with Lara Kelly,
Head of Data Analytics, HealthBeacon
In January, we launched our adoption series, assessing the multiple considerations required to create useful technologies within the complex landscape of healthcare. This was followed by HealthBeacon CEO, Jim Joyce, reflecting on the importance of keeping technology human, making reference to the significance of simple acts, such as the inclusion of a smile.
Next, Dr. Zara Kinsella Fullerton does a Q&A session with Lara Kelly, head of Data Analytics at HealthBeacon to learn about the benefit of a data driven approach, when it comes to maximising patient adoption.
TELL ME ABOUT YOURSELF
I am the head of Data Analytics at HealthBeacon. My background is in Biomedical Engineering which I studied at Trinity College in Dublin.
For a very long time, I thought that I wanted to be a doctor. However, as time passed, I became passionate about using the data captured from digital tools to support and improve patient care.
I like to think of the HealthBeacon as the key that unlocks the data, which enables us to make really smart decisions!
FROM A DATA PERSPECTIVE, HOW WOULD YOU DEFINE ADOPTION?
Adoption is something that is often referenced, but the meaning can be unclear, particularly when it comes to digital health technologies. Defining adoption is a topic that has certainly led to much debate amongst the team – taking a purely data driven approach we would classify a patient as a “HealthBeacon adopter” if they consent to the service, their HealthBeacon is communicating with us (we are a connected device after all!) and the patient has continued to use the device for a period of time. But we are dealing with the human psyche after all, so it is not as straightforward as this!
The big question we grapple with, is how long does the patient need to use the HealthBeacon for us to classify them as an adopter? It is a matter of individual patient characteristics. According to a study published in the European Journal of Social Psychology, it takes 18 – 254 days for a person to form a new habit – and on average, 66 days for a new behaviour to become automatic. ¹
As a result of this broad time range, there is no hard and fast rule on the period of time required before we can classify a patient as being a true adopter. At HealthBeacon, we use a threshold of 90 days. Some might argue we are being hard on ourselves, however, as maximising patient adoption is one of our core values, we would rather under-estimate our true adoption rates and consistently strive to improve them than reach a level of complacency.
HAS HEALTHBEACON PERFORMED ANY RESEARCH RELATING TO ADOPTION?
We completed a study to assess our adoption rates and presented our findings at the Connected Health Conference in Boston, MA in October 2019.
The key finding from an analysis of
If you would like to find out more information about HealthBeacon’s research relating to patient adoption, please email email@example.com t
IN YOUR OPINION, WHAT IMPACTS ADOPTION OF HEALTH TECHNOLOGIES?
There are many factors that can impact adoption. Patient and disease related factors play a big part as well as device usability – how easy it is for someone to incorporate that technology in their daily lives.
As we now have > 10,000 devices and patient experiences to learn from, we have formed a theory on why the HealthBeacon is so widely adopted or “USED”
USER NEED: Patients have to put their sharps somewhere and often are legally obliged to do so
SMART INTERVENTIONS: The system only intervenes when required
EASY: All the patient has to do is plug it in
DISCREET DESIGN: Replacing the conventional sharps bin
Designing patient-centric tools while capturing actionable data can often be difficult to achieve simultaneously. Over the last year we have spent a lot of time reviewing and introducing improvements to the patient experience – much of this is in the form of additional and smoother patient touch-points – this means we are collecting a lot more data but we must continuously ensure that the data we capture is actionable.
To help us achieve the perfect balance, we opened HB Labs which facilitates extensive collaboration between the data and product teams. Once devices have been tested in the user lab and are released for patient use, the data team constantly monitors and identify new behaviours and insights. These are fed back to the product team to ensure all learnings are incorporated in the design of future products.
Additionally, any findings from our research relating to adoption is also integrated into this process.
WHAT'S NEXT FOR THE HEALTHBEACON DATA TEAM?
The data will keep flowing and we will keep learning and improving!
One project that I am particularly excited about, is the use of machine learning to predict if a patient is likely to take their next dose on-time.
Today, the HealthBeacon enables us to identify which patients forget to take their medication and intervene accordingly – this work will enable us to identify them and intervene before they have even forgotten to take their medication! This will be our smartest intervention yet but more importantly it should improve the patient experience which ultimately is the key to successful adoption!
WHEN YOU AREN'T IN THE HEALTHBEACON DATA LABS, WHERE MIGHT WE FIND YOU?
When I am not at work, my three favorite things to do are exercising, cooking and travelling – in no particular order! You may find it hard to believe, but I spent over a year researching the potenti
This article was written By Lara Kelly and Dr. Zara Fullerton Kinsella at HealthBeacon.
Dr. Zara Fullerton Kinsella, has a degree in Business and Economics from Trinity College Dublin and a Medical Degree from University College Cork. She is the Medical Science Liaison at HealthBeacon.
Lara Kelly, has a degree in Mechanical Engineering and a Masters in Biomedical Engineering from Trinity College Dublin. She is Head of Data Analytics at HealthBeacon.
Lally. P., 2009, How are habits formed: Modelling habit formation in the real world, European Journal of Social Psychology
Kooiman, T., and Schans, C.,2018, The use of self-tracking technology for health. 1st ed.: Rijksuniversiteit Groningen, p.12.