Thank You – This is Not the End

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Thank you for an amazing weekend. This event would not have been possible without the enthusiasm, ideas, and participation of people like you.

We, the organizers, deeply enjoyed putting on this event for the community.

Please fill out the form and we’ll put you on our mailing list for news and future events:

This is not the end. Hacking Medicine is a movement dedicated to raising the profile of medical entrepreneurship throughout Boston and beyond. We had attendees from as far as California and the Netherlands and we want to spread the idea that systemic medical problems can be solved today.

We’d love for you to give us feedback and get involved.

Feel free to get in touch with us directly at

Looking forward,
-The Hacking Medicine Team

Guidelines for Your Pitch

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Now that you’re in groups focusing on a specific project, the goal of the weekend is to hash out as much as possible about your idea in preparation for your funding pitch and beyond.

If you can build a demo or a product, prioritize that. Straight from Sutha Kamal, one of the judges.

Your pitch is only 3 minutes long so you’ll need to be concise. We want three slides max. You don’t have to include everything below, but think about these points.

Potential Impact:
What is the fundamental problem you are addressing and how big is the market?
What are the best existing solutions? How is your method superior to what already exists?

Technical Information:
What are the technical specifications for your product?
What is unspecified in your design? What will it take to further specify the components?
What are the drawbacks in your design?

Next Steps:
What are your next steps moving forward? Consider the following events (if applicable):
• Finish underlying research
• Lean startup – Customer validation
• Validate technical implementation
• Create prototype of product
• Launch to market

Break down each task into actionable items you’ll be doing in the future.

Good luck and we look forward to your great ideas!

Less than 24 Hours to Hacking Medicine: Final Thoughts

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Don’t forget to join us for the Massive Health happy hour at Mead Hall in Cambridge at the end of Saturday (approx 6pm).

Healthcare costs are skyrocketing.  In 2009 we spent 17.9% of GDP and rising on healthcare, and for all this expense we aren’t even getting high quality care.

We can do better.  We MUST do better.  We need to dramatically bend the cost curve in healthcare, expand access to care and improve the quality of care across the board.  To do this we need innovation and entrepreneurship to move at the speed we see in software and on the web.  We can do this in healthcare and medicine.

We created Hacking Medicine at MIT in order to help students and members of the community learn about the hackable areas of medicine, find like minded team members, and dive in on new projects that hold the promise to become disruptive healthcare companies.

MIT, the birthplace of hacking, is the perfect place to launch this effort.  Entrepreneurship is in our blood here and we are also home to some of the world’s leading research medical devices, software, and biosensors.  By combining our hacking culture with the research happening on campus, with Harvard Medical School, MGH and the rest of the medical community around Boston we can dramatically impact healthcare for the better.

As we have seen across this week’s blog posts, the intersection of Mechanical Engineering, Electrical Engineering and Computer Science, Materials Engineering, and Biological Engineering is creating new opportunities in Healthcare Automation, Big Data, Biosensing, and Synthetic Biology.  These technologies hold the promise to enable us to drive costs down while simultaneously improving access and quality of care.  This is possible because YOU a group of optimistic engineers, scientists, and entrepreneurs refuse to accept a trade-off between quality, access, and cost.  We refuse to live in a world of no-win scenarios.

The response to this call for action has been overwhelming.  We had more than twice the number of applications than we can accomodate at this year’s event.  We truly appreciate the energy and dedication that everyone has shown leading up to this weekend and are extremely excited to see the projects that come out of the weekend.

The agenda is posted here:

Come ready to share ideas and create new ones.

Bring your laptop, bring your creativity, and most importantly bring your passion to change healthcare.

Written by Elliot Cohen, a Founding Hacker for Hacking Medicine.

If you could program a cell to do anything, what would you have it do?

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Such is the perennial challenge of synthetic biology – to program living entities and biological materials to achieve novel function. How does this apply to healthcare? Engineered viruses that target and destroy infectious bacteria have spawned startups targeting industrial applications and bacterial infection. Human cells have been rewired to secrete insulin in response to flashes of blue light. This might lead to an implant for diabetics that monitors blood glucose and secretes insulin on demand – an artificial pancreas. Other biomedical examples are gene circuits that detect and kill cancer cells and regulates chemical levels in blood to prevent gout.

Most of these applications are created by combining parts. Much like electrical engineers build circuits with resistors and capacitors, synthetic biologists hook up elements that control gene expression, sensors for inputs like chemicals and light, and actuators like toxin release or gene transcription.

Biology unfortunately gets in the way. Putting these parts together isn’t predictable – most parts are not well characterized, meaning a biologist can’t model the response of his circuit. This is why independent organizations like the Parts Registry are leading the effort to characterize parts. Some startups like Gingko Bioworks can even build and test the circuits for your application.

An even bigger challenge is what to do with the synthetic components once you build them. If you build a genetic circuit, how do you safely introduce it into a live organism safely and effectively? How do you make sure it still works in this new environment? How do you prevent it from mutating into something useless, or worse, harmful?

Due to these challenges the field is still waiting for its killer app. So far large-scale biosynthesis has taken most of the attention. Genetically engineered organisms can make biofuels out of biomass and even just sunlight and waste CO2. The same principles can be applied to making artemesinin, a critical drug for malaria, vaccines, and other protein therapeutics like antibodies.

But when can we inject modified cells and circuits into humans to treat and cure diseases that aren’t as treatable with drugs? The field is getting there – Intrexon is working on a cancer treatment in which synthetic DNA molecules are injected around a tumor. A patient takes a pill and activates the DNA, stimulating the immune system to destroy the tumor.

Clearly there are numerous issues and barriers to applying synthetic biology to biomedicine. What can we do with the tools available now to get around these barriers?

Written by Allen Cheng, a Founding Hacker for Hacking Medicine.

Healthcare Automation: Siri will see you now

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Healthcare is one of the last major industries to truly embrace automation. The retail and banking industries have long supported the use of automation, which allows us the convenience of self-checkout at the supermarket and depositing a paycheck at an ATM. Robotics is currently used in the manufacturing and production of nearly every product under the sun, yet we still have pharmacy technicians counting by fives at the local drugstore. In addition, the majority of physicians are still handwriting prescriptions, chart notes, and diagnostic tests, which are then filed by a receptionist in a one department, only to be misplaced by a different receptionist, never to be seen again. This  outdated system leads to incredible inefficiencies including duplicate procedures, unnecessary hospital admissions, and ultimately ends up adding a huge amount of unnecessary burden to the healthcare system.

Depending on the source, it is estimated that administrative costs account for 7% of total healthcare costs in the United States, which equates to approximately 175 billion dollars annually. In addition, there is a huge amount of waste not included in the administrative costs due to massive overhead at healthcare institutions and inefficient models of care. We need next generation technology that both automates repetitive tasks and is scalable.

Currently in this space, Athenahealth is automating practice management, Allscripts is finally making e-prescribing a reality, companies like TCGrx and McKesson are automating pharmacy workflow and startups like Drchrono are trying to pickup where Google Health left off. There is a huge amount of opportunity in for other clever healthcare hacks to automate repetitive tasks.

The winning combination for a successful healthcare automation company is better care at a lower cost. There was an interesting article in the Atlantic this week, which discussed an innovative new practice in California that has cut both hospitalization rates and amputation rates among diabetics significantly while spending 18% less than the national average per patient. They monitor patients remotely through the use of wireless scales and blood pressure cuffs, and are exploring ways to remotely monitor patient’s glucose levels. This allows them to catch a problem before it escalates, leading to decreased costs downstream. Remote monitoring is a good example of a healthcare hack, because none of the technology (wireless scales, blood pressure cuffs, and glucose monitors) is particularly novel. The value is in the unique combination of technologies, which allows for an increased level of care at a lower cost.

Another good example of healthcare automation that is efficient, scalable, and eliminates the need for repetitive tasks is multi-dose medication packaging. These machines package a patient’s medications in sealed packets based on the time of administration, while decreasing the personnel needed to fill the same number of prescriptions. The packaging increases patient compliance, especially in the elderly, because they are no longer required to organize their multitude of pills (decreased human error), and are able to easily track whether they missed a dose (date/time is printed on each packet). The increased patient compliance leads to decreased hospital admissions, which is associated with significant cost savings for both the patient and the overall healthcare system.

How can automation be used to bring about the most effective changes in healthcare? Will you eventually dial up Watson for healthcare advice, or pick up your prescriptions from an automated prescription kiosk?  What specific legal and regulatory hurdles do we face as we attempt to change healthcare for the better? And most importantly, how can you personally be a part of this paradigm shift and help lead healthcare boldly into the 21st century?

Written by TJ Parker, a Founding Hacker for Hacking Medicine.

Health: Don’t just measure it — make it meaningful, and actionable!

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A decade ago, articles first started appearing in the popular press about “lifeblogging”. By 2008, the Washington Post reflected on the growing number of people measuring and recording information about themselves:

Oh, look, this guy’s doing this, too, and he’s actually making plots of it. Plotting was cool,”…. The ability to visualize trends over time… Bloggers go to extreme means to record infinitesimal events throughout the course of a day. Microsoft engineer Gordon Bell famously (at least in very small circles) wears a SenseCam around his neck, which automatically snaps a photograph every 60 seconds of wherever Bell happens to be and whatever he happens to be doing.

The article scoffs at this extreme “high-tech navel gazing” but then heralds the arrival of self-tracking, which includes not just the collection and plotting of data, but also “the analysis that goes on after the recording.”


Tracking concentration related to butter consumption. Data + Analysis. (Via 2010 National Post Article.

We’ve made disappointing progress.  The focus still seems to be on the sensors: Wallstreet has (historically) cheered companies like DexCom which developed technology to maximize the number, and minimize the size, of biosensors.  While their market-approved product is for diabetes, it’s no secret that Dexcom believes the future is lots of sensors, for lots of reasons, on lots of people.  And living laboratories have received glowing press for throwing sensors on just about everything: measuring how and when people move around the house, when they open the refrigerator, and even noting if they lay down on the bed alone or with a second “human-sized” mass.

These technological advances are important: they help lower the cost of sensors, educate the market, and prove out the technology.  


But it’s time to move beyond making measurements and focus on making meaning.  And not just making meaning, but making these data actionable.


via Nudgeblog: The Toyota Prius is often cited as using good design to make gas consumption data actionable

We ought to apply our entrepreneurial mettle to figuring out what people should do, not just what they did, or what those data mean.  Psychologist Barry Schwartz explains the paradox of choice: plenty of times, less is more. Instead of drowning people with data, we need to offer data-driven action-steps with outcomes like less waste, better health, or more happiness.  Even Kevin Kelly, co-creator of the self-tracking movement Quantified Self, argues that when he puts the weather on tv, he doesn’t really want to know the temperature or the pollen prediction.  And he doesn’t even want the meterologist to tell him if it’s going to rain.  It’s simpler than that: Kevin just wants to know if he should carry an umbrella that day.  Kevin doesn’t value the data as much as he does the analysis.  But what he really values, above everything else, is knowing what actions he should take.

It’s easy to get caught up in new technologies that help us more precisely, rapidly, or seamlessly measure health data.  And plenty of efforts have attempted to make it easier, faster, or cheaper to make meaning of peoples’ data. That’s a noble cause, but it’s not enough.  The future is in helping people figure out what to do — or not to do — as a result of all this passive bio-sensing and beautiful graphing.

How do we orient to action?  What does your customer, user, patient, or friend really want?  If she wants to be healthier, do everything you can to take her to that point.  Don’t just leave her with a nifty collection of elegantly-packaged and slickly-interactive data.  Ask yourself “so what?”  And keep asking.   Ask until you offer something she can actually do that will help her reach those goals. 

Make it meaningful.  And make it actionable.


Written by Andrew J. Rosenthal, a Founding Hacker for Hacking Medicine.

Big data

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Big data is getting bigger

Technology is prevalent everywhere in our lives. For instance, we use increasingly sophisticated smartphones for communication, our cars contain more and more sensors and microprocessors to make driving safer and today many companies depend on technology like the Internet as a major part of their business model.

While data collection is relatively cheap, the art of harnessing large datasets – or: big data – still remains a great challenge. Companies like Google, Amazon & Co. collect lots of data to gain deeper insights into complex problems and their success heavily depends on how they exploit big data. Google did not invent search, Amazon did not invent retail, Netflix did not invent movie rental and Facebook did not invent social. However, due to the increased popularity, prevalence and progress of the Internet as well as their outstanding efforts and success in dealing with big data, these companies were able to create tremendous added value for their customers. They and their competitors work hard to find new ways to leverage big data to improve their products and services.

Big data and healthcare

Big data can be extremely helpful if we know how to deal with it. Despite the fact that we are collecting massive data like never before – about patients, hospitals, treatments etc. – in the healthcare domain, the question how we can extract meaningful information from this data on a domain wide basis remains unsolved.

Fortunately, we see promising examples such as or that demonstrate how big data can leverage improvements in and create new useful use-cases for technology for the healthcare domain. Another impressive example is the case of physician Jeffrey Brenner, who discovered several patterns in medical data from the three main hospitals of Camden, New Jersey. For instance, he revealed that a single building in Camden sent more persons with serious fall injuries than any other building, namely 57 elderly in two years resulting in three million dollars in healthcare bills. Such insights render efficient improvement possible. And this is just one of several findings Brenner made while studying the data but it gives an idea of the possibilities and power of harnessing big data.

More and more data becomes publicly available. For instance, as a priority Open Government Initiative for President Obama’s administration, the website provides an increasing number of datasets collected by the Federal Government for the public. The Department of Health and Human Services released several datasets such as Medicare Cost Reports, Product Recall Data or Chronic Conditions Data. In the context of Open Data Initiatives all around the world, governments start to release large datasets for useful applications in various domains. Some private companies use open data, other build proprietary datasets. But the vision is clear: excavate the hidden treasure in large datasets to learn about and improve healthcare.

Joshua Rosenthal, who is “Products Engagement Guru” at Eliza Corporation, develops data-driven approaches and programs that engage people in interactive discussions about their health. He is experienced with handling qualitative and quantitative data. We are proud to have him on our speakers list and excited about what he has to say about big data. Use the chance to talk, discuss and think about healthcare, big data and related topics with experts like him at our conference in a creative and inspiring atmosphere.

Big data + healthcare = lots of open questions and opportunities

The phrase “killer app” commonly refers to applications that demonstrate the core value of a technology. Admittedly, in health care, this term sounds odd but the question remains: What is the “paramedic app” for big data in healthcare? How can we improve healthcare by using technology and incorporating big data? How can we disrupt the broken healthcare system and create a new, better one? Wich legal, technological and organizational issues do we have to deal with in order to use big data effectively and efficiently?