I wrote about Proactive Sleep a while back and wondered how it would be to combine a sensor to automatically record sleep pattern. Well, Zeo gets one step closer to that.

Zeo system consists of a wearable headband that measure brain’s natural electrical activity. Although their blog has a high-level explanation of how it works, my understanding is that its a single-channel EEG, which seems to be a reasonable way to do sleep analysis in healthy individuals. That data is wirelessly transmitted to a bedside display and stored on an SD card.

Algorithms based on proprietary logic churn out a personal sleep score (called ZQ) to quantify the type of sleep you get. The display unit looks like a bedside alarm clock and shows current and past 2 weeks worth of sleep analysis. Also has some smart alarm clock features like SmartWake alarm that wakes you up at the most suitable time within half-hour of set time. You could upload the data to an online sleep journal through the SD card. The website gives graphs, trends and the ability log other supplementary lifestyle data that can affect your sleep. All that for $249. An additional $100 would get lifetime access to a personal sleep coaching program, which includes regular assessments, goal tracking, email tips etc.

There is no question that Sleep Science is a serious, mature field. Zeo can find its place as a useful adjunct for plenty of sleep-related disorders that affect people who are otherwise healthy. It’s not an FDA approved 11 channel medical grade polysomnogram, and it’d be a mistake to compare it to one. It’s perhaps a closer analog to Actigraphy where a wearable sensor measures overall motor activity during sleep. An actigraph unit is an accelerometer based device like the FitBit, WakeMate or Axbo.

Accurate or not, Zeo is yet another proof that healthcare is slowly being transformed by sensor-based, portable devices that are capable of analyzing data in a consumer-oriented way to enable individual patient to manage their conditions better.

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.

David M. Eddy, MD, PhD is a legend when it comes to Evidence-Based Medicine. He coined the term in 1980s, actually. Being exceptionally skilled in mathematics, it was perhaps natural for him to apply it to medicine. The result is Archimedes Model- a mathematical simulation of the human physiology and how it interacts with healthcare interventions.

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.

PharmaSurveyor is a free service that analyzes your medications to point out potential drug interaction and side-effect risks. It was founded in 2006 by Linda and Erick Von Schweber to commercialize the ‘knowledge surveying’ technology they have developed over the last 25 years or so.

Given the fact that Adverse Drug Effects (ADE) are one of the leading cause of death in the US, there is significant market opportunity in consumer education and support around it. Couple of nifty features that I like:

• Direct meds import from Healthvault
• Community Knowledge Base – an aggregation of information and experiences from people who are on multiple drugs. This feature is currently in private beta, but I think that it can be a great revenue opportunity once it gets some traction in terms of number of users. There are plenty of pharma companies who would pay good money for getting early (even though informal) insight into side-effects, efficacy, interactions, and usage patterns of their drugs.

Seems like they are planning to integrate with DestinationRx and Polka, which is a good idea since the traffic from those sites will already be primed for the services that PharmaSurveyor provides. The advisory board has some significant names, including Barney Pell, Matthew Holt and Mark Musen.

They do have some interesting marketing techniques like analyzing celebrity cocktails, not requiring registrations, etc. Business model seems to be only google ads for now, which is no surprise given their research-oriented background. The site is more a proof-of-concept for the underlying technology (although I’m not sure what it is exactly). It’ll make a lot of sense to integrate this service with commercial CIS offerings, and take it one step beyond just using RxNorm.

TrialX.org is an fantastic example of how the web enables linking specific demand with relevant supply. The services matches users (patients, affected individuals) to ongoing clinical trials using their submitted personal health information.

What a great startup idea. Service demand can be tapped easily since users are searching the web for highly specific keywords (almost all include the keyword “trials”, so bit of SEO and keyword advertising would direct the traffic effectively). Other sources are the rapidly growing PHR platforms like Google Health and Microsoft Healthvault- both encourage developers to write apps that provide such value-added services based on user’s health information. Supply is readily available on ClinicalTrials.gov, a government-sponsored online public registry of clinical trials in US.

TrialX.org is completely free for users (patients). They let investigators create free accounts to post their trial information directly, but charge a fee for providing access to the interested potential trial enrollee. It’s hard for trial investigators to find eligible patients who are motivated to stick around for the complete trial. TrialX solves both the problems for them.

Imagine the possibilities if this service gets integrated into CIS vendor products. A patient coming in for advanced  breast cancer treatment can be flagged right at admission and be given the option to enroll in an experimental drug trial right then, if they so choose. If nothing else, it’ll give the medical research community a much more real-time opportunity to advance the science.

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.

SugarStats is a hawaii-based startup inspired by one diabetics’ unfulfilled need to manage his disease data better. Marston Alfred (the site’s creator) found the online diabetes management solutions boring and inaccessible, so he embarked on crating a clean, user-friendly website where diabetes could track their sugar levels and network with other affected individuals.

They have various analytical graphs and trends, messaging, mobile edition and even twitter integration. I think the concept would have been much more powerful if there was a way to eliminate manual data entry by patients. So integration with blood glucose monitors that can sync data to the website would be great- perhaps using platforms like Healthvault?

Side note: Diabetes is perhaps one of the most well-represented diseases online. There are a ton of resources out there- David Mendosa has a good list here.