Nuna has the right mix of all the tailwinds a startup can ask for: talented team, big funding cushion, valuable data and huge market.
Founder Jini Kim was one of the Google PMs who was poached to rescue Healthcare.gov. That inroad, plus personal experience taking care of her autistic brother led to Nuna’s current shape.
Nuna has the enviable access to Medicaid’s records of 74 million beneficiaries. The cloud-based data warehouse that Nuna is building would be able to generate deep insights that probably have never been surfaced before.
Nuna just announced $90M funding round right out of the gate with the legendary KPCB name backing them. That should give enough runway to make a big dent in a stolid, opaque space.
Adding it to the 10 Year Watchlist. Should be interesting.
Most 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.
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:
- 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.
- 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.
- 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.
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.
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.
The US healthcare system has spent decades digitizing clinical documentation and records. Now that most of the data generated during a patient visit is capable of being stored in some electronic manner, the next logical question becomes ‘what do we do with this data?’. There are an increasing number of startups recently that attempt to answer that specific question. 360Fresh uses data-mining technology with the same objective.
Believe it or not, a lot of electronic medical record archives today consists of documents in free text format- no structure or organization, just vanilla narrative text. 360Fresh uses their proprietary data-mining logic to extract meaning from that. Generally speaking, I think there is potential for such offerings; especially when presented in a focused manner. For example, a service that identifies high-risk patients in ED or Labor & Delivery patients could be enormously useful for hospitals. And ‘risk’ can go beyond just clinical perspective, like this vendor that focuses on malpractice risk. And if its near real-time data-mining based on output from existing systems, even better.
Of course, ideally we would want (and expect) such intelligence to be inherent in the multi-million dollar enterprise Healthcare IT systems that hospitals buy to record the data in the first place. But most of them are either distracted by industry fads (like RHIOs or Comparative Effectiveness) or bogged down by existing product support to innovate in this direction.
Google.org’s flutrend is an attempt to model flu activity across US based on the search terms that Google.com users enter around flu symptoms, treatment etc. The underlying premise is that there is a relationship between how many people search for flu-related topics and how many people have flu symptoms. Think of it as a virtual public health surveillance proxy. If you are not convinced that this is a brilliant idea, take a look at how their analysis relates to CDC reporting.
In case you didn’t know, Google.org is the philanthropic arm of Google, and it was formed with the commitment of 1% of Google.com’s profits to address some of world’s most urgent problems (read the famous 2004 IPO letter by Larry and Sergey where they mention it). The site humbly admits that the Flutrends system is experimental. Nevertheless, it’s impressive that in some instances Flutrends was actually predicting flu before CDC.
Of course, not all people who search for flu have flu necessarily, but the power of this analysis comes from the coverage and promptness, not the granular accuracy. The basic idea of harnessing the collective thought (a.k.a. search needs) of the population to predict/monitor health events is fantastic. And this is just the beginning, IMHO. When a population is connected real-time and discussing what they think/want/need, abstracting that information can yield powerful insights- not just for prediction and monitoring, but for most aspects of healthcare (diagnosis, prognosis, news, followup etc).
The concept is applicable to domains outside of healthcare too. Take twitter for example. Twitter is another platform with mass adoption where people are having real-time conversations about what they are thinking/doing. Just look at what intelligent twitter mashups did for getting real-time snow report of the Feb’09 storm in UK or the Dec’08 Ice Storm in New Hampshire. There are health related examples too- the feb’09 salmonella-in-peanut-butter recall could be tracked promptly on a Twitter feed (btw, this slideshare presentation by PF Anderson at the University of Michigan explains ‘Twitter for Health’ in detail. Thanks to Christine Gorman for the link).