mie2014 workshop: gap analysis of personalized health services through patient-controlled devices
DESCRIPTION
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Pei-Yun Sabrina HSUEH, , Michael MARSCHOLLEK, Yardena PERES, Stefan von CAVALLAR and Fernando J. MARTIN-SANCHEZ IBM T.J. Watson Research Center, Yorktown Heights, NY, USA Hannover Medical School, Germany IBM Research Lab in Haifa, Israel IBM Research Lab in Melbourne, Australia Melbourne Medical School, Australia Mobile computing, wearable and embedded tech entail new and different styles of healthcare data processing, clinical and wellness decision support, and patient engagement schemes. This is especially important to the preventive and disease management scenarios that require better understanding of disease progression previously unable to achieve due to the lack of reliable means to capture granular patient-generated data in non-clinical settings. The new sources of data, when coupled with a framework to integrate analytical insights with feasible service models, enable reliable detection of inflection points, habit formation cycles and assessments of treatment efficacy. Research into data collection, recording, management and analysis of behavioral manisfestations and triggers will help address these challenges in areas spanning from simple fall detection to situations requiring complicated, multi-modal health monitoring such as Alzheimer’s progression and other adherence management cases. Leveraging recent advance in health devices and sensors as well as expertise in healthcare practice and informatics, the proposed workshop will help form a deeper understanding of requirements on patient-controlled devices to address unique healthcare challenges, identify care flow gaps and translate these findings to the design of platforms for patient-controlled devices and a portfolio of potential service models for preventive care and disease management.TRANSCRIPT
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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)….
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Healthcare is being re-imagined by bringing together high-
growth, high-value patient generated information and EMR data
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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).
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
INTRODUCTION • 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).
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Service classes of health-enabling technologies –
relevant applications in the future
Michael Marschollek
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Wearables – just nice toys?
????
Good medicine and good healthcare
demand good information
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Haux R et al.. Inform Health Soc Care. 2010 Sep-Dec;35(3-
4):92-103. PubMed PMID: 21133766.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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)
• …
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
join our IMIA WG: www.wearable-sensors.org
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Stefan von Cavallar Advisory Software Engineer, IBM Research Australia
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
The title of Stefan von Cavallar’s Presentation
will be:
Mobile health: Solution requirements and challenges
for scale-up
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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… !
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Mobile Health Solution Considerations
• Health information privacy
• Health information security
• Standardization
• Interoperability
• Device fragmentation
• Data fragmentation
• Geography
• Budgets $
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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?
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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…
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
• 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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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”
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Enablers for applications in research and
potential clinical use:
Standardised reporting guidelines in self-
monitoring experiments
Prof. Martin-Sanchez
Melbourne Medical School
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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 !
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
A perfect storm awaits…..Data Deluge from Patient-generated information
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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.
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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)
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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)….
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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?
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
Thank You
Merci
Grazie
Gracias
ObrigadoDanke
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Suggestions on next step?
Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
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