role of standards: tackling the barriers to adoption of...
TRANSCRIPT
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session 2
June 29,2016 1:30-3:00 pm
1
http://standards.ieee.org
http://www.embs.org/
http://lifesciences.ieee.org
http://bigdata.ieee.org/
IEEE Entities Supporting the First IEEE Conference on Connected Health
2
Session Chair: Carole C. Carey, IEEE EMBS Standards Committee Chair, Former Senior Scientific Reviewer U.S. FDA, Center for Devices and Radiological Health
Co-Moderator: Hung Trinh, USPHS, Chief Engineer for DoD/VA Interagency Program Office, Former DoD Technical Lead for the EHR Data Sharing
Meet the Facilitators
3
4
Interoperable Connected Healthcare: Social and Behavioral
Determinants
William Riley, National Institutes of Health, Director of Behavioral and
Social Sciences
Implementing Connected Health Solutions in the Clinical Practice
Paolo Bonato, Harvard Medical School, Director of the Motion Analysis
Laboratory at Spaulding Rehabilitation Hospital
Too Much Data and How the IEEE Big Data Initiative is
Addressing it
Kathy Grise, IEEE Staff Senior Program Director, Future Directions, IEEE
Technical Activities
Standards-based Medical Device Communication Facilitates
Better Clinical Decision
John Zaleski, Bernoulli Enterprise, Chief Informatics Officer
Perspectives from ONC’s Health IT Frontline
Karson Mahler, Office of the National Coordinator for Health Information
Technology, Senior Policy Advisor
FDA’s Regulatory Perspectives
Bakul Patel, U.S. FDA Center for Devices and Radiological Health,
Associate Director for Digital Health
Meet the Panel Team
Session Quick Overview
In a nutshell…
– DoD/VA Interagency Office approach to adoption of standards in healthcare data integration
– IEEE/IEEE-SA standards development paradigm, life cycle, IEEE 11073 standards WGs and global collaborations
Panel Presentations
Interactive panel discussion and audience participation
5
Department of Defense / Department of Veterans Affairs Interagency Program Office (IPO) Mission and Statistics
6
To lead and coordinate the two Departments’ adoption of and contribution to national health data
standards to ensure seamless integration of health data between DoD, VA and private health care
providers
1,230+ Care LocationsIncluding care locations on ships and
submarines
1,400+ Care LocationsIncluding care locations in each state
9.5M Eligible BeneficiariesDoD primarily cares for the younger, active duty
population and their families
22M Eligible Beneficiaries, 9M
Enrollees VA primarily cares for a population that has long
term medical claims
60% Private Sector CareA majority of the DoD population receives some
or all of their care in the private sector
60% Private Sector CareA significant percentage of the Veteran
population receives some or all of their care in
the private sector
70+ Electronic Healthcare
SystemsAs EHR functionality evolved, DoD incorporated
new systems into the portfolio to meet functional
requirements
1 Electronic Healthcare System
with 100+ Modules As EHR functionality evolved functionals at VA
incorporated new modules into VistA to meet
requirements
Department of Defense Department of Veterans Affairs
7
HDIStandards
Lifecycle
Private Sector
Partnerships
IPO Partnerships
IEEE IEEE-SA
The world’s largest professional and technical organization.
IEEE global reach is 426,000+ members in 160+ countries.
39 technical societies and 7 technical councils.
Led by a diverse body of elected and appointed volunteer members.
IEEE Standards Association
Globally recognized standards-setting body within IEEE.
Over 20,000 standard developers worldwide.
Over 1200 active standards and 550 in development.
IEEE – A Global Organization
8
Participants are the Driving Force Behind the Development of Standards
Initiating the
Project
Mobilizing the
Working Group
Drafting the
Standard
Balloting the
Standard
Gaining Final
Approval
Maintaining the
Standard
9
Basic Principles Guide Standards Development
• Openness• Due Process• Balance• Right of Appeal• Consensus
The IEEE Standards Development Lifecycle
IDEA!
INNOVATE!
CREATE!
GET INVOLVE!
IEEE 11073 Family of StandardsPHD, Upper Layer and Lower layers Working Groups
Health informatics, point-of-care, medical device communications standards
– Real-time plug and play interoperability for patient-connected medical devices
Health informatics, personal health device communication standards
– Plug and play with mobile phones and home hubs, using Bluetooth and USB specifications
10
IEEE 11073 WGs Collaboration
ISO (International Standards Organization)
IHE (Integrating the Healthcare Enterprise
LOINC (Logical Observation Identifiers Names and Codes)
HL7 (Health Level Seven)
CEN (European Committee for Standardization)
IEC (International Electrotechnical Commission)
DICOM (Digital Imaging and Communications in Medicine)
SNOMED (Systematized Nomenclature of Medicine)
Continua Health Alliance
11
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session
12
Interoperable Connected Healthcare:
Social and Behavioral Determinants
William Riley, Ph.D.
Director, Office of Behavioral and Social Sciences
Research
Electronic Health Record Core to Connected
Health – and to the Precision Medicine Initiative
• Initial Data Types:
– Demographics
– Diagnosis
– Prescribing
– Encounter and Procedure
– Vital
– Lab Results
• Expand to include additional structured data and unstructured data as capacities improve
• HPOs:
– Learn from HPO Data Sprint
– Transmission from HPO to CC in specified format
– Make available to participants
• DVs:
– Sync for Science (S4S)
– Develop a FHIR-based API
• Single “S4S” indication from DV
• Indicate EHR home
• Permission and authentication
• Secure transmission
• Make available to participants
Proposed Flow for S4S Pilot at Harvard
Department of Biomedical Informatics
Capturing Social and Behavioral Domains and
Measures in Electronic Health Records: Phase 2
Suggested citation: IOM (Institute of Medicine). 2014. Capturing
Social and Behavioral Domains and Measures in Electronic Health
Records: Phase 2. Washington, DC: The National Academies Press.
www/iom.edu/EHRdomains2
Slides from IOM
CORE DOMAINS & MEASURESWITH SUGGESTED FREQUENCY OF ASSESSMENT
DOMAIN/MEASURE MEASURE FREQUENCY
Alcohol Use
Race and Ethnicity
Residential Address
Tobacco Use
3 questions
2 questions
1 question (geocoded)
2 questions
Screen and follow up
At entry
Verify every visit
Screen and follow up
Census Tract-Median Income
Depression
Education
Financial Resource Strain
Intimate Partner Violence
Physical Activity
Social Connections & Social Isolation
Stress
1 question (geocoded)
2 questions
2 questions
1 question
4 questions
2 questions
4 questions
1 question
Update on address change
Screen and follow up
At entry
Screen and follow up
Screen and follow up
Screen and follow up
Screen and follow up
Screen and follow up
NOTE: Domains/Measures are listed in alphabetical order; domains/measures in the shaded area are currently frequently collected in
clinical settings; domains/measures not in the shaded area are additional items not routinely collected in clinical settings.16
Social Determinants of Health in EHRs
HealthLandscape Geocoding API (OCHIN)
More Data to Connect and Make Interoperable – and
Implications for Persuasive Technologies
Ecological Momentary Assessment (EMA) methods improved and delivered on cell phones
Capture of digital traces from daily interactions with technology Social media
Call data records
Consumer sensors
Sensors that can passively and continuously behaviors in context Physical activity sensors
Smoking sensors
Environmental exposure sensors
Applying these and other sensor technologies to changing behavior:
• Greater reach and scalability
• Just-in-time Adaptive Interventions (JITAI)
19
NIH CDE Collections & Efforts
Broadly applicable & formally evaluated collections
PhenX Toolkit > 350 standard measures of
phenotypes & exposures
PROMIS Validated patient reported outcome
measures, ~ 100 computerized adaptive tests
NIH Toolbox – Validated measures of cognitive,
emotional, sensory and motor functions
More narrowly focused collections
NINDS CDEs for disease-specific studies
NCI Early Detection Research Network
NEI eyeGENE ophthalmic phenotype CDEs
NIDA substance use disorders CDEs for EHRs
NCATS Global Rare Diseases Patient Registry
BMIC CDE Resource Portal
Information about CDEs from across NIH:
Glossary of terms
Specific CDE use guidance from ICs
Organized and sorted information & links to
NIH/IC CDE collections
NIH CDE tools & resources
NIH CDE Repository
Structured human & machine readable definitions of
NIH CDEs allowing
Search for individual CDE or sets per FOA, etc.
Compare & harmonize similar but distinct CDEs
Select or create CDEs with minimal duplication
Etc.
• The DUA is a legal binding agreement between the OPDIV and an external entity that requests the use of personal identifiable data that is covered by a legal authority (e.g., Privacy Act, Economy Act)
• The agreement delineates the confidentiality requirements of the relevant legal authority, security safeguards, and the OPDIV’s data use policies and procedures.
• The DUA serves as both a means of informing data users of these requirements and a means of obtaining their agreement to abide by these requirements.
• The DUA serves as a control mechanism for tracking the location(s) of the OPDIV’s data and the reason for the release of the data. A DUA requires that a System of Records (SOR) be in effect, which allows for the disclosure of the data being used.
Purpose of a Data Use Agreement (DUA)
23
Thank you
2
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session
25
Too Much Data and How the IEEE Big Data Initiative is Addressing it
Panel 2: The Role of Standards
Tackling the Barriers to Adoption of Interoperable Connected Healthcare
IEEE CHASE, Arlington, VA
Kathy Grise, IEEE - Senior Program Director
29 June 201626
IEEE Big Data Initiative (BDI)
27
Data touches upon a broad spectrum of areas throughout IEEE and beyond.
Objectives
1.Nurture and curate collaboration across all interested groups for a well-coordinated approach and message for big data.
29 June 2016
2.Launch new initiatives across IEEE in Conferences, Education, Publications, and Standards that address in a comprehensive way the many opportunities and different dimensions of Big Data.
3.Identify and develop new business models based on big data (examples: data portal, data analytics and visualization)
4.Develop and grow IEEE’s technical community on big data, and to serve as a forum for discussion on the social implications of big data.
5.Ensure IEEE is a leader driving for consistent handling of data, its privacy and security.
Biomedical Data Explosion and Interconnected Healthcare
More Sensors and Lower Cost Sensors Lots of DATA
28 29 June 2016
New Report: Within 15 Years, Health Data Goes Up to 1 Yottabyte!
1,000,000,000,000,000,000,000,000 bytes
Biomedical Data Explosion and Interconnected Healthcare
More Sensors and Lower Cost Sensors Lots of DATA
29 29 June 2016
New Report: Within 15 Years, Health Data Goes Up to 1 Yottabyte!
1,000,000,000,000,000,000,000,000 bytes
It’s really not about
the data…
Interconnected World – Driving Standardization
30 29 June 2016
Services and
Applications
Access
Networks
Wired: xDSL, Cable,
Fiber, etc
Thin
g/O
bjec
t Dom
ain
(Phy
sica
l or V
irtua
l)
Wireless
Sensor Networks
(e.g. environment monitoring)
Access
Networks
(Wireless: 3G/4G,
Satellite, etc
Local
Data
RFID Networks
(e.g. supply chain
management)
Body Area Networks
(e.g. eHealth/mHealth)
Vehicular Networks
(e.g. smart transportation)
RFID Reader
Raw Data
Raw Data
Raw Data
Local
Data
Core
Networks
(Software-Defined Networks,
Content-Centric Networks,
etc)
Raw and
Processed
Data
Servers in the
Cloud Providing
Various Services
Gateway
Gateway
Thing DomainDevice Domain
(Generate Data)
Network Domain
(Collect Data)
Service Domain
(Manage Data)
User Domain
(Access Data)
End-to-End IoT System
Div
erse
Ver
tical
App
licat
ions
Big Data Measurement
Big Data Networking
Big Data Management
Big Data Analytics
Big Data Visualization
Edge/Fog Computing
Cloud Computing
Big Data Privacy
and Security
Challenges Drive Opportunities: Pre-standardization Examples
Mobile Health Platform (N. Keshava, C. Carey, D. Hudson, W. Malik)
The development of mobile technology as platforms for measuring physiological and behavioral parameters is a rapidly growing area. While there is great interest in using mobile technology platforms to collect persistent measurement, translating those measurements reliably into clinical insight is a major leap.
Areas of potential interest and applicability:1.Inference of physiological parameters during clinical trials as
possible endpoints and surrogate biomarkers2.Estimation of cognitive state (e.g., after injury, during recovery)3.Interoperability between platforms4.Visualization5.Methods for addressing missing data, artifacts, etc.
31 29 June 2016
Challenges Drive Opportunities: Pre-standardization Examples
32 29 June 2016
Curation of EHRs for Reuse (N. Keshava, C. Carey, D. Hudson, W. Malik)
EHRs and payer/claims databases are a potential source of value to a wide variety of health care stakeholders. Longitudinal patient records suffer from a wide variety of distortions ranging from missing data, gaps in coverage, inconsistent medical coding, and different standards of care. Different commercial vendors currently employ different formats and schema for collecting data.
Propose developing standards for curation of EHRs and payer/claims databases to enable key information products to be derived with the least variation. These products can provide the foundation for advanced applications and services. Examples of possible curation algorithms could include patient matching algorithms, data imputation algorithms, and algorithms that generally reconstruct the patient journey through the medical system using medical records.
Challenges Drive Opportunities: Pre-standardization
Mobile Health PlatformThe development of mobile technology as platforms for measuring physiological and behavioral parameters is a rapidly growing area. While there is great interest in using mobile technology platforms to collect persistent measurement, translating those measurements reliably into clinical insight is a major leap.
Areas of potential interest and applicability:1. Inference of physiological parameters
during clinical trials as possible endpoints and surrogate biomarkers
2. Estimation of cognitive state (e.g., after injury, during recovery)
3. Interoperability between platforms4. Visualization5. Methods for addressing missing data,
artifacts, etc.
33 29 June 2016
Curation of EHRs for ReuseEHRs and payer/claims databases are a potential source of value to a wide variety of health care stakeholders. Longitudinal patient records suffer from a wide variety of distortions ranging from missing data, gaps in coverage, inconsistent medical coding, and different standards of care. Different commercial vendors currently employ different formats and schema for collecting data.
Propose developing standards for curation of EHRs and payer/claims databases to enable key information products to be derived with the least variation. These products can provide the foundation for advanced applications and services. Examples of possible curationalgorithms could include patient matching algorithms, data imputation algorithms, and algorithms that generally reconstruct the patient journey through the medical system using medical records.
Learn, participate, contribute!
34
@ieeebigdata IEEE Big Data IEEE Big Data Initiative
bigdata.ieee.org web portal
29 June 2016
ieee-collabratec.ieee.org/
Email: [email protected]
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session
35
Standards-based medical device communication facilitates better
clinical decisions
John R. Zaleski, Ph.D., CAP, CPHIMS
Chief Informatics Officer
Bernoulli Enterprise, Inc.
http://bernoullihealth.com
IEEE Chase 2016 Conference on Connected Health:
Applications, Systems, and Engineering Technologies
Hyatt Arlington
1325 Wilson Blvd
Arlington, VA 22209
DATE: Friday June 29th, 2016
ROOM: Salon B & C
TIME: 1:30-3:00 pm
Patient Care Devices Used in Operating Rooms, ICUs
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Data from Medical Devices Used in Patient Care
Is temporal
Can be real-time or near real-time
Can be multivariate & multi-source
Can have varying data collection frequencies
Can require non-standard methods for collecting
Is objective, for the most part
Can be integrated with other data to provide better context
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
The Challenge
Much of patient care device data is trapped in silos
– Unique Protocols
– Unique Physical Connectivity
– Unique Clock Times
– Unique Time frequency of output
– Unique Terminology
Data cleaning, alignment & harmonization must occur before data can be used
– Semantic interoperability
– Temporal synchronization
– Key for importation into electronic health record systems, data warehouses
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Physical Architecture
MDI Middleware
DevicesElectronic Health Record orClinical Data Warehouse
Health Level Seven (HL7) Solicited or
Unsolicited Transaction
Nuvon VEGA Server
IDM-MG 3000
IDM-MG 4000
Enterprise Clinical Information Systems /Electronic Medical Record Systems
IDC
NativeData
NativeData
NativeData
NativeData
IDM-SpecificHL7 Data
HL7 Data
Infusion Pumps
Physiological Monitors
Ad-Hoc Vitals Monitors
Mechanical Ventilators
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Examples of Data Collection Appliances
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Logical MDI Architecture
Patient Care
Device
Data Aggregation
, Formatting & Routing
(DAG)
Monitoring Server
Electronic Health Record System
CSV, HL7,TXT or XML or TXT
Device Drivers & Monitoring
Monitoring
polled
Data Collection Appliance
(DCA)Pull or push & acknowledge
TXT orBINARY
Time stamp observations(e.g.: OBX times)
Time stamp messages(e.g.: MSH & OBR times)
push
Network Time Server
Time Updates
TXT
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Example: PB 840 Mechanical Ventilator Raw Data to HL7
MSH|^~\&|VEGA^I3E3VEGA00000592_005|IDMMG4000^I3E3VEGA00000592|||20100903133103||ORU^R01|IDC00501801|P|2.3
PID|1111|005|||^^
PV1||I|
OBR|1||IDC^VEGA||||20100903133103||||||||IDC
OBX|1|ST|DEV-TIME^Ventilator time^100-5||13:27|
OBX|2|ST|DEV-ID^Ventilator ID^100-6||840 3510083675|
OBX|3|ST|DEV-DATE^Ventilator Date^100-8||SEP 03 2010|
OBX|4|TS|DEV-TS^Device Timestamp^100-107||20100309132700|
OBX|5|ST|VNT-MODE^Ventilator Mode^100-9||BILEVL|
OBX|6|NM|SET-RR^Respiratory rate^100-10||12.0|/min
OBX|7|NM|SET-TV^Tidal volume^100-11||0.00|L
OBX|8|NM|SET-MFLW^Peak flow setting^100-12||0|L/min
OBX|9|NM|SET-O2^O2% setting^100-13||21|%
OBX|10|NM|SET-PSENS^Pressure sensitivity^100-14||0.0|cmH2O
OBX|11|NM|SET-LO-PEEP^PEEP Low (in BILEVEL) setting^100-104||3.0|cmH2O
OBX|12|NM|IN-HLD^Plateau^100-16||0.0|cmH2O
OBX|13|NM|SET-APN-T^Apnea interval^100-21||22|s
OBX|14|NM|SET-APN-TV^Apnea tidal volume^100-22||0.60|cmH2O
OBX|15|NM|SET-APN-RR^Apnea respiratory rate^100-23||12.0|/min
OBX|16|NM|SET-APN-FLW^Apnea peak flow^100-24||60|L/min
OBX|17|NM|SET-APN-O2^Apnea O2%^100-25||21|%
OBX|18|NM|SET-PPS^Pressure support^100-26||0|cmH2O
OBX|19|ST|SET-FLW-PTRN^Flow pattern^100-27|||
OBX|20|ST|O2-IN^O2 Supply^100-30||OFF|
OBX|21|NM|VNT-RR^Total respiratory rate^100-34||12|/min
OBX|22|NM|TV^Exhaled tidal volume^100-35||0.33|L
OBX|23|NM|MV^Exhaled minute volume^100-36||5.64|L/min
OBX|24|NM|SPO-MV^Spontaneous minute volume^100-37||0.0|L
OBX|25|NM|SET-MCP^Maximum circuit pressure^100-38||20.0|cmH2O
OBX|26|NM|AWP^Mean airway pressure^100-39||7.2|cmH2O
OBX|27|NM|PIP^End inspiratory pressure^100-40||20.0|cmH2O
OBX|28|NM|IE-E^1/E component of I:E ^100-41||5.80|
OBX|29|NM|SET-HI-PIP^High circuit pressure limit^100-42||50|cmH2O
OBX|30|NM|SET-LO-TV^Low exhaled tidal volume limit^100-45||0.20|L
OBX|31|NM|SET-LO-MV^Low exhaled minute volume limit^100-46||1.0|L
OBX|32|NM|SET-HI-RR^High respiratory rate limit^100-47||40|/min
OBX|33|ST|ALR-HI-PIP^High circuit pressure alarm status^100-48||NORMAL|
OBX|34|ST|ALR-LO-TV^Low exhaled tidal volume alarm status^100-51||NORMAL|
OBX|35|ST|ALR-LO-MV^Low exhaled minute volume alarm status^100-52||NORMAL|
OBX|36|ST|ALR-HI-RR^High respiratory rate alarm status^100-53||NORMAL|
OBX|37|ST|ALR-NO-O2^No O2 supply alarm status^100-54||ALARM|
OBX|38|ST|ALR-NO-AIR^No air supply alarm status^100-55||NORMAL|
OBX|39|ST|ALR-APN^Apnea alarm status^100-57||RESET|
OBX|40|NM|SET-FLW-BASE^Ventilator-set base flow^100-70||4|L/min
OBX|41|NM|SET-FLW-TRG^Flow sensitivity setting^100-71||3|L/min
OBX|42|NM|PIP^End inspiratory pressure^100-84||20.00|cmH2O
OBX|43|NM|SET-PIP^Inspiratory pressure or PEEP High setting^100-85||18|cmH2O
OBX|44|NM|SET-INSPT^Inspiratory time or PEEP High time setting^100-86||0.74|s
OBX|45|NM|SET-APN-T^Apnea interval setting^100-87||22|s
OBX|46|NM|SET-APN-IP^Apnea inspiratory pressure setting^100-88||0|cmH2O
OBX|47|NM|SET-APN-RR^Apnea respiratory rate setting^100-89||12.0|/min
OBX|48|NM|SET-APN-IT^Apnea inspiratory time setting^100-90||0.00|s
OBX|49|NM|SET-APN-O2^Apnea O2% setting^100-91||21|%
OBX|50|NM|SET-PMAX^High circuit pressure limit^100-92||50|cmH2O
OBX|51|ST|ALR-MUTE^Alarm silence state^100-93||OFF|
OBX|52|ST|ALR-APN^Apnea alarm status^100-94||RESET|
OBX|53|ST|ALR-VNT^Severe Occlusion/Disconnect alarm status^100-95||NORMAL|
OBX|54|NM|SET-HL-HI^High component of H:L (Bi-Level) setting^100-105||1.00|
OBX|55|NM|SET-HL-LO^Low component of H:L (Bi-Level) setting^100-106||5.76|
OBX|56|ST|SET-APN-IEI^Inspiratory component of apnea I:E ratio^100-98||0.00|
OBX|57|ST|SET-APN-IEE^Expiratory component of apnea I:E ratio^100-99||0.00|
OBX|58|ST|SET-CONST^Const. during rate set. chn. for PCV mandatory brths^100-100||I-TIME|
OBX|59|ST|IE^Monitored value of I:E ratio^100-101||1:5.80|
4D 49 53 43 41 2C 37 30 36 2C 39 37 2C 02 31 33 MISCA,706,97,.13
3A 32 36 20 2C 38 34 30 20 33 35 31 30 30 38 33 :26 ,840 3510083
36 37 35 20 20 20 20 2C 20 20 20 20 20 20 2C 53 675 , ,S
45 50 20 30 33 20 32 30 31 30 20 2C 43 50 41 50 EP 03 2010 ,CPAP
20 20 2C 30 2E 30 20 20 20 2C 30 2E 34 34 20 20 ,0.0 ,0.44
2C 36 35 20 20 20 20 2C 32 31 20 20 20 20 2C 30 ,65 ,21 ,0
2E 30 20 20 20 2C 33 2E 30 20 20 20 2C 30 2E 30 .0 ,3.0 ,0.0
20 20 20 2C 20 20 20 20 20 20 2C 20 20 20 20 20 , ,
20 2C 20 20 20 20 20 20 2C 20 20 20 20 20 20 2C , , ,
32 32 20 20 20 20 2C 30 2E 36 30 20 20 2C 31 32 22 ,0.60 ,12
2E 30 20 20 2C 36 30 20 20 20 20 2C 32 31 20 20 .0 ,60 ,21
20 20 2C 30 20 20 20 20 20 2C 52 41 4D 50 20 20 ,0 ,RAMP
2C 20 20 20 20 20 20 2C 20 20 20 20 20 20 2C 4F , , ,O
46 46 20 20 20 2C 20 20 20 20 20 20 2C 20 20 20 FF , ,
20 20 20 2C 20 20 20 20 20 20 2C 31 32 20 20 20 , ,12
20 2C 30 2E 35 33 20 20 2C 36 2E 33 33 20 20 2C ,0.53 ,6.33 ,
30 2E 30 20 20 20 2C 32 35 2E 30 20 20 2C 37 2E 0.0 ,25.0 ,7.
39 20 20 20 2C 32 32 2E 30 20 20 2C 33 2E 35 30 9 ,22.0 ,3.50
20 20 2C 35 30 20 20 20 20 2C 20 20 20 20 20 20 ,50 ,
2C 20 20 20 20 20 20 2C 30 2E 32 30 20 20 2C 31 , ,0.20 ,1
2E 30 20 20 20 2C 34 30 20 20 20 20 2C 4E 4F 52 .0 ,40 ,NOR
4D 41 4C 2C 20 20 20 20 20 20 2C 20 20 20 20 20 MAL, ,
20 2C 4E 4F 52 4D 41 4C 2C 4E 4F 52 4D 41 4C 2C ,NORMAL,NORMAL,
4E 4F 52 4D 41 4C 2C 41 4C 41 52 4D 20 2C 4E 4F NORMAL,ALARM ,NO
52 4D 41 4C 2C 4E 4F 52 4D 41 4C 2C 41 4C 41 52 RMAL,NORMAL,ALAR
4D 20 2C 20 20 20 20 20 20 2C 20 20 20 20 20 20 M , ,
2C 31 33 3A 32 36 20 2C 20 20 20 20 20 20 2C 53 ,13:26 , ,S
45 50 20 30 33 20 32 30 31 30 20 2C 30 2E 30 20 EP 03 2010 ,0.0
20 20 2C 30 2E 30 20 20 20 2C 33 36 2E 30 20 20 ,0.0 ,36.0
2C 31 37 2E 30 30 20 2C 30 20 20 20 20 20 2C 30 ,17.00 ,0 ,0
2E 30 30 30 20 2C 30 20 20 20 20 20 2C 34 20 20 .000 ,0 ,4
20 20 20 2C 33 20 20 20 20 20 2C 20 20 20 20 20 ,3 ,
20 2C 20 20 20 20 20 20 2C 20 20 20 20 20 20 2C , , ,
20 20 20 20 20 20 2C 20 20 20 20 20 20 2C 20 20 , ,
20 20 20 20 2C 20 20 20 20 20 20 2C 20 20 20 20 , ,
20 20 2C 20 20 20 20 20 20 2C 20 20 20 20 20 20 , ,
2C 20 20 20 20 20 20 2C 4E 4F 4F 50 20 43 2C 32 , ,NOOP C,2
32 2E 30 30 20 2C 30 20 20 20 20 20 2C 30 2E 30 2.00 ,0 ,0.0
30 20 20 2C 32 32 20 20 20 20 2C 30 20 20 20 20 0 ,22 ,0
20 2C 31 32 2E 30 20 20 2C 30 2E 30 30 20 20 2C ,12.0 ,0.00 ,
32 31 20 20 20 20 2C 35 30 20 20 20 20 2C 4F 46 21 ,50 ,OF
46 20 20 20 2C 41 4C 41 52 4D 20 2C 4E 4F 52 4D F ,ALARM ,NORM
41 4C 2C 30 2E 30 30 20 20 2C 30 2E 30 30 20 20 AL,0.00 ,0.00
2C 30 2E 30 30 20 20 2C 30 2E 30 30 20 20 2C 20 ,0.00 ,0.00 ,
20 20 20 20 20 20 20 20 2C 31 3A 33 2E 35 30 2C ,1:3.50,
03 0D
DCADAG
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR:
Enabling Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Mapping Patient Care Device Semantics
HR
SpO2
NBPs
NBPd
NBPm
ARTs
ARTd
ARTm
CO
PVC
etCO2
HR
SpO2
fR
Mve
Tve
fRe
PIP
etCO2
HR-ECG
NBPs
NBPd
NBPm
RR
MVe
TVe
Device 1
Device 2
Mapped Output
SpO2-1
SpO2-2
HR-SPO2
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR: Enabling
Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 201629-Jun-1644
HL7 Standards
29-Jun-1645
HL7 FHIR
29-Jun-1646
ISO/IEEE 11073 Standards
29-Jun-1647
Integrating the Clinical Environment(ICE) ASTM F2761
29-Jun-1648
ISO/IEC 80001
29-Jun-1649
IHE.NET Domain: Patient Care Devices (PCD)
29-Jun-1650
Continua Health Alliance
29-Jun-1651
Thank you!
John R. Zaleski, Ph.D., CAP, CPHIMS
Chief Informatics Officer
203-343-9225
http://www.bernoullihealth.com
Book III:
Published 2015 by HIMSS Media
Title:
Connected Medical Devices:
Integrating Patient Care Data in
Healthcare Systems
29-Jun-1652
Logical Architecture with Time Synchronization
Medical Device-1 Network Time Service
UTC UTC
…
DCADAG
EHR
Source: R Richards, J Zaleski, S Peesapati, “Liberating medical device data for clinical research: an architecture for semantic and temporal
synchronization.”AMA-IEEE Conference, Park Plaza Hotel, Boston, MA. 16-18 October, 2011.
Medical Device-2
Medical Device-N
Patient Care Device Data Spectrum
Real-Time Ad Hoc
< 1 sec
Waveforms,
Interventional alarms
30-60 sec
Anesthesia
Charting
1 - 60 min
Critical Care
Charting
1x – 4x / shift
General Ward
Charting
Source: J.R. Zaleski, “Medical Device Integration Beyond the EHR: Enabling
Real-Time Healthcare.” Bernoulli Webinar. June 22nd, 2016
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected HealthcarePanel Session
55
Perspectives from ONC’s Health IT FrontlineIEEE-CHASE Conference on Connected Health
Karson F. Mahler, JDSenior Policy Advisor, Office of the National Coordinator for Health IT (ONC)
June 29, 2016
Disclaimer
The views expressed herein are my own and do not necessarily represent the
position of ONC, the Department of Health and Human Services, or the United
States government.
57
Overview
• National goals for health IT and connected health
• Key motivations for health IT standards
• Government role in advancing standards for interoperability
• Beyond standards: Addressing “non-technical” barriers to interoperability
58
National health IT goals (HITECH Act)
A nationwide health IT infrastructure that, inter alia —
• Enables the exchange and use of electronic health information
• Protects the privacy and security of patients’ health information
• Improves healthcare quality, outcomes, and efficiency, such as by providing
information to:
» Guide medical decisions at the point of care
» Provide coordinated, patient-centered care
» Manage chronic conditions
» Reduce medical errors and health disparities
» Reduce waste and inefficiency
• Supports research and public health
• Promotes a more effective marketplace
See § 3001(b) of the Public Health Service Act (42 USC § 300jj–11(b)).59
Key motivations for health IT standards
• Protect the public and promote trust in the health IT infrastructure (e.g.
safety, privacy & security)
• Provide technical solutions to health IT challenges (e.g. interoperability)
• Enable innovation and competition in health IT capabilities, products, and
services
60
Government role in advancing standards for interoperability
• Set clear policy direction
» Interoperability Roadmap
• Support standards development
» Participate directly in standards development activities
» Encourage harmonization and alignment
• Encourage adoption and use
» Recognize/endorse standards for federal compliance and certification
» Create demand-side incentives for standards-based technologies
» Foster a supportive regulatory environment
» Fund pilots, challenges
• Leverage markets and competition
61
Beyond standards: Addressing the “non-technical” barriers to interoperability
• Legal uncertainty and disuniformity
» HIPAA misconceptions
» State privacy laws
» The patient identifier quandary
• Information sharing policies and practices (a.k.a. “governance”)
» Lack of consistent principles and rules for exchanging information
• Lack of (or perverse) incentives to share information
» Vertical vs. horizontal interoperability
» Healthcare consolidation
» Platform dynamics in health IT industry
» Information blocking
62
Resources
• Interoperability Roadmap
https://www.healthit.gov/sites/default/files/hie-
interoperability/nationwide-interoperability-roadmap-final-version-1.0.pdf
• Report to Congress on Health Information Blocking
https://www.healthit.gov/sites/default/files/reports/info_blocking_040915.
• Interoperability Standards Advisory
https://www.healthit.gov/standards-advisory/2016)
• Interoperability Proving Ground
https://www.healthit.gov/techlab/ipg/
63
Resources (continued)
• Innovation challenges
» Consumer Health Data Aggregator
https://www.challenge.gov/challenge/consumer-health-data-aggregator-
challenge/
» Provider User-Experience
https://www.challenge.gov/challenge/provider-user-experience-challenge/
» Move Health Data Forward
https://www.challenge.gov/challenge/move-health-data-forward-challenge/
• Funding opportunity announcements
» High-impact Pilots (HIP)
https://www.healthit.gov/techlab/innovation/high-impact-pilots
» Standards Exploration Award (SEA)
https://www.healthit.gov/buzz-blog/interoperability/tech-lab/1-5-million-
available-advance-health-interoperability-standards-implementation-
experience/ 64
@ONC_HealthIT @HHSONC
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session
66
67
Bakul PatelAssociate Director for Digital Health
Office of Center Director
Center for Devices and Radiological Health
Medical DeviceInteroperability
68
• Enable “patient centered” public
health as digitization touches every
aspect of health care
• Foster trust in innovative
technologies as an enabler of a new
healthcare paradigm
• Prepare a "digital-future ready”
infrastructure @CDRH that
understands innovators needs and
expectations
CDRH
Objectives
Why Interoperability?
New benefits
– Lowered cost
– Allows best of breed coexistence
– Smart interventions…
New opportunities
– Device safety behavior
– Detecting errors in a interconnected environment
– Maintaining safe interoperable use…
69
Medical Device
Medical Device
Enterprise Info systems
CDRH Interoperability Objective
Advance the role and ability of medical devices in a connected system to exchange and use
information safely and effectively
With other medical devices and other information technology
to increase safety and efficiency in patient care.
70
Milestones on the Journey
In 2010, the FDA hosted a 3-day workshop on medical device interoperability
In 2012, FDA - AAMI Summit on Medical Device Interoperability
In 2013, the agency officially recognized a set of standards manufacturers could use to improve patient care by making sure devices work well together.
In 2015, final policy on medical device data systems (MDDS) encouraging manufacturers to share data.
71
Next milestone –Draft interoperability Guidance
72
Manage and minimize risks
Create transparent or standards based medical device interface
Anticipate interoperable scenarios
Design for interoperability
Working Together
73
Understand
Needs
Engage
Advance
Patient
Safety
Business
Research
Providers
Manufacturers
Standards developers
Researchers
Regulators
Patients
Hospitals systems
Medical device interoperability
02/23/2016
Collective Progress
74
Manufacturers
Standards developers
Researchers
Regulators
Patients
Hospitals systems
Certifiers
Role of Standards: Tackling the Barriers to Adoption of Interoperable Connected Healthcare
Panel Session
75
Implementing ConnectedHealth Solutions in theClinical Practice
Paolo Bonato, PhD
06/29/2016 - IEEE CHASE
The Healthcare System Transformation
The soaring costs of
acute care, the significant
increase in life
expectancy, and the high
prevalence of long-term
medical conditions in
older adults have created
a “perfect storm” that is
causing profound
changes in the healthcare
system.
Management of Long-Term Conditions
Individuals who lose a lower limb
secondary to diabetes mellitus are
at high risk to have to undergo
amputation of the contralateral limb.
The combination of high-tech
prosthetic solutions and
compliance with an exercise
program are meant to substantially
decrease this risk.
Parkinson’s Disease
PD affects about 3% of the population
over the age of 65 years and more
than 500,000 people in the US alone
The primary biochemical abnormality
in PD is deficiency of dopamine due to
degeneration of neurons in the
substantia nigra pars compacta
The characteristic motor features are
the development of tremor,
bradykinesia, rigidity, and impairment
of postural balance
Current therapy of PD is based
primarily on levodopa and other drugs
which activate dopamine receptors
Motor Fluctuations in Parkinson’s Disease
Therapies are effective for some time, but most patients eventually
develop motor complications
These complications include wearing off, the abrupt loss of
efficacy at the end of each dosing interval, and dyskinesias,
involuntary and sometimes violent writhing movements
OFF OFF
ON
Levodopa
IntakeOnset
Dyskinesia
End-of-Dose
Dyskinesia
Peak-Dose
Dyskinesia
WEARING-OFF
OFF OFF
ON
Levodopa
IntakeOnset
Dyskinesia
End-of-Dose
Dyskinesia
Peak-Dose
Dyskinesia
WEARING-OFF
Motor Fluctuations in Parkinson’s Disease
OFF
UPDRS=0ON
UPDRS=2
OFF
ON
LevodopaIntake
WEARING-OFF
OFF
LevodopaIntake
-
UPDRS
Dyskinesia
Motor Fluctuations in Parkinson’s Disease
Day 3
0 60 120 1800
1
2
3
Day 2
0 60 120 1800
1
2
3
Feb
Jan
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Day 1
0 60 120 180 2400
1
2
3
Tremor Bradykinesia Dyskinesia MedicationWe have deployed wearable sensors and
successfully monitored patients with late
stage PD in the home. Sensor data was
collected during the performance of
UPDRS motor tasks.
Record ACC data
during standardizedmotor tasks
0 906030
test 4test 3test1 test 2
Time (min)
Time
(months)Day 1 Day 2 Day 34
Months
4
Months
Implementing The “Radio Doctor”
2-Jul-1683
Courtesy of
Jay Sanders, MD
2-Jul-1684
http://srh-mal.net/
Paolo Bonato, PhD
Dept. of PM&R
Harvard Medical School
Spaulding Rehabilitation Hospital
