Today, clinical professionals have tools (like Ovid, ScienceDirect, UpToDate, Trip) that help answer complex questions and are connected to validated knowledge bases derived off of sources like PubMed. But how does a patient, with no access or expertise in the domain find and leverage this information? Medify tries to solve that.

The value proposition of Medify is not easy to describe. In fact, the ‘What is Medify‘ description on the site was banal enough to be dismissed, just like most other online social health startup marketing. They do a better (albeit prolix) job on the ‘How it works‘ page. Medify will appeal to the well-informed patients who are not afraid to sift through piles of academic articles burdened with medical jargon to understand and manage their own disease. Medify gives them a dashboard of existing literature – with it they can monitor things like which treatments are gaining traction in the provider community, which institutions are on the forefront of relevant research, etc. Affiliated web 2.0 functionality like faceted search, social sharing, tracking, annotating are bundled in to make it more personal.

Under the hood, it is smartly leveraging what public knowledge bases are already out there. The citation and abstract are free from PubMed. Interstitial phrases and terms in the content are further linked to sources like Wikipedia and MeSH. Brief outcomes or summaries are synthetically constructed from the article text.

Medify is not alone. There are other sites that try to help patients navigate the vast sea of research literature. PubMed’s parent NLM runs MedLinePlus,  UpToDate has a patient-oriented version, and niche startups like MyDailyApple, PatientsLikeMe are also tackling this to some extent.

In 2001 Brian Haynes, MD, PhD wrote an article describing the landscape of such ‘pre-appraised’ resources through a hierarchical structure that had four layers (called “4S” Model):

• Original ‘Studies’ (what PubMed provides) at the base
• ‘Syntheses’ (systematic reviews sources like The Chochrane Library) of evidence just above that
• ‘Synopses’ (like EBM, EBN Online) of studies and syntheses next up, and
• the most evolved evidence-based information ‘Decision Support Systems’ at the top.

He later expanded the model to 2 more layers (read about the “6S” paper here), but the basic argument remained same – Information seekers should begin looking at the highest level resource available for the problem that prompted their search. That is a good framework to understand why services like Medify are needed.

The skeptics would argue that offerings like Medify will do little more than empower hypochondriacs. But I believe that well-served health information only makes outcomes better. The lag time between published research being implemented in real-world medical practice can be in the order of decades. As consumers, we are entrusted to make choices about other important topics like money, and the market provides personal finance tools/services to help. Same can apply to healthcare, without diminishing the role of experts.

Alliance Health offers services to create health-related social networks. So it’s like Ning (Marc Andreessen‘s online platform for custom social networks) for healthcare, except that users can’t self-service and start a health-related network on Alliance Health.

They currently operate separate networks on over 40 physical and mental health issues (see screenshot below). Each community offers the standard fare expected in a online social networking site- discussion forums, videos, posts, reviews, etc. The star performer in their portfolio is DiabeticConnect, which is reportedly the largest diabetic community online with around a million unique visitors monthly.

If you look at the top most active social networking websites today, there are hardly any entries related to health (DXY.cn, DailyStrength, CaringBridge are the only ones). Yet health information is one of the most commonly sought-after content online. So there is definitely scope and need for leveraging social features of internet for health. Doing so on a combined platform increases viability, since the mainstay of revenue for social networking is still advertising. Cumulative audience across all networks is a better sell to potential advertisers.

I have high hopes for Alliance Health. Once the user base is there (like in DiabeticConnect), there are significant revenue opportunities in data analytics and mining. Beyond niche topic-based networks, I think they can extend services to private organizations like large care delivery organizations, insurers to create white-label social networks.

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.

There are plenty of precedents to social networking websites for patients, so nothing new from that perspective. But as I read more about this one, it stood apart. The idea of a patient collective focusing on obscure, lifestyle-affecting, painful, chronic and under-researched diseases  and making their raw data available is pretty cool. This WSJ article talks about trend of ‘Personal Informatics’ emerging- where affected individuals obsessively record everything about their life and share it with others. This may usually sound useless and weird, but given the fact that there is no definitive causal understanding of conditions like migraine (even though it affects millions of Americans each year), I find it novel and exciting.

I like this bottom-up, organic approach to furthering research on obscure conditions. Their call for Open-Source Health Research is also an interesting read.  Bit worried by the fact that CureTogether is self-funded. Hopefully they will stay around long enough to claim a large-scale success for one of the diseases.

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.

At the heart of the model are a set of ordinary and differential equations that represent the physiological pathways relevant to diseases and their complications. The ‘variables’ in this model include signs, symptoms, patient behaviors (including adherence), provider behaviors, provider performance, encounters (e.g.  ER visits, office visits, admissions), protocols, guidelines, tests, treatments, etc. Basically, it tries to incorporate all aspects of diseases and healthcare system that are needed to analyze downstream clinical outcomes, utilization and costs.

A more loaded one-line description of Archimedes (taken from his original paper in 2002): “It’s an object-oriented, continuous-time, full simulation model for addressing a wide range of clinical, procedural, administrative, and financial decisions in health care at a high level of biological, clinical, and administrative detail.” Phew. I’ll confess that I don’t know what exactly is under the hood. But I know enough about the informatics field to believe that this approach is viable and very exciting.

This YouTube video explains how the model can be used to run virtual clinical trials. Kasier has already backed the findings of Archimedes to change their diabetes care delivery.  I think there are fantastic, unlimited opportunities for applying such a fundamental model to medicine- personalized health predictions, public health, health policy, cost-effectiveness and what not.  As a startup, they are doing fine. With an impressive list of partners/clients, and a $15.6M RJWF grant (2007), they have a good runway and momentum. They have all the right ingredients to be a change agent for next-generation Healthcare IT.

Jan 2011 Update: The FDA and Archimedes entered into a research agreement to understand the benefits of weight loss compared to the long-term risks of cardiovascular outcomes in patients treated with weight loss drugs.

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).

Healthmap extracts real-time information from more than 10,000 sites every hour and text mines them for disease and location patterns using bayesian filtering. The interface is clean and intuitive mashup with google maps. Links to the source of alert and a ‘heat index’ (composite score for each incidence based on things like recency of alerts, number of sources etc) are provided.

There are limitations too-  dependance on other sources, unstructured text, lack of integration between sources, not comprehensive, etc. But if you think of it as a free resource that supplements existing public health systems, its a great asset for general public and clinical professionals.

The business idea is to mine the community generated data and monetize it by letting sponsors directly participate and gain real-time insights into physician attitudes and perceptions. They provide online CME (free), journal articles and news/meetings/conference coverage. The standard WebMD fare of Medscape Drug Reference, expert columns and interviews are included too.

PatientsLikeMe is a social networking site that enables patients to share information around disease conditions. It was started in 2004 by concerned family members of an individual with ALS (Lou Gehrig’s disease). Users can register for free and interact around eleven conditions: ALS/Motor Neuron Disease, Anxiety, Bipolar, Depression, HIV/AIDS, Multiple Sclerosis, OCD, Parkinson’s disease, PLS, PMA and PTSD. For a site focused on limited number of disease conditions, they get some serious traction:

PatientsLikeMe enables affected individuals to find others with similar condition, share experiences, and learn what works and what doesn’t. Users can also track their progress and keep up with relevant research. The knowledge of first hand information about a disease experience, treatment effectiveness or side effect is very powerful for managing these complex multi-system diseases.

Needless to say, the rich data generated by such a focused and engaged community is invaluable for research. So no surprise that one of the revenue models behind PatientsLikeMe is the sale of anonymized data from and permission-based access to their user community.

June’10 Update: Case in point about the usefulness of sites like PatientsLikeMe. Last month Lancet Neurology published a study showing that the generic drug lithium did nothing to slow the course of ALS. PatientsLikeMe community had reached the same conclusion 18 months earlier, much more quickly and at much less cost. In 2008, after a small Italian study suggested that lithium could delay the progression of ALS, about 10 percent of PatientsLikeMe’s ALS users began taking the drug, not wanting to wait for a larger trial to confirm the results. Inspired by a member in Brazil who wanted to know if lithium was truly helping, the company rolled out a number of tools to allow patients to track their progress. You can see their fantastic report page here.

So what is special about Sermo? For one, it has been forming all the right partnerships. Take its alliance with the country’s largest physician organization (AMA), collaboration with pharmaceutical giant Pfizer and the agreement with main regulatory authority FDA. Second, it’s been successful in getting traction from a very fickle and tech-resistant group of professionals- physicians. Their compete.com report is pretty impressive:

The site claims 65,000 users and generates revenue by charging outsiders for access to the community. The data generated by physician interactions around medical events, treatments, observations, opinions provide valuable insights to commercial organizations. Its a information arbitrage or ‘Engagement Model’ model, as Sermo calls it.

Sermo is not alone in trying to aggregate and monetize the lucrative US physician community. Docsboard, SocialMD, DoctorNetworking are others in the game. There is competition in adjacent spaces too: NurseGroups, NurseLinkup are focusing on the registered nursing professionals, StudentDoctor.net focuses on medical students. Even finer niches like women in medicine are online (MomMD).