Medinfo2015 workshop-adherence mangement-patient_driven-publicized

Addressing Patient Adherence Issues by Engaging Enabling Technologies
Effective Patient Adherence
Management by Engaging Enabling
Technologies
MEDINFO 2015 Workshop
Aug 22 Saturday 14:30 - 16:00
Pei-Yun Sabrina Hsueha, Marion Ball, Vimla L. Patelb, Fernando Sanchezc,
Marcia Itod,e, Chohreh Partoviana, María V. Giussi Bordonig, Henry Chang

2
Senior Advisor, Research Industry Specialist,
Healthcare Informatics, IBM Research
 Professor Emerita, Johns Hopkins University
 Affiliate professor, Division of Health Sciences
Informatics, Johns Hopkins School of Medicine
 Member, Institute of Medicine
 Serve on the Board Of Regents of the National Library of
Medicine
 Past President, International Medical Informatics
Association ( IMIA)
 Board member of American Medical Informatics
Association (AMIA)
Fellow: American College of Medical Informatics (ACMI),
Past Board member and Fellow of the Health Information
Management and Systems Society( HIMSS), American
Health Information Management Association (AHIMA)
Medical Library Association (MLA) and the College of
Health Information Management Executives (CHIME),
American Academy of Nursing (FAAN)
Marion J. Ball, Ed.D

MEDINFO 2015 Workshop 14 Room 8
Vimla L. Patel, PhD DSc
(New York Academy of Medicine, USA)
Pei-Yun Sabrina Hsueh, PhD (Organizer)
Review of gap analysis from big data to
“small” patient-generated data
(IBM T.J. Watson Research, USA)
Fernando Martin Sanchez, PhD
(University of Melbourne, Australia.)Marion Ball, Ed.D (Moderator)
(Johns Hopkins University)
Henry Chang, PhD
(IBM T.J. Watson Research Center, USA)
Victoria Giussi, MD
(Hospital Italiano de Bueno Aires, Argentina)
Marcia Ito, MD PhD
(IBM Brazil Research Lab/University of Federal Sao
Paulo)

Agenda
• 14:30-14:45 Opening Remark
– Dr. Marion Ball: Intro of the workshop
– Dr. Pei-Yun Sabrina Hsueh: from big data to small data for including patients in
evidence-based conversation
• 14:45-15:45 Presentations
– Dr. Vimla Patel: Understanding people for technology support
– Dr. Fernando Martin Sanchez: Enabling technology and data standards for
patient-generated data
– Dr. Henry Chang: Adherence management proof-of-concept using technology
– Dr. Victoria Giussi: Personal Health Record at HIBA
– Dr. Marcia Ito: Proposed chronic adherence management model
• 15:45-16:00 Workshop discussion/audience Q&A
– Dr. Marion Ball as moderator

Pei-Yun (Sabrina) Hsueh, PhD
Wellness Analytics Lead
Global Technology Outlook Healthcare Topic co-Lead
Healthcare Informatics PIC co-Chair
Computational Behavioral and Decision Science Group
Health Informatics Research Dept.
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

Source: Based on McGinnis et al, The Case for More Active Policy Attention to Health Promotion, Health Affairs, 2002.
Health Determinants Mismatches Today’s Spending“We need to invest in addressing all
determinants of health…”
BIG DATA
Clinical + behavior
driven
Wellness Management
Slide credit: Henry Chang
CLINICAL
GENETIC
EXOGENOUS

Credit: John Rogers, Univ. of Illinois

Effective Patient Adherence Management by Engaging Enabling Technologies
Ion ~1 A
Protein ~10 nm
Synapse ~1 m
Compartment ~10 m
Dendrite ~100 m
Neuron ~500 m
Microcircuit ~1 mm
Network ~5 mm
Brain Region ~1 cm
Brain Tissue ~5 cm
Whole Brain ~10 cm
Organism ~1 m
DEVICES
MOLECULES
Multiscale Multimodal Brain Systems Modeling
Clinical Inputs
SIMULATION
+ ANALYTICS
Clinical Prediction
Clinical Data
PET
MRI
BEHAVIOR
Credit: James Kozloski, IBM

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
Hyper-Personalization
N of 1 clinical trial

IBM Confidential12
Recap from MEDINFO 2013 PANEL:
Personalized Healthcare: Issues and Challenges
The true benefit of consumer technologies and consumer health informatics is
not in the quantity of data they provide, but in how they transform data into useful
information that can make a difference, and improve value and care.
Review physician cognitive model and the need to understand consumers
challenge in physician-patient communication: the lack of social context --
“christmas problem”
challenge in cross-culture communication
challenge in designing instructions for medication management
The potential of using technologies (e.g., mobile text messaging) to increase
adherence

IBM Confidential13
Recap from MIE 2014 WORKSHOP: 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)

MEDDIN 2015 Focus Area:
Adherence risk mitigation
- 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?

15
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

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

Case Study: 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
17
Credit: Michael Marschollek Prof. Dr.
(Director of Hanover Medical School,
Institute for Medical Informatics)

18
This analysis was performed using data from our program, supplemented with data from respected
sources to estimate cost savings.
Analysis
:
Results
:
Compared to the control group, patients in the intervention group
demonstrated 2x rehab completion, ¼ no-show rate at 3-month follow-up
appointment, better exercise tolerance, & lower depression scores
Increased rehab completion rate reduces utilization → meaningful savings
Entity: Hospital
Population: Commercial +
Medicaid; Urban + Rural
Average age: 55
n = 100
English level: 6th grade
Benefit to hospital: Reduced
30-day readmissions for AMI,
CABG, Stent, CHF
Cost savings: ~ $1,300 annual
savings per member
Case Study: Promoting cardiac rehab program
adherence through mobile text messaging
Credit: Bern Shen (CEO, HealthCrowd)

What we are looking for at the individual level…….
Slide courtesy credit: Prof. Lange (UCI)

Stages of Change Model

Source: Based on McGinnis et al, The Case for More Active Policy Attention to Health Promotion, Health Affairs, 2002.
Health Determinants Mismatches Today’s Spending“We need to invest in addressing all
determinants of health…”
BIG DATA
Clinical + behavior
driven
Wellness Management
Slide credit: Henry Chang

Vimla L. Patel, PhD, DSc, FRSC
• Senior Research Scientist, The New York Academy of Medicine
• Director, Center for Cognitive Studies in Medicine and Public health
• Adjunct Professor, Biomedical Informatics. Columbia University, NY
• Adjunct Professor, Public Health, Weill Cornell Medical Center, NY
• Professor of Biomedical Informatics, Arizona State University
• Fellow of the Royal Society of Canada (Academy of Social
Sciences)
• Fellow, American College of Medical Informatics
• Associate Editor, Journal of Biomedical Informative (JBI)
• Editorial Boards of Journal of Artificial Intelligence in Medicine
(AIM), Advances in Health Science Education (AHSE), Topics in
Cognitive Science.
• Past Vice-President (Member services), International Medical
Informatics Association (IMIA)
• Past Vice-Chair, AMIA Scientific Program Committee
• Past Editorial Boards: International Journal of Medical Informatics
(IJMI), Journal of Medical Decision Making (MDM)

Understanding People for Technology Support

Knowledge Infrastructure Continuum
Knowledge
Generation
– Scientific—Journals
– Informal—Community
– Purpose—Real World ContextUtilization
Transmission
– Through Technology
– Face-to-face
Communication
– Mental Models—UsersRepresentation

Impediment to Medication Adherence (2)
Implementation of medication administration
instructions without understanding the nature of
the users social context
Patel, V.L., Eisemon, T.O. & Arocha, J.F. (1988) Causal reasoning and treatment
of diarrheal disease by mothers in Kenya. Social Science & Medicine, 27(11),
1277-1286.

Case 1: Oral Rehydration Therapy
(ORT)
• Used in the treatment of dehydration in
children with diarrhea
• Correct implementation involves
preparation of sterile media (boil water)
and the administration of a constant
dosage at irregular intervals
• Patients are required to execute a
complex procedure

Pharmaceutical Instructions for ORT
The solution replaces body water and body salts lost during
diarrhea. How to use this solution (for children up to 5-years old).
Boil a tumbler of water up to mark (300ml). Add all powder from
sachet to cool water. Stir.
Give two or three tumblers during the first 4 to 6 hours. Give 2 or 3
more tumblers over the next 18 to 24 hours. Give 2 more tumblers
in the following 24 hours. Do not give more than 6 tumblers in 24
hours.
IMPORTANT Always use as instructed unless otherwise directed by your
doctor. Give slowly to prevent vomiting during treatment. Use clean spoon
to give the solution to small babies. If baby is thirsty between drinks of the
solution give plain boiled and cooled water. Begin normal feeding as soon
as possible.

Procedural Representation of ORT Instructions
Procedures Sub- Procedures
1. How to prepare the solution
1.1 Fill tumbler with water
1.2 Boil water
1.3 Cool water
1.4 Add powder
1.5 Stir
2. How to administer the solution
2.1 Give 2-3 tumblers, first 4-6 hours
2.2 Give 2-3 more tumblers, next 18-24 hours
2.3 Give 2 or more tumblers, following 24 hours

Results
Observations (in the wild) of Mothers in Kenya (Africa)
and Montreal (Canada) while preparing medication
– Most of the participants correctly followed the
instructions for preparation the ORT solutions
– Only 50% of the mothers were able to correctly
administer the first stage of ORT
– Only those with graduate degree were able to
correctly administer medication for all stages of
therapy

Mean Level of Accuracy in Interpreting the ORT Procedure
Level of education
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Graduate Degree All other levels
Urban Canadian
Urban Kenyan
Rural Kenyan

Problem Identification: Challenge
• Non-uniformity of instructions: Too
complex for the most needy patient
context, leading to lack of adherence
• Instructions insensitive to socio-
cultural context: boiling a pre-
determined amount of water, leading to
adverse events in small babies

Impediment to Medication Adherence (3)
Different (sometimes conflicting) mental models
of medication administration for the patients,
healthcare providers, and the designers of
instructions
Patel, V.L., Eisemon, T.O. & Arocha, J.F. (1990) Comprehending instructions
for using pharmaceutical products in rural Kenya. Instructional Science, 19,
71-84.

Case 2: Antipyretic Drops
• Over-the-counter medicine used in
the treatment of the common fever
• Correct implementation involves a
simple procedure and calculation but
requires an appropriate medication
plan for the child
• Mothers were asked to follow the
instructions for youngest child in the
family

Pharmaceutical Instructions for Antipyretic Drops
EACH 1 ml DOSE CONTAINS: 80 mg acetaminophen
INDICATIONS: For fast and effective relief of children's fever and pain.
DOSAGE: Administer single dose orally according to age as listed, 4 to
5 times daily, for maximum of 5 days.
Age Maximum Single Dose
Under 2 years as directed by Physician
2 to 3 years 2.0 ml (160 mg)
4 to 5 years 3.0 ml (240 mg)
6 to 8 years 4.0 ml (320 mg)
9 to 10 years 5.0 ml (400 mg)
11 to 12 years 6.0 ml (480 mg)
Consult a physician if the underlying condition requires use for more than five
days. It is hazardous to exceed recommended dose unless advised by a
physician.

Results
– The majority of participants (77.7%) were
unable to correctly suggest therapy
schedules for the administration of the proper
amount of medication
– The participants consistently identified the
frequency of administration recommended in
the instructions as “too much”, since the
suggested plan did not make intuitive sense

Dosage Accuracy for Antipyretic Drops
Accuracy
0
10
20
30
40
50
60
70
80
Under
dose
Correct
dose
Slight
overdose
Extreme
overdose
English
East Asian
Greek

Usage Picture of Antipyretic Drops as
Provided by a Primary Care Physician
1 2 3 4
TIME IN DAYS
SERUM
LEVELS

Usage Picture of Antipyretic Drops as
Provided by a Pharmacologist
SERUM
LEVELS
1 2 3 4 5 6 7 8
TIME IN HOURS

Think-Aloud Protocol Quotation #1
The doctor told us how much to give her, but I
wouldn't give it to her five times a day. The
maximum four and probably we might give it to
her twice in the daytime and once before she
went to bed. I wouldn't give it to her unless I
thought she needed it. I have never given it five
times a day to any of my children.

Think-Aloud Protocol Quotation #2
I would give him 3 milliliters using the
pharmaceutical measuring spoon I have. I
only give fever medicine when it is
necessary. I don't believe in giving a lot of
medicine to the children. I am really cautious
when it comes to that, I only treat the fever
when it needs it. If my son looks ok, then I
don’t give anything

Problem Identification: Challenge
– Both under-dose and overdose of medication , leads
to the child not getting the best medical care
– The frame of referent situation intended by the
instruction was with inaccurate understanding that
it is shared by all readers (users), such that the
identification of the appropriate representation will
be facilitated
– The Medication instructions do not "make contact"
with the subjective or intuitive models used by
readers (patients) when interpreting them, and so
they will fail to have their intended effects

Thank You!
vpatel@nyam.org
www.lodhia-patel.net

Digital Medicine (Convergence of digital revolution
and medicine)
• We have witnessed the impact of the
digital revolution in other domains
(banking, insurance, leisure,
government,…)
• Although digital technology has greatly
affected healthcare at the hospital or
research centre level.
• The digital revolution has not yet reached
medicine at the patient/citizen level
• THIS IS STARTING TO HAPPEN NOW
!!!
Shaffer, D.W., Kigin, C.M., Kaput, J.J. & Gazelle, G.S. Stud. Health Technol. Inform. 80,195–204 (2002)

Participatory
Health
Regina Holliday
The Society for Participatory
Medicine defines participatory
medicine as a movement in
which networked patients shift
from being mere passengers to
responsible drivers of their
health, and in which medical
care providers encourage and
value them as full partners.

History of Participatory Health
• September 2009 – California Healthcare Foundation Report:
“Participatory Health: Online and Mobile Tools Help
Chronically Ill Manage Their Care”
• “Partnership between patients and providers and trusted
experts, one in which participation is enabled and enhanced
by technology and information”
• “Patients are the most under-utilized resource, and they have
the most at stake. They want to be involved and they can be
involved. Their participation will lead to better medical
outcomes at lower costs with dramatically higher
patient/customer satisfaction”
Charles Safran MD

Patients are
impatient

Patient advocacy

Interest from Governments
US
Australian
MyHealth
Record
People are
managing their
own health
better.

IOM Workshop & Report 2013
Partnering with Patients to Drive Shared
Decisions, Better Value, and Care Improvement
- Workshop Proceedings
Shared
decision
making

Effective Patient Adherence Management by Engaging Enabling TechnologiesHealth Informatics and Participatory
health
I. Personal genome services (23andMe)
II. Personal diagnostic testing
III. Personal medical image management
IV. Personal sensing and monitoring (QS)
V. Personal health records
VI. Patient reading doctor’s notes (OpenNotes)
VII. Patient initiating clinical trials (PLM)
VIII. Patient reporting outcomes (PROMIS)
IX. Patient sharing data (Social Media)
X. Shared decision making
Collecting
data
Exchanging
and using
information
Participatory
health

Open Notes – Patients reading
Doctor’s notes

Patient reported outcomes
• Health services and
outcomes research
• Measuring quality
of care from the
patient perspective
NHS PROMs
NIH

Shared decision making

Visualising personal health risks profiles
(Univ. Missouri)
(Juhan Sonin, MIT)

Effective Patient Adherence Management by Engaging Enabling TechnologiesTherapeutic affordances of social media
Merolli M, Gray K, Martin-Sanchez F. Developing a Framework to
Generate Evidence of Health Outcomes From Social Media Use in
Chronic Disease Management. Med 2.0, 2013. 2(2): e3.
1 2 3

White Paper
http://www.broadband.unimelb.edu.au
Activity Theory
+
Patient Activation

DeviceSample
Data
Where is
it stored
Units
Location
Time
Body part
(FMA)
Method
Name
Model
Manufacturer
Technical
Specs
Taxonomy
Body structure
Body function
Around body
(based on WHO)
Who/Which
part/Where/When?
What
How?
Processed
Raw
Minimum Information about a
Self Monitoring Experiment (MISME)
Procedures
EXPERIMENT
Measurement

Tensions
Patient advocatesClinicians’ resistance
to change

Australian Doctors the least open toward patients updating
the information in their EHRs

MEDICINE PARTICIPATORY HEALTH
Provider-centric Patient or Consumer-centric
Curative Proactive
Passive role of the patient Active
Clinical decision making Shared decision making
Electronic medical record Patient Health Record
Adherence, compliance vs activation
Literacy vs Clarity
Research n=they vs n=me and n=we
Patient-generated data

AMA
• it must be recognised that as a
design feature of the PCEHR,
patient control means that the
PCEHR cannot be relied on as a
trusted source of key clinical
information.
• The absence of specific
remuneration for medical
practitioner contribution to the
PCEHR reinforces the need to
ensure that using PCEHR
functions does not impose any
additional workflow
requirements on them.
Consumer Health Forum, Consumer
e-health Alliance
• The ‘personally controlled’
aspect of the eHealth
record is what makes it such
a powerful consumer
resource.
• Patients and potential
patients – health consumers
– must be informed and
engaged as the ultimate
users of the PCEHR.
Submissions to Australian PCEHR
Review - Nov 2013

Evolution
Shenkin B, Warner D.
Giving the patient his
medical record: a proposal
to improve the system.
NEJM, 1973

Benefits
• Better outcomes
• Lower costs
• Better patient experience
• Motivation
• Deepening understanding of their health
• Self-improvement
• Risk profiling
• Prevention
• Shift terciary  secondary  primary  home care
• Data donors for research

• Privacy
• Security
• Education
• Cyberchondria
• Equity
• Regulation, accreditation
• Role of the clinician
• Infrastructure needs
• Therapeutic gap (ethics)
Issues

Dr. Charles Safran, AMIA

© Copyright The University of Melbourne 2015
Thank you for your attention!

Agenda
• 14:30-14:45 Opening Remark
– Dr. Marion Ball: Intro of the workshop
– Dr. Pei-Yun Sabrina Hsueh: from big data to small data for including patients
in evidence-based conversation
• 14:45-15:45 Presentations
– Dr. Vimla Patel: Understanding people for technology support
– Dr. Fernando Martin Sanchez: Enabling technology and data standards for
patient-generated data
– Dr. Henry Chang: Adherence management proof-of-concept using technology
– Dr. Victoria Giussi: Personal Health Record at HIBA
– Dr. Marcia Ito: Proposed chronic adherence management model
• 15:45-16:00 Workshop discussion/audience Q&A
– Dr. Marion Ball as moderator

MEDINFO 2015 Workshop 14 Room 8
Vimla L. Patel, PhD DSc
(New York Academy of Medicine, USA)
Pei-Yun Sabrina Hsueh, PhD (Organizer)
Review of gap analysis from big data to
“small” patient-generated data
(IBM T.J. Watson Research, USA)
Fernando Martin Sanchez, PhD
(University of Melbourne, Australia.)Marion Ball, Ed.D (Moderator)
(Johns Hopkins University)
Henry Chang, PhD
(IBM T.J. Watson Research Center, USA)
Victoria Giussi, MD
(Hospital Italiano de Bueno Aires, Argentina)
Marcia Ito, MD PhD
(IBM Brazil Research Lab/University of Federal Sao
Paulo)

Hungyang (Henry) Chang , PhD
• Senior Research Staff member, Healthcare Informatics,
IBM T.J. Watson Research center
• Program leader of WellVille initiatives for mobile health based
improvements of community health
• Research lead of IBM Connected healthcare analytics for chronic
disease management
• Program Director of IBM research collaborator in Taiwan for Health
and Wellness (2010-2013), conducting technology development for
chronic disease patient engagement via health literacy intelligence
• Research manager of business performance monitoring and management
with technical responsibility to provide innovation leadership to IBM
Websphere BPM suits and IBM internal supply chain visibility initiatives.
• IBM Innovate Awards for his works on model-based business
transformation and B2B collaboration solution.

77
Medical Service Providers Wellness Service Providers Exercise Service ProvidersDietary Service ProvidersService Device Providers
Service
Components
Disease
Mgnt.
Disease
Prevention
Disease
Treatment
Wellness
Mgnt.
Exercise
Mgnt.
Dietary
Mgnt.
Personalized CareElder Care
Service
Scenarios
Service
Processes
Dietary
Recommendation
Exercise PlanDisease Mgnt.
Wellness Ecosystems– Research Framing

78 78
Diabetes Mellitus case manager service flow (Outpatient Clinic)
Health
EducationClinic
(Certified
Diabetes
Educator)
Outpatient
Clinic
(Doctor)
Diabetes
Mellitus
SharedCare
Network
Health
Promotion
Management
Center
(Casemanager)
PatientHospital
Clinical
Chemistry
Laboratory
Nutrition
Counseling
Clinic Outpatient Clinic Stage
Start
Appointments
& Registration
Referral
patients?
Outpatient
Clinic
Diagnosis
Diabetes
Mellitus?
Cashier
OPD
Dispensary
N
End
Meet DM
Case
Criteria?
Y
Collect Batch
Case
Y
Health
Education
Clinic
N
Transfer Case to Diabetes
Mellitus Shared Care
Network by Batch System
Diabetes
Mellitus
Shared Care
Network
Education
Clinical
Chemistry
Examination
Nutrition
Counseling
Clinic
Physiology
Examination
Clinical
Chemistry
Examination
Report
Physiology
Examination
Report
Nutrition
Health
Education
Referral
Form
Y
N
Enrolled Case
in Case
Management
System
End
Diabetes Mellitus case manager service flow (Follow up)
Health
Education
Clinic
(Certified
Diabetes
Educator)
Outpatient
Clinic
(Doctor)
Diabetes
Mellitus
SharedCare
Network
Health
Promotion
Management
Center
(Casemanager)
PatientHospital
Clinical
Chemistry
Laboratory
Nutrition
Counseling
Clinic
Follow up Stage
Start
Outpatient
Clinic
Diagnosis
Cashier
OPD
Dispensary
End
Collect Batch
Case
Health
Education
Clinic
Transfer Case to Diabetes
Mellitus Shared Care
Network by Batch System
Clinical
Chemistry
Examination
Clinical
Chemistry
Examination
Report
Health
Education
Clinic Referral
Care Plan
Check
Appointments
& Registration
Referral ? Y
N
N
Update Case
End
As-Is
(in-site Hospital)
To-Be
(IBM Mobile Health Pilot)
A DM Management Pilot for Newly Onset Patients
Start from Research Methodology to design innovated DM service procedure (2012-14)
Diabetes Mellitus case manager service flow (To-Be) (Follow up)
Health
Education
Clinic
(Certified
Diabetes
Educator)
Metabolism
OutpatientClinic
(Doctor)
Diabetes
Mellitus
SharedCare
Network
Health
Promotion
Management
Center
(Casemanager)
Patient
Hospital
GeneralDepertment
ClinicalChemistry
Laboratory
HIS
(System)
PHMCloud
(System)
Cardiology
Outpatient
Clinic
(Doctor)
Clinical
SupportSystem
(System)
Follow up Stage
Start
Outpatient For
Ongoing
Management
and Follow-Up
Cashier
OPD
Dispensary
Collect
Batch Case
Health
Education
Clinic for
Referral
Transfer Case to
Diabetes Mellitus
Shared Care Network
by Batch System
Clinical
Chemistry
Examinatio
n
Clinical
Chemistry
Examinatio
n
Report
Issue
Referral for
Outpatient
Clinic
Care Plan
Check
Appointments
& Registration
Should
Patient
Be
Referral
?
Y
N
N
Update Case
End
Invoke Data
Integration
process
Request
Clinical
Support
Report
Generate
Clinical
Support
Analytic Report
Provide Clinical
Data
Analyze
compiled
information
Clinical
Support Report
Outpatient
Clinic
Diagnosis
Consider
Referral to
Diabetes
Care Team
or
Specialists
PHM Data
Integration
Cardiology
Outpatient
Clinic for
Referral
Outpatient
Clinic
Diagnosis
Invoke PHM
Integration
process
Update PHM
Care Plan
Based on Care
Plan issue notice
Receive Notification
from PHM
Upload Glycemic and
Blood Pressure Data to
PHM
Collect &
Update PHM
Data
End
1 2
3
4
Hospital site IBM Cloud Platform

Lack of theoretical models has
hampered wider use of patient-
generated data in lifestyle
interventions
Susceptibility
Severity
Benefits
Barriers
Demographics
Triggers/Cues
Self-efficacy
Likelihood of
Adherence
Curated data provides significant opportunity
for foundational behavioral analytics

Value-add services in exercise management for weight loss
(adherence/personalization)
●Evaluate the disease risk
●Provide personal health
plan
●Base on health screen
results and personal
behavior
Health screening and
personalized disease
risk assessment
/
Metabolism assessment
and personalized effective
exercise plan design
●Real time
heart rate
monitoring
●Exercise
plan
guidance
●Heart rate
data
recording
Exercise plan
execution
(Devices and
environment)
●Plan execution,
adherence tracking
and management
Plan adherence
and outcome
tracking
●Establish personal metabolism
profile
●Provide personal effective
exercise plan
●Base on personal resting &
exercise metabolism
assessments

Group B & C show the difference
“Active service intervention (on site coach)” shows the improvement of plan
adherence for +42%~51%
“Active service intervention (on site coach)” shows the delay of adherence attrition
for average 7 weeks
•0%
•20%
•40%
•60%
•80%
•100%
•120%
•1 •4 •7 •10 •13 •16 •19 •22 •25 •28
•Group B
•Group C
•Linear regression of B
•Linear regression of C
•C. Trend of average time spend % over time
•B. Trend of average complied exercise event % over time
•A. Trend of average exercise event % over time •D. Trend of average total calories burnt % over time
•E. Trend of average fat calories burnt % over time
•Slope C = -1.08
•Slope B = -1.34
•50% drop = 23.1 weeks
•1 •4 •7 •10 •13 •16 •19 •22 •25 •28
•0%
•20%
•40%
•60%
•80%
•100%
•120%
•1 •4 •7 •10 •13 •16 •19 •22 •25 •28
•0%
•20%
•40%
•60%
•80%
•100%
•120%
•140%
•1 •4 •7 •10 •13 •16 •19 •22 •25 •28
•0%
•20%
•40%
•60%
•80%
•100%
•120%
•140%
•1 •4 •7 •10 •13 •16 •19 •22 •25 •28
•0%
•20%
•40%
•60%
•80%
•100%
•120%
•140%
•Slope C = -0.72
•Slope B = -1.51
•Slope C = -0.87
•Slope B = -1.04
•Slope C = -0.94
•Slope B = -1.25
•Slope C = -1.50
•Slope B = -1.61
•Delay 4.4 weeks
•Delay 4.8 weeks
•Delay 1.1 weeks
•Delay 18.3 weeks
•Delay 6.5 weeks
81
Significant improvement from active monitoring

How to leverage community data for actionable health Insight?

María Victoria Giussi
- MD, Buenos Aires University
- Family Physician
- Medical Resident at Health
Informatics Department from
Hospital Italiano de Buenos Aires.

Personal Health Record at HIBA
2001
HIS- EHR
2007
PHR
2012
PHR-
UCD
• Web based & “in house”
developed tools

Some numbers

What makes our PHR important in
Engaging Patients to their Healthcare?

Our Strategy to achieve an Effective Patient
Adherence is…
 Knowing what patients really need
 The integration with the EHR

We focus on:
• Collect information about expectations and perceptions of both
physicians and patients in order to improve the tool
• Incorporate features based on real patient needs
• Encourage the active role of patients in the design and
functionality of the PHR
• Knowing the impact of new technologies in the daily workflow of
the physicians
• Empowering the patients to manage their health
information

 User Centered Design
 Focus Groups
Interviews in waiting room
 Workshops
 Analizing our data base
 Effective management of patient suggestions

• Patients are the owners of their Health Data
- Entry Health Data
- Access to their Health Data
- Give access at their EHR to the Healthcare Professionals
- Share the access to their PHR with other people

• Active role in their Healthcare
• Engage in the design of the PHR
- Rediseño Centrado en el Usuario de un Portal Personal de Salud. Goldenberg J, et al. CBIS 2012
Menu without UCD Menu after UCD

• PERSONALIZED HEALTH INFORMATION
from MedlinePlus
• INFOBUTTONS
- Implementación de Arquitectura Orientada a Servicios (SOA) en un proyecto de E-Salud. Gómez A, et al. INFOLAC 2008.
- Integrating personalized health information from MedlinePlus in a patient portal. Borbolla et al. Stud Health Technol
Inform. 2014;205:348-52.PMID: 25160204

Desktop
Tablets
Smartphones
Portal Personal de Salud del Hospital Italiano. Evaluación del uso de su versión “Mobile” Gómez A, et al. INFOLAC 2014

• Web Message Service
-Diseño y evolución de un sistema de mensajería electrónico entre médicos y pacientes del HIBA. Giussi MV, et al.
CBIS 2014.
-Understandig how physicians respod messages sent by their patients. Almerares A, et al. CBIS 2014
-¿Qué opinan los médicos acerca de la comunicación electrónica con sus pacientes? Khorsadnia B, et al. CBIS 2014

• Suggestions for improvement
through the Helpdesk
•Solve some problems with the use
of the PHR

• Administrative Consultations
• Schedule an appointment with
physician
• View study results from the EHR
and upload results
• Consult physicians directory
• Update vital signs
• View and manage prescriptions
• View all referrals

• Remote Consultations
• Digital literacy of the Elderly
• Improving conditions of life for the Elderly through the use
of ICT: Active Assisted Living programme (AAL)
• Improvements for the PHR Mobile App
• Integration of patient health data generated by wearables
and external devices to the EHR trough PHR
• Medication Reconciliation by patients
• Accuracy of the Problem List
• Health Forums
• Patient Access to progress notes
• Health Assets GIS Mapping
Working on…

• Data generated per patient (per life)
Genomics
6 Terabytes
Exogenous Data (Behavior,
environment, etc)
1.100 Terabytes
Extracted from IBM Watson for Oncology.
Clinical Data
0.4 Terabytes

Conclusion
Strategic
Decision
Thinking,
developing,
testing and
implementing
the tool
Evaluate use
Measure
Outcomes

Giussi María Victoria, MD
maria.giussi@hospitalitaliano.org.ar
Thank You

Márcia Ito, MD, PhD • Formation
– Medical Doctor – EPM-UNIFESP/Brazil
– PhD in Electronic Engineering – USP/Brazil
– Data processing technologist – Fatec-SP/Brazil
• Research Scientist at IBM Brazil Research Lab
• Visitor Professor at Health Informatics Department of EPM-UNIFESP
• Teacher at MBA in Health Management at FGV-SP
• Coordinator of Health Computation Applied Special Interest Group of the
Brazilian Computer Society (SBC)
• Co-chair of HL7 Brazil
• Member of Special Committee in Health Informatics Standardization at
ABNT (ISO/TC-215) – Working Group 1 and 2
• Master degree advisor at IPT-USP
• College Professor at Fatec-SP
• Past Executive Secretary of the Brazilian Health Informatics Society
(2012-2014)
• Past Vice-Coordinator of Health Computation Applied Special Interest
Group of the Brazilian Computer Society (2012-2014)
• Past Coordinator of the Research Laboratory Sciences Service in Paula
Souza Center (2007-2011)

The title of Dr. Ito’s Presentation will be:
• A Collaborative System based on Chronic Patient
Relationship Management Model as a form to
engage patient adherence to his treatment

• Chronic diseases require the care of several healthcare
professionals, in addition we need to increase the
engagement of patient adherence to his treatment
– We must understand the patient as a human being and not
only disease evolution of him – personalized care and patient
centered care
– Increase the interaction between the patient and his care team
– collaborative relationship and care coordination
– The electronic medical records of the patient were not meeting
those needs. Creates a set of objectives to develop useful EHR.
– Meaningful Use
Healthcare Current Scenario

• Care Coordination
– decrease the fragmentation of care and improve the delivery of health services
– Sucess programs:
• the relationship between the care coordinator and the patient was beyond medical
service
• the care coordinator knows the needs of the patient and connected to him personally
• long lasting relationships and trust between the patient and the care team and among
members of the care team
– The relationship between the coordinator and the patient is the key, because
the treatment involve change behavior and choices that made by the patient.
• Meaningful use is using certified electronic health record (EHR)
technology to:
– Use the information to engage patients and their families in their care
– Improve quality, safety, efficiency, and reduce health disparities
– Improve care coordination, and population and public health
– Maintain privacy and security of patient health information
Healthcare Current Scenario

Chronic Patient Management – CPRM (Chronic Patient
Relationship Management) Model – Conceptual Approach
• A model that makes the appropriate
coordinate patient care so that we
can prevent the disease, its
complications or deaths:
– Everybody is responsible for theirs health
(prevention, treatment adherence, etc.)
– Participate in their treatment decisions
(collaborative relationship between
doctor and patient)
– adequate control of the disease, based
on best practice (translational medicine
and evidence-based medicine) and the
psychosocial context of the patient.
– Monitoring, assessment and control of
laboratory tests, physical and
psychosocial status of the patient
– Patient and physician behavior change
– Better quality of health and life
• For all these phases we will need new integrated
technologies.
Adapted from IBM, 2006: Healthcare
2015: Win-win or lose-lose?
Font: Adapted from ITO, M., MARTINI, J. S. C., IOCHIDA, L. C. CPRM: A Chronic Patient's Management
Model Based on the Concepts of Customer Relationship In: 2008 ACM SIGAPP - Symposium on Applied
Computing, 2008, Fortaleza. 2008 ACM SIGAPP - Symposium on Applied Computing. , 2008.

Concept Actual Model New Model
Focus Disease/illness Pacient
Strategy Disease Control in accordance
with existing standards
(epidemiologic studies)
Control of the health considering the person
biological context and psychosocial
(individual/personalized analytics)
Approach Use of medications and
guidelines "standardized"
Interactivity, confidence, awareness, credibility
and personalized guidance
Collection of
information and
orientation
Information and data scattered
throughout the organization or
between organizations
Get the information only in the
health attendence
Integrated all the informations – personalized
healthcare information
New ways to communicate with each other in
any time
Relationship distrust and authoritarianism Partnership, collaborative
Indicators Results of tests and clinical
assessments sporadic
Results of tests and clinical assessments
frequent, satisfaction and adherence
Adapted from: ITO, M., MARTINI, J. S. C., IOCHIDA, L. C. CPRM: A Chronic Patient's Management Model Based on the Concepts of Customer Relationship In: 2008
ACM SIGAPP - Symposium on Applied Computing, 2008, Fortaleza. 2008 ACM SIGAPP - Symposium on Applied Computing. , 2008.
CPRM Model – Comparison between the new and
the old

Use Case 1: Define Care team
Care Coordinator
(CC)
Choose the patient
that will be in the
program –
elegibility analise
Define the
care team
Patient
(P)
CC
P1
F1 M1
D1
(TM1)
Care team
(TM)

Care Coordinator
(CC)
care monitors
Care team
(TM) Patient
(P)
Care Coordinator
(CC)
coordinate
CC
P1
F1 M1
D1
(TM1)
Use Case 2: Patient Monitoring and Care

Use Case 3: Collaborative Network of Patient
Care
CC
P1
F1 M1
D1
(TM1)
P2
D2 M2
F2
(TM2)
P3
(TM3)

Use Case 4: Collaborative Network of Care
Coordination
CC
F1 M1
D1
(TM1)
D2 M2
F2
(TM2)
(TM3)
Institution 1
Instittuion 2
DF3
DF2
DF1
COd

Adapted from: ITO, M., MARTINI, J. S. C., IOCHIDA, L. C. CPRM: A Chronic Patient's Management Model Based on the Concepts of Customer Relationship In: 2008 ACM SIGAPP -
Symposium on Applied Computing, 2008, Fortaleza. 2008 ACM SIGAPP - Symposium on Applied Computing. , 2008.
Patient +
caregivers
phone assistant
Patient
Care team
Colaborative Network
Analytic
Component
Collaborative
component
Care Coordinator
Operational
Component
Health System:
- Government
- Hospitals
- Assurance
- Health
Institutions
- others...
Chronic Patient’s Relationship Central Service (CPRC)

Operational
Coordination Patient Care System
Adapted from: ITO, M., MARTINI, J. S. C., IOCHIDA, L. C. CPRM: A Chronic Patient's Management Model Based on the Concepts of Customer
Relationship In: 2008 ACM SIGAPP - Symposium on Applied Computing, 2008, Fortaleza. 2008 ACM SIGAPP - Symposium on Applied Computing. , 2008.
Text Conversion System
AppsSocial
Net
Virtual
environment
Direct
interaction
sensors ????
CPRM extended Model - Architecture
Genoma
Map
Phone, Urgency and
Emergency Alerts,
Whatsapp...
Patient Care
Collaborative System
Posts
Patient Care
Management
Patient
Information
Care team
Management
Hospital
Informations
Systems
Clinic’s
Systems
Health
Government’s
Systems
Health
Institutions
Systems
Eletronic Health
Record
(EHR)
Personal Health
Record
(PHR)
Patient Eletronic
Record
(PER)
Specialized
Monitoring Systems
Educational
Systems
Primary
Monitoring
Systems
Health
Promotion Systems
Analytical
Collaborative

1st Prototype – Patient Page

1st Prototype – Care Coordinator Page
Care Coordinator Communities

1st Prototype – Care Coordinator Page

1st Prototype – Medical Page

Preliminary Results – Medical Page
Doctor Applications

Expected Results
• Improve the patient care coordination
• Improve the data visualizations about the patient
use
Using that information to
track key clinical
conditions
Electronically
capturing health
information in a
standardized format
Care team
Communicating that
information for care
coordination process

Expected Results
• Know more about
patient pathway and
dynamic demand on the
service structure

Expected Results
• Initiating the reporting of clinical quality measures
and public health information
Care team
Infra structure
Initiating the reporting of
clinical quality measures
and public health
information
Using information to
engage patients and their
families in their care
Electronically
capturing health
information in a
standardized format

Thank You
Merci
Grazie
Gracias
Obrigado
Danke
Japanese
English
French
Russian
German
Italian
Spanish
Brazilian PortugueseArabic
Traditional Chinese
Simplified Chinese
Hindi
Tamil
Thai
Korean
Hebrew

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?
• What are the minimal description of patient-generated data sources to make the insights
relevant in the patient-physician conversation? Any difference in terms of specialty?
• What are the good frameworks of patient engagement to be used for this purpose?
• Are there information governance initiatives we can start inserting ourselves into?
A follow-up workshop/panel with a more focused area wherein filling in the gap has
been perceived as priority  MEDINFO 2015
https://goo.gl/Aj88Zs

Summary on Workshop Theme (1)
• (1) Implications and lessons learned from the case studies --
especially the gaps you perceived as barriers of entry
• (2) Requirements for successful redesign of healthcare
systems to accommodate patient-generated information (with
a sub-goal of identifying the areas where such information
can make most impacts).
• (3) Identify action items and initiate proposals for enabling
evidence-based conversation with
patients/physicians/providers in the loop

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?
• What are the minimal description of patient-generated data sources to make the insights
relevant in the patient-physician conversation? Any difference in terms of specialty?
• What are the good frameworks of patient engagement to be used for this purpose?
• Are there information governance initiatives we can start inserting ourselves into?
A follow-up workshop/panel/tutorial  MEDINFO 2015
https://goo.gl/Aj88Zs

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