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• Freescale Semiconductor has released its Kinetis com- puter chip that measures only 1.9 x 2 mm and is working with customers and partners to develop products that can be swallowed.

• External sensors are a valuable source of data when com- bined with personal sensor data to understand and get insight about the impact of environmental infl uences. One application combines this data with an IoT asthma inhaler to better understand asthma triggers.

Mobile devices will continue to help improve healthcare, but

there are also challenges. One key issue is that the Internet is funda- mentally not secure. Devices must prevent eff orts to intentionally cause malfunctions, to collect secure data for malicious uses or to otherwise infringe on freedom and privacy. Over the next few years, these new types of highly personal mobile computing will become ubiquitous and will profoundly change how and where healthcare is delivered, while supporting goals of improving quality and reducing costs. Big data meets three criteria: volume, velocity and variety.

Together, these create vast potential but also challenges. Big data means large volumes of diff erent types of data in diff erent formats arriving in an endless stream. T is creates not only processing chal- lenges, but also challenges around how to meaningfully interpret the data – much of it not described using consistent standards or metadata – into information and recommendations while eliminating noise and erroneous data. T ere are also challenges particular to using big data for healthcare: • Accuracy: People tend to understate negative factors, such as smoking and failure to comply with treatment. People also tend to overstate positive factors, such as exercise. T ese biases need to be identifi ed and corrected, or passive techniques need to be used in order to acquire data that does not have self-reported bias.

• Privacy: People are reluctant to divulge personal informa- tion because of concerns about privacy. Incentives, security and privacy guarantees must be developed to address these concerns.

• Consistency: Standards need to be developed and imple- mented to promote consistency, increase usefulness and facilitate data usage.

• Facility: Mechanisms need to be developed to make it easy for patients to accurately self-report data. T is includes evolving passive mobile computing devices that require no eff ort. Ideas, such as fostering community among patients to encourage self-reporting, accuracy and sharing, are also needed. Techniques are needed to get data from healthy people to make the populations truly representative and not biased by the ill.

• Fragmentation: Healthcare data is notoriously fragmented. T ere is also unwillingness for healthcare participants to share data. T is should improve as new payment models,

such as ACOs, emerge and encourage players to cooperate, but other incentives need to evolve.

Big data makes vast amounts of facts available, some useful and some less so. Some of this information has always existed, but was stored in non-electronic formats that made it diffi cult to act on in a timely manner. T e convergence of mobility and big data extends both the volume of and type of data and increases potential information that can be extracted. As data becomes more current, it becomes important to get information to people who can use it for purposes, such as clinical decision support, and for patients who are active in their health management. Improved quality of care and improved outcomes result from providers and patients having better access to this information. Additionally, big data allows data from diff erent regions and countries to converge. Integrating data across disparate data re- positories allows local and regional best practices to be identifi ed and leveraged on a broader scale. Researchers benefi t from larger populations for clinical studies, trending and disease monitoring for epidemics, and other improved results. Challenges, such as metadata and data quality, security, technology and language barriers, need to be addressed. Regardless of the challenges, mobile computing and big data

are central to improving healthcare outcomes and quality. Mobile computing acts as both a source of big data and as the channel for distributing information and recommendations from big data. Mobile computing provides data that is aggregated into big data warehouses with other data, including medical records and demographics. Analytics are then used to extract information, draw conclusions, spot trends and make predictions that can then be shared with healthcare constituents to improve quality and outcomes and increase competitiveness. Healthcare organizations need to devote time and resources to visioning and planning implementation of big data and mobile computing solutions and realize potential benefi ts. Some key recommendations for healthcare organizations include: • Establish a business intelligence center of excellence with a focus on mobile computing and big data.

• Decide on an appropriate strategy based on the organiza- tion’s current and target business, technological maturity and objectives.

• Assess various initiatives for meeting corporate objectives, focusing initially on quick wins.

• Work with partners that understand the full range of big data and mobile computing technologies and implications, including trends, security, internal and external system integration, hosting and development platforms, and ap- plication and solution development.

T is will provide the foundation needed for strong execution.

Without this preparation, organizations will not realize benefi ts and will risk being left behind their competitors.


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