SlideShare ist ein Scribd-Unternehmen logo
1 von 18
Health IT Seminar Review

      CLIFF KAUFMAN
Focus on NC
 NC Strategy for HIT
      Steve Cline, DDS, MPH
      HIT Coordinator, NC DHHS

 Using Telehealth Technology for Rehabilitation
      Helen Hoenig MD, MPH
      Durham VA Med Ctr Duke University

 CCNC Informatics Center
      Annette DuBard, MD, MPH
      North Carolina Community Care Networks, Inc.

 NCB Prepared
      Steve Potenziani, PhD
      Executive Director, NCB-Prepared Collaborative
NC Strategy for HIT

 GOALS                             PROBLEMS
 Improved healthcare quality    Paper is inefficient
 Better health outcomes         Duplicate tests
   Individuals                  Medical errors
   Populations
                                 Lack of information
 Control costs
                                 Too much information
 Better engage health care
                                 Consumer engagement
  consumers
                                 Quality-Quality-Quality
The 12-Step Approach
1.  Admit we have a problem
2. Must get clinical information into an electronic sharable format.
3. Incentivize targeted providers to adopt EHRs and meaningful use
4. Create a new standard for EHR vendors
5. Build a mechanism for sharing health information electronically
6. Make sure healthcare providers know how to use the new systems
7. Make sure the network has the capacity for all these new users
8. Make good use of the data (Data Analytics)
9. Make good use of the technology to improve health
10. Children as a priority
11. Learn from the leaders
12. Sustainability
Keys to Success

 EHR Adoption
 Consumer Engagement
 Change Leadership
 Strengthen the “Trust Fabric” of health info exchange
 GOOD USE OF THE DATA!

              And the Winner Is . . .
• Whoever can figure out how to take the tsunami of new health
  data that is heading our way and turn it into actionable health
  information.
• Whoever can help us move from surveillance and reaction to
  event prediction and prevention.
Telehealth Technology for Rehabilitation
  Public Health Problem
 It is difficult for persons with physical disability, particularly in remote areas, to
  access health care.
 High cost and burden of travel.
 Limited rehab specialists in remote areas.
 Clinicians have limited insight into how individual is functioning in home
  environment.

  What is Telehealth?
 Telehealth is comprised of diverse technologies that allow health care to be
  provided in situations where distance separates those receiving services from
  those providing services.
 Telehealth changes the location for providing health care services from the
  doctor’s office or hospital to the local clinic or the patient’s own home.
Telehealth Encounters by VA Providers
Telehealth – Rehab Clinical Trials

 Telerehabilitation for exercise & functional training:
 4 RCTs with Televideo alone or with other Teletechnology.
 4 different populations (geriatric gait disorder, post-stroke, ICU
  survivor, post-op orthopedic surgery).
 Non-inferiority in clinical outcomes compared to Standard PT.
 Better functional outcomes , performance-based & self
  report, compared to Usual Care (no PT).
 Equipment reliability and visual clarity a challenge in all studies
Teletechnology QI Study
 3 types physical function tested
     Fine motor coordination: finger taps (front view)
     Gross motor coordination: gait (lateral view)
     Spatial relationship: cane height (front & lateral views)
 Reliability & validity determined
 3 common Internet speeds (64, 384, 768 kps)
 In person (community standard) and slow motion videotape (gold standard)
 Internet bandwidth had a strong effect on validity and reliability for the fine
  motor and gross motor tasks.
 Fine motor coordination - Reliability & Validity comparable to Standard Care
  @768 kps
 Gross motor coordination (gait ) – Validity not comparable to Standard Care
 Still spatial relationships - Reliability & Validity comparable to Standard Care
  at all of the bandwidths
Teletechnology Infrastructure

Security
 HIPPA
 Full face image and/or Voice = PHI
 Can’t post cell phone video to U-tube for review
 Skype isn’t HIPPA compliant


Costs
 Equipment
 Internet access
 Who pays?
CCNC Informatics Center

 Information Support for Patient-Centered Care
 Develop a better healthcare system for NC starting with public payers
 Strong primary care is foundational to a high performing healthcare system
 Additional resources needed to help primary care manage populations
 Must build better local healthcare systems ( public-private partnership).
  Community Care is a clinical partnership, not a regulatory management agency.
 Physician leadership is critical. Providers who are expected to improve care
  must have ownership of the improvement process
 Achieve savings through better quality and efficiency of care
 Timely data is essential to success
CCNC Informatics Center Data Flow
HC Data for Population Mgmt and QI

1. Identification of High-Risk/ High-Opportunity Patients for
   Targeted Services (Examples: Identification of individuals with
   above-expected preventable utilization, Hypertension Self-
   Management Support)

2. Cost/utilization performance measurement coupled with
   actionable information (Examples: Pharmacy Initiatives, In-
   patient and ED Reporting)

3. Quality Measurement and Feedback coupled with actionable
   information (Examples: Practice Views with
   County, Network, and State Benchmarks; i.e., % eye exams for
   diabetes patients)
ID of Patients for Case Mgmt
                                                                 Historically, case management efforts have
  = Historical or predicted costs for an individual              been targeted at the highest utilizers



  $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K




CRG#1    $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K


                        Expected potentially preventable costs

CRG#2
        $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K

                                                                  Priority patients for care management


CRG#3    $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K
NCB Prepared

A Public/Private Consortium (UNC, NCSU, SAS, DHS) focused on
bio-surveillance – accurately detect and rapidly analyze biological
hazards to ensure public health and safety.
                         • Improve early recognition of
                           outbreaks augmenting bio-
                           surveillance
                         • Improve situational awareness
                         • Faster and more accurate information
                           for decision makers
                         • Integration with emergency
                           management and law enforcement
Analytics – Reactive vs. Proactive
Data Value

PROCESS
Get Data
Use Analytics
Provide Information




CLIENT OPPORTUNITIES (?)   Food      Pharma
                           Finance   Pub Health
                           EMS       News
Focus on NC – Recurring Themes

 Government (US & NC) Funding
 Fundamental Change tied to Technology
 Big Data used predictively not reflexively
 Improve patient care
 Security
 Cost Models


 Opportunities!

Weitere ähnliche Inhalte

Was ist angesagt?

Resource Guide to Informatics Standards
Resource Guide to Informatics StandardsResource Guide to Informatics Standards
Resource Guide to Informatics Standardsjetweedy
 
Personalized Medicine with IBM-Watson: Future of Cancer care
Personalized Medicine with IBM-Watson: Future of Cancer carePersonalized Medicine with IBM-Watson: Future of Cancer care
Personalized Medicine with IBM-Watson: Future of Cancer carejetweedy
 
Shaping Informatics for Allied Health - Refining our voice
Shaping Informatics for Allied Health - Refining our voiceShaping Informatics for Allied Health - Refining our voice
Shaping Informatics for Allied Health - Refining our voiceHealth Informatics New Zealand
 
Health Informatics Mobile Health, Telemedicine, and the Consumer
Health Informatics Mobile Health, Telemedicine, and the ConsumerHealth Informatics Mobile Health, Telemedicine, and the Consumer
Health Informatics Mobile Health, Telemedicine, and the Consumerjetweedy
 
Big Data to Artificial Intelligence in Healthcare
Big Data to Artificial Intelligence in HealthcareBig Data to Artificial Intelligence in Healthcare
Big Data to Artificial Intelligence in Healthcarejetweedy
 
Mobile Phone Applications for Diet and Weight Control
Mobile Phone Applications for Diet and Weight ControlMobile Phone Applications for Diet and Weight Control
Mobile Phone Applications for Diet and Weight Controljetweedy
 
Real Time Location Systems in Healthcare
Real Time Location Systems in HealthcareReal Time Location Systems in Healthcare
Real Time Location Systems in Healthcarejetweedy
 
Peter Embi: Leveraging Informatics to Create a Learning Health System
Peter Embi: Leveraging Informatics to Create a Learning Health SystemPeter Embi: Leveraging Informatics to Create a Learning Health System
Peter Embi: Leveraging Informatics to Create a Learning Health SystemPAÍS DIGITAL
 
Paul Coplan, VP, Johnson & Johnson_mHealth Israel
Paul Coplan, VP, Johnson & Johnson_mHealth IsraelPaul Coplan, VP, Johnson & Johnson_mHealth Israel
Paul Coplan, VP, Johnson & Johnson_mHealth IsraelLevi Shapiro
 
HXR 2017: Juhan Sonin, GoInvo
HXR 2017: Juhan Sonin, GoInvoHXR 2017: Juhan Sonin, GoInvo
HXR 2017: Juhan Sonin, GoInvoHxRefactored
 
Consumer Health Wearables
Consumer Health WearablesConsumer Health Wearables
Consumer Health Wearablesjetweedy
 
Electronic Medical Records - Paperless to Big Data Initiative
Electronic Medical Records - Paperless to Big Data InitiativeElectronic Medical Records - Paperless to Big Data Initiative
Electronic Medical Records - Paperless to Big Data InitiativeData Science Thailand
 
Connected Health & Me - Matic Meglic - Nov 24th 2014
Connected Health & Me - Matic Meglic - Nov 24th 2014Connected Health & Me - Matic Meglic - Nov 24th 2014
Connected Health & Me - Matic Meglic - Nov 24th 2014ipposi
 
Quality Health Care: Technology and Data Drive Improvement by Stephen Lieber
Quality Health Care: Technology and Data Drive Improvement by Stephen LieberQuality Health Care: Technology and Data Drive Improvement by Stephen Lieber
Quality Health Care: Technology and Data Drive Improvement by Stephen LieberApollo Hospitals Group and ATNF
 
Using the EMR in early recognition and management of sepsis
Using the EMR in early recognition and management of sepsisUsing the EMR in early recognition and management of sepsis
Using the EMR in early recognition and management of sepsisHealth Informatics New Zealand
 
Electronic Medical Records - MxSecure
Electronic Medical Records - MxSecureElectronic Medical Records - MxSecure
Electronic Medical Records - MxSecureCraig Mercure
 
Clinical analytics–innovating to support clinical research
Clinical analytics–innovating to support clinical research Clinical analytics–innovating to support clinical research
Clinical analytics–innovating to support clinical research Kent State University
 

Was ist angesagt? (20)

Resource Guide to Informatics Standards
Resource Guide to Informatics StandardsResource Guide to Informatics Standards
Resource Guide to Informatics Standards
 
Personalized Medicine with IBM-Watson: Future of Cancer care
Personalized Medicine with IBM-Watson: Future of Cancer carePersonalized Medicine with IBM-Watson: Future of Cancer care
Personalized Medicine with IBM-Watson: Future of Cancer care
 
Shaping Informatics for Allied Health - Refining our voice
Shaping Informatics for Allied Health - Refining our voiceShaping Informatics for Allied Health - Refining our voice
Shaping Informatics for Allied Health - Refining our voice
 
Health Informatics Mobile Health, Telemedicine, and the Consumer
Health Informatics Mobile Health, Telemedicine, and the ConsumerHealth Informatics Mobile Health, Telemedicine, and the Consumer
Health Informatics Mobile Health, Telemedicine, and the Consumer
 
Big Data to Artificial Intelligence in Healthcare
Big Data to Artificial Intelligence in HealthcareBig Data to Artificial Intelligence in Healthcare
Big Data to Artificial Intelligence in Healthcare
 
Mobile Phone Applications for Diet and Weight Control
Mobile Phone Applications for Diet and Weight ControlMobile Phone Applications for Diet and Weight Control
Mobile Phone Applications for Diet and Weight Control
 
Real Time Location Systems in Healthcare
Real Time Location Systems in HealthcareReal Time Location Systems in Healthcare
Real Time Location Systems in Healthcare
 
Improving EMRs 2009
Improving EMRs 2009Improving EMRs 2009
Improving EMRs 2009
 
Peter Embi: Leveraging Informatics to Create a Learning Health System
Peter Embi: Leveraging Informatics to Create a Learning Health SystemPeter Embi: Leveraging Informatics to Create a Learning Health System
Peter Embi: Leveraging Informatics to Create a Learning Health System
 
Paul Coplan, VP, Johnson & Johnson_mHealth Israel
Paul Coplan, VP, Johnson & Johnson_mHealth IsraelPaul Coplan, VP, Johnson & Johnson_mHealth Israel
Paul Coplan, VP, Johnson & Johnson_mHealth Israel
 
HXR 2017: Juhan Sonin, GoInvo
HXR 2017: Juhan Sonin, GoInvoHXR 2017: Juhan Sonin, GoInvo
HXR 2017: Juhan Sonin, GoInvo
 
Consumer Health Wearables
Consumer Health WearablesConsumer Health Wearables
Consumer Health Wearables
 
Electronic Medical Records - Paperless to Big Data Initiative
Electronic Medical Records - Paperless to Big Data InitiativeElectronic Medical Records - Paperless to Big Data Initiative
Electronic Medical Records - Paperless to Big Data Initiative
 
Surveillance of social media: Big data analytics
Surveillance of social media: Big data analyticsSurveillance of social media: Big data analytics
Surveillance of social media: Big data analytics
 
Connected Health & Me - Matic Meglic - Nov 24th 2014
Connected Health & Me - Matic Meglic - Nov 24th 2014Connected Health & Me - Matic Meglic - Nov 24th 2014
Connected Health & Me - Matic Meglic - Nov 24th 2014
 
gursimran kaur
gursimran kaur gursimran kaur
gursimran kaur
 
Quality Health Care: Technology and Data Drive Improvement by Stephen Lieber
Quality Health Care: Technology and Data Drive Improvement by Stephen LieberQuality Health Care: Technology and Data Drive Improvement by Stephen Lieber
Quality Health Care: Technology and Data Drive Improvement by Stephen Lieber
 
Using the EMR in early recognition and management of sepsis
Using the EMR in early recognition and management of sepsisUsing the EMR in early recognition and management of sepsis
Using the EMR in early recognition and management of sepsis
 
Electronic Medical Records - MxSecure
Electronic Medical Records - MxSecureElectronic Medical Records - MxSecure
Electronic Medical Records - MxSecure
 
Clinical analytics–innovating to support clinical research
Clinical analytics–innovating to support clinical research Clinical analytics–innovating to support clinical research
Clinical analytics–innovating to support clinical research
 

Andere mochten auch

Eye Tracking Tests for Cognitive Impairment
Eye Tracking Tests for Cognitive ImpairmentEye Tracking Tests for Cognitive Impairment
Eye Tracking Tests for Cognitive Impairmentjetweedy
 
Framework Use in Clinical Research
Framework Use in Clinical ResearchFramework Use in Clinical Research
Framework Use in Clinical Researchjetweedy
 
Physiological signals and patients' information behavior
Physiological signals and patients' information behaviorPhysiological signals and patients' information behavior
Physiological signals and patients' information behaviorjetweedy
 
Health Disparities in the United States
Health Disparities in the United StatesHealth Disparities in the United States
Health Disparities in the United Statesjetweedy
 
Application of bio informatics
Application of bio informaticsApplication of bio informatics
Application of bio informaticsZeeshan Ahmed
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessProf. Dr. Basavaraj Nanjwade
 
Application of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesApplication of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesSobia
 
Bioinformatics
BioinformaticsBioinformatics
BioinformaticsJTADrexel
 

Andere mochten auch (8)

Eye Tracking Tests for Cognitive Impairment
Eye Tracking Tests for Cognitive ImpairmentEye Tracking Tests for Cognitive Impairment
Eye Tracking Tests for Cognitive Impairment
 
Framework Use in Clinical Research
Framework Use in Clinical ResearchFramework Use in Clinical Research
Framework Use in Clinical Research
 
Physiological signals and patients' information behavior
Physiological signals and patients' information behaviorPhysiological signals and patients' information behavior
Physiological signals and patients' information behavior
 
Health Disparities in the United States
Health Disparities in the United StatesHealth Disparities in the United States
Health Disparities in the United States
 
Application of bio informatics
Application of bio informaticsApplication of bio informatics
Application of bio informatics
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And Process
 
Application of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciencesApplication of Bioinformatics in different fields of sciences
Application of Bioinformatics in different fields of sciences
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 

Ähnlich wie Health IT seminar review

McGrath Health Data Analyst SXSW
McGrath Health Data Analyst SXSWMcGrath Health Data Analyst SXSW
McGrath Health Data Analyst SXSWRobert McGrath
 
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...Levi Shapiro
 
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...Health IT Conference – iHT2
 
Analytics in healthcare bhuvaneashwar 11th_march
Analytics in healthcare  bhuvaneashwar  11th_marchAnalytics in healthcare  bhuvaneashwar  11th_march
Analytics in healthcare bhuvaneashwar 11th_marchBhuvaneashwar Subramanian
 
What eHealth strategies work and do not work, and what should be implemented ...
What eHealth strategies work and do not work, and what should be implemented ...What eHealth strategies work and do not work, and what should be implemented ...
What eHealth strategies work and do not work, and what should be implemented ...Plan de Calidad para el SNS
 
Neale Chumbler Regenstrief 2007 Presentation
Neale Chumbler Regenstrief 2007 Presentation Neale Chumbler Regenstrief 2007 Presentation
Neale Chumbler Regenstrief 2007 Presentation ShawnHoke
 
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08Keynote-Brookstone-Physician-Voice-SingaporeITSummit08
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08alanbrookstone
 
Stfm april 28 2011
Stfm april 28 2011Stfm april 28 2011
Stfm april 28 2011Paul Grundy
 
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...Health IT Conference – iHT2
 
Oslo paul grundy nov 2014
Oslo paul grundy nov 2014Oslo paul grundy nov 2014
Oslo paul grundy nov 2014Paul Grundy
 
Information Technology in Hospitals
Information Technology in HospitalsInformation Technology in Hospitals
Information Technology in HospitalsVijay Raj Yanamala
 
New Technologies and Healthcare and Delivery Alternatives
New Technologies and Healthcare and Delivery AlternativesNew Technologies and Healthcare and Delivery Alternatives
New Technologies and Healthcare and Delivery Alternativesevinsshequanda
 
"Enabling Individual Wellness through Computational Systems Biology, Cloud An...
"Enabling Individual Wellness through Computational Systems Biology, Cloud An..."Enabling Individual Wellness through Computational Systems Biology, Cloud An...
"Enabling Individual Wellness through Computational Systems Biology, Cloud An...Hyper Wellbeing
 
2012 02 11 EHRs - healthcare system chicken soup or rotten egg
2012 02  11 EHRs - healthcare system chicken soup or rotten egg2012 02  11 EHRs - healthcare system chicken soup or rotten egg
2012 02 11 EHRs - healthcare system chicken soup or rotten eggdvreeman
 

Ähnlich wie Health IT seminar review (20)

McGrath Health Data Analyst SXSW
McGrath Health Data Analyst SXSWMcGrath Health Data Analyst SXSW
McGrath Health Data Analyst SXSW
 
State of the Nation: Health Sector Leaders Panel
State of the Nation: Health Sector Leaders PanelState of the Nation: Health Sector Leaders Panel
State of the Nation: Health Sector Leaders Panel
 
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...
mHealth Israel_Mony Weschler_Montefiore_How Data Exchange Is Essential In Sup...
 
Re-visioning Radiology
Re-visioning RadiologyRe-visioning Radiology
Re-visioning Radiology
 
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organization...
 
Analytics in healthcare bhuvaneashwar 11th_march
Analytics in healthcare  bhuvaneashwar  11th_marchAnalytics in healthcare  bhuvaneashwar  11th_march
Analytics in healthcare bhuvaneashwar 11th_march
 
What eHealth strategies work and do not work, and what should be implemented ...
What eHealth strategies work and do not work, and what should be implemented ...What eHealth strategies work and do not work, and what should be implemented ...
What eHealth strategies work and do not work, and what should be implemented ...
 
Neale Chumbler Regenstrief 2007 Presentation
Neale Chumbler Regenstrief 2007 Presentation Neale Chumbler Regenstrief 2007 Presentation
Neale Chumbler Regenstrief 2007 Presentation
 
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08Keynote-Brookstone-Physician-Voice-SingaporeITSummit08
Keynote-Brookstone-Physician-Voice-SingaporeITSummit08
 
Stfm april 28 2011
Stfm april 28 2011Stfm april 28 2011
Stfm april 28 2011
 
ACO Assessment Toolkit Webinar
ACO Assessment Toolkit WebinarACO Assessment Toolkit Webinar
ACO Assessment Toolkit Webinar
 
Patient generated-data
Patient generated-dataPatient generated-data
Patient generated-data
 
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...
Health IT Summit New York 2014 - Case Study “Investment in a Health IT Infras...
 
Oslo paul grundy nov 2014
Oslo paul grundy nov 2014Oslo paul grundy nov 2014
Oslo paul grundy nov 2014
 
Information Technology in Hospitals
Information Technology in HospitalsInformation Technology in Hospitals
Information Technology in Hospitals
 
New Technologies and Healthcare and Delivery Alternatives
New Technologies and Healthcare and Delivery AlternativesNew Technologies and Healthcare and Delivery Alternatives
New Technologies and Healthcare and Delivery Alternatives
 
2015 EMS 3.0
2015 EMS 3.02015 EMS 3.0
2015 EMS 3.0
 
IBM - 9iun2011
IBM - 9iun2011IBM - 9iun2011
IBM - 9iun2011
 
"Enabling Individual Wellness through Computational Systems Biology, Cloud An...
"Enabling Individual Wellness through Computational Systems Biology, Cloud An..."Enabling Individual Wellness through Computational Systems Biology, Cloud An...
"Enabling Individual Wellness through Computational Systems Biology, Cloud An...
 
2012 02 11 EHRs - healthcare system chicken soup or rotten egg
2012 02  11 EHRs - healthcare system chicken soup or rotten egg2012 02  11 EHRs - healthcare system chicken soup or rotten egg
2012 02 11 EHRs - healthcare system chicken soup or rotten egg
 

Mehr von Carolina Health Informatics Program @ UNC (10)

Med X Keona Health
Med X Keona HealthMed X Keona Health
Med X Keona Health
 
Clinical Research Management System
Clinical Research Management System  Clinical Research Management System
Clinical Research Management System
 
Clinical Informatics: Education, Experience, Expectations
Clinical Informatics: Education, Experience, ExpectationsClinical Informatics: Education, Experience, Expectations
Clinical Informatics: Education, Experience, Expectations
 
NC AHEC Email Tagging
NC AHEC Email TaggingNC AHEC Email Tagging
NC AHEC Email Tagging
 
Healthcare business intelligence
Healthcare business intelligenceHealthcare business intelligence
Healthcare business intelligence
 
Nc tracs project_spring2012
Nc tracs project_spring2012Nc tracs project_spring2012
Nc tracs project_spring2012
 
HIE Practicum
HIE PracticumHIE Practicum
HIE Practicum
 
A Case for linked Data for Medical Devices in the IVD Market
A Case for linked Data for Medical Devices in the IVD MarketA Case for linked Data for Medical Devices in the IVD Market
A Case for linked Data for Medical Devices in the IVD Market
 
Implementing EHR Modifications to Support Self-Care to Patients with Chronic ...
Implementing EHR Modifications to Support Self-Care to Patients with Chronic ...Implementing EHR Modifications to Support Self-Care to Patients with Chronic ...
Implementing EHR Modifications to Support Self-Care to Patients with Chronic ...
 
Carolinas Comfort Scale
Carolinas Comfort ScaleCarolinas Comfort Scale
Carolinas Comfort Scale
 

Health IT seminar review

  • 1. Health IT Seminar Review CLIFF KAUFMAN
  • 2. Focus on NC  NC Strategy for HIT Steve Cline, DDS, MPH HIT Coordinator, NC DHHS  Using Telehealth Technology for Rehabilitation Helen Hoenig MD, MPH Durham VA Med Ctr Duke University  CCNC Informatics Center Annette DuBard, MD, MPH North Carolina Community Care Networks, Inc.  NCB Prepared Steve Potenziani, PhD Executive Director, NCB-Prepared Collaborative
  • 3. NC Strategy for HIT GOALS PROBLEMS  Improved healthcare quality  Paper is inefficient  Better health outcomes  Duplicate tests  Individuals  Medical errors  Populations  Lack of information  Control costs  Too much information  Better engage health care  Consumer engagement consumers  Quality-Quality-Quality
  • 4. The 12-Step Approach 1. Admit we have a problem 2. Must get clinical information into an electronic sharable format. 3. Incentivize targeted providers to adopt EHRs and meaningful use 4. Create a new standard for EHR vendors 5. Build a mechanism for sharing health information electronically 6. Make sure healthcare providers know how to use the new systems 7. Make sure the network has the capacity for all these new users 8. Make good use of the data (Data Analytics) 9. Make good use of the technology to improve health 10. Children as a priority 11. Learn from the leaders 12. Sustainability
  • 5. Keys to Success  EHR Adoption  Consumer Engagement  Change Leadership  Strengthen the “Trust Fabric” of health info exchange  GOOD USE OF THE DATA! And the Winner Is . . . • Whoever can figure out how to take the tsunami of new health data that is heading our way and turn it into actionable health information. • Whoever can help us move from surveillance and reaction to event prediction and prevention.
  • 6. Telehealth Technology for Rehabilitation Public Health Problem  It is difficult for persons with physical disability, particularly in remote areas, to access health care.  High cost and burden of travel.  Limited rehab specialists in remote areas.  Clinicians have limited insight into how individual is functioning in home environment. What is Telehealth?  Telehealth is comprised of diverse technologies that allow health care to be provided in situations where distance separates those receiving services from those providing services.  Telehealth changes the location for providing health care services from the doctor’s office or hospital to the local clinic or the patient’s own home.
  • 8. Telehealth – Rehab Clinical Trials  Telerehabilitation for exercise & functional training:  4 RCTs with Televideo alone or with other Teletechnology.  4 different populations (geriatric gait disorder, post-stroke, ICU survivor, post-op orthopedic surgery).  Non-inferiority in clinical outcomes compared to Standard PT.  Better functional outcomes , performance-based & self report, compared to Usual Care (no PT).  Equipment reliability and visual clarity a challenge in all studies
  • 9. Teletechnology QI Study  3 types physical function tested  Fine motor coordination: finger taps (front view)  Gross motor coordination: gait (lateral view)  Spatial relationship: cane height (front & lateral views)  Reliability & validity determined  3 common Internet speeds (64, 384, 768 kps)  In person (community standard) and slow motion videotape (gold standard)  Internet bandwidth had a strong effect on validity and reliability for the fine motor and gross motor tasks.  Fine motor coordination - Reliability & Validity comparable to Standard Care @768 kps  Gross motor coordination (gait ) – Validity not comparable to Standard Care  Still spatial relationships - Reliability & Validity comparable to Standard Care at all of the bandwidths
  • 10. Teletechnology Infrastructure Security  HIPPA  Full face image and/or Voice = PHI  Can’t post cell phone video to U-tube for review  Skype isn’t HIPPA compliant Costs  Equipment  Internet access  Who pays?
  • 11. CCNC Informatics Center Information Support for Patient-Centered Care  Develop a better healthcare system for NC starting with public payers  Strong primary care is foundational to a high performing healthcare system  Additional resources needed to help primary care manage populations  Must build better local healthcare systems ( public-private partnership). Community Care is a clinical partnership, not a regulatory management agency.  Physician leadership is critical. Providers who are expected to improve care must have ownership of the improvement process  Achieve savings through better quality and efficiency of care  Timely data is essential to success
  • 13. HC Data for Population Mgmt and QI 1. Identification of High-Risk/ High-Opportunity Patients for Targeted Services (Examples: Identification of individuals with above-expected preventable utilization, Hypertension Self- Management Support) 2. Cost/utilization performance measurement coupled with actionable information (Examples: Pharmacy Initiatives, In- patient and ED Reporting) 3. Quality Measurement and Feedback coupled with actionable information (Examples: Practice Views with County, Network, and State Benchmarks; i.e., % eye exams for diabetes patients)
  • 14. ID of Patients for Case Mgmt Historically, case management efforts have = Historical or predicted costs for an individual been targeted at the highest utilizers $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K CRG#1 $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K Expected potentially preventable costs CRG#2 $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K Priority patients for care management CRG#3 $0 $1K $2K $3K $4K $5K $6K $7K $8K $9K $10K $11K $12K $13K $14K $15K $16K $17K $18K $19K $20K
  • 15. NCB Prepared A Public/Private Consortium (UNC, NCSU, SAS, DHS) focused on bio-surveillance – accurately detect and rapidly analyze biological hazards to ensure public health and safety. • Improve early recognition of outbreaks augmenting bio- surveillance • Improve situational awareness • Faster and more accurate information for decision makers • Integration with emergency management and law enforcement
  • 16. Analytics – Reactive vs. Proactive
  • 17. Data Value PROCESS Get Data Use Analytics Provide Information CLIENT OPPORTUNITIES (?) Food Pharma Finance Pub Health EMS News
  • 18. Focus on NC – Recurring Themes  Government (US & NC) Funding  Fundamental Change tied to Technology  Big Data used predictively not reflexively  Improve patient care  Security  Cost Models  Opportunities!

Hinweis der Redaktion

  1. Simply automating what we currently do will not fix the problem.Use AHEC for rural areasMedicaid paymentsCertification programNC HIE (e-prescribing, structured labs, clinical records, PH reporting)NC Community College systemMiddle mile connectivity – broadbandEvidence-based medicine, best practicesRural health strategy12. ROI, Patient centered; lower cost
  2. Real time Televide – skype, facetimeStore & Forward Telehealth – image/data xfer to central server for later reviewIn-home messaging – central server upload/download questions to patient – responses via keypad linked to telephoneemail,Virtual Reality (wheelchair training)
  3. PM&R = Physical Medicine & RehabAmp = AmputationPT/OT = Physical Therapy/Occupational Therapy
  4. RCT = Randomized Control Trial
  5.  Reliability refers to the confidence we can place on the measuring instrument to give us the same numeric value when the measurement is repeated on the same object. Validity on the other hand means that our measuring instrument actually measures the property it is supposed to measure. 
  6. CCNC takes a lot of data from many different sources and delivers web reports