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AMCP 2016

Driving Preventive Care Through Digital Medicines

Laura Joszt
The current reactive healthcare system is driving digital health innovations, Yoona Kim, PharmD, PhD, head of clinical modeling and analytics with Proteus Digital Health, said at the AMCP Managed Care & Specialty Pharmacy Annual Meeting 2016.
The current reactive healthcare system is driving digital health innovations, Yoona Kim, PharmD, PhD, head of clinical modeling and analytics with Proteus Digital Health, said at the AMCP Managed Care & Specialty Pharmacy Annual Meeting 2016.
 
There are 3 main factors driving the increased interest in digital health:
  1. Requirement to curtail increasing healthcare costs
  2. Need to find new ways of handling the growing number of individuals with chronic diseases
  3. Desire to provide better and safer medical care
 
These align with the Institute for Healthcare Improvement’s triple aim to improve care quality, reduce per capita healthcare costs, and improve population health.
 
The fastest growing category in digital health is personal health tools, which are providing tons of data that can be used to help the industry understand who is at highest risk and who is costing the system the most money. The challenge is that we still don’t know why they are risky and what the best intervention might be for them.
 
“And in order to understand why they are risky, we have to understand other risk factors—social and behavioral risk factors—that we don’t necessarily get with medical diagnostic tools,” Dr Kim said. “That will feed into what intervention will best impact that patient.”
 
Current care management efforts take the highest risk patients and through a number of programs and types of providers at them. The problem is that all of that becomes difficult for the patient to keep straight, which leads to a fragmented experience.
 
Digital biomarkers—consumer-generated measures—are a way to deliver personalized medicine to these patients. Data becomes a digital biomarker when a relationship is drawn to a health-related outcome, explained Dr Kim.
 
This data is gathered mostly through smartphones as 50% of US smartphone owners use at least 1 health app. There are also a few different types of smart wearables that collect data and send it to a central hub:
 
  • Loose worn: watches or bands, necklaces, and apparel, such as FitBit. They have high user familiarity, but not the best accuracy.
  • Body worn: adhesive patches, partial implants, and rehab devices. These have direct sensor contact, but low user familiarity and a high cost.
  • Phone-based: apps and algorithms, sensing cases, and embedded sensors. These have a lot of users, but data accuracy is questionable and the market is oversaturated, leading to fragmentation and lack of quality standards.
  • Portable devices: home devices like digital weight scales or glucose monitors. They are specific plug and play solutions that have a single functionality.
 


 
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