Archive for October, 2010

The day when a cost efficient technology for reading and sequencing DNA is available may be closer at hand. Intense research is focused on refining the most promising techniques. While IBM’s DNA Transistor Technology is in the forefront, efforts at Oxford Nanopore, Sandia National Labs and elsewhere are validating the trend.

The goal is to reduce costs to where an individuals’ complete genome can be sequenced for between $100 and $1,000. Once this becomes a reality the impact could be so significant as to create a brand new generation of health care capabilities. While there is no way to predict exactly how fast this technology will become available to the average researcher, Manfred Baier, head of Roche Applied Science maintains an optimistic position:

“We are confident that this powerful technology…will make low-cost whole genome sequencing available to the marketplace   faster than previously thought possible”

The technology involves the creation of nanometer sized holes in silicon based chips and then drawing strands of DNA through them. The key rests with forcing the strand to move through slowly enough for accurate reading and sequencing. In this case researchers have developed a device that utilizes the interaction of discrete charges along the backbone of a DNA molecule with a modulated electric field to trap the DNA in the nanopore. By turning these so named ‘gate voltages’ on and off scientists expect to be able to slow and manipulate the DNA through the nanopore at a readable rate. The effort combines the work of experts in nanofabrication, biology, physics and microelectronics.

A clarifying cross-section of this transistor technology has been simulated on the Blue Gene supercomputer. It shows a single-stranded DNA moving in the midst of (invisible) water molecules through the nanopore.

No matter how long it takes for the technology to become a cost-effective reality, it will be a true game-changer when achieved. While researchers express both optimism and caution on the timing, there is one inevitable result for which keen observers in related fields are preparing. When peoples’ individual genetic codes can be economically deciphered and stored, the amount of data generated will be massive. The consequent demands on data storage, mining and analytics will in turn generate their own new challenges.

Using the huge influx of new data to make more informed life science decisions is a key, long-range benefit of the current research efforts in sequencing technology. In health science alone revolutionary new approaches are expected to allow:

  • Early detection of genetic predisposition to diseases
  • Customized medicines and treatments.
  • New tools to assess the application of gene therapy
  • The emergence of DNA based personal health care

An equally critical benefit is the potential cost savings expected when sequencing technology, data storage and advanced predictive analytics combine allowing truly preventive medicine to take its place as the new foundation of health care.

A recent article in the WSJ once again highlights the steadily growing list of applications for predictive analytics in health/life science. This time the goal is identifying patients likely to stop their medication regimen before they do so.

According to a report from the nonprofit New England Healthcare Institute, an estimated one half to one-third of Americans don’t take their medications as prescribed by their doctors…contributing to about $290 billion a year in avoidable medical spending including excess hospitalization.

Is it any wonder with that level of cost at stake along just one vector in health care science that the demand for the best in predictive analytics is becoming more and more critical?

Significant cost savings across the entire spectrum of health care science, as well as more individualized service options for patients, are expected as inevitable results of the steady powering-up now seen in predictive analytics. There are many opportunities to review the positive results in applied case studies. These provide just a glimpse at the successes ahead.

Take for instance the results of SSPS technology and software at Texas Health Services, where the challenge was to limit health care costs without reducing the quality of service provided to patients. As Texas Health Services relates:

 

“With IBM SPSS Statistics Base, Texas Health Resources has greatly enhanced its ability to support its process-improvement initiative. Today, it not only detects process variations, but can determine the underlying causes such as a sicker-than-normal patient population.”


Data quality was improved while data mining costs were reduced by 50 percent.

These high yield results make it clear why developments in predictive analytics have become so worthy of front and center news for so many segments of the health care industry.  Other endeavors are focused on identifying those individuals most likely to develop specific illnesses such as develop diabetes or cancer. Ingenix, a unit of United Health Group, and a customer of Netezza, has already successfully launched an effort to mine and analyze the underlying risk factors in patient data before an illness develops or advances.

As health care science, quality patient care and advancing medical technology struggle with cost factors, the value of predictive analytics in lowering costs increases exponentially.