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Mie2014 workshop: Gap Analysis of Personalized Health Services through Patient-Controlled Devices

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.

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Mie2014 workshop: Gap Analysis of Personalized Health Services through Patient-Controlled Devices

  1. 1. 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
  2. 2. 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)….
  3. 3. 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
  4. 4. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Elder population and care costs are growing annually, but no reliable solutions for early detection and efficacy monitoring 4 IBM Confidential 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.
  5. 5. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Holistic View of Determinants of Health to Personalized Services Endogenous determinant (e.g., genetics predisposition) Clinical determinant (e.g., care flow, care delivery) Exogenous determinant (e,g, environment, behavioral social factors) 30% 10% 60% SA Schroder. We can do better - Improving the Health of the Amarican People. NEJM 2007;357:1221-8. Cardiovascul ar 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 Cardiovascular complication (42-57%) (UKPDS, US EDIC) Eye complication (76%), Kidney complication (50%), Nerve complication (60%) (UKPDS, US DCCT) Huge opportunity space for risk reduction: Progress impeded by the lack of granular data capturing tools!
  6. 6. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices 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 6 IBM Confidential stretch sensors gait analysis in a pocket vitals sensor in t-shirt insole sensors e-textile wireless ECG Cardiac monitoring systems Requires ultra-low power adaptive circuits, non-intrusive form factors OpenBCI
  7. 7. 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
  8. 8. 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
  9. 9. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Healthcare becoming both Personal and Collaborative 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 Service Disease-Centric Guideline Century of behavior change Precision Medicine Degree of personalization Degree of collaboration (data dimension) Data-Driven Evidence
  10. 10. 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).
  11. 11. 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).
  12. 12. 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
  13. 13. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Wearables – just nice toys? ? ? ? ? Good medicine and good healthcare demand good information
  14. 14. 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.
  15. 15. 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.
  16. 16. 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.
  17. 17. 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.
  18. 18. 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.
  19. 19. 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) • …
  20. 20. 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 source: Institute for Hea plthr eMveterincst iaondn E!valuation, healthmetricsandevaluation.org
  21. 21. 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
  22. 22. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices join our IMIA WG: www.wearable-sensors.org
  23. 23. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Stefan von Cavallar Advisory Software Engineer, IBM Research Australia
  24. 24. 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
  25. 25. 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… !
  26. 26. 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 $
  27. 27. 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
  28. 28. 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?
  29. 29. 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…
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Cognitive Healthcare Hub Interface Gateway Governments Interaction Communication Visualisation Mobile Internet Community Radio TV Statistics Modelling Cognitive Computing Analytics Machine Learning Simulation Prediction Business Intelligence Unified Data View Security Access Quality Environment Mobile Social Media Indigenous Knowledge Guidelines Publications Remote Sensors Registries
  34. 34. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Watson: Question Answer Hospitals Deep Thunder: Climate Modeling Cognitive Healthcare Pharmacies Health Workers Community Health Centers Patients Prepare for patient increase Hub Optimized drug distribution Support untrained Advice for rare conditions STEM: Epidemiological Modeling Public Health Boards Optimized Resource Allocation
  35. 35. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices
  36. 36. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices GPS Healthcare worker entry Sensors 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 Community General questioning Tests Diagnosis Aftercare Oral Zinc supplements Rehydration Salts Rehydration schedule Tracking; how? Reminders Local Push Treatment Calculate therapy Public Health Water supply analysis Pathogen outbreak Pathogen identification Individual App; decision tree Intravenous fluids
  37. 37. 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
  38. 38. 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
  39. 39. 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”
  40. 40. 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
  41. 41. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices 41 Manager, Mobile Big Data Solutions IBM 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
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. 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
  46. 46. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices 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 46 IBM Confidential stretch sensors gait analysis in a pocket vitals sensor in t-shirt insole sensors e-textile wireless ECG Cardiac monitoring systems Requires ultra-low power adaptive circuits, non-intrusive form factors OpenBCI
  47. 47. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Significant growth in exogenous data poses challenges to existing BigData storage and analytics solutions Determinants of Health Outcomes Exogenous (Behavior, Socio-economic, 60 Environmental, ....) % Fitness/WellnessPatient-controlled medical devices Affinity (digital)Affinity (retail) Employ ment Socio-econo mic databa ses Data Sources Endogenous (-omics) 30 % Clinical (EMR) 10 % Exogenous Data Growing Fast 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 dynamic Data generation ~ uncontrolled environment !
  48. 48. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices A perfect storm awaits….. Data Deluge from Patient-generated information
  49. 49. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices 49 Patient generated information are effective for self-management and personalized intervention/adaptation Increase awareness to self-monitoring (Prestwich et al., 09; Burke et al., 05) Promoting behavioral change (Dietary intake: Burke et al., 05; Physical activity: Prestwich et al., 09; Michie et al., 09) Triggering reminders to care plans (Consolvo et al. 09; Hurling et al., 07) Personalizing communication messages and education materials (Thaler and Sustein, ‘08) Nudge: Improving Decisions About Health
  50. 50. 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.
  51. 51. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Data-driven Calibration and Personalization Process: From Population-based evidence to individualized alerting/adaptatio Population Data-driven Insight Feature Abstraction (Candidate feature generation) Complete feature set Feature Optimization 1 2 (Optimal set construction) Construction of features based on variance over time Analyze and select variance features from the complete set of constructed features Optimized Feature subset Identify input data sources from the optimal feature set and configure the input of data sources Feature 3 Population (data source configuration) Individual Data-driven Personalization Individual data captured based on input configuration Alert Setting (individual-based calibration) Individual-based alert Learning for Adaptation Slide 51 IBM CONFIDENTIAL Monitoring biomarker/patient-generated info operational DB EHR/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) 4 5 Verified feature set for the target individual
  52. 52. 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 Monitoring device Intra-individual variability calibration (evidence-based) • Service Scenarios: Input for monitoring feedback generation and diagnosis/intervention – 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)
  53. 53. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices 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 53 IBM Confidential 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)
  54. 54. 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 future cloud 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
  55. 55. 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
  56. 56. 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 QA • 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)….
  57. 57. 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
  58. 58. 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
  59. 59. 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?
  60. 60. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Suggestions on next step? Traditional Chinese Thank You Merci Grazie Gracias Obrigado Danke English Japanese French Russian German Italian Spanish Arabic Brazilian Portuguese Simplified Chinese Hindi Tamil Thai Korean Hebrew
  61. 61. Gap Analysis of Insight-Driven Personalized Health Services through Patient-Controlled Devices Questions?

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