Healthcare has an abundance of unique opportunities to leverage data to be impactful for providers and patients.
There is no arguing that healthcare involves a prodigious amount of data. According to an IDC report, 4.4 ZB of data was produced in 2013, and it estimated that 44 ZB of healthcare data will be produced yearly by 2020—a tenfold increase in seven years. In a New England Journal of Medicine article, the authors estimated that a single patient typically generates close to 80 MB in imaging and electronic medical record (EMR) data each year. With all the data that is being collected, healthcare organizations have the opportunity to leverage it to drive patient outcomes and lower overall healthcare spend.
When thinking of healthcare data, I frequently recall the Tolstoy quote, “Happy families are all alike; every unhappy family is unhappy in its own way.” Healthcare entities are not homogenous and most have their own unique set of data quirks, concerns, and institutional roadmaps and use cases. Thus, the question becomes, 'How can healthcare data move beyond static noise to become a business asset to improve patient adherence?'
Data does not speak; it is interpreted. In a Stanford Medical School Trend Report, it was estimated that 2,314 exabytes (one exabyte = one billion gigabytes) of healthcare data will be produced by 2020. It is of little wonder that extrapolating clinically useful, interoperable, meaningful, and time-sensitive insight from that prodigious amount of data seems like a daunting task.
As healthcare organizations move from volume to value, data has to be actionable and accurate. Additionally, there are numerous regulatory, privacy and security requirements that need to be considered. And while there cannot be a one-size-fits-all approach, that should not stop healthcare entities from implementing best practices, specifically as they relate to how to reconcile siloed data, maximize existing stored data, and generate actionable, strategic processes for collecting future patient data.
Parsing through large quantities of data to solve complex, analytical tasks is becoming easier with solutions like artificial intelligence (AI) and machine learning (ML) tied to a secure cloud. Consider, for example, patient no-shows cost an estimated $150 billion annually in the US and roughly £216 million in the UK. With a growing chronically ill population, patient no-shows can delay disease detection, impact the quality of healthcare delivery, and increase overall healthcare costs.
Healthcare organizations have the data to determine what percentages of their overall practice has a patient no-show problem. Therefore, it is no longer acceptable to say patient no-shows are “part and parcel of doing business.” Let us not forget, the business of healthcare is improving patient outcomes, and reducing no-shows will do just that.
With advances in technology, making sense of healthcare data does not have to be a Sisyphean effort. Healthcare organizations can and should use their data assets to drive patient adherence and better outcomes. Healthcare data is “noisy” and at times, overwhelming. However, with AI and machine learning, healthcare organizations can:
To improve operational and clinical efficiencies it might be advantageous if healthcare organizations stop vacillating between healthcare data being a blessing and a curse. Healthcare has an abundance of unique opportunities to leverage data to be impactful for both providers as well as patients. There are decidedly numerous use cases on how to leverage healthcare data, but each healthcare organization will need to determine what technologies and use cases are right for their patients and providers.
A version of this article originally appeared in the Journal of mHealth.
With nearly 18 years of Healthcare IT product marketing experience, Alison Nicole Haughton has worked across the continuum of healthcare for companies such as IMS Health, Harvard Medical School, Parexel, and a variety of early stage healthcare companies implementing m-Health clinical. Ms. Haughton received her bachelor’s degree from American University and master’s from Tufts University School of Medicine/Emerson College.
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