Difference between revisions of "Open Source Medicine/Intro"

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(New page: Unbelievably, it takes 10-15 years for a drug to go from development to availability. <sup>[http://www.microsoft.com/casestudies/ServeFileResource.aspx?4000001258]</sup><sup>[http://pinkar...)
 
 
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Unbelievably, it takes 10-15 years for a drug to go from development to availability. <sup>[http://www.microsoft.com/casestudies/ServeFileResource.aspx?4000001258]</sup><sup>[http://pinkarmy.org/]</sup>. The problem of cancer, AIDS and the other killer diseases we face is not a purely scientific problem; part of the problem is the inefficient drug development pipeline. Drugs are now developed by competing corporations who carefully guard their research from public eyes, lest their methods be copied and they miss out the opportunity to profit financially from it.  
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Two key principles of this wiki are [[advanced automation]] and open collaboration. This page explores the application of these to healthcare, looking at how [[artificial intelligence]] can help automate healthcare and open collaboration can provide a more effective translation of knowledge into actual patient care.
  
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There is an alternative medical research methodology, one that is open rather than secretive, collaborative rather than competitive, and done with the aim of solving the problem at hand, rather than profiting from it. [http://openwetware.org Open WetWare] is a massive hub of open collaboration between biologists and biological engineers. [http://pinkarmy.org/ Pink Army] is an open research project dealing with developing drugs for breast cancer. [http://www.osdd.net/ The Open Source Drug Discovery Network] is a large collaborative project that successfully mapped the genome of tuberculosis.
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The medical systems now in existence are fraught with problems. The enormous amounts of data they gather remain disconnected and scattered, doctors are completely overwhelmed, much research (especially pharmaceutical) is done by private companies who take decades to develop drugs, and an unnecessary [[scarcity]] of healthcare lifts it out of the reach of the poor
  
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Open medical research brings the potential of high-quality development of drugs for treating the 'neglected diseases'. These are diseases that occur exclusively among poor communities and go unresearched by pharmaceutical companies because it is not feasible financially to develop drugs for people who can't afford them.
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There is a way out: open source medicine and advanced automation. Use modern information technology to make medical knowledge freely available and share it globally by a standardized system. Develop drugs openly, so that people can pursue the research that matters to them, rather than base it on bottom-line considerations. Organize the medical knowledge of humanity intelligently and put it at the fingertips of every doctor. Automate as much of the medical system as possible (but no more!) to take the strain off doctors.

Latest revision as of 17:00, 20 May 2010

Two key principles of this wiki are advanced automation and open collaboration. This page explores the application of these to healthcare, looking at how artificial intelligence can help automate healthcare and open collaboration can provide a more effective translation of knowledge into actual patient care.

The medical systems now in existence are fraught with problems. The enormous amounts of data they gather remain disconnected and scattered, doctors are completely overwhelmed, much research (especially pharmaceutical) is done by private companies who take decades to develop drugs, and an unnecessary scarcity of healthcare lifts it out of the reach of the poor

There is a way out: open source medicine and advanced automation. Use modern information technology to make medical knowledge freely available and share it globally by a standardized system. Develop drugs openly, so that people can pursue the research that matters to them, rather than base it on bottom-line considerations. Organize the medical knowledge of humanity intelligently and put it at the fingertips of every doctor. Automate as much of the medical system as possible (but no more!) to take the strain off doctors.

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