Mie2015 workshop-adherence engaging-publicized

Addressing Patient Adherence Issues by Engaging Enabling Technologies
MIE 2015 Workshop WS13
May 28th 2015 4:45pm - 6:15pm Room Frankfurt
Chohreh Partovian, MD PhD
(IBM T.J. Watson Research Center, USA)
Pei-Yun Sabrina Hsueh, PhD
Review of gap analysis from big data
to “small” patient-generated data
(IBM T.J. Watson Research, USA)
Michael Marschollek Prof. Dr. med Dr. Ing
(Director of Hanover Medical School, Peter L.
Reichertz Institute for Medical Informatics)
Fernando Martin Sanchez, PhD
(Director, Healthcare and Biomedical
Informatics Center, University of
Melbourne, Melbourne, Australia.)

Agenda
• 4:45-5:00pm Opening Remark
– Pei-Yun Sabrina Hsueh: A view from big data to small data (IBM T.J.
Watson Research)
• 5:00-6:00pm Presentations / interactive Q&A
– Chohreh Partovian: physician’s POV on adherence management using
technology (IBM T.J. Watson Research Center)
– Michael Marschollek: examples of adherence management via
patient-generated information (Hanover Medical School)
– Fernando Martin Sanchez: an update of self-quantifiable movement –
enables and imminent challenges (University of Melbourne)
• 6:00-6:15pm Workshop discussion/audience Q&A

Pei-Yun (Sabrina) Hsueh, PhD
Wellness Analytics Lead
Global Technology Outlook Healthcare Topic co-Lead
Healthcare Informatics PIC co-Chair
Health Informatics Research Group
IBM T. J. Watson Research Center
• Research focus: Insight-driven Healthcare service design, Patient-generation
info from wearables and biosensor devices/implants, Personalization analytics
framework for lifestyle intervention, Patient engagement & Adherence risk
mitigation
Opening Remark

A perfect storm awaits…..
Healthcare Landscape Shift driven by Patient-generated information

Apple iOS 8 HealthKit Samsung sHealth
The mHealth Data Platform Race!
Google Fit

Recap from MIE 2014: Gaps observed in the use of Patient-Generated Data
in Personalized Service Design
Q: How to re-create healthcare service and user experience through
Patient-generated data from non-clinical settings?
30%
10%
60%
Endogenous  Personal Genomics
Care Delivery Clinical Care
Exogenous (Behavior, environment, social)
MobileFRR
Personalized
Healthcare
Outcome-based
Model
Outcome-driven service models that can account for exogenous data (60% of
healthcare determinants) are crucial to effective healthcare.
Unreliable detection of inflection points, habit formation cycles
and assessments of treatment efficacy
Reliable means for providing granular patient
understanding in daily contexts.
Not an imagined problem! (Otsuka/Proteus/LLoyds, J&J Alz early detection,
Samsung app/platform, Apple HealthKit/ResearchKit/Watch, etc.)

It’s Data. Big Data!
lso not just Big Data!
1240
PB
1800
PB
6800 PB
(annual)
Clinical:
Episodic; care pathways
in controlled settings
Genomic: Mostly static
data, but critical for
personalized medicine
Exogenous data
(behavioral, social,
environmental)
Social and behavioral
phenotypes + Exposome
informatics
Exogenous Data Growing Fast !
NOISY, LARGE VOLUME,
UNCONTROLLED
Need minimum description
& quality control

Turning big data to actionable small data
1990 Empirical
MedicineIntuitive
Medicine
Personalized Service
Personalized service
(Individualized Calibration)
Knowledge-driven
Guideline
Precision Medicine
Degree of personalization
Degreeof
collaboration
(datadimension)
Data-Driven
Evidence
Century of
behavior change
Hypothesis
Modeling

IBM Confidential9
Recap from MIE 2014: Gaps observed in the use of Patient-Generated Data in
Personalized Service Design
• The lack of reliable means to capture granular patient-generated data in non-clinical
settings (user’s daily life contexts)
– Leads to unreliable detection of inflection points, habit formation cycles and assessments of
treatment efficacy.
• Need for a framework to integrate analytical insights with feasible service models.
– Progress impeded by the lack of modular design and data standardization in existing
healthcare systems
Customer/
Patient
Adherence
Theme
#1
Theme
#2
Theme
#3
Personalization for
risk stratification
(from population to
individual evidence)
Personalization for in-
context recommendation
(from disease-centric to
patient-centric)
Personalization for
adherence risk
mitigation
(from status-insensitive
to status-sensitive)

More questions to think & Suggestions on next step?
• Do provider beliefs and support of these technologies and approaches affect
patient usage?
• Will patient interactive reported data improve provider and patient
communications, reduce risks and increase early interventions?
• Can adherence to care plans for patients with chronic health conditions be
increased through technology-mediated techniques?
• Can analytics based on patient characteristics and adherence behavior be used to
identify patients at risk for adverse health events, as well as identify “model”
adherers who are more effective than the average patient at remaining healthy?
• Can dynamically configured software improve health outcomes for the patient
and help control costs?
• How will real time patient reported data shift communications, culture, care
processes and the patient – provider partnership?
A follow-up workshop/panel with a more focused area wherein filling in the gap has
been perceived as priority  MIE 2015

MIE 2015 Focus Area:
Adherence risk mitigation opportunities
- Less than 50% of patients adhere to clinical recommendations
- 20 to 30% of prescriptions are never filled
- 194,500 deaths a year and an additional 125 billion (EU)
- 69% of adverse event-related hospital admissions, $100-$290 billion annually (US)
- $30 - $594 billion dollars annually (global)
- UK, France and Belgium have started including pharmacists as a mean to gather
additional information on patient adherence
How to bring patients and clinicians into the loop for evidence-based conversation?

12
Key Challenges in Adherence Risk Mitigation
 Existing system’s lack of capabilities to account for case history has resulted in not
being able to differentiate urgent cases.
 Care coordinators have to handle all case exceptions equally; this is a costly
process given the sheer number of guideline violations per day.
• Personalized continuous feedback
loop mechanism
• Adherence monitoring on an
individual basis
• Accommodate individual
differences in the way users
behave
• Instant feedbacks on non-
adherence
• Detect changes in personal
activity model and identify
problems
• Specify problem areas in
physical activity segments and
replay correct sequences
Collaborative Care
• Provide an evidence re-examination
mechanism
• Update the current personal activity
model in PWR according to latest
behavioral changes
• Recommended services w.r.t. changes
revealed in the monitoring context
Evidence Delivery
• Reuse evidence generated from population
databases
• Save time and cost in training
• Learning from the coach-based (or
population-based) model.
Evidence Generation

How do you proactively leverage patient data
(individual, population) and guidelines into actionable
insights based on risk and disease progression?
Risk
Stratification
User-Centered
Service
Personalization
Monitoring
How do you generate a specific personalized
plan?
how do you monitor effectiveness, Adherence risk and
adaptation points?
13
Theme
#1
Theme
#2
Theme
#3
13

Slide 14
What factors are characteristic of current adherence behavior?
Adherence
Outcome
Measure
Control Variables
(Demographics)
Age
Gender
Patient characteristics
Socioeconomic, Location, Benefits
Health/wellness status
Past adherence behavior
Utilization profile
Drug utilization
Drug delivery channel
Drug cost
Healthcare utilization in the previous year
Psycho-active clinical care
Professional characteristics
Professional responsibility
Stress & Sense of Control Factors
Routine Disruption Factors
Medication/Disease management
Medication Access Factors (Impact of retail/mail
order, days supply by doctor behavior)
Medication Management Burden (in observation
window)
Disease Management Factors (in look-back period):
Charlsen Index, Secondary prevention criteria

Context-based Wellness/health Services ~ tracking the “data value chain”
15
Insight-driven health/wellness services
Clinical
provider
Payer Affinity
Services
Person
Generated
Billing data
Lab data
Imaging data
Inpatient EMR data
Outpatient EMR data
Claims Data
HIE Data
External pharmacy data
Geno -mics Data
Exercise assessment data
Retailer (Food, trainer, etc)
Environmental data
Wearables – psychological
Patient reported symptom data
Family and Lifestyle data
Wearables – physiological
User preferences/implied habits
Patient reported outcome data
Risk stratification,
intervention
assignment
Intervention efficacy &
disease progression
Personal Health
tracking
Lifestyle baselining
(without self-efficacy
context)

Personalized Healthcare and Adherence: Issues and challenges
Ambient and Wearable Sensor Systems –
improving patient adherence?
• Some example studies
• Discussion
Michael Marschollek (Peter L. Reichertz Inst. for
Med. Informatics at Hannover Medical School, GER)

The NATARS study
• Ambient sensors (retrofitted) in homes and
wearables
• long-term mobility and activity monitoring of
patients who recover from mobility-impairing bone
fractures
• 24 patients (recruitment: some 400), each 3 months
• Primary outcomes: acceptance, feasibility
• Secondary: relationship sensor data – clinical
outcome

devices used
19
inexpensive, backfitting possible, wireless
home automation sensors
base station (data collection) wearable accelerometer
power meters

Example: motion sensors in bathroom, kitchen
20

outcome
• Technical feasibility
• Good acceptance, once installed
• First hints on relationships btw.
clinical outcome and sensor
data
Marschollek M, et al. Inform Health Soc
Care, 2014; 39(3–4): 262–271

Clinical outcome and sensor data

promoting physical activity in children
• multitude of projects, e.g. Plischke et al, 2008, Stud
Health Technol Inform, cyberMarathon study,
wearable sensor data feedback
• results:
– change in BMI over a year in intervention group
– +11.4% daily physical activity MET level
23

AGT Rehab study

Point-of-Care study – Remote monitoring
of liver transplant children
• Point-of-care blood testing
• Decision support and communication for
parents, doctors
Marschollek et al., ESPGHAN 2013

POC study – results (excerpt)
• Home monitoring following liver transplantation isfeasible
and is accepted by parents and physicians.
• Cons:
– time consuming (physisican), expensive
– two POC devices were damaged despite training
– possible interference of communication with local health care
provider (patient may fall in-between responsibilities)
• Pros:
– data securely transmitted
– one infection alarm was generated successfully
• enabled timely diagnosis and anti-viral treatment
– good parent satisfaction, acceptance
„It was so soothing to
have it here and know
that everything is ok with
our son. It really helps
people in our situation
and gives them security.
Thank you for letting us
use it!”

Questions…
• Do provider beliefs and support of these technologies and approaches
affect patient usage?
• Will patient interactive reported data improve provider and patient
communications, reduce risks and increase early interventions?
• Can adherence to care plans for patients with chronic health conditions
be increased through technology-mediated techniques?
• Can analytics based on patient characteristics and adherence behavior be
used to identify patients at risk for adverse health events, as well as
identify “model” adherers who are more effective than the average
patient at remaining healthy?
• Can dynamically configured software improve health outcomes for the
patient and help control costs?
• How will real time patient reported data shift communications, culture,
care processes and the patient – provider partnership?
• YES
• YES,
maybe
• YES
• Maybe
• YES
• subtan
tially

But…
• Lack of data integration in HIS (semantics…?)
• Lots of data = lots of information? > „analytic gap“
• Issues of data quality
• Adherence only in case of clear benefit (e.g. POC
study), potentially strong study bias?
≠

MIE 2015 Workshop WS13
May 28th 2015 4:45pm - 6:15pm Room Frankfurt
Chohreh Partovian, MD PhD
(IBM T.J. Watson Research Center, USA)
Pei-Yun Sabrina Hsueh, PhD
Review of gap analysis from big data
to “small” patient-generated data
(IBM T.J. Watson Research, USA)
Michael Marschollek Prof. Dr. med Dr. Ing
(Director of Hanover Medical School, Peter L.
Reichertz Institute for Medical Informatics)
Fernando Martin Sanchez, PhD
(Director, Healthcare and Biomedical
Informatics Center, University of
Melbourne, Melbourne, Australia.)

The problem-centered interviewing and treatment
The “Doc Martin” Approach
Symptoms Past medical historDemographics
Clinical ExaminationDiagnostic Tests: Lab, Imagi
Diagnosis
Treatment
Recommendations for behavioral and lifestyle change
±
Medications
Patient DATA
Physician Order
Decision Making
Process
???

Adherence is a key mediator between medical
practice
and patient outcomes
For every 100
prescriptions
written
50-70
go to a
pharmacy
48-66
come out of
the pharmacy
25-30
are taken
properly
15-20
are refiled
as prescribed

Five interacting dimensions of Adherence
Low literacy - Language barrier
Lack of insight into illness
Belief medications are harmful or not important
Lack of belief in benefit of treatment
Fear of medication side effects
Forgetfulness
Tired of taking medications
Anger, stress, anxiety
Substance abuse, psychiatric disease, Depression
Complexity of medication regimen
Inconvenience of medication regimen
Inadequate follow-up / discharge planning
Barriers of access to care
Cost of medications
Provider’s inadequate techniques, theory, and
relationship skills, failing to assess the patient’s needs
not sharing decision making with patients

How to promote maintained health behavior change?

Self determination theory suggests the need for fostering Autonomy
• intentional change as
opposed to societal,
developmental, or imposed
change
• Intrinsic motivation or well-
internalized extrinsic
motivation
• autonomously motivated
people are more engaged,
persistent, and efficacious

How to promote autonomy
Creating conditions for internalization of the recommendations
Refraining from
pressuring to think,
feel, or behave in
particular ways via
coercion or
seduction
Taking interest in the barriers
to change and their
motivations behind it
Acknowledging their internal
frame of reference and
Respecting their choices
Encouraging them to explore
and to make choices about
how to behave
Providing guidance and support
Providing relevant information in a
dispassionate way, Translating the
Scaffolding the presentation of fac
facilitate more reflective lifestyle c
≠ leaving them alone to decide and act for themselves
≠ being permissive or neglectful
Control Autonomy
Independence
Dependence
External influence and individual commitment

Patient-centered interviewing and treatment
An integrated (biopsychosocial) approach to clinical reasoning and
patient care

Can technology-
enabled patient-
generated data help
the provider-patient
duo to better manage
adherence?
Technologies that aim to enable real-time, adaptive management,
engage and influence patients, and enhance clinicians cognitive processes

Patient-Generated Data
• Health-related data created, recorded, gathered by/from patients to
help address a health concern
• Patients and not providers are primarily responsible for capturing or
recording these data
• Patients direct the sharing or distributing of these data to providers
and other stakeholders
• Personal Health Records (PHR), Interactive Web-Based Patient Portals
• Remote monitoring using passive low-cost Mobile sensors, Streaming
biometric data with manual or automatic download from medical
devices, Geo-location tagging, question-asking systems using mobile
phones to create personal profiles from questionnaires, quality of life
scores and other patient-reported outcomes

Will patient-generated data help?
• Parity of information access is important to effective engagement
• The fact of creating, managing, and reporting data has the potential to
empower patients, to engage and “activate” them
• “Patients who read their notes, collected personal health data, and
maintained a record became more aware of their conditions and
behaviors => felt more in control of their care, and showed increased
participation”
• Can address information gap and ensure continuity of care after discharge
from hospital or between visits
• Leverage untapped patient experience for shared decision making

Provider’s perception of PGD
• “It’s not just me learning
about them, they are
learning about
themselves”
• “They discover it because
they pull it out of the data,
which is much more
powerful than me figuring
it out and telling them”

Shared Information alone does NOT insure benefit
• Expanding technology use alone does not guarantee
that people will become more adherent
• A patient having limited health literacy or low
patient activation may not engage effectively in
capturing and sharing PGD that require
understanding and engagement

Perhaps more automated methods could help…

Understanding the process of conversion
from uninvolved to highly engaged
patient
is critical
Underlying structure of change is neither
technique-oriented nor problem specific

The transtheoretical model of change
– a cyclical pattern of
movement through
specific stages of
change
– a common set of
processes of change
– a systematic
integration of the
stages and processes of
change
Prochaska and DiClemento

Transtheoretical Model: People are perceived as
moving through a series of stages
• stages of change
represent a temporal
dimension: when a
particular shifts in
attitudes, intentions,
and behaviors occur
• The more clients
progressed into action
early in therapy, the
more successful they
were in losing weightProchaska and DiClemento

Processes of Change: how these shifts occur
• Covert and overt
activities and
experiences that
individuals engage in
when they attempt to
modify problem
behaviors
• Repeatedly identified
across diverse areas
• The processes used
early in treatment, and
the stages of change
scores were the bestSource: Reproduced from Prochaska, J.O. et al. (1992). “In Search of How People Change.”

Efficient change depends on doing the right
things (processes) at the right time (stages)
• 10%—15% of smokers are prepared for action,
• 30%—40% are in the contemplation stage, and
• 50%—60% are in the precontemplation stage
• The amount of progress clients make following
intervention tends to be a function of their
pretreatment stage of change
• Helping people progress just one stage in a
month can double the chances of participants
taking action on their own in the near future
Not really
thinking
About
change now
Precontempl
ation
only action-oriented programs are likely to underserve,
misserve, or not serve the majority of their target population
Prochaska, DiClemente, Velicer, Rossi, & Guada

Mismatching Stage and Treatment
• Some appear to rely primarily on change processes most
indicated for the contemplation stage–consciousness raising, self-
reevaluation–while they are moving into the action stage.
Insight alone does not necessarily bring about behavior change
• Others rely primarily on change processes most indicated for the
action stage–reinforcement management, stimulus control,
counterconditioning–without the requisite awareness, decision
making, and readiness provided in the contemplation and
preparation stages
Overt action without insight is likely to lead to temporary change

Dynamic measures of the processes and stages of
change outperform static variables
• Four major patterns of behavior change in a two-
year longitudinal study of smokers
(a) Stable patterns: remained in the same stage for the
entire two years
(b) Progressive patterns: linear movement from one stage to
the next
(c) Regressive patterns: movement to an earlier stage of
change
(d) Recycling patterns: two or more revolutions through the
stages of change over the two-year period
Prochaska, DiClemente, Velicer, Rossi, & Gua

Need to assess the stage of a client's readiness for
change and to tailor interventions accordingly

Providers’ concerns: An information system is
only as good as the information stored in it
• Risk of Information overload
• Challenge of finding useful information in a
large amount of data
• Quality and trustworthiness of data: need to
trust that the information content is pertinent
and the extra time spent worthwhile
• Additional work not being reimbursed
• How is it going to fit with the workflow
• Usability
• Interoperability between PHR and EMR

Design Issues: What information?
• Customization may be necessary: Information needs vary
across specialties
• Medication, medical history, records of past tests and
treatments
• Subjective information: patient’s goals, values and
preferences, feelings, moods, experiences, perceptions about
their conditions, aspects of their lives related to their
conditions, quality of life
=> to better understand individual patients and provide more
“personalized” service

How the information should be presented?
• How easily the information can be retrieved, viewed,
processed
• What thresholds are useful for timely review of received
information and for automated alerting for out of range
information
• Presenting in a chronological manner: Timelines, Calendars
• Importance of data visualization: Tables, Charts, color coding,
trends over time and patterns
• Integration of various types of information to identify
relationships among multiple factors surrounding a patient
specific behavior in order to identify triggers

Design issues: What functions should be
included
• Ability to exchange data between EMRs and PHRs via an easy
interface
• Ability to distinguish between PGD and data coming from
other providers into EMR
• Ability to prompt patients to supply specific pieces of
information during data entry
• Ability to filter data based on certain criteria, and to
implement rules to detect conflicting data points
• Function that would relay information directly to the care
team when urgent care is needed and workflow created to
respond quickly and reliably

Policy, Privacy, and Liability Issues
• Who is accountable for each step in handling of
information
• Security and privacy issues
• Policies guiding review and documentation of
relevant information in a consistent way across
organizations
• Reliable and trusted mechanisms for identifying and
acquiring unadulterated, unambiguous, time-
stamped data from known sources

Providers’ Role: Achieving high adoption impacts usefulness
Just as providers discuss and recommend medicines, lab tests,
exercise, and other health-related activities, their impact on
promoting patient use of PGD is likely to be important in
terms of marketing the use of PGD to their patients or
responding enthusiastically if their patients suggest PGD use

Provider’s Readiness to Change?
Changes in workflow: “The staff came to work one day and
nobody knew how to do their job” Richard Baron
Social relationships and communication patterns will change:
“Medical interns spent 12% of their times talking to patients
vs. more than 40% of their time on computers”
Power dynamics “who controls what” have to be redefined
and renegotiated: team work and shared decision-making
Policy changes and realignment of incentives: Pressure to be
cost-effective, outcomes and quality measurements

Needs for education, creativity, and innovation
Use of simulation and
experiential learning to make
the process of adopting the
innovation (web-enabled
patient reported
measurement systems)
easier for individual clinics to
accomplish
Double Loop Learning

It takes time….
In 1834, speaking of the stethoscope, Times of London
wrote:
“ This diagnostic advance was not well received. That it
will ever come to general use, not withstanding its
value, is extremely doubtful because its beneficial
application requires much time and gives a good bit of
trouble both to the patient and the practitioner,
because its hue and character are foreign and opposed
to all our habits and associations.”
The Digital doctor, Robert Wachter

Thank you!

Thank You
Merci
Grazie
Gracias
Obrigado
Danke
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