Medicine 2.0™ Congress: Social Networking and Web 2.0 Applications in Medicine and Health

I am speaking at the Medicine 2.0™ conference on adapting and adopting social networking methods for biosurveillance. The conference is taking place in Ontario, CA on Sept4-5, 2008. "Medicine 2.0™ is an international conference on Web 2.0 applications in health and medicine, organized and co-sponsored by the Journal of Medical Internet Research, the International Medical Informatics Association, the Centre for Global eHealth Innovation, CHIRAD, and a number of other sponsoring organizations." The congress was organized by Dr. Gunther Eysenbach.

Here is a list of the final accepted abstracts and the Medicine 2.0™ Blog site...

I am very interested in hearing your thoughts and any input you can provide to help me better present the topic. Definitely if you know of existing or related work that I can reference will be much appreciated...

And as my friend Susanne Jul would say: Gunalchéesh [thank you, Tlingit]

A roadmap toward a European healthgrid

This is an exciting work towards building an environment of sharing of resources across heterogeneous and dispersed health data:

• molecular data (e.g. genomics, proteomics)
• cellular data (e.g. pathways)
• tissue data (e.g. cancer types, wound healing)
• personal data (e.g. PHR, EHR)
• population data (e.g. epidemiology)

Also, an application which can be accessed by all users as a tailored information system according to their level of authorization and without loss of information. Collaboration is the heart of this especially across multiple disciplines. In addition, many standards have not yet been realized (e.g., HL7 (Health Level Seven), the US based Standards Development Organization that currently offers asynchronous messaging), this makes a grid approach far more powerful where organizations join the grid and make their information available for querying, processing, analysis, etc.

Few challenges remain, such as:

• How do we secure and maintain high performance of such distributed structure of data integration and computing?
• How do we close the gap between grid standards and health-related standards [some nice work's been done here by Power, et al]?
• How do we go about next-generation open source ontologies for medical informatics?
• How do we close the gap between hospital policies, public health policies, etc. and the grid approach?
• How do we go about consumerism and patient ownership of her or his data?

If anything, I hope this raises more awareness of the grid application in health and public health - much still remains to be answered.

Successes are already underway in the health community, for example:

• in the European health community (HealthGrid©)
• US CDC [presentation on the Public Health Grid by Ken Hall (BearingPoint, Inc) and Dr. Tom Savel (US CDC) at the recent HimSS February 2008 meeting]

But also there are lots of lessons which we can learn from the innovative thinking of efforts like the Google Cloud (click here to learn more about cloud computing).

I'd be very interested to hear your thoughts on this...

What is Biosurveillance? What is InSTEDD' Role?

Dating back to Hippocrates, biosurveillance, the detection and observation of disease outbreaks, is not an entirely new concept. In recent history, the geographical isolation between plants and animals has been gradually broken by the intentional or natural transport of organisms caused by human travel, tourism or trade. Today, the rate at which species are moving between different bio-geographic regions is unprecedented, resulting in adverse ecological, economic, and human health consequences. Additionally, global environmental changes have continued to grow rapidly throughout the past five years. These changes for example to climate, transport networks, disease pathogens and their vectors do not respect administrative boundaries and their influences and impacts are best addressed at the global scale. These factors have contributed to an environment where a new disease threat can spread globally within hours and days.

Over the last decade, automated biosurveillance systems have been developed to provide more timely detection of disease outbreaks by monitoring clinical systems and other sources. To date, the bulk of these systems have been regionally deployed, limited to areas where clinical data is readily available, and internet connectivity and high bandwidth are widespread. Unfortunately, these systems do not support regions of low bandwidth, limited connectivity, and sparse use of clinical information systems-the regions where globe-threatening outbreaks typically originate and where timely interventions are most needed. What is needed now is a global information infrastructure and scientific methods to support timely detection and monitoring of events world-wide, as close to real time as possible.

InSTEDD is working with its partners on addressing these problems and promise of timely biosurveillance. InSTEDD's suite of technologies and informatics approach will have a global reach that will revolutionize retrieval and integration of information across multiple disciplines, and will have a high degree of penetration into regions were enhanced biosurveillance is sorely needed. InSTEDD is taking the ancient art of biosurveillance to the global level, focusing on key biosurveillance targets such as Infectious and Respiratory Diseases (Influenza, Malaria, West Nile Virus, Lyme Disease (in animals is indicative of global warming), Rift Valley Fever, Schistosomiasis (schis·to·so·mi·a·sis (shĭs'tə-sə-mī'ə-sĭs)), Asthma)), Extreme Weather Events (heat waves and floods), and Natural and Managed Systems (forests, agriculture, marine ecosystems, and water).

Finally, I leave you with this historic map - John Snow used statistics to argue that "something was in the water" -- although no one had yet identified cholera.

1854 Broad Street cholera outbreak

Pandemic Preparedness and Response Models

Last month, Gerardo Chowell (School of Human Evolution and Social Change, Arizona State University) and Hiroshi Nishiura (University of Utrecht) published their work on quantifying the transmission of a pandemic influenza. Although we have learned a lot from the 1918 Spanish flu pandemic, we still lack the understanding of some basic probabilities necessary to plan an effective response to a pandemic. For example, if one has the flu, what is the probability that the disease will manifest itself clinically? Once the disease manifest itself clinically in an individual, what is the probability of dying from the flu. In the case of bird flu, the virus can spread across different host species, what is the probability of transmission? Right now we don't have all these answers and we can only make assumptions. This is what makes it very difficult for responding authorities to have an effective response plan to a pandemic, like the H5N1 potential pandemic influenza. Additionally, current intervention optimization tools are limited by the fact that they can't maximize realism, generality and precision at the same time. Public health authorities and planners are in need of an optimal and realistic combination of these properties in order to form the appropriate and timely response plan. Is closing schools the right strategy? When do we issue a quarantine? etc. etc.

At minimum, a pandemic planning model (and supporting tools) should:

1. allow investigation of time-dependent variables (such as; surge capacity, incidence, height of a pandemic peak, availability of drugs or vaccination)
2. be adjustable to support different local conditions and assumptions, localities and countries will vary in their preparedness and response plans
3. be publicly available
   

Network model for disease transmission and control

The natural history of an infectious disease can be described by the so-called SEIR model: susceptible individuals (S) become exposed and acquire the infection (E). After a latent period, they become infectious (I) and pass on the infection. If they survive the disease, they will acquire a permanent immunity (R). Furthermore, we can adopt a slightly more sophisticated model where we split the infectious state into three different compartments according to different symptoms. Infectious cases can remain asymptomatic or they can develop mild or severe disease. Infectious individuals of these three states may differ in their biological contagiousness and in their ability to contact and infect others. Severely ill cases may stay at home or are transferred to a hospital where they can only infect people that are regarded as “close contacts”. Asymptomatic and mildly ill cases, on the other hand, retain most of their mobility and can also infect “casual contacts”. The recognition of symptoms may also influence the individual’s and the public health system’s ability to intervene against further spreading the disease by targeted application of drugs or vaccinations. Prophylactic use of drugs or vaccination can modify the infection process and the natural history of disease. Prophylaxis can reduce the susceptibility to infection and the treatment of cases can reduce their infectivity. Even though prophylactic drug use may not always prevent infection, it can effect the development of symptoms, so that a larger fraction of infected cases will remain asymptomatic or develop only mild symptoms.