mie2014 workshop: gap analysis of personalized health services through patient-controlled devices

Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices

Gap Analysis of Insight-Driven

Personalized Health Services through

Patient-Controlled Devices

MIE 2014 Workshop 510 W17 25

TUESDAY 17:00 - 18:30

Pei-Yun Sabrina Hsueh, Michael Marschollek, Yardena Peres, Stefan Von Cavallar,

Fernando Martin Sanchez


Logistics • 17:00-17:15 Opening Remark

• Key drivers and problems being addressed (Dr, Hsueh, IBM T.J. Watson Research)

• 17:15-18:10 Presentations• Overview of service classes for health-enabling technologies for elderly and a physician’s view

in relevant applications in the future (Prof. Marschollek, Hanover Medical School).

• Enablers for successful development of mobile health solution– mobile health solution requirements and challenges for scaling up and realizing its full potential (Mr. von Cavallar, IBM Research Australia)

• Enablers for applications in research and potential clinical use – the need for standardised reporting guidelines in self-monitoring experiments (Prof. Martin-Sanchez, Melbourne Medical School)

• Business aspects of insight-driven Personalized Health Services through Patient-Controlled Devices (Dr. Peres, IBM Research Haifa)

• Development of Temporal Context-based Feature Abstractions for Enabling Monitoring and Managing of Interventions (Dr. Hsueh)

• 18:10-18:30 Workshop discussion (gap analysis, requirement gathering)/audience Q&A

• 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).

Please leave your email and questions (if any)….


Pei-Yun (Sabrina) Hsueh, PhD

Wellness Analytics Lead

Global Technology Outlook Healthcare Topic co-Lead

Health Informatics Research Group

IBM T. J. Watson Research Center

• Research focus: Insight-driven Healthcare service design via

wearables and biosensor devices/implants, Patient-generation info,

Personalization analytics, Patient engagement & Adherence risk

mitigation


IBM Confidential4

Elder population and care costs are growing annually, but no reliable

solutions for early detection and efficacy monitoring

4

Elderly population expected to double by 2030 in US

Annual per capita healthcare costs grows significantly with age

Early detection and efficacy

monitoring are key

Cognitive health is imperiled by the

lack of reliable solutions

1 in 3 seniors dies with Alzheimer’s or other dementia. Up to 72% of cases are misdiagnosed at the PCP level

In 2013, Alzheimer’s will cost US $203 billion. This number is expected to rise to $1.2 trillion by 2050.


Clinical determinant

(e.g., care flow, care delivery)

Endogenous determinant

(e.g., genetics predisposition)

Exogenous determinant

(e,g, environment, behavioral social factors)

30%

10%

60%

Cardiovascular disease(73-83%)

(NHS, NEJM 2000)

Type II Diabetes(58-91%)

(Finland DPS, NEJM 2001, 2007) (US NHS, 2000; CDC DPP, 2002)(China Da-Qing, 2001)

Cancer(60-69%)

(HALE, JAMA 2004; de lorgeril Arch

Intern Med, 1998)

Personalized Medicine

Personalized Care

Personalized Prevention and Disease Management

Eye complication (76%), Kidney

complication (50%), Nerve complication

(60%)(UKPDS, US DCCT)

Cardiovascular complication (42-57%)

(UKPDS, US EDIC)

Holistic View of Determinants of Health to Personalized Services

Huge opportunity space for risk reduction:

Progress impeded by the lack of granular data capturing tools!

SA Schroder. We can do better - Improving the Health of the Amarican People. NEJM 2007;357:1221-8.


IBM Confidential6

Technology barriers are lower than ever.

A whole array of patient-controlled devices are on the rise….fall sensor in a pocket

adhesive vitals sensor

stretch sensors

vitals sensor in t-shirt

gait analysis in a pocket

insole sensors

e-textile wireless ECG

Cardiac monitoring systems

Requires ultra-low power adaptive

circuits, non-intrusive form factors

OpenBCI


7

Wearable/IOT computing is the new mobile

“Three medical technology stories to watch in these areas will be wearable technologies for fitness, aging-in-place technologies, and

real-time monitoring. ”

— Forbes, “Medical technology stories to

watch in CES 2014” (Jan 2, 2014)

“Wearable tech will be as big as the smartphone.”

—Wired, Cover story (Dec 17, 2013)

• Quantified self (27% of US users) - IDC Report, 2014

• From IOT to “Internet of Everything” (IOE): 30-50 bn devices in 2020

- Gartners Report, 2014

• IoT enabled “Connected Life” market forecast in 2020: Clinical Remote Monitoring

and Assisted Living to be the 2nd and 3rd largest mkt

- IDC Report, 2014


Healthcare is being re-imagined by bringing together high-

growth, high-value patient generated information and EMR data


The Creative Destruction of Medicine: How Digital Revolution will Create Better Healthcare (Eric Topol, 2012)

(1) What are the implications and lessons? What are the gaps as barriers of entry?(2) What are the Requirements for successful redesign of healthcare systems to

accommodate patient-generated information? What are the areas where such information can make most impacts?

1990 Empirical Medicine

Intuitive Medicine

Personalized Service

Patient-Centric

ServiceDisease-Centric

Guideline

Precision Medicine

Degree of personalization

Degre

e o

f colla

bora

tion

(data

dim

ensio

n)

Data-Driven

Evidence

Century of behavior change

Healthcare becoming both Personal and Collaborative


Workshop Theme

• 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).


INTRODUCTION • 17:00-17:15 Opening Remark












Service classes of health-enabling technologies –

relevant applications in the future

Michael Marschollek


Wearables – just nice toys?

????

Good medicine and good healthcare

demand good information


Wearables – just nice toys?

�more data, (hopefully) more information

�more accurate diagnoses

� early detection of subtle changes, disease onset

� better, targeted treatment

• Niilo Saranummis‘s 3 ‚P‘s:– pervasive technologies

shall enable semantically interoperable platforms to communicate and store health data

– personal services

using sensor technologies for continuously measuring health-related data of an

individual; to support her or him at specific health problems

– personalized decision support

adapted, ‘tuned’ to the individual’s norm, not to averages in populations (not one-size-

fits-all)

Saranummi N. IT applications for pervasive, personal, and

personalized health. IEEE Trans Inf Technol Biomed. 2008; 12: 1-4.


Haux R et al.. Inform Health Soc Care. 2010 Sep-Dec;35(3-

4):92-103. PubMed PMID: 21133766.


Service classes

• Basic services:

– Emergency detection and alarm

– Disease management (chronic diseases)

– Health status feedback and advice

• Other services:

– Communication and social interaction

– Support for daily life and activities

– Entertainment, information and communication

S. Koch et al. Methods Inf Med, 2009.

Ludwig W et al. Comput Methods Programs Biomed. 2012,

May;106(2):70-8.


Example: emergency detection – falls

• Feldwieser F, Gietzelt M, Goevercin M, Marschollek M, Meis M, Winkelbach S, et al.

Multimodal sensor-based fall detection within the domestic environment of elderly

people. Z Gerontol Geriatr. 2014 Aug 12. PubMed PMID: 25112402.

• Kangas M, Korpelainen R, Vikman I, Nyberg L, Jämsä T. Sensitivity and False Alarm Rate

of a Fall Sensor in Long-Term Fall Detection in the Elderly. Gerontology. 2014 Aug 13.


Example: disease management

• Whole System Demonstrator (WSD) study (UK):

– Different chronic diseases (e.g. heart failure)

– ‚Telehealth‘ intervention (oximeters, scales, glucometers, …)

– Lower mortality and admission rates, higher cost

– Steventon et al. BMJ 2012; 444:e3874

• NATARS study (Germany):

– Geriatric home rehabilitation after mobility-impairing

fractures

– Wearable sensor, smart home sensors

– Marschollek et al. Inform Health Soc Care. 2014 Sep;39(3-

4):262-71.


Example: early detection/ diagn., prevention

• Fall risk assessment/ fall prediction:

– medium-scale prospective studies, e.g. Greene et al, 2012,

Gerontology; Marschollek et al, 2012, Meth Inf Med; Gietzelt

et al, 2014, Inf Health Soc Care

• Rehabilitation Monitoring/ relapse identification:

– Steventon et al. BMJ 2012 (WSD study)

– Marschollek M et al. Inform Health Soc Care. 2014

– Calliess et al. Sensors, 2014

• Physical activity promotion (Plischke et al. 2008)

• Aftercare, paediatric liver TX patients (Marschollek et al. 2013)

• …


Epidemiologic perspective: future diseases

• increase of chronic diseases

• increase of “age-related deficits”

• decrease of health professionals

• application areas:

– cardiovascular diseases (e.g. congestive heart disease)

– neuropsychiatric disorders (dementia, uni-/bipolar

depressive disorders, anxiety disorder)

– diabetes (and follow-up conditions)

– musculoskeletal diseases (arthritis and esp. follow-up

conditions (e.g. post-implant rehabilitation))

• but: this is only secondary/ tertiary prevention!source: Institute for Health Metrics and Evaluation,

healthmetricsandevaluation.org


Gaps and Pitfalls (subjective!)

• Translating (diagnostic) knowledge into action

• Lack of integration into health information systems,

especially on semantic level (modeling)

– E.g. Marschollek M. Inform Health Soc Care. 2009

• Psychological:

– the right not to know

– trust, security

• and still: Device interoperability


join our IMIA WG: www.wearable-sensors.org


Stefan von Cavallar Advisory Software Engineer, IBM Research Australia


The title of Stefan von Cavallar’s Presentation

will be:

Mobile health: Solution requirements and challenges

for scale-up


Mobile Health

Benefits

•Unprecedented opportunities

•High growth usage in developing countries = health

service delivery in regions where otherwise limited

•Improved access to health services

•Improved patient communication, ie. Reminders, Care

plans

•Monitoring of treatment compliance

•And MORE… !


Mobile Health Solution Considerations

• Health information privacy

• Health information security

• Standardization

• Interoperability

• Device fragmentation

• Data fragmentation

• Geography

• Budgets $


Specifically...

The exchange and collection of data from different

systems and platforms will be…

*Essential for users with multiple clinical

requirements

*Key to preventing further fragmentation between

health programs


What are we trying to solve?

Consider this use case:

•Mother, with daughter

•Daughter sick for several days with lots of fluid loss

•They know nearest medical health center is 60Km away, they have no

transport

•Both walk to health center, and wait for a further 24 hours until seen due to

understaffing and high patient numbers

•Assessment made, treatment given and returned home

•Mother has no care plan or guidance on next steps

What happens next?


What do we want to do?

1. Improve health!

How about the previous use-case becomes:

• Mother, with daughter

• Daughter sick for several days with lots of fluid loss

• They know nearest medical health center is 60Km away, they have no transport

• Mother uses mobile health credits to send message to a Cognitive Healthcare

Hub where it is analyzed. Identify open questions to determine severity

• Message sent back requesting additional information and includes guidance on

how to gain that information (e.g. how to perform a pinch test)

• Mother carries out tests and responds. Guidance is given to seek medical

assistance in the nearest healthcare center. Details for the center are different

to what the mother knows, its closer (8Km), but in a different direction…


• Details of daughters condition are recorded and monitored via the Cognitive

Healthcare Hub

• At the health center social worked collect biometric data of waiting patients

• Information collected and presented to physician for accelerated diagnosis

• Information fed into Cognitive Healthcare Hub

• Diagnosis and treatment options presented through the Cognitive Healthcare Hub

to the healthcare worker. Support diagnosis by checking guidelines, hilight

treatment options and assemble care plan

• Daughter is being treated for diarrhea and dehydration

• The Cognitive Healthcare Hub allows healthcare worker or physician to select a

recommended care plan that the Hub has personalized for the daughters

conditions

• The mother is sent the care plan via wifi

• Mother and daughter are discharged, complete with a take-home plan for on-

going treatment

• At points of time afterwards, the Hub sends out reminders and short enquiries to

follow up and if necessary request that a health worker check on them


Solution Requirements

The solution must engage:

•A unified data view

•Health information privacy

•Health information security

•Standardized

•Interoperable

•Defined device and data structure

•The users and fulfil their use cases


Solution Requirements

• Provide information collecting, learning and sharing infrastructure (ie,

cognitive healthcare hub)

• Include historical disease, climate and population data

• Include continuous disease surveillance and drug consumption data

• Learn from historical and continuous data

• Two-way information flow

• Mobile sensing (eg, occurrence of certain symptoms in a region) and multi-casting

• Practitioner support (eg, recent weather condition and high number of reported

infections with same symptoms in the region suggest particular diagnosis)

• Value proposition

• Support health workers and the need for diagnosis

• Provide visibility and forecasting of disease outbreaks and drug demand & supply

• Enable macro-level priority setting and investment support

• Monitor the ROI of health investments

• Provide sustainable infrastructure for data collection and dissemination


Cognitive Healthcare Hub

Interface Gateway

Mobile Internet Community Radio TVInteraction Communication Visualisation

StatisticsModelling

Machine LearningPredictionSimulation

Business Intelligence

Cognitive Computing & Analytics

Unified Data View

Security Access Quality

Environment Mobile & SocialMedia

IndigenousKnowledge

Guidelines &Publications

RemoteSensors

Registries

Governments


Hospitals

Pharmacies

HealthWorkers

CommunityHealth

Centers

PatientsPrepare for patient increase

Optimized drug distribution

Support untrained

Advice for rare conditions

Cognitive Healthcare

Hub

Watson: Question & Answer

Deep Thunder: Climate ModelingSTEM: Epidemiological Modeling

PublicHealthBoards

Optimized Resource Allocation


Dehydration*Healthcare/trained worker only

Visual

inspection

Skin pinch

timer; App

Blood viscosity;

Infra-red sensors;

camera modified*

Image analytics on

lips, eyes; camera;

MMS

General

questioning

Tests

Diagnosis

Aftercare

Rehydration schedule

Tracking; how? Reminders

Local Push

Treatment

Calculate therapy

Public Health

Water supply analysis

Pathogen outbreak Pathogen identification

Individual

Community

App;

decision

tree

Intravenous fluids

GPS Healthcare worker entry Sensors

Oral Rehydration SaltsZinc supplements


Challenges for Scale Up

• Data Fragmentation/Distribution

• Data inconsistencies

• Education/Training i.e. Hardware, software

• Differing working practices

• Infrastructure, i.e. Easily no data reception

• Costs, incentives and funding $$$$$

• Not everyone has the same level of access to

technology


Summary – Implications and lessons learnt

from this case study

• Assume nothing… i.e. users with smartphones

• While countries want the same thing, how they get

there varies greatly…

• Technology uptake is not always as easy or advanced

as one might think

• Infrastructure is not as mature as required

• Limited funding/incentives available for adopting

these technologies/infrastructures

• Integrating the fragmented data


Summary – Requirements for successful

redesign of healthcare systems

• Everyone to want to contribute

• Analytics engines using structured and unstructured

data

• A system that enables contributors and provides

tailored data to consumers

• Data consumption and feedback for improved

analytics

• Education and “buy-in”


Enablers for applications in research and

potential clinical use:

Standardised reporting guidelines in self-

monitoring experiments

Prof. Martin-Sanchez

Melbourne Medical School


41

Manager, Mobile Big Data SolutionsIBM Research - Haifa

� B.A., M.Sc., Technion – Israel Institute of Technology� Senior Researcher, IBM Research – Haifa� Focus on leveraging state-of-the-art IT to solve industry

pain points� Mobile, Cloud, Big Data, Analytics� Standards & Interoperability� HC/Wellness, Retail

� Prolific EU FP6, FP7 and H2020 research activities

Yardena Peres


Research project funded by the

EU (Nov 2013 - Oct 2016)

• DAPHNE Consortium:

– Sensor partners: Evalan, UPM

– IT partners: IBM Research – Haifa, TreeLogic, Atos, SilverCloud

– HC partners: Nevet, Bambino Gesu, University of Leeds, IASO

• DAPHNE Objective:

– Develop a novel IT platform for delivering personalized guidance

services for lifestyle management (focused on reducing

sedentariness) to the citizen/patient by means of:

• Advanced sensors and mobile phones to acquire and store data on

lifestyle aspects, behavior and surrounding environment

• Individual models to monitor health and fitness status

• Intelligent data processing for the recognition of behavioral trends

and services for personalized guidance on healthy lifestyle and

disease prevention

• Use Case:

– The system receives clinical parameters from the selected

sensors, stores health markers, learns personal preferences, and

generates feedback and recommendations.

42


Business aspects of insight-driven Personalized Health

Services through Patient-Controlled Devices

• Patient-Controlled Devices are generating large

amounts of new data

• This poses several IT challenges

– Cope with large amounts of varied data while maintaining

data quality

– Connect with existing Healthcare Systems (e.g., EHR, HIS)

– Handle security, privacy and consent management


Business aspects of insight-driven Personalized Health

Services through Patient-Controlled Devices

• Monetize data, e.g. Data as a

Service (DaaS) Model

– Patients generate new data

– IT companies manage it

– HC providers, Pharma, Payers,

Retailers, Governments,

Scientific Research, etc.

consume it

– All stakeholders are part of the

same value-chain


Development of Temporal Context-based

Feature Abstractions for Enabling Monitoring

and Managing of Interventions

MIE 2014

Pei-Yun Sabrina Hsueh

Ke Yu

Marina Akushevich

Shweta Shama

Peter Mooiweer

Sreeram Ramakrishnan

IBM GBS BAO/Watson Research


IBM Confidential46

Technology barriers are lower than ever.

A whole array of patient-controlled devices are on the rise….fall sensor in a pocket

adhesive vitals sensor

stretch sensors

vitals sensor in t-shirt

gait analysis in a pocket

insole sensors

e-textile wireless ECG

Cardiac monitoring systems

Requires ultra-low power adaptive

circuits, non-intrusive form factors

OpenBCI


Determinants of Health Outcomes

Exogenous(Behavior, Socio-economic,

Environmental, ....)60% Fitness/WellnessPatient-controlled medical

devices

Affinity (digital)Affinity

(retail)Employment

Significant growth in exogenous data poses challenges to existing BigDatastorage and analytics solutions

Socio-

econo

mic

databa

ses

Data Sources

Clinical (EMR)

10%

Endogenous(-omics)

30%

1240 PB

1800

PB

6800

PB(annu

al)

Episodic; care pathways in controlled settings

Mostly static data, but critical for personalized medicine

Significant volume(every step, heart rate, meals,….) and variety(physiological, psychological, socio-economic) and dynamicData generation ~ uncontrolled environment

Exogenous Data Growing Fast !


A perfect storm awaits…..Data Deluge from Patient-generated information


49

Promoting behavioral change(Dietary intake: Burke et al., 05;Physical activity: Prestwich et al., 09; Michie et al., 09)

Increase awareness to self-monitoring(Prestwich et al., 09; Burke et al., 05)

Triggering reminders to care plans(Consolvo et al. 09; Hurling et al., 07)

Personalizing communication

messages and education materials(Thaler and Sustein, ‘08)

Patient generated information are effective for self-

management and personalized intervention/adaptation

Nudge: Improving Decisions About Health


Existing tools lack capabilities to determine appropriate

metrics most sensitive to individuals

• Especially true for those require artful interpretation of the temporal context of measurement– E.g., Hypertension => blood pressure; Diabetes => SMBG; Metabolic

syndrome => weight, cholesterol level

• Need new capability to calibrate intra-individual variability– E.g., Heart rate variability (HRV) � detect abnormal symptoms of

autonomic nervous system that are correlated with lethal arrhythmias

– E.g., The variability of B-type natriuretic peptide (BNP) detect cardiac ischemia

• Barriers:– (1) No unifying theoretical models exists for enabling such interpretations

– (2) The process from feature abstraction to individualized prognosis is non-trivial.


IBM CONFIDENTIALSlide 51

Feature

Optimization (Optimal set construction)

Construction of features

based on variance over

time

Analyze and select

variance features from

the complete set of

constructed features

Identify input data

sources from the optimal

feature set and configure

the input of data sources

Feature

Population (data

source configuration)

Population Data-driven Insight

Feature

Abstraction (Candidate feature

generation)

1

2

3

Complete feature set

Optimized Feature subset

Data-driven Calibration and Personalization Process:

From Population-based evidence to individualized alerting/adaptatio

Monitoring biomarker/patient-

generated info operational DBEHR/PHR

Repository

Learn from baseline to understand

normal variance and use the info to

determine when to send alerts

Verify if the selected abstraction is the

right one for the individual according to

the KPI. Create time gates events,

triggers to check if the selected feature

is the optimal one.

Individually adapted

plan (alert and

intervention)

Alert Setting (individual-based

calibration)

Learning for

Adaptation

Individual Data-driven Personalization

4

5

Individual data captured based on input configuration

Verified feature set for the target individual

Individual-based alert


Enabling personalized temporal context interpretation

by data-driven calibration and personalization

• Need to streamline the process from population-based feature abstraction

to individualization

• Enable more effective monitoring and management of interventions

• Service Scenarios:

– 1. Development of adherence programs for patient self-management

– 2. Enablement of intervention design for care coordinators/care givers

– 3. Understanding efficacy for care givers to adapt suggested

interventions for an individual

– 4. Evidence-generation for intervention efficacy (population data)

Monitoring

device

Intra-individual

variability calibration

(evidence-based)

Input for monitoring

feedback generation

and

diagnosis/intervention


IBM Confidential53

Summary: Gaps observed in 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/Customer/PatientPatient

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)


Summary: New requirement of a modular framework to

accelerate personalized service design

Technologies to enhance wellness services

– Guide the identification of customization points in clinical workflow and deployment of the Analytics and IM offerings

– Create new tools and infrastructure for client engagements

– Explore light-weight approach to connect the components (to prepare for futurecloud offerings)

New solutions and services

– Bring together clients and researchers to understand clinical touch points

– Demonstrate how to leverage customization points to engage users and possibly improve health literacy and outcomes

Replicable patterns for patient engagement deployment

– Create ETL procedures to be repeatedly use in other provider settings

– Explore both hosted and internal deployment possibilities

Plug-in for other tools

– Create a recipe from data collection to summarization to customization to engagement to outcome measurement

– Each component can be singled out as a standalone process for other tools


Gap Analysis of Insight-Driven

Personalized Health Services through

Patient-Controlled Devices

MIE 2014 Workshop 510 W17 25

TUESDAY 17:00 - 18:30

Pei-Yun Sabrina Hsueh, Michael Marschollek, Yardena Peres, Stefan Von Cavallar,

Fernando Martin Sanchez


Logistics • 17:00-17:15 Opening Remark











Please leave your email and questions (if any)….


Workshop Theme

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).

• Workflow

– Knowledge actionable?

– Integration

– Lack of modular design

• User

– Right not to know, trust, security,

consent management

• Data

– Fragmented, lack of EHR interoperability

– Beyond big data, uncontrolled env.

• Device

– Interoperability, infrastructure

• Service

• Resource


Summary:

Gap analysis and HC re-design requirement• Workflow

– Lack of integration into health information systems, especially on semantic level (modeling)

– Lack of modular design of existing healthcare system

• User – Manage the right not to know, trust, security, consent

– Assume nothing from the start

– Country/Cultural differences

• Device– Fragmentation ; Lack of interoperability

– Immature infrastructure

• Data– Fragmented data sources (need to integrate with EHR / HIS)

– Ecosystem platform (enabling contributors, tailoring data to consumers)

– Need to create personalization analytics framework (and engine) (data consumption feedback)

– BigData: large amounts of varied data while maintaining data quality

– Beyond Bigdata storage and processing, in uncontrolled env.

– Beyond Bigdata analytics, in uncontrolled env.

• Service– Touchpoint redesign to integrated Clinical/Wellness Service

• Resource– Lack of funding/incentives


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?

Consider publishing our summary report in MEDINFO 2015? (any other venue?)

A follow-up workshop/panel with a more focused theme on the gap and

requirement perceived as priority?


Thank You

Merci

Grazie

Gracias

ObrigadoDanke

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Suggestions on next step?


Questions?

mie2014 workshop: gap analysis of personalized health services through patient-controlled devices

Healthcare

keycognitive health

array of patient

patientgeneration info

us nhs

us users idc report

us ediceye complication76

selfmonitoring experiments

us dccthuge opportunity