Amino

The service transaction between a patient and physician has always been looked at closely from a billing perspective. Relatively few attempts have been made to enhance the match-making aspect of it: like which physician is best suited for the given patient? Tweaking this may have cascading effects downstream in the service experience and actual outcome.

Years ago, Zocdoc impressed me with their simple solution in this space. It was making it easier to get an appointment with retail physician of your choice. It’s hard to scale the retail small/medium clinics, so they dabbled in employee wellness for a bit and finally settled its focus on hospitals and health system customers.

Amino focuses on figuring out the right physician first and then help with cost-estimation and scheduling. It claims to have analyzed a trove of insurance claims data to figure out the attributes of past interactions of care providers. Using the information that patient’s type in to Amino website (health problem, insurance, zip code, etc.) it can align it with the best physician profile in its database.

The approach has merits since most patients are inherently biased (“my sister says this doctor is great”), lazy (“I’ve always gone to this nearby clinic”) or arbitrary (“I just googled it”, “His name was on the first page of my insurance directory”) in the way they select physicians. Having a good partner in healthcare can make a difference. My curiosity is about their business model – who pays for this ultimately? For now, Amino has enough runway to not worry about it ($20M in three rounds so far).

PS: Kyruus does similar stuff, but for enterprise.

 

AmplifyHealth

AmplifyHealthLogoMost of the time, Health IT spawns artificial concepts – born as a result of relentless media hype, each reaches a precocious peak of publicity and then quickly fades away. Buzzwords like RHIO, NHIN, PHR, Chronic Disease Management, etc. were all touted as game changing at one point or other in the past. Now it’s more about patient engagement, HIE, Analytics, Care Collaboration. One stands out in my mind though – Population Health Management (PHM). I think that even though it may be riding the hype cycle like all others, it has signs of legitimacy.

Think of it this way. For decades, we have endured and participated in a healthcare system that is geared towards encounter-based medicine. Patient comes in with complaint X, gets treated and billed for complaint X. Now with changing payment models though, it is important for the payers and providers to broaden their perspective. They need to keep track of patient (member) over a period of time, and keep them out of hospitals/ERs. As a result they need a “Longitudinal Health Record” that spans across encounters. This is what HIEs promise to provide and interoperability standards promise to enable.

From a Health IT vendor perspective, PHM means tools that help user do two things:

This is done by analyzing a population in a given care context. Like HbA1c tests for diabetics. PHM construct is based on the premise of looking beyond those who need immediate care (i.e. are having an encounter) and provide insights on the entire cohort under care.
This is where the analytics graduates into what it should be – Actionable Analytics. The ideal PHM tool will not only help find at-risk individuals, but also make it easy to do something about it. So if the PCP user has found the 50 at-risk diabetics in his/her 1000 patient panel, they now need to send reminder letters or queue them up for some kind of outreach. This workflow integration is what really legitimizes the emerging niche of PHM. Just analytics on it’s own doesn’t cut it.

But the devil is in the details, of course. One can argue why EHRs, the perennial stolid incumbents of health IT world, don’t have this as native capability. The answer is clear if you’ve ever used an EHR. They were (and are) built as transactional systems that focus on the current visit billing and documentation. Doing a parallel meta-analysis of how this patient fits into a population profile and what they need outside the context of this visit is a humungous leap for almost all EHRs. And that is why a new crop of startups have started to focus on this niche.

 

AmplifyScreenshot

AmplifyHealth says all the right things on it’s website. They point out the need for finding patients that are going off-track. Like most startups, it avoids putting a live demo video on the site (so frustrating) so I’m going off of what the webpages claim as capabilities. The three areas they speak of:

  1. Patient Management: Seems like this provides ability to create custom lists, akin to registries. That is a valid value-add, aligned with actionable analytics as described above. But the website description veers off into “engage new patients, influence productive behavior, establish relationship” which is confusing. All those belong to the foundational practice management and EHR system.
  2. Measuring Outcomes: This would be the ‘meta-analysis’ that doesn’t come native with EHRs. Tracking outcomes based on measures is just starting to get engrained into the EHR DNA, thanks to the bullying by Meaningful Use regulation. But even that is a very regimented approach to this meta-analysis, and may not suffice for an ideal user. Hence the value-add opportunity.
  3. Client-Sales Support: Very interesting. This seems to be an administrative dashboard for provider groups, self-insured employer groups to analyze of potential savings for a population. So it goes beyond just the clinical aspect of Population Health Management. I can see that as a separate sell to administrative, non-clinical users.

Buoyed by the hype that usually accompanies anything new Health IT, PHM is ready to bask in media limelight. But this may be one of the rare occurrences where there is actual substance underlying the claim to fame. Of course, only time will tell. One thing is for sure – you will see this term splattered across a lot of vendor booths in HIMSS 2014.

Simplee

Consumer tools that help deal with healthcare system complexity are unquestionably needed. A recent niche has focused on dealing with healthcare bills.

Simplee helps it’s users track medical expenses in an friendly online dashboard. The aggregated data and management tools can help manage health care costs and perhaps be used for finding the right medical plan and services for an individual or family. The service can also be used to pay medical bills since it has an integrated payment platform.

Obvious comparisons can (and have) been made to personal finance management websites like Mint. No surprises there since managing health and wealth are equally daunting tasks, riddled with complicated verbiage and stressful decision-making for most. The need is obvious and there is competition (CakeHealth, HealthExpense and Quicken Health for example). Payer coverage is key ground to cover quickly- I couldn’t find my insurer in Simplee, for example.

Regardless, the real utility of a service like this is in its integration with existing channels that push healthcare billing information to patient. A white-labeled Simplee would be fantastic for Payers so they can evolve the annoying EOB letters sent to patients. PHR or Patient Portals (whether provided by the insurer or provider’s EHR) would be another channel for using Simplee’s service to explain the bills. Without channels partnerships like these, I’m less optimistic about Simplee’s uptake in the real world. Another perplexing topic is business model. Providing free management tools can only get a user base, and to monetize that Simplee will need to add more services – perhaps become a shopping engine for health services, provide comparisons and ratings, etc. That can’t be a viable option for short-term since building a value proposition like that would need significant traction in a given healthcare market.

As a patient do I want a new, independent, smaller company to access, analyze and archive my healthcare bills? How comfortable am I want to give them my credit card info? The answer would probably be no for a significant part of conventional patient population, unless this useful ‘billing translation service’ was embedded in my usual interaction channel with the healthcare system. I’m looking forward to the partnerships that Simplee can muster going forward.

 

Apixio

With regulatory push for EHR adoption, there is an impending avalanche of healthcare data coming in the next few years. Some believe it’s already here. But data can come in different flavors: from the frighteningly common free text to loosely categorized documents to well structured messages. The less structure it has, more hard it becomes for a machine to understand the real meaning (semantics) of the content. The combined effect of increasing quantity and poor quality makes this a bigger problem than what most anticipate.

Apixio is one of the few startups tackling this issue. Their analytics engine indexes the underlying data, processes queries and provides context-relevant results. The core technology is supposedly based on Apache’s Pig (a data-flow language and execution framework for parallel computation), Hadoop (a framework that allows for the distributed processing of large data sets across clusters of computers) and Cassandra (a scalable multi-master database).

There are a number of terminologies (read ontologies) in healthcare, trying to specify the concepts and relationships from a particular perspective. LOINC, ICD, SNOMED, CPT are common examples, but see a pretty comprehensive list of all human-related ontologies at BioPortal (filter by category ‘Health’).

So a medical-grade search service offering would need to traverse such terminologies and surface all relevant, normalized data related to the query. For example, a search for keyword “breathlessness” in a patient with long, complicated medical history would bring up documents and encounters that mention items like wheezing, PEFR, smoking, asthma management. It’s no short order to do all that analytical crunching.

Sophisticated data transformation and abstraction offerings are certainly needed for making sense of complex healthcare data. Niche efforts like Apixio, 360Fresh, are signs of growing market realization that the era of just trying to digitize healthcare data is getting over. Now we start figuring out what the heck to do with all the incoming bytes.

PS: Advanced analytics offerings in healthcare are an interesting topic. See this wiki page for a living list of relevant companies in this space.

 

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Cellnovo

People with type 1 diabetes need to take insulin in a similar way their pancreas would produce if it were normal. Older therapy used to be multiple daily injections, which were poor approximation of the insulin need. In Continuous Subcutaneous Insulin Infusion (CSII) or Insulin Pump therapy, a small device delivers a constant stream of rapid-acting insulin through a tiny tube; according to a programmed plan unique to each wearer. Insulin pumps are not automatic but they let patients make immediate adjustments, enabling them to lead a more spontaneous lifestyle.

Companies like Cellnovo represent the key role IT is playing in the evolution of medical devices. UK-based Cellnovo began in 2002 as Starbridge Systems Ltd. to develop a novel micropump with only one moving part that made it smaller, more accurate and less expensive. Somewhere along the line, their conventional medical device transformed into a mobile health offering. It now consists of:

1. Pump: A small, waterproof device that can be easily applied, removed, and repositioned on the body. Also includes a built-in accelerometer that registers and stores user activity data.

2. Handset: A hand-held device that communicates wirelessly to control the pump and sends data to a secure website. User can manage dosage, schedule, log supplemental data like food intake, activities, emotions, etc. through this device. The look-and-feel has been compared to today’s appealing smartphones with icon-driven intuitive graphical display and touch screen ability.

3. Online: Websites customized for various participants that are usually involved in managing diabetes- provider, patient, caregivers, etc. Given the variety of people that can be involved in the care team (primary doctor, dietitian, diabetes nurse educator, eye doctor, foot doctor, endocrinologist, exercise trainer…), communication and coordination is an often under-served part of diabetes management. Seems like Cellnovo Online is an attempt to improve just that.

The overall concept is not new. OmniPod by Insulet Corporation (a public company) has a pump and handset. Big players like Medtronic, Sanofi-Aventis, J&J have shown signs of moving in similar direction. With the February 2011 series B financing round of $48.4 million, Cellnovo also seems to have enough runway in this space. As an interesting aside, combining insulin pumps with Continuous Glucose Monitoring System (CGMS) makes a terrific combo- uninterrupted sensing and coordinated, intelligent drug delivery. OmniPod does this.

Solutions like Cellnovo provide not just a way to deliver therapy, but a novel way to collect detailed data about given patient population. Analyzing aggregate data like that can lead to insights at multiple levels- clinical evidence (EBM), provider performance, population health, etc. An interesting decision fork in this evolution would be whether manufacturers leverage commercial computing hardware like smartphones or create their own (like Cellnovo). The former gives wider reach, while latter provides better, medical-grade control (something that FDA probably mandates).

But the key point in all this is about the future of traditional consumer medical devices. The next-generation devices seem to be less conspicuous, continuously connected, more personalized and come with an integrated online component that becomes the window to interaction with multiple parties (caregivers, friends, insurers…like an evolved, niche form of social networking). The new value proposition doesn’t stop at just a hardware device, but becomes a continuous service for managing chronic disease.

One can argue that managing all chronic diseases requires understanding an ever-changing constellation of information continuously generated by a whole ecosystem of participants. This ever-connected disease management approach that removes the burden of keeping journals and pushes information to healthcare professionals can to be applied to many diseases besides diabetes. I’m sure a number of those are already underway.