Covering Disruptive Technology Powering Business in The Digital Age

A breath of relief

Tracy said that for him, that desired outcome is simple: Make the lives of people who live with asthma or chronic obstructive pulmonary disease a little bit better, one data point at a time.

Tracy is the co-founder and chief technology officer of Propeller Health, a burgeoning health care technology company located in a small brick office building on West Main Street. There, a team of about 50 employees provides clients with data-driven reports about their respiratory problems, which they do by analyzing data collected from sensors attached to patients’ inhalers.

When patients use their inhaler, the Propeller Health-engineered sensors collect data about when the attack took place. That gets transmitted to a phone or Bluetooth device, which collects location data, and then sends the information back to Propeller’s databases. The company’s engineers then merge that data with weather and air quality data, and after analyzing the resulting cocktail of information, they’re able to report trends back to the patient.

“We’re doing this data collection to try to unearth insights for them, and to help them better understand what might be causing them to flare up when they’re not doing well,” said Tracy.

The company also uses its system to help patients keep abreast of potential attacks.

“We might know that 90 percent of your events are occurring when air quality is bad,” said Tracy. “Once we know that about you, you wake up the next day, and we know the air quality is bad where you are, we can notify you and let you know, ‘Be careful — air quality’s bad. Don’t forget your inhaler.’”

Propeller sends alerts to health care providers, providing doctors with specific information about when and where attacks are happening — information that’s otherwise tricky for practitioners to pin down, given that asthma attacks are episodic by nature.

All things considered, Propeller Health is a striking example of a big data company. Its sensors make it part of the trend known as “the internet Of Things” — the proliferation of “smart objects” ranging from Fitbits to coffeemakers that are able to collect data and transmit it via the internet. Many experts see the spread of iOT — and the subsequent spread of new physical data collection points — as a prominent factor in the increased clip of data collection.

On top of that, Propeller is representative of what could be described as one of the big data niches that Madison occupies: the intersection of big data and medicine. Madison has become something of an enclave of biotechnology and health IT companies.

Epic Systems, the Verona-based giant health care IT giant, by definition serves as a massive big data platform, hosting millions of electronic health records. On top of that, company representatives say it’s also working on a number of big data initiatives. For example, it’s merging patient data with things like socioeconomic information and insurance claims data to generate a more holistic understanding of health care in action, and to better assess and predict health risks for patients.

There are also smaller health IT companies like Redox, which is working in an area that many trade experts say is the biggest issue in the realm of big data and medicine: interoperability. Essentially, the company is creating an interface for accessing electronic health record data from different record-keeping systems — data that would otherwise be incompatible.

Besides health tech companies, examples of companies both big and small utilizing big data abound. The startup Export Abroad analyzes data to help clients that want to sell products or services overseas identify markets that could be lucrative. American Family Insurance, one of the biggest employers in the Dane County area, created a research team to work on big data initiatives.

Then there are companies that have established their identity through work with big data. Take MIOsoft, which has quietly become an industry leader within the field of data quality.

Data sets that companies and institutions want to work with are often messy, disorganized, or seemingly incompatible. MIOsoft has made a business out of cleaning and organizing those data sets into something that’s friendly and useable, to make it possible for clients to parse signal from noise.

Maurice Cheeks is a city alderman who also serves as MIOSoft’s vice president of business development. He said that in a world that hinges on data, there’s a growing demand for MIOSoft’s services.

“In today’s age, people are recognizing that data are an asset,” said Cheeks. “In the same way that people, property, inventory are all assets to a company, the data that they hold about their customers, about their suppliers, about their energy usage — that’s an asset to their company.”

Rethinking diagnoses

Then there’s the work in big data that exists outside of the private sector.

The Center for Predictive Computational Phenotyping, housed within the UW-Madison’s Medical Sciences Center, was created in 2015 as part of a National Institutes of Health initiative to improve medicine through the use of big data. Currently, a group of about 40 biostatisticians and researchers are working within the center to take large data sets and information on raw, observable traits — in other words, phenotypes — and using them to better understand or better predict health outcomes.

Much of the center’s activity is happening in health clinics across south-central Wisconsin. One project involves merging data on things like demographics, health imaging and genetic information to figure out a new standard for breast cancer screening.

“As you look at more and more data sources, you get a better idea of who’s more likely to get breast cancer,” said Mark Craven, the director of the center.

The center has also been working with the Marshfield Clinic to use electronic health records to predict disease. That’s where Robert Nowak — the same Robert Nowak who co-created BeerMapper — comes in.

Nowak is seen as something of an innovator in the realm of machine learning, the growing computer science subfield that’s recognized as one of the core elements of big data analysis. It’s defined as the teaching of computers to learn on their own, enabling them to independently improve algorithms as they parse complex sets of data.

Nowak has developed a platform called NEXT that refines the science of active learning — in other words, when humans give machines input to help them improve their performance.

“Much like people in a classroom, computers learn best when they’re given active feedback,” said Nowak.