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).

Healthmap is a perfect example of what technology can do to adavnce healthcare information. It aggregates online media reports to enable infectious disease intelligence on a global level. Its a near real-time internet-based infectious disease surveillance that is free from political and geographical restraints.

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.

Ever since Google and Microsoft jumped into it, the PHR (Personal Health Record) space has become red hot. So while PHRs try to move from hype phase to reality, startups like ClearSense are positioning themselves to leverage all those rich, complex details about your health.

ClearSense aims to help you make sense of your health information by providing the data analysis technology called REDBOX (developed at Bioinformatics Research Center at the Medical College of Wisconsin). I couldn’t find any information around what exactly makes REDBOX unique- it seems like it consists of data analysis models and algorithms that are optimized for health related data (although the devil is in the details for a technology like this). The company behind both of them is Point One Systems, which seems to have spun off from the Research Center.

The sample reports look Web 2.0-ish, with simple interface and layout. The actionable items and alerts are clearly outlined along with tips and educational material. I dont have enough health information in my Google Health account (fortunately I’m in the pink of health) so there was not point in taking ClearSense for a spin on my info.

Although there is minimal info about ClearSense’s unique selling point, the overall concept may actually prove to be useful if/when PHRs take off in future. Hopefully, ClearSense will survive to see that day.