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 8Addressing Patient Adherence Issues by Engaging Enabling Technologies

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










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



Pei-Yun (Sabrina) Hsueh, PhD

Wellness Analytics LeadGlobal Technology Outlook Healthcare Topic co-LeadHealthcare 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 DATAClinical + behavior

drivenWellness Management

reduce

expand

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

Addressing Patient Adherence Issues by Engaging Enabling TechnologiesIt’s Data. Big Data!

It’s also 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

Degree of

collaboration (data dim

ension) Data-Driven Evidence

Century of behavior change

Hypothesis Modeling

++

Hyper-PersonalizationN 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 managementThe 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. med Dr. Ing

(Director of Hanover Medical School, Peter L. Reichertz 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 savingsEntity: HospitalPopulation: Commercial + Medicaid; Urban + RuralAverage age: 55n = 100English level: 6th gradeBenefit to hospital: Reduced 30-day readmissions for AMI, CABG, Stent, CHFCost 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 DATAClinical + behavior

drivenWellness Management

reduce

expand

Slide credit: Henry Chang










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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 ORTThe 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 InstructionsProcedures 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

Mea

n A

ccur

acy

Graduate Degree All other levels0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Urban CanadianUrban KenyanRural 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 DropsEACH 1 ml DOSE CONTAINS: 80 mg acetaminophenINDICATIONS: 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 DoseUnder 2 years as directed by Physician2 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

Perc

enta

ge

01020304050607080

Underdose

Correctdose

Slightoverdose

Extremeoverdose

English East AsianGreek


Usage Picture of Antipyretic Drops as Provided by a Primary Care Physician


Usage Picture of Antipyretic Drops as Provided by a Pharmacologist


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!

[email protected]

www.lodhia-patel.net










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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 areimpatient


Patient advocacy

• Gimme my damn data!

• The patient will see you now…

• Let patients help

• Nothing about me without me!


Interest from GovernmentsUS Australian

MyHealthRecord

People are managing their own health better.


IOM Workshop & Report 2013Partnering 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 managementIV. 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

Collectingdata

Exchangingand using information

Participatoryhealth


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 structureBody functionAround body

(based on WHO)

Who/Which part/Where/When?

What

How?

Processed

Raw

Minimum Information about a Self Monitoring Experiment (MISME)

Procedures

EXPERIMENT

Measurement


TensionsPatient 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 activationLiteracy vs ClarityResearch 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!











Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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

ServiceComponents

DiseaseMgnt.

DiseasePrevention

DiseaseTreatment

WellnessMgnt.

ExerciseMgnt.

DietaryMgnt.

Personalized CareElder CareServiceScenarios

ServiceProcesses

Dietary Recommendation

Exercise PlanDisease Mgnt.

Wellness Ecosystems– Research Framing

78


78

Diabetes Mellitus case manager service flow (Outpatient Clinic)

Healt

h Ed

ucatio

n Clin

ic(Ce

rtified

Dia

betes

Ed

ucator

)

Outpa

tient

Clinic

(Do

ctor)

Diabe

tes

Mellit

us Sha

red Ca

re Ne

twork

Healt

h Pro

motio

n Ma

nagem

ent

Cente

r(Ca

se ma

nager

)

Patie

ntHo

spital

Clinic

al Ch

emistr

y Lab

orator

y

Nutriti

on

Coun

seling

Cli

nicOutpatient Clinic Stage

Start

Appointments& Registration

Referral patients?

Outpatient Clinic

Diagnosis

Diabetes Mellitus?

CashierOPD

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

ExaminationReport

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

Edu

cation

Clinic

(Certifi

ed Dia

betes

Educat

or)

Outpa

tient

Clinic

(Docto

r)

Diabet

es Me

llitus

Shared

Care

Netwo

rk

Health

Pro

motio

n Ma

nagem

ent

Center

(Case m

anager

)

Patient

Hospi

talClin

ical

Chemis

try

Labora

tory

Nutriti

on Cou

nseling

Clin

ic

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

ExaminationReport

Health Education

Clinic Referral

Care Plan Check


Referral ? Y

N

N

Updat e Case

End

As-Is(in-site Hospital)

To-Be(IBM Mobile Health Pilot)

A DM Management Pilot for Newly Onset PatientsStart from Research Methodology to design innovated DM service procedure (2012-14)

Diabetes Mellitus case manager service flow (To-Be) (Follow up)

Heal

th

Educ

ation

Cl

inic

(Cer

tified

Di

abet

es

Educ

ator

)

Met

abol

ism

Out

patie

nt C

linic

(D

octo

r)

Diab

etes

M

ellit

us

Shar

ed C

are

Net

wor

k

Heal

th

Prom

otion

M

anag

emen

t Ce

nter

(Cas

e m

anag

er)

Patie

ntHo

spita

lGe

nera

l Dep

ertm

ent

Clin

ical

Che

mis

try

Labo

rato

ryHI

S(S

yste

m)

PHM

Clo

ud(S

yste

m)

Card

iolo

gyO

utpa

tient

Cl

inic

(D

octo

r)

Clin

ical

Supp

ort S

yste

m(S

yste

m)

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

Examination

Clinical Chemistry

Examination

Report

Issue Referral for Outpatient

Clinic

Care Plan Check


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

CardiologyOutpatient 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

34

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

Addressing Patient Adherence Issues by Engaging Enabling TechnologiesValue-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

• 50% drop = 18.7 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

• 50% drop = 34.8 weeks

• 50% drop = 16.6 weeks

• Slope C = -0.87

• Slope B = -1.04

• 50% drop = 28.8 weeks

• 50% drop = 24.0 weeks

• Slope C = -0.94

• Slope B = -1.25

• 50% drop = 26.5 weeks

• 50% drop = 20.1 weeks

• Slope C = -1.50

• Slope B = -1.61

• 50% drop = 16.7 weeks

• 50% drop = 15.5 weeks

• 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?










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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

2001HIS- EHR

2007PHR

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


http://www.ncbi.nlm.nih.gov/pubmed/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)

Genomics6 Terabytes

Exogenous Data (Behavior, environment, etc)1.100 Terabytes

Extracted from IBM Watson for Oncology.

Clinical Data0.4 Terabytes



Conclusion

Strategic Decision

Thinking, developing, testing and

implementing the tool

Evaluate use Measure Outcomes



Giussi María Victoria, [email protected]

Thank You










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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 monitors

Care team(TM) Patient

(P)


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



Patient + caregivers

phone assistant

Patient Care team

Colaborative Network

Analytic Component

Collaborativecomponent

Care CoordinatorOperationalComponent

Health System:-

Government- Hospitals- Assurance- Health

Institutions- others...

Chronic Patient’s Relationship Central Service (CPRC)


Operational

Coordination Patient Care System


Text Conversion System

AppsSocialNet

Virtual environment

Directinteraction

sensors ????

CPRM extended Model - Architecture

Genoma Map

Phone, Urgency and Emergency Alerts, Whatsapp...

Patient Care Collaborative System

Posts

Patient Care Management

Patient Information

Care teamManagement

HospitalInformationsSystems

Clinic’sSystems

Health Government’s Systems

HealthInstitutionsSystems

Eletronic HealthRecord(EHR)

Personal HealthRecord(PHR)

Patient EletronicRecord(PER)

SpecializedMonitoring Systems

EducationalSystems

PrimaryMonitoringSystems

Health Promotion Systems

Analytical

Collaborative


1st Prototype – Patient Page


1st Prototype – Care Coordinator PageCare Coordinator Communities


1st Prototype – Care Coordinator Page


1st Prototype – Medical Page


Preliminary Results – Medical PageDoctor Applications


1st Prototype – Medical Page


Expected Results

• Improve the patient care coordination • Improve the data visualizations about the patient

useUsing 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

MerciGrazie

Gracias

Obrigado Danke

Japanese

English

French

Russian

German

Italian

Spanish

Brazilian PortugueseArabic

Traditional Chinese

Simplified Chinese

HindiTamil

ThaiKorean

Hebrew










Henry Chang, PhD


Victoria Giussi, MD


Marcia Ito, MD PhD



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


Thank You

MerciGrazie

Gracias

Obrigado Danke

Japanese

English

French

Russian

German

Italian

Spanish

Brazilian PortugueseArabic

Traditional Chinese

Simplified Chinese

HindiTamil

ThaiKorean

Hebrew


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