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In-the-moment mobile research: Capturing real-time emotional and rational decision making in healthcare Jessica Ma, Director Megan Long, Senior Analyst Janssen SKIM Group
In-the-moment mobile research:
Capturing real-time emotional and rational
decision making in healthcare
Jessica Ma, Director Megan Long, Senior AnalystJanssen SKIM Group
5x5 Reality
56%57%57%57%
58%60%62%
65%65%66%67%68%68%
69%70%72%74%75%
80%85%
AustriaFinland
USACanada
New ZealandSwitzerland
IsraelNetherlands
IrelandAustralia
TaiwanUK
NorwayDenmark
ChinaSpain
Hong KongSweden
South KoreaSingapore
Global smartphone penetration
Source:
Consumer
Barometer, 2014
Decision making is changing
Background and Theory
of In-the-Moment
Case Studies in
Healthcare
Benefits and Outcomes
of Mobile Research
Outline
Background and Theory
Conventional methods don’t cut it
Too simple Too complex
- Emotions wear off
- Recall fades
- Answer solely based on rational factors (e.g. hard clinical facts)
- Does not capture changes in fast moving markets
+ Immediately after decision is made
+ Easier to recall why
+ Answer based on a combination of soft (irrational) and hard (rational) factors
+ Immediate access to real-time data in fast moving markets
OCCURRENCE MEASUREMENT
OCCURRENCE MEASUREMENT
Research
Facility
Post-hoc:
Patient
Consultation
Patient
Record
Patient
Consultation
Mobile
App
Ad-hoc:
Data collection is changing
Traditional
Quant
Traditional
Qual
Blending qual and quant insights
Hard clinical facts Generalized
perceptions
Patient level
insights
providing key
drivers to
prescribing
Case Study:Capturing in-the-moment decisions
New entry into
crowded market
Unclear what
drives prescribing
decisions
Need to
understand drivers
Business Problem
3 minutes of
closed questions
2 minute
voice memo
Data collected via mobile app
50 physicians 65 physicians
254 262
Physicians complete 3-5 records
Example screens
Simplify data collection, but
account for market complexity
physician makes
decision based on
patient characteristics
physician presents
options to patient and
discusses options
patient proactively
suggests treatment
Physician identifies options and then . . .
Findings
Tweak messaging to better define
target patient and key benefits
Increase
in patient related
materials and
programs
Actions taken by client
Increase patient related
materials and programs
Benefits and Outcomes
Benefits
Robust data – patient records
without associated problems
Applications in market potential,
segmentation, treatment pathway
Less post-rationalisation responses
close to point of prescription
Accounts for indications with wide variety of patient types, or highly
individualised treatment (e.g. biomarker-based treatment)
Accommodates changes to fast moving markets
Practical considerations
Some
over-recruitment
needed
Incentives to
maximise
participation
Not necessarily
cheaper than
current methods
Additional questions this approach can answer
What is the patient share for each currently available therapy?
Which patient types get which currently available therapy?
What patient characteristics are correlated with different treatment options?
What is the future patient share of your new product & key competitors?
What patient characteristics correlate with your product?
How does this differ from key competitors?
What do physicians think of new agents?
Other applications for mobile
Telecom Consumer
goodsFinancial
services
Questions?Contact Megan Long at [email protected]
