The healthcare model is undergoing an inversion. Previously, facilities and other providers were incented to keep patients in treatment – more in-patient days translated to more revenue. The trend with new models, including accountable care organizations (ACO), is to incent and compensate providers to keep patients healthy.
Providers and payers are increasingly embracing mobile computing to improve outcomes. Patients are increasingly demanding information and real-time access to healthcare data so they understand their choices and can participate in decisions about their care. As a result of this focus on information, all healthcare constituents are impacted by big data, which supports analytics that predict how these players are likely to behave, encourage desirable behavior and minimize less desirable behavior. Big data is radically changing healthcare delivery and research. All of these factors are changing the way healthcare is delivered, while putting new burdens on IT departments to make the most of data to drive operational excellence.
Big data and mobile computing have a natural synergy that will continue to evolve as those industries mature. Figure 1 shows mobile computing and big data working together: Mobile computing is used to both gather data and present information back to end-users, while big data is the foundation for the analysis used to improve outcomes and quality.
|Big data and mobile computing work together.|
When many people think about mobile computing, they think of smartphones and tablets. While these technologies are certainly an important part of the mobile computing landscape, they also have sophisticated capabilities for generating useful data from applications, cameras and sensors (including GPS), as well as presenting information through sophisticated mechanisms to end-users. The increased coverage and performance of cellular phone networks and the increasing availability of Wi-Fi networks keep mobile computing devices connected and ensure the data they capture is available when needed.
But just because a device can be carried and allows easy access to information doesn’t completely define mobile computing in today’s world. The definition is limited; there is nothing particularly revolutionary or game changing about being able to access or track your claim history from your smartphone, and it’s not going to have much of an impact toward improving healthcare quality and outcomes. The bar for mobile computing is continually rising together with peoples’ expectations; enterprising developers and companies deliver groundbreaking functionality as people get more accustomed to mobility.
The emerging Internet of Things (IoT) connects physical objects to the Internet and uniquely identifies them. As the IoT expands to include new types of devices – including wearable, implantable and swallowable computers – these devices will find uses to monitor and improve health when combined with each other and with other patient information through big data. Some mobile computing examples:
- Wearables are unobtrusively embedded into a user’s outfit or accessories and used to recognize user state, activity, location and surrounding situation. Applications are quickly moving from research and novelty to practical ones, especially in healthcare where emerging uses include health monitoring and mobile treatment.
- Implantables are widely used in healthcare to treat chronic ailments such as diabetes, cardiac ailments and seizures. Small embedded computers collect physiological data and control therapies.
- Dissolvables are remote-controlled implantable devices that dissolve once their job is done. One application currently being studied has these devices delivering thermal therapy to help heal wounds.
- Proteus Digital Health got FDA approval in 2012 for their 1-mm2 ingestible sensor that is embedded into a pill as part of its Helius solution; it can relay information about your digestive tract, initially helping to manage compliance with medication regimens.
- Freescale Semiconductor has released its Kinetis computer 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 combined with personal sensor data to understand and get insight about the impact of environmental influences. 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 fundamentally not secure. Devices must prevent efforts 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 different types of data in different formats arriving in an endless stream. This creates not only processing challenges, 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. There 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. These biases need to be identified 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 information because of concerns about privacy. Incentives, security and privacy guarantees must be developed to address these concerns.
- Consistency: Standards need to be developed and implemented 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. This includes evolving passive mobile computing devices that require no effort. 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. There is also unwillingness for healthcare participants to share data. This 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 difficult to act on in a timely manner. The 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 different regions and countries to converge. Integrating data across disparate data repositories allows local and regional best practices to be identified and leveraged on a broader scale. Researchers benefit 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 benefits. 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 organization’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 application and solution development.
This will provide the foundation needed for strong execution. Without this preparation, organizations will not realize benefits and will risk being left behind their competitors.
About the author
Bill Hamilton is director, healthcare consulting, Cognizant. For more on Cognizant, click here.