Personalized Medicine:  What to Expect

In 2018, big data investments in healthcare and pharmaceutical industries will be nearly $4.7 billion.  Innovations from big data analytics combined with scientific discovery have fueled a fundamental shift from disease-centered care to patient-centered care. The Human Genome Project was designed to increase efficiency in disease prevention, diagnosis, and treatment.  This project has helped scientists discover over 1,800 disease genes.  And companies like 23AndMe sell direct-to-consumer DNA testing kits to help individuals benefit from having an understanding of the human genome. The company also collects customers’ DNA potentially making it the Amazon of healthcare in the future.  Targeted therapies in oncology have already identified that nine percent of genetic mutations are actionable.  And, clinical trials are underway to understand if precision medicine can live up to its promise.

Generally speaking, companies are trying to leverage large datasets to find variations that may create new therapies or drugs that fit the individual patient’s biology.  As part of that fine-tuning, healthcare in the next 15-20 years could change everything from the amount of drugs they give to individual patients to how a patient’s own stem cells are used to regain quality of life.

The digital revolution has created enough data that can be analyzed through advanced algorithms and machine learning to tailor healthcare strategies to the individual.  In the future, electronic medical records may be used across the globe to predict diseases before they occur.  Public health departments could play a new role in using this data to implement targeted solutions before diseases even start.

The era of personalized medicine is changing the future of everything from diagnostics to home care. The key to success will be how organizations foster the innovation and development of machine learning applications.  Also, challenges such as healthcare data security and privacy, data leakage, handling of medical imaging data, and other ethical dilemmas will need to reach some resolution so that they do not become barriers to the advancement of these innovative technologies.  Regardless, big data is changing the world, in ways that could very likely save your life.

#PersonalizedMedicine #BigData #PrecisionMedicine

Google’s Artificial Intelligence Bench Strength

As the age of digital services puts pressure on companies to become more agile, Google is an excellent case study to understand how the intersection of machines and humans continues to evolve to create value for customers.

Google has been an industry leader in machine learning.  As a result, Google can control 90 percent of the internet search market in Europe.  And, 85 percent of Americans go to Google or Amazon for product searches.

While Google started as just an index of web pages, the business successfully evolved to a central hub for real-time data feeds. When a search is conducted, Google algorithms study an array of data sources to provide insights relevant to the search.  Many businesses rely on Google to provide strategically placed advertisements based on big data algorithms that have tracked user website browsing and interests. Google also used inbuilt algorithms supported by big data in their translation services.

However, they aren’t just a search engine as Google continues to innovate with new products and services. For example, they leveraged their competitive advantage with big data analytics to optimize the machine performance for Google’s self-driving car.

Big data is also fueling Google’s vision to ship 3 million models of their new smart speaker to compete with the Amazon Echo.  With Google owning YouTube, they have the competitive advantage over the Amazon Echo since this new device offers a user-friendly screen to view videos.

Google has also entered the wearable device space with their Google Glass eyewear.  Recently they announced a new focus for the glasses for manufacturing workers powered by Google’s cloud computing business and artificial intelligence capabilities.  The glasses can show manufactures where there are product issues that require immediate attention as well as covering other basic operational needs.

Google also recently built a cooling system based off of artificial intelligence to cool their data centers.  These new algorithms have reduced their energy usage to around 40 percent.

It doesn’t stop there. This year Google launched a new job search tool that functions off of artificial intelligence and machine learning.  And, to reduce the biases inherent in machine learning, Google has donated millions of dollars to try to reduce future discrimination in artificial intelligence algorithms.

Google is also rumored to soon enter the OS wearable device market with the launch of the Google watch.  Others speculate that they may also make a play in the online retail space.

As connectivity increases along with the internet of things, organizations will continue to leverage big data analytics to diversify their business and drive future growth.


#MachineLearning #AI #BusinessIntelligence #BigData #Google



The Rise Of The Machines:  The Future of Clinical Medicine

It is well known that big data will change healthcare as we know it. But, how will the next decade transform patient care?  Dr. Pratik Shah develops new products that exist in the intersection of healthcare and machine learning by creating technology algorithms that run off of structured and unstructured data from a variety of sources. This kind of artificial intelligence advancement has the potential to revolutionize the healthcare industry. What can we learn from his success?

Dr. Shah stresses that the artificial intelligence architecture is critical first to strategically understand to eliminate red tape and potential barriers. He provides the example of both liver cancer and oral cancer patients that could benefit from deep machine learning approaches.  To make better diagnoses he needed to reduce the number of images needed for machine learning and the expensive technologies so often required to screen patients.

Traditional methods would suggest 10,000+ images would be needed to provide a baseline of the disease, but Dr. Shah had a different vision. The solution involved starting with just one image of a patient that had the disease, and from that, he found a way to extract billions of data points just from that one image.  Also, to reduce the burden of expensive machine screening, instead of using an expensive CT scanner for the undiagnosed patient, he started with just a photo from the undiagnosed patient’s mobile phone. He overlaid the pictures of the diseased mouth with the undiagnosed mouth to make a composite image.  The results were that he only needed fifty of the original diseased images to compare with the one picture from the undiagnosed patient’s phone to get a higher diagnosis efficiency than currently exists today.

The forces at play include technological advancements like iPhones and new algorithms for interpreting data. Forces playing against the innovation include the economics of the health care system that has invested in these expensive machines and need volume to sustain their business models. Also, health plans currently do not know how to reimburse for these innovative kinds of services. There are some legal barriers as well, as the government is still trying to catch up in the mHealth regulation which could affect the success of these kinds of technologies.

However, generally speaking, the customer defines value. And, if the customer can “double check” their healthcare at a relatively low cost, there may be a market for these type of services as long as Dr. Shah’s business model is sustainable.  Renowned big data scientist Dr. Obermeyer observed, “We are optimistic that patients, who generously – if unknowingly – donate the data underlying algorithms, will ultimately emerge as the biggest winners as machine learning transforms clinical medicine.”

#MHealth #MachineLearning #AI

Demystifying Big Data

What is Big Data?

Big data revenues are estimated to reach $135 billion by the end of 2019. While the technical definition of big data varies by company, there is general agreement that it is defined by the volume, variety, and velocity of the data. The concept of big data arose from a combination of a number of advancements over the years including technology becoming more affordable, the rise mobile computing, increased social networking, and the ease cloud computing which resulted in increased volumes of stored transactional data which now can be analyzed with tools that combine open-source technology and commodity hardware.

Traditional data systems can be slow whereas big data tools can store, analyze and process large volumes of data at a fast pace. Big data analysis can seek to extract insight out of petabytes of data or more. It is usually based on distributed database architecture where large groups of data need to be broken down into smaller pieces for analysis. Several different computers are often present within the network and work together to find solutions. Whereas a traditional approach might include fixed data fields, with big data there are billions and billions of unstructured data sources that can provide valuable insights into business problems. Big data often leverages this semi-structured and unstructured data to produce data insights.

Free Access to Big Data

There are hundreds of free big data sets available today which transforms the playing field for producing new insights. For example:

What are your favorite big data analytics tools?

#BigData #BigDataAnalytics


Digital Regulations in mHealth Use


Of the over 3 million active mobile apps available in the iTunes app store, and the over 3.5 active apps in the Google Play store, 95,851 and 105,912 respectively, were categorized as Health and Fitness. Not surprisingly, those numbers are increasing exponentially. Overall, the digital health revolution is worth somewhere around $25 billion globally. As mobile health (mHealth) technological innovations increase, these tools can be transformative to patient care, especially for clinical trials and drug development.

With an increasing focus on mobile regulations, it is essential to stay up to date with the U.S. Food and Drug Administration (FDA). Where the FDA used to have to approve applications on a case-by-case basis, in 2017 the FDA started running pilot programs for digital health pre-certifications program. Big companies like Apple, Johnson & Johnson and Samsung were early participants. Most likely these big companies have been early participants as they would like approval for their development processes overall versus to have the red tape involved with case by case evaluations.

The new administrator at FDA, Dr. Scott Gottlieb, is a believer in innovation has already provided guidance on clinical decision support tools subject to regulation as wells as initial guidance as it relates to the use of mobile technologies in clinical trials.  Pilot programs under his watch also include reducing regulation for mobile solutions that automate simple tasks for providers or help patients track their health. He has also made it clear that mobile health apps that are designed for just encouraging a healthy lifestyle fall outside the scope of FDA regulation.

While the European Commission has adopted multiple mobile strategies in delivering ‘citizen-centric’ health care, there is likely to be an ongoing dialogue in Europe as it relates to social-demographic backgrounds in the use of e-health solutions. Dr. Gottlieb has the advantage of seeing how these conversations play out abroad, to help eliminate red tape in the United States.

These digital solutions are appealing to multiple stakeholders as they have potential in the United States to cut healthcare cost by over $7 billion. Given this wave innovation to improve client-centric care and reduce costs, regulators will need to provide more flexible solutions.

As the previous CEO of Rocky Mountain Cancer Centers, it always troubled me that the cost of bringing a new cancer drug to market could cost up to $2.7 billion and the median time of development was 7.3 years.

Implementing mHealth technologies in areas like clinical trials could provide game-changing care to patients in keeping stride with innovations in genomic and immunotherapy.  With the FDA’s new administrator at the helm, mHealth has the promise to transform patient’s lives.

#mHealth #ClinicalTrials #FDA