Automation has already taken the healthcare manufacturing sector by storm—thumb through an issue of Pharmaceutical Technology, and you will see solution after solution, all of which claim to cut costs and boost production rate. With the advent of AI and big data, healthcare automation is becoming faster, smarter, and more versatile. In this week’s Innovara Digest, we’ll cover the various ways healthcare is using automation in 2017.
Simplifying workflows: In an IDC Health Insights survey, nearly 1/3 of participants claim to use robots in their practice or hospital. “Robots can deliver value by automating manual and laborious tasks,” asserts Mutaz Shegewi, IDC research director. “But they will also be increasingly adopted for direct clinical applications and emergent use cases.”
Deriving insights: Healthcare automation may be useful for simplifying workflows, but Sandy Allerheiligen, VP of Predictive and Economic Modeling at Merck, has much higher hopes. “We will know more about our individual genetics, long-term patient response, and adherence to regimens, as well as compliance,” predicts Allerheiligen. “For me, it’s how do we actually learn how to translate a clinical-trial patient for a real-world patient? When we’re projecting doses, it’s what’s the right dose of the drug and how do you find that? It’s still a journey, but one that is within reach.”
Performing research: In research, the opportunities for automation are endless. Merck and others have begun using WCG’s Site Feasibility Solution to automate the site selection process, cutting the time spent from 8-10 weeks to 2-3 days. Pfizer is using AI to pull data from electronic health records (EHRs) and generate clinical study reports. Kai Langel, director of eClinicalHealth, is experimenting with the idea of eliminating trial sites altogether, instead using a patient portal to collect and process data, as well as send automated status updates and reminders.
Clean up existing data: Healthcare organizations often struggle with data governance—according to mobihealthnews, “many organizations have troves of electronic health records that can’t avail themselves of analytics because they can’t be matched with other records.” AHIMA interim CEO, Pamela Lane, believes that using cloud-based non-healthcare databases, such as credit bureaus, is the answer to healthcare’s patient matching problems.
Monitor symptoms: Bigfoot Biomedical is developing an automated diabetes management system that communicates with wearable insulin delivery and glucose monitoring to monitor diabetes symptoms and deliver insulin in real time. A companion app provides information to the patient and allows them to tweak settings. Abbott is partnering with the California-based startup to provide the wearables and promote the system. Eko Devices has developed a heart monitor that works in a similar way—Duo monitors patients at risk for heart failure, communicates with the patient via a companion app, and sends information to their clinician.
Improve adherence: Ensuring that patients have easy access to their medication when they need it is crucial to improving adherence. Gregory Morris, Chief Commercial officer of VirMedica, recommends using a HUB system to connect patient, provider, and pharmacy. “The on-demand, self-help model is expanding because it offers lower long-term costs, improves services, and is highly scalable, says Morris. “New tools with automated workflows are helping the provider understand how to acquire the product for the patient in real time, regardless of preference – referral to provider; white bag delivery, and so on.” A product called PillsyCap uses a Bluetooth-enabled pill bottle to sense when patients take their medication. The companion app sends automatic reminders and checks in with patients to ensure adherence.
Share information: Countless apps across the market allow patients and providers to share information with one another, eliminating the need for pen and paper processes that waste valuable time and resources. According to Stephen Wurtz, New Hampshire state registrar, “real-time data could transform public health surveillance and disease prevention.”
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