitors, which are drugs that block immune checkpoints. These checkpoints are a normal part of the immune system and keep immune responses from being too strong. By blocking them, these drugs allow immune cells to respond more strongly to cancer.

  Learn more about immune checkpoint inhibitors.

• T-cell transfer therapy, which is a treatment that boosts the natural ability of your T cells to fight cancer. In this treatment, immune cells are taken from your tumor. Those that are most active against your cancer are selected or changed in the lab to better attack your cancer cells, grown in large batches, and put back into your body through a needle in a vein.

  T-cell transfer therapy may also be called adoptive cell therapy, adoptive immunotherapy, or immune cell therapy.

  Learn more about T-cell transfer therapy.

• Monoclonal antibodies, which are immune system proteins created in the lab that are designed to bind to specific targets on cancer cells. Some monoclonal antibodies mark cancer cells so that they will be better seen and destroyed by the immune system. Such monoclonal antibodies are a type of immunotherapy.

  Monoclonal antibodies may also be called therapeutic antibodies.

  Learn more about monoclonal antibodies.

• Treatment vaccines, which work against cancer by boosting your immune system’s response to cancer cells. Treatment vaccines are different from the ones that help prevent disease.

  Learn more about cancer treatment vaccines.

• Immune system modulators, which enhance the body’s immune response against cancer. Some of these agents affect specific parts of the immune system, whereas others affect the immune system in a more general way.

  Learn more about immune system modulators.

  Another recent classification shows key classes within Immunotherapies below (source unknown unfortunately)

  
  
  

  
  

Insights: SaMD Regulatory Strategy

The regulators seem to consider a medical Software as a device provided it meets certain criteria (trying to diagnose, treat or enables clinical decision making etc).

1. The 30,000 feet view:

Source: Yale

Note: The FDA will not actually run your software but rely on your documentation.

2. Qualifying as a Medical device

There are three influential bodies when it comes to Medical Device Software matters.

The FDA, the EMA, and the International Medical Device Regulators Forum (IMDRF)

2.1 IMDRF and SaMD

SaMD definition: “Software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.”

More about IMDRF:

The International Medical Device Regulators Forum (IMDRF) is a voluntary group of medical device regulators from around the world who have come together to reach harmonization in medical device regulation. IMDRF develops internationally agreed-upon documents related to a wide variety of topics affecting medical devices. In 2013, IMDRF formed the Software as a Medical Device Working Group (WG) to develop guidance supporting innovation and timely access to safe and effective Software as a Medical Device globally. Chaired by the FDA, the Software as a Medical Device WG agreed upon the key definitions for Software as a Medical Device,

• Framework for risk categorization
• Quality Management System
• Clinical evaluation

2.2 FDA and SaMD (Software as Medical Device)

The FDA refers to the IMDRF definition and does not have its own separate definition.

2.3 EMA and MDSW (Medical Device Software)

In Europe (MDCG 2019-11), MDSW is defined as:

“Medical device software is software that is intended to be used, alone or in combination, for a purpose as specified in the definition of a “medical device” in the medical devices regulation or in vitro diagnostic medical devices regulation.”

3. Class of Medical Device Software

3.1 The FDA: Here again, the FDA borrows a framework for risk categorization from the IMDRF, as noted on the FDA website.

source:imdrf.org, page 14

For SaMD pathway

 

3.2 For EMA

 

Source:ec.europa.eu, page 26

4. Important Standards

 

The key standards in medical software are listed below

• ISO 13485: this is the quality management systems description for medical devices.
• ISO 49721: this is the application of risk management to medical devices. These two are known as so-called management standards.
• IEC 62304: this is the system lifecycle process. 
• IEC 62366: this is usability engineering, how we design software to avoid user errors and keep them safe.

These are what some SMEs call a soft laws. They do not have the force of law, but the regulatory agencies look at compliance with the standards if you follow the procedures laid out in the standards as evidence of good work, and the standards are recognized.

You have a much easier time with many regulatory policies. Well, our software process follows IEC 62304, they just know exactly what you're doing and they're very happy when you say something like that. The standards may not be 100 percent in fulfilling the needs of a particular jurisdiction. ISO 13485, is not one-for-one, the same as what the FDA would look for in an equivalent system, but it's close and it gets you most of the way there.

5. The Quality Management

FDA’s guidance is 7 pages long

• There are 10 sections
• Contains very high-level instructions
• Also called CFR part 810

EU’s Medical Device Regulation

• Also called 2017/745 MDR
• 225 pages long
• Also contains 10 sections

From the doc: “The quality system regulation inclusive requirements related to the methods used in the facilities and controls used for designing, manufacturing, packaging, labeling, storing, installing, and servicing of medical devices intended for human use.” Note the process begins at design.

Requires signed and dated review documents for design (and also other aspects).


Subpart C: one of the 10 sections, and very important as it is for design controls.

source: fda.gov

6. General Principles of Software Validation

(Also called GPSV)

The scope of this guidance is somewhat broader than the scope of validation in the strictest definition of the term the way with defining. Planning, verification, testing, traceability, configuration management, and many other aspects of good software engineering discussed in this guidance are important activities that together help to support a final conclusion that the software is validated.

The updates are a very significant part of the software lifecycle, and updates can be a big source of trouble. The maintenance process is treated with the same level of seriousness as

the original process of creating the software.

The section 5 of the GPSV talks about tasks and activities in detail:

The Trifecta for FDA approval

Fundamentally the FDA wants to ensure three things,

• Efficacy: does it work effectively and performs as per the claims
• Safety: it does not cause harm to the patient or caregiver (usability)
• Security: sign to prevent malicious use (cybersecurity)

7. SaMD FDA approval pathways

We have three different review processes for the different types of software.

1. 510k or PMN

2. De Novo

3. PMA

The most common one in software, I'm sure many of you have heard the expression 510k. Here the FDA determines that the device, the software and tell me other what device you can insert the word software in your mind is substantially equivalent and has similar safety profile to a previously commercialized product. This is called the predicate.

In this particular case, if you have a piece of software that does something, for example, detect tumors from MRI images in this a previous piece of software, different products somebody else's marketed that does more or less the same thing, you can claim that that is your predicate

and all you have to do is say, we're similar to something you have already approved.This is a 510k process. A lot of time in this business is spent looking for the predicate because it makes life simpler. In the cases where there is not a predicate, but we're still in class 1 or class 2, this is type 1 type 2 classification, there's something called the De Novo process.

Is a newer process here. We'll talk a little bit about more detailed in a video clip in a minute and your device here is reviewed on its own, but you have to provide evidence of safety and effectiveness for the intended use. There is no predicate here so you say,

Pre-market Approval.

This is where we must demonstrate reasonable assurance of safety and effectiveness. This is really complicated, expensive, and your company has to be set up with a full college system as if your product was already on the market.

Approximate distribution on the 3 classess:

Commercializing AI in Medical Imaging

AI has been used in medical imaging for a while now. Dozens of well funded companies operate in this space. Although the new machine learning techniques, massive compute power and rise is electronic medical images has created many impressive solutions, commercialization is a whole different ballgame. In this article I hope to review how multiple companies are commercially positioning themselves in this space. Mainly what are the common commercialization pathways and if it can lead us to sort of a playbook.

Since last one decade the key focus of AI application in medical imaging has remained mostly on the diagnostic. Either computer-aided diagnostic or more independently executing algorithm doing at least as good of job as human reviewers. We can call this as the first generation of AI on medical imaging products and companies. In last few years however we see companies going beyond disease diagnostic and thinking hard about better integration with end-to-end workflow, creating what we can call as the next generation of companies.

Let's start with PathAI, a company started in 2016 who has built a platform to enable substantial improvements to the accuracy of diagnosis. The company has expanded into the measurement of therapeutic efficacy for complex diseases, leveraging the recent advancements in machine learning. The company raised $165 million is 2021 and also acquired Poplar Healthcare, a laboratory services company.

They started with a narrow scope of how can you identify cancerous cells in histopathology images. [?] And now have some impressive algorithms which can help you identify other relevant biomarkers like PD-1 expression on tumor or immune cells at least on par with human pathologists. One more cool thing about Path.ai is they hav developed a platform to collect exhaustive annotations (of PD-L1 positivity in this case) from a crowdsourced network of pathologists for analytic validation.

In September, in a historic milestone, the FDA approved the first ever AI-based pathology product by Paige.ai for clinical use. Paige Prostate is the first and only AI-based pathology product to receive FDA approval for in vitro diagnostic use in detecting cancer in prostate biopsies. Paige Prostate is a clinical-grade AI solution designed to identify foci that could indicate cancer, enabling fast, accurate in vitro diagnosis. In the clinical study submitted to the FDA, pathologists using Paige Prostate were shown to increase over 7 percentage points in sensitivity in correctly diagnosing cancer (from 89.5% to 96.8%). Pathologists using Paige Prostate had a 70% reduction in false negative diagnoses and a 24% reduction in false positive diagnoses.

Although PaigeAI and PathAI are destined to compete with each other, they seem focussed on generating their own value differentiators. Paige is primarily after diagnostics through FDA-approval path. Although Path has companion diagnostics as part of their core value proposition they seem to be focussed on offering variety of solutions like 

• Identify new insights on pharmacodynamics, mechanism of action, and patient stratification
• Quantify biomarkers for patient enrollment (quantify HER2 expression, and measure stain intensity, artifact content, tumor area, and DCIS)
• Surrogate endpoint prediction (e.g. MPR in NSCLC)
• Correlative studies in prospective clinical trials
• Measuring efficacy of treatments through various strategic partnerships. 

In December 2021, Gleamer AI demonstrated that their algorithm can quickly detect and flag X-rays with positive fractures helping hospitals reduce missed fractures by 29%. Due to the large number of X-rays that have to be read by radiologists, patients often have to wait hours in the ER before they can be seen, evaluated, and receive treatment. Fracture interpretation errors represent 24% of harmful diagnostic errors in the ER.

Dental industry is also seeing increased interest in companion diagnostics. Overjet a startup spun off from Harvard's innovation lab went through series A and series B in 2021 and now values at 425 million. Started in 2018 Overjet has been hyperfocussed on execution. In May of last year, FDA has authorized one of the first artificial-intelligence-based technologies for use in dentistry, a software platform from Overjet. With the 510(k) clearance, Overjet will be able to market its Dental Assist software for clinical use, selling it directly to dental practices. The software is already employed by insurance companies to make claims processing more accurate and efficient. 

Generation 2

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. For example using the radiology images can solve,  

• prognostication of outcome across multiple cancers
• prediction of response to various treatment modalities
• Discrimination of benign treatment confounders from true progression
• Identification of unusual response patterns and prediction of the mutational and molecular profile of tumours.

AI powered workflow

Finally, my favorite example of Viz.ai. This company is a textbook case of how AI needs to be seamlessly incorporated into medical workflow. In 2020, Viz.ai came up with a workflow for stroke victims. Essentially it is AI-charged push notifications + a group chat. It’s deceptively simple…but that works. Average time to notify a specialist in standard of care was 58.72 minutes vs. 7.32 minutes with Viz.ai. In 2021 Viz.ai successfully replicated this workflow for pulmonary embolism. commercial launch of AI-driven solutions for acute pulmonary conditions. Diagnosis and care coordination of patients suffering from pulmonary embolism (PE) can be challenging, with the average arrival-to-treatment times lasting more than 8 hours. Viz.ai uses deep learning to identify suspected pulmonary embolism disease in under two minutes. The Viz Platform is now utilized in over 850 hospitals across the U.S. and Europe. Obviously the approval from CMS has helped tremendously where Viz.ai demonstrated how this tech creates new workflow and measurably better outcomes for patients.

AI powered enrollment and more

Tempus released new product to identify potential therapeutic and clinical trial options for your patients. The platform claims to serve the customers in 3 keys ways:

• AI algorithms for molecular biomarker prediction: Their developing portfolio of digital pathology algorithms use a single whole slide H&E image to predict biomarkers, such as MSI or HRD status, for patients who are not ordinarily sequenced.
• AI algorithms for clinical trial enrichment: Their algorithms are being developed to use an H&E whole slide image to identify patients likely to contain characteristics relevant for clinical trial eligibility through our unique biomarker prediction technology.
• Solutions to digitize provider practice: They can serve as a partner to your practice in digitizing pathology workflow, from scanning to integrating algorithms into the existing workflow to uncover more potential options for patients.

Multi-modal model development 

On 20th May 2021 Google held its developer conference I/O and announced a new algorithm for their search engine: MUM, a Multitask Unified Model. For the two previous years, BERT was the underlying model for their search engine. BERT was a breathtaking release and was state-of-the-art until now, until MUM came. One of the most appealing aspect about MUM is the multi-modal data compatibility. This data point acts as a teaser of things to come making multimodal models part of mainstream.

Clinical decision-making in oncology involves multimodal data such as radiology scans, molecular profiling, histopathology slides, and clinical factors. Despite the importance of these modalities individually, no deep learning frameworks until last year has combined them all to predict patient prognosis.

Industry Update - March 27

Timedoc Health raises $48.5 million

Company: 

• The company offers virtual care management solutions to providers overseeing patients with chronic health conditions, working with approximately 120 providers across 35 states.

Problem Space:

• Chicago-based TimeDoc helps the overstretched, overworked primary care physician better manage their chronic care patients in between appointments as per the co-founder and CEO Will Boeglin says. 
• Ensuring virtual care programs stick can ultimately contribute to fewer hospitalizations.

The Products:

• Unlike other care coordinators, TimeDoc works directly with PCPs, and offers a broad array of services, including virtual care. 
• The three lines of business are chronic care management, remote patient monitoring, and the integration of behavioral health with primary care.
• TimeDoc collects data from remote monitoring devices into its platform, which pushes the data into an electronic health record (EHR). 
• It also staffs more than 150 care coordinators that work with patients, capture social determinants of health and make appointments.

Future Plans:

• Currently operating in 35 states, the company plans to use the capital injection to expand operations and support new hires. 
• It will add to its 150 care coordinators today with 20 to 40 new hires a month, along with other new staff, Boeglin says. 
• TimeDoc will grow its enterprise customer base by partnering with more traditional health systems, innovative primary care providers (such as those with value-based care-centric models), and accountable care organizations. 
• "Our big goal is to be helping 1 million patients a month by end of 2025," the CEO says.Who pays?

Afterlife raises $22 million

Avive raises $22 million

Antidote Health raises $22 million.

Health data and analytics companies

This is a very interesting space in the industry. I have attempted to create a classification framework. It is primarily based on three pillars - data (relationship/closeness with source), scale of technical solution, level of insights. A lot of the data and analytics companies would fall into one of the three categories. 

1. Data Providers

For the data providers I am using the Nikhil Krishnan's work here.

Originators 

• Providers or insurance companies trying to record/document the medical or claims data related to patient
• Hospitals, Clinics, Labs, Clinical Research Sites 

Data Collectors

• Being the tech side of originators, these collectors will always have access to the data flowing through their system
• Depending on the characteristics of the data it might be biased (geography, demographics etc).
• Examples include the EMR vendors like Epic, Cerner, Meditech or EDC vendors for clinical trials.

Data Brokers

• Expensive per patient record but likely to present a more complete and less biased picture of the landscape
• Tokenization works better at the stage due to multiplicity of sources
• The risk here is total dependence on data originators and data collectors
• Example include HealthVerity, Datavant etc.

Collectively these 3 segments can be called as patient registry market.

source: outofpocket health

2. Data + Analytic Solutions providers

• These companies are taking the value generated for the customer to another level. There is no value of a data unless we can draw an actionable insight from it. So that is what these companies sell - think of them as data-insights-as-a-service.
• Group 1 - IQVIA, Optum, Premeire
• Generally you can ask multiple questions but confidence-level of the answer would be moderate to high. Kind of old guards of the industry.
• Group 2 - Clarify Health, Komodo Health, Tempus (comprehensive insights-as-a-service)
• Generally you can ask multiple questions but confidence-level of the answer would be moderate to high.
• Technology is the cornerstone of these companies compared to group 1.
• Group 3 - Aetion, Flatiron Health, Kota, Concerto (Evidence-as-business)
• You can ask one precise question and confidence-level of the answer would be significantly high.
• These companies either will do lot of hand-holding while heading towards the answer or they will derive it for the customer. The customer doesn't have to worry about getting the right data or tech or experiment planning
• A few of them generate their own data having a highly stable business as compared to providers who buy the data.
• Some companies started in #2 but are now well known names in #4 are FlatIron, Medidata/AcornAI

3. Analytics Solution providers

• BYOD where D stands for data
• These companies provide software or platform services. You can come up with multiple interesting ways to group these companies.
• Group 1: SnowFlake, DataBricks etc. The platform stack for data analytics. 
• Group 2: Innovaccer, Health Catalyst etc. The application stack for data analytics.
• Group 3: HuggingFace, H2O etc. High-end tech providers (AI)
• Group 4 - Syntegra, Synthesized, Owkin High-end tech providers (Privacy)
• Unless you grow quickly, offering variety of solutions it would be difficult to survive in this section. Because as you do not have a differentiating data the success to some extent depends on being one of the first one with better execution in your swim-lane.
• Better execution is this case means extreme ease of use, significant gain in value (performance, privacy whatever is the metric) and continuous modernization of your tech stack.
• Advantage of being part of this class is you don't share the burden to ensure your data is comprehensive, high-quality, relevant and recent. All previous 4 categories would have to consider these factor seriously.

General notes on aggregated health data analytics business 

• Big players can be categorized as legacy players and new companies. Old guards include IQVIA, Optum, Premiere. New wave companies include Komodo Health, Clarify solutions 
• Key difference with the new wave companies is they are technology-first companies - started with the vision of technology being the centerpiece of the platform. And now offers SaaS platform based solutions. 
• New companies and their tech generally creates a lot of buzz but also at times FDA admissible evidence and commercially viable business  
• Business is generally generated by providing aggregated and de-identified data or insights based on that data or tech-powered-workflow which can enable generating insights.
• Out of the 5 listed above, only number 4 offers complete outsourcing options. All other categories involve the end-users getting their hands dirty while trying to figure out the data piece or the tech-piece. In a way you can view these four categories as whatever is your weakness, tech or data, the solution provider would help you deal with it. 

Industry Update - March 19

Telehealth platform Doctolib reaches €5.8BN valuation

Company:

• Started out as an appointment booking and scheduling platform in 2013.
• Doctolib has added additional services including telemedicine, instant messaging service, and back-office tool for administrative tasks.
• The company has reached a valuation of €5.8 billion, or $6.4 billion at today’s exchange rate. That makes Doctolib the highest valued French startup.

Problem Space:

• The company’s main product is a software-as-a-service platform for doctors and medical workers. 
• The company wants to help them (doctors and the medical staff) tackle admin tasks. 
• For patients, Doctolib acts as a booking platform that connects doctors with patients. Like ZocDoc.

The Product Evolution:

• Started out as a doctor's appointment booking and scheduling platform in 2013.
• A couple of years ago, the company launched remote appointments with a telemedicine add-on. Doctors who choose to pay a bit more can start video calls and use Doctolib’s payment systems for remote appointments.
• Last year, Doctolib launched Doctolib Médecin, a back-office tool for administrative tasks. For instance, it lets you centralize documents for each patient, see a patient’s history, take notes and issue invoices.

The traction:

• The startup also works with 250 public hospitals. And if you’re living in France, you know that Doctolib has become ubiquitous.
• During the COVID pandemic Doctolib ranked among the top three most-used video-consultation services in the world.
• In January the firm announced that 300,000 medical professionals in Europe use its monthly €129 per month software as a service (SaaS), including GPs, psychologists, pharmacists and dentists. 
• The company estimates a growth of 100,000 medical users in 2022.

Future Plans:

• The funding will be used to further fuel Doctolib’s recruitment drive, as part of its ambition to become an indispensable part of the healthcare industry. 
• It plans to take on 3,500 new employees in France, Germany and Italy over the next five years.
• Doctolib relies on a vast network of offices in major and mid-sized European cities so that they can talk with doctors all around France, Germany and Italy. Doctolib plans to operate across 30 cities.

Who pays?

• Providers pay. They get operational efficiency through suit of SaaS products offered by Doctolib and also patients through the booking portal.

Analysis: 

• Doctolib represents what ZocDoc could have been if ZocDoc had a clear vision and expansive strategy. Perhaps. Five years ago ZocDoc had 6 million monthly active users. They could have gone down the route of Ribbon Health and be a validator of in-network or out-of-network services. They could have gone down the route of Rupa Health or GetLabs and connect providers with labs. They could have gone down the route of Doctolib (which they are now) and be an early entrant in tele-health. Seems like they wasted a huge opportunity here. 

Clarify Health acquires Embedded Healthcare to use behavioral economics for value-based care

Company: 

• Enterprise analytics company
• Launched in 2015, Clarify unites longitudinal data from more than 300 million patients drawn from government and commercial claims, electronic health records and prescriptions.
• Raised $115 million series C in March 2021 ($178 million to date)
• Embedded Healthcare was spun out of the University of Pennsylvania’s Healthcare Transformation Institute in 2019 
• Embedded Healthcare based on research in behavioral economics & value-based care model design.
• Its behavioral science tools, provide data and incentives to clinicians to simplify value-based contracting and reduce the cost of care for patients.

Value Generated:

• Provides enterprise analytics solution on aggregated data collected from multiple sources.
• Komodo claims one of the largest dataset (300M+ patients), more longitudinal data (up to 6 years), rich set of features (like demographics)  

The traction:

• During the COVID pandemic Doctolib ranked among the top three most-used video-consultation services in the world.
• In January the firm announced that 300,000 medical professionals in Europe use its monthly €129 per month software as a service (SaaS), including GPs, psychologists, pharmacists and dentists. 
• The company estimates a growth of 100,000 medical users in 2022.

Future Plans:

• The funding will be used to further fuel Doctolib’s recruitment drive, as part of its ambition to become an indispensable part of the healthcare industry. 
• It plans to take on 3,500 new employees in France, Germany and Italy over the next five years.

Who pays? 

• Payers (50% of the customers), providers (30% of the customers), life science companies (remaining 20%).

Comments:

• Value-based care models picked up steam during the pandemic as mounting evidence demonstrated their potential to lower costs and improve outcomes.

Interoperability startups, TEFCA and QHIN

Health Gorilla grabs $50M for national data-sharing network

Company: 

• The startup’s national health information network works to allow seamless data sharing between patients, payers, providers, digital health companies and labs.
• Its platform uses a suite of FHIR-based application program interfaces (APIs) that digital health developers can integrate into their own solutions to aggregate each patient’s entire clinical history in one place.

Problem Space:

• Your doctor might not have access to accurate medical records. For patients to receive the best care possible, all players in the ecosystem need to work together
• The right people having access to the right health data is critical so that the right decisions can be made in a more timely and informed manner. It is rarely the case.

The technology: 

Value generated:

• Secure, compliant, and standardized way to share healthcare data between patients, payers, providers, digital health companies and labs.
• The startup says it prioritizes aspects of data sharing including high security and patient identity matching to simplify the task of extracting a patient’s health information from another clinical records system.

Future Plans:

• The company is also applying to become a qualified health information network, a federal designation introduced this year to mark networks that connect to one another to support national health information exchange.
• The startup said it plans to use the series C funding to continue to build its team and invest in its product development and go-to-market strategie

Who pays? 

• payers, providers, digital health companies and labs

General Catalyst, a16z and Rock Health back Ribbon Health's $43.5M series B to build out health data platform

Company: 

• The startup offers a health data API platform that delivers comprehensive data across providers, insurance
• what data - conditions and procedures treated as well as cost and quality metrics.

Problem Space:

• Despite significant investments in digital health and focused innovation specific to patient experience, patients struggle with the seemingly basic task of finding an in-network and conveniently located provider or facility that focuses on appropriate care. This confusion is caused by fundamentally flawed and disparate data that have become particularly burdensome as digital health advances. 
• Data on medical providers, including information as basic as address and phone number, have been cited as being only 48% accurate. These provider data flaws directly impact patients’ ability to compare procedure prices before seeing a clinician, according to Ribbon Health. 
• With 1 in 3 patients skipping care due to cost concerns, there is a critical need for accurate data on providers, specialties and insurance that takes price transparency and quality into account. 
• “While consumers can easily find the address for a local restaurant, the same can’t be said for patients who are looking for a phone number to make an appointment for a critical MRI or to confirm they are seeing a clinician who is in-network and of high-quality, so there are no unexpected costs associated with their care visit,” said Nate Maslak, CEO and co-founder at Ribbon Health, in a statement.
• “This doctor data problem harms patients as it often leads to people delaying care, opting out of treatments, or dealing with unnecessary and burdensome financial impact," he said.The technology: 

Value generated:

• Customers use Ribbon as the core infrastructure to build healthcare solutions, and Ribbon continues to expand and enhance its data set to support many different use cases across the industry. 
• With Ribbon, companies can seamlessly integrate and quickly update provider data directly into their existing workflows, according to the company.
• “Ribbon helps us connect members with high-quality providers and ensure patients are making knowledgeable decisions on their care, leading to improved outcomes, reduced costs and peace of mind,” said Marcia Otto, vice president of product strategy at Health Advocate.

Ribbon Health's data elements

Future Plans:

• Ribbon plans to use the fresh capital to prioritize team expansion 
• technology investments to build a best-in-class data platform 
• to simplify healthcare decisions across the industry

Who pays? 

Short answer is payers, providers, and digital health companies. Ribbon Health has 4 products as of now and each is directed towards a specific B2B need.

• Provider directory; customers include
• Health Insurance Companies
• Third-Party Administrators
• Digital Doctor Finders
• Referral Management; customers include: 
• Referral Manager Software
• Clinics and Hospitals
• EHR and EMR Systems
• Care Navigation
• Care Navigators & Advocates
• Doctor Search Platforms
• Health Savings Accounts
• Insurance Enrollment
• Benefits Administrators
• Health Insurance Exchanges
• Enrollment Platforms

Comments:

• It sounds like services like ZocDoc should have built this functionality years ago and could have offered as part of premium subscription. The process of finding a convenient provider is really cool but not expand vertically and ensure other aspects like Ribbon Health?
• The problem space is really hot mess, mostly because it is hard to solve but it also represents a huge opportunity. Although some claims by Ribbon health seem far-fetched around the problem space they might be very true. Yeah, it is that bad.
• To live up to the potential of the opportunity Ribbon need to expand much beyond provider and insurance data. Although it is a great start, real mess most likely happens when we look at the clinical data. For example, in clinical trials standardizing clinical sites data is hard but manageable problem. Standardizing clinical data is another ballgame, much much harder.

 

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What's a QHIN? (Qualified Health Information Network)

A Qualified HIN (QHIN) is a network of organizations working together to share data. QHINs will connect directly to each other to ensure interoperability between the networks they represent.

The title is part of the Trusted Exchange Framework and Common Agreement (TEFCA), released by the Office of the National Coordinator for Health IT in January 2022, five years after the first draft.

With the release of TEFCA, healthcare entities will soon be able to apply to be qualified health information networks (QHINs). These networks will connect to one another to support health information exchange nationwide.

Goal of TEFCA

• Provide a single 'on-ramp' to nationwide connectivity
• Electronic Health Information securely follows you when and where it is needed
• Support nationwide scalability

TEFCA isn’t binding, but the publication of the health information exchange principles marks a critical step toward creating a nationwide data-sharing network.

Companies leveraging national data-sharing networks have grown in popularity in recent years among organizations seeking easy access to patient data. Investors are pouring more money into interoperability solutions, too. The global market is expected to grow to $5.8 billion by 2028, more than doubling from $2.8 billion in 2020.

Industry Update - March 12

Elemy reaches unicorn status boosted by $219M investment

Company:

• Online + in-home behavioral health therapy for children
• provides care services for children with autism and other behavioral health conditions by pairing best-in-class clinicians with better technology.
• launched a little more than a year ago 
• Formerly named Sprout Therapy
• Valued at $1.15 billion

Problem Space:

• Just in the U.S. alone, Elemy estimate there are 1.8 million families with children with autism 
• beyond that, one in five children have some sort of pediatric behavioral health need, according to the CDC as per Yakubchyk's interview. 

The technology: 

• Elemy offers a tech-enabled platform for personalized care. 
• Elemy’s clinicians create customized treatment plans that can be administered both online and in-home, where children are more comfortable and have fewer distractions. 
• Data collected are used to measure efficacy and inform the evolution of both individual treatment plans and broader clinical strategies.

Value generated:

• By simplifying the onboarding process and more quickly pairing expert clinicians with patients, Elemy’s platform can reduce the patient onboarding process to as little as 12 weeks from an industry average of six months to two years
• On demand virtual or at-home care

Future Plans:

• plans to use the fresh capital to invest in significantly growing its staff 
• to expand its national reach. 
• the company will also invest in R&D to further enhance its technology offerings
• to expand into new areas of behavioral care

Who pays? 

• It looks like the patients pay in this case
• The provider gets paid for their time, and Elemy keeps some commission 

Competitors:

It seems the competition is really heating up in the space of online/at-home/in-clinic behavioral therapy for children or teens.

• Pediatric teletherapy provider DotCom Therapy recently secured $13 million. DotCom Therapy provides speech, behavioral, mental health and occupational therapy to children through its online, face-to-face platform, Zesh.
• Hazel Health operates virtual care clinics inside the school nurse's office to connect students to a physician via telehealth and last year landed a $33 million funding round in September 2020.
• Daybreak Health is another startup trying to tackle the gaps in mental health care for kids. The company launched in February 2020 and specializes in providing online counseling services, especially for teens.
• Brightline recently scored $72 million to fuel the national expansion of its virtual behavioral health solution designed specifically to support children, teenagers, and their families. 
• On-demand mental health care startup Ginger also unveiled a new offering designed for users age 13 to 17. Called Ginger for Teens, the service resembles the startup’s primary platform with the inclusion of digital self-care materials, behavioral health coaching, therapy and psychiatry accessible through a smartphone app.

Analysis: 

• CDC has compiled some numbers and this indeed seems to be a big issue. It is a welcome change to see the underserved population of kids and teens getting the attention they deserve and care coming to them in virtual or at-home or in-school setting. 
• In order to make the commercial model sustainable the specific programs and interventions needs to be backed by peer-reviewed research based on large enough sample sizes. Or eventually people might have trust issues with the specific solutions offered by the companies (not the behavioral therapy itself).
• The certified behavioral analysts have more incentive to be part of Elemy network, where they not only get competitive pay and flexible work hours, but other perks associated with a Unicorn (paid customer cancellation, BCBA tuition reimbursements etc).
• Elemy doesn't seem to have have their own self-created interventions (which might change with the new round of funding), they just offer a platform where board certified behavioral analysts can connect with kids (and their families) who need the services. Basically they act as intermediary trying to generate value for both sides using technology, and providing flexibility.

Industry Update - March 6 - Labwork startups

Rupa Health Raises $20M to Bring Root Cause Medicine to the World

Company: 

• Rupa health defined itself as a healthcare company that is building the digital platform for the next evolution of medicine, root cause medicine. 
• Basically it enables providers to order, track & get results from 25+ lab companies and 3000+ tests in one place.

Science:

• A root cause approach looks at a person’s health holistically and employs advanced, comprehensive diagnostics to understand everything from genetics to diet, stress and cortisol levels to the microbiome, and more.
• Rupa is at the forefront of the exploding root cause medicine movement. Major medical journals like JAMA are publishing research on the benefits of root cause medicine, and major hospital groups like the Cleveland Clinic, the University of California System, and the Mayo Clinic have added root cause departments.

The technology: 

• This is platform which act as a test aggregator marketplace currently and removes some friction at touchpoints like collecting specimen or payment from patients, sending to labs, transferring results back to providers etc.

Future plans

• Since RCM is a field about to take off, when/if it does Rupa will be one of the frontrunners.
• Rupa will use its distribution to root cause practitioners to create products that will enable more and more patients to access this new approach to healthcare. The new funds will be used to help make root cause medicine more affordable and accessible to all.

Value generated:

• It generates value for providers by saving their time (from 15 hours to 15 minutes as per the claim). [1] Rupa health takes care of the entire labwork experience from beginning to end for a comprehensive list of tests. All providers/physicians has to do is select tests from Rupa's one stop shop platform.

Who pays? 

• Rupa allows orders to be sent directly to patient for online payment, 
• or provider practice can pay up-front and charge patients separately.

Analysis: 

• Generally providers might not have incentive to order variety of tests and spend more time analyzing the mountain of results data due to fee-for-service model. What Rupa offers is variety of tests, some of which less common than the others. That could be the reason why Rupa health positions itself as a platform for root cause medicine. 
• This also means there might be a highly skewed distribution of tests orders from Rupa's platform. In order to make the less frequently ordered tests more attractive Rupa could offer comparatively less service fee - which is flat 7% for all tests currently. This was they can build a data cube including results of less common tests and potentially generate another revenue stream.

Getlabs will build out its at-home blood testing network with $20M Series A

Company: 

• Phlebotomist on demand
• Founded in 2018, Getlabs aims to become the boots-on-the-ground accompaniment to telehealth. 
• For example: Imagine you’ve just had a telehealth visit, and your healthcare provider thinks it might be time for a blood test. Instead of trekking to a clinic, Getlabs will come to your home and get the draw done, for an out of pocket “convenience fee” (to use company parlance) starting at $25.

Market:

• Lab work is an important part of clinical decision-making. One commonly cited statistic is that some 70% of clinical decisions are based on lab work. Some scientists have pointed out that no one can really find the origin of that number, but it’s been echoed from the Mayo Clinic to CDC website.
• In the U.S., there are about 14 billion lab tests ordered every year, per the CDC. And there’s evidence that more lab tests are being ordered each year.
• Between 2013 and 2018, spending on U.S. laboratory tests has increased more than 15%

Expected trend:

• Some telehealth companies, like Amwell, are beginning to realize that hybrid care models will facilitate telehealth’s bleed into areas like chronic care management, for example. It’s not just Amwell. 
• There has also been investor speculation that the future of telehealth isn’t virtual only, but a hybrid model that combines virtual appointments with in-home remote patient monitoring, or visits from trained specialists. 

The technology: 

• Rather than having patients book lab visits themselves, Getlabs is aiming to become fully integrated into a telehealth platform by launching its API. That API, he says, would allow for companies to schedule lab tests directly after patients’ virtual sessions.

Future plans

• The major goal of this round, says Michelson, is to increase the amount of healthcare workers hired by the platform. This funding will allow the company to bring on more phlebotomists to expand coverage, and pursue more partnerships with emerging telehealth companies looking for an in-person component.

Customer segment:

• Telehealth users with chronic conditions (or need of labwork due to any other reason)

Who pays?

• Getlabs costs nothing to providers and charges patients a low convenience fee (starting at $25) for collecting their samples and delivering to the laboratory.

Competitors

• Last year (in 2021) many reputed investors have invested in same space where digital health companies are trying to come to patients for at-home-care.
• Andreessen Horowitz, GV invest in Sprinter Health to scale up at-home lab, testing services
• DispatchHealth brings in $200M to scale up in-home medical care

Analysis

• Ideally Rupa health should be able to eat Getlab's lunch since the former is comprehensive end-to-end platform. Right from physicians selecting and ordering the tests, getting the specimens, payments and delivering the results Rupa health does everything. But maybe the devil is in the details - Rupa health provides self-testing kits. As per Getlab's pitch this is where is most error arise. 
• Getlabs seems to be narrowly focussed. They do no have as many partners as Rupa, but they have the biggest ones (Labcorp, QuestDiagnostics etc).
• Getlabs also seems to have different plans on platform vs API. Their preference is integrating seemlessly within existing ecosystem and so they are going down the path of API where as Rupa health is building the platform. So providers basically would have one less thing to worry about when it comes to Getlabs.
• There is no definitely better way but the answer for this significantly different approaches might be in founder's backgrounds. Getlabs is founder Kyle Michelson's third startup, and he has fundraising experience with previous companies as well. This could be a reason why Rupa Health seems to offer more comprehensive solution but Getlab's story seems investor friendly.