# Stop Thinking, Just Do!

Sung-Soo Kim's Blog

# Article Source

[1] Healthcare Does Hadoop, Hortonworks Blog.

Use Apache Hadoop to Save Lives While Delivering More Efficient Care

Difficult challenges and choices face today’s healthcare industry. Hospital administrators, technology and pharmaceutical providers, researchers, and clinicians have to make important decisions—often without sufficient accurate, transparent data.

At the same time, consumers are experiencing increased costs without a corresponding increase in health security or in the reliability of clinical outcomes.

At Hortonworks, we see our healthcare customers ingest and analyze data from many sources. The following reference architecture is an amalgam of Hadoop data patterns that we’ve seen with our customers’ use of Hortonworks Data Platform (HDP). Components shaded green are part of HDP.

Here are some ways that Hadoop makes data less expensive and more available, so that patients have more choices, doctors have more insight, and pharma and device manufacturers can deliver more effective, reliable products:

## Access Genomic Data for Medical Trials

If we read that a given drug is “40% effective in treating cancer,” another interpretation could be that the drug is 100% effective for patients with a certain genetic profile.

Matching a particular drug to a specific genomic profile is a big data problem. Each individual’s genome is about 1.5 gigabytes of data. Massive data storage and processing power is required to analyze data on a drug’s interactions with different genetic combinations. For example, just focusing on 20 genes is a 20,000-choose-20 calculation, with 4.3 x 10\^67 possible combinations.

Researchers are turning to Apache Hadoop as a cost-effective, reliable platform for storing genomic data and combining that with other data sets (e.g. demographics, trial outcomes) to find out which drugs and treatments work best for groups of patients across the genetic spectrum.

## Monitor Patient Vitals in Real-Time

In a typical hospital setting, nurses do rounds and manually monitor patient vital signs. They may visit each bed every few hours to measure and record vital signs but the patient’s condition may decline between the time of scheduled visits. This means that caregivers often respond to problems reactively, in situations where arriving earlier may have made a huge difference in the patient’s wellbeing.

New wireless sensors can capture and transmit patient vitals at much higher frequencies, and these measurements can stream into a Hadoop cluster. Caregivers can use these signals for real-time alerts to respond more promptly to unexpected changes. Over time, this data can go into algorithms that proactively predict the likelihood of an emergency even before that could be detected with a bedside visit.