Welcome! [asatn.org]asatn.org/system/files/news-documents/2017 Steering Mtg...–Transition...
Transcript of Welcome! [asatn.org]asatn.org/system/files/news-documents/2017 Steering Mtg...–Transition...
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This activity is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under cooperative agreement UA3 MC11054 – Autism Intervention Research Network on Physical Health. This information or content
and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government. This work was conducted through the Autism Speaks Autism Treatment Network serving as the
Autism Intervention Research Network on Physical Health.
Welcome!
Autism Intervention Research Network on Physical Health (AIR-P) Autism Treatment Network (ATN)
2017 Network Steering Committee Meeting
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Acknowledgement
• This meeting is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under cooperative agreement UA3 MC11054 – Autism Intervention Research Network on Physical Health and Autism Speaks.
• This information or content and conclusions are those of the presenter and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government
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Day 1 Agenda
• Welcome / Opening Comments
• AIR-P Network Accomplishments
• HRSA/MCHB (Michael Kogan and Romey Azuine)
• Autism Speaks (Thomas Frazier)
• Network of Networks (Peter Margolis)
• Panel – Future Directions
• Group Discussions – Registry
– Research
– Dissemination
• “The Family Next Door” movie screening
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Day 2 Agenda
• Care Algorithms / Standardization (Evie Alessandrini)
• Breakout Sessions
– Diagnostic Evaluation
– Anticipatory Guidance
– Transition
– Parent Training
• Closing Comments
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Participants in 2017 Steering Meeting
• Network Steering Committee representatives (2 per site)
– Clinical
– Research
• Family Advisory Committee (FAC) Members
• Coordinator Co-Chairs
• Clinical Coordinating Center
• Data Coordinating Center – MGH Biostatistics Team
• HRSA/MCHB
• Autism Speaks
• James M. Anderson Center – CCHMC
• Massachusetts League of Community Health Centers
• Guest Speakers – Evaline Alessandrini, Romey Azuine, Thomas Frazier, Peter Margolis
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Who’s Who? • Clinical Coordinating Center
– Karen Kuhlthau AIR-P PI /Co-Director, Clinical Coordinating Center – Dan Coury Co-Director, Clinical Coordinating Center – Brian Winklosky Research Program Manager – Audrey Wolfe Research Coordinator – Alyssa Taubert Administrative Coordinator – Kristin Hasselschwert Grant Manager
• HRSA
– Jessica DiBari Program Officer, Health Scientist
• Autism Speaks
– Thomas Frazier Chief Science Officer – Donna Murray Vice President, Head of Clinical Programs – Angie Fedele Director of Operations - Clinical Programs – Naomi Jackenthal Project Manager - Clinical Programs
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Data Coordinating Center Staff AIR-P/ATN Role
PI Robert Parker PI for the ATN/AS Data Coordinating Center
Contact on study design and analysis
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Eric Macklin Senior statistician, contact on study design and analysis
James Chan Statistician
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Hilda Gutierrez Project manager for AIR-P, AS ATN Registry and AS studies, study management, regulatory
Frances Lu Data manager
Info
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Richard Morse Data systems developer
Adrian Lagakos Data management
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Carolyn Hintlian Grants manager and senior administrator
Jenna Pedrin Research coordinator
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Meeting Goals
• Identify/ refine Network priorities for the coming year
• Examine in depth key issues for children and youth with ASD and related conditions
• Identify strategies to improve reach of underserved populations
• Develop plan for family integration into all network activities
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Network Accomplishments Dan Coury Karen Kuhlthau Donna Murray
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Research ECHO Autism
Dental Study
Transition Study
RFA ATN-AIR-16-09 Studies
DSM-5
Registry Call Back Study
Family Navigation
Research/Improvement:
Learning Network
Disparities Report Dissemination
ATN/AIR-P Current
Activities
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AIR-P Objectives
• Research
– Protocol-driven Network research
– Clinical research portfolio focused on improving treatment
• Mentor new investigators
• Quality and Practice Improvement
– Learning Network
– Evidence-based guidelines and toolkits
• Disseminate guidelines and research findings
– Pediatrics Supplements
– Other publications
– Capacity-building through training and dissemination activities
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ATN Objectives Creating Best Practice Standards of Care
– Manualization of ATN Care Model • A technical manual to assist other medical center develop or improve services to individuals with ASD
– Medical Guidelines in ASD
Research • Provide a ready platform for research
– Registry/Longitudinal Study • Better understand the medical issues in ASD over a lifetime
• Understand how medical complications in ASD relate to behavioral symptoms
– DSM5 Study • Compare diagnostic criteria for Autism Spectrum Disorders of the DSM-IV and DSM5 in 250 children
– Family Navigator Study • Evaluate the current state of family Navigation services within the ATN network and evaluate the outcome of family impact of
receiving family navigation services
Broadening the Reach • Disseminate information/build capacity Tool kits, Blogs, guidelines, outreach and trainings
– Provide support and training for medical providers serving individuals with ASD – Disseminate expertise & practice guidelines across nation & world – Disparities project
• Identifying disparities within the ATN and improving healthcare care for underserved populations
Family Engagement – Family Advisory committee (ATN/AIR-P and local)
• We have family collaboration on all network activities – Book of Hope
• A project of our FAC to share inspiration stories with families of the newly diagnosed
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Network Research Accomplishments
• Support for 35 AIR-P research studies in 8 years – 17 involving junior faculty as PI
– 2,000+ participants enrolled in AIR-P studies
• 155+ network abstracts at various scientific meetings
• 100+ network publications in academic journals
• Extensive study development of ATN Registry
• Advances in Autism Research & Care webinar series – Includes AIR-P, AIR-B, DBPNet, LEND, DBP Training, AUCD, AMCHP and AAP
audiences
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Network Accomplishments
• Served nearly 50,000+ patients with ASD
• Released 23 toolkits with 300,000+downloads (and counting!)
• 5 practice guidelines
• In 2016, held 1,000+ community events & training sessions throughout US and Canada - reaching 50,000+ professional & community members, mentored over 1,300 investigators and students
• Improvement – Insomnia and constipation screening and care plan development
– Anti-psychotic medication monitoring
– Improved access at 2 sites
– Enhanced network capacity to carry out activities using QI methodology
– Launching Learning Network
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Network Tool Kits A survey revealed that 93% found the ATN/AIR-P tool kits helpful and would recommend to others. 60% of the
users were professionals.
ATN/AIR-P Tool Kits ABA Guide for Parents a c
Behavioral Health Treatments b c
Blood Draws for Parents
Blood Draws for Providers a
Constipation Guide for Parents
Delivering Feedback - A Professionals' Guide and Videos
Dental Provider's Guide
EEG Guide for Parents
EEG Guide for Providers
Feeding Behavior a
Medication Decision Aid
Pica Guide for Parents a
Pica Guide for Providers a
Puberty & Adolescence
Safe Medication Use
Sleep Quick Tips for Parents a
Sleep Strategies for Children with ASD a b
Sleep Strategies for Teens with ASD a
Toileting Guide for Parents a b c
Video: Vision Exam for Individuals
Visual Supports
Melatonin * Available in Spanish
** Available in French
+ Available in Somali
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Network Assets • ATN Registry • HRSA grant to act as the AIR-P • Expert Clinical Team to develop guidelines and resources • Ready Research Platform • Network of Mentors in Clinical Care and Research in ASD • Platform for Training, Outreach, and Dissemination • Network of Family Navigators • Family Advisory Committee (Institutional and Network
level) • Network Clinical and Data Coordinating Centers • Network of Providers Trained in Quality Improvement
Methodology in Healthcare
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ATN Signature Research 2014-2017
ATN Registry (ongoing) - redesign underway for next cycle A clinical registry including medical and behavioral data on over 7,000 individuals (2-17 years of age) from ATN centers that meet criteria for ASD. A number of secondary data analysis is conducted by internal and external researchers using this registry. This is the first and largest registry of its kind.
Longitudinal Study of ATN registry participants (Phase 1 completed and data being analyzed, currently in Phase 2) A study to better understand long-term outcomes associated with ASD and the relationship of medical co-morbidities to these outcomes. Nearly 600 children from registry enrolled in phase one. Phase 2, to collect second longitudinal visit, is underway. This study will strengthen the longitudinal aspect of the ATN and provide information on the design of on-going data collection
Comparison of DSM-IV-TR and DSM-5 Diagnostic Criteria for Autism Spectrum Disorder (Completed – Manuscript completed and in review with SRC) The study examined whether specificity and sensitivity of DSM-5 ASD diagnosis relative to DSM-IV-TR ASD diagnosis is associated with IQ, comorbid behavior problems, age, or ASD symptom severity. This is an important study in providing information to clinicians using the DSM5 in making ASD diagnoses.
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Current ATN Signature Research and Projects 2014-2017
Family Navigation Study (Completed phase 1, currently in Phase2) Phase 1 qualitative descriptions of models of Family Navigation services. Phase 2 evaluating impact of family navigation services on caregiver activation/engagement and stress. This is the first study in describing “real world” models of Family Navigation service delivery in this complex clinical population. Healthcare Disparities Project (Completed, manuscript in preparation) To identify health disparities among families served by ATN sites and to identify patients from catchment area that do not access specialty care at ATN centers to inform ways to reduce barriers to healthcare for all individuals with ASD.
Development of a Care Model Manual (Phase 1 in final editing) Develop a technical manual to assist medical facilities in developing an autism center or restructure existing clinics to help better serve individuals with autism spectrum disorders and their families. Provide “one pagers” from manual chapters that are a practical tool that can be used by primary care physicians to care for children with ASD in their setting.
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Goals for 2017 and Beyond
• Improving and disseminating evidence base for care – Research (ECHO Autism, Dental Study, RFA9)
– Learning Network to facilitate improvement and translation of research to practice
– Dissemination and implementation of ATN care model
• Evaluate potential treatments and rapidly disseminate findings to practice
• Successful close-out of 2 AIR-P research projects
• Expand 1 AIR-P RFA9 study
• Growing presence at scientific meetings – IMFAR/PAS/APHA/ISBNPA 2017: 14 accepted abstracts
• Broaden reach to primary care and underserved populations
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Questions?
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MCHB Updates
Romey Azuine
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Autism Intervention Research for Physical Health (AIR-P): Steering
Committee Meeting
Romuladus E. Azuine, DrPH, MPH, RN Director, Division of Research
Office of Epidemiology and Research Maternal and Child Health Bureau
Health Resources and Services Administration U.S. Department of Health and Human Services
April 27, 2017
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Presentation Outline
• Introduction
• MCHB transformation and research networks
• MCHB and its research networks
• Emerging issues
• Demonstrating and communicating impact
• Discussions
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Background
• Family of teachers and commitment to public service
• Global journey across three ‘worlds’
• Education and training
• Father of two “MCH kids” (something personal)
• Health Scientist Administrator/Project Officer
• 2 MCH Research Networks
• PROS
• DBPNet
• Other duties
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Uniqueness of MCHB Research Networks
• Research Networks (RNs) provide unique national forums for scientific collaboration to advance practice, programs, and policies on critical MCH issues.
• RNs are MCHBs largest research investments.
• RNs have a national impact, and perform cutting-edge MCH research.
• RNs help inform MCHB’s agenda for research investments.
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MCHB Transformation
• In 2013 MCHB engaged stakeholders in a visioning process aimed at improving, innovating, and transforming the Title V MCH Services Block Grant.
• Triple aims of the transformation were to:
1. Reduce burden,
2. Maintain flexibility, and
3. Increase accountability.
• Fostered a culture of continuous quality improvement.
• To tell a compelling story of the impact of our programs on the nation’s mothers, children, and families.
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Transformation & MCHB RNs
• Strategic relationship
• Impact and outcome-driven
• Flexibility in outlook
• Collaboration across other RNs
• Adaptability to address emerging MCH policy and practice
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Emerging Issues – US Preventive Services Task Force Report
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Demonstrating & Communicating Impact
• Engage with MCHB leadership in building consensus for defining impact across stakeholders;
• Engage with MCH leadership in identifying specific measures of research impact applicable to clinical and non-clinical-based networks;
• Identify case studies of RNs’ contribution to specific physical, mental and behavioral health outcomes among pediatric and MCH populations.
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Autism Speaks
Thomas Frazier
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Network of Networks
Peter Margolis
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Panel Discussion: Future Directions
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This activity is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under cooperative agreement UA3 MC11054 – Autism Intervention Research Network on Physical Health. This information or content
and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government. This work was conducted through the Autism Speaks Autism Treatment Network serving as the
Autism Intervention Research Network on Physical Health.
Family Advisory Committee 2017
Family Integration and the Future
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Family Integration! • WE have done good family integration and engagement to date!
– HOW will this continue?
• Individual site development of FAC integration
– Utilizing the FAC Development Guide and assessing effectiveness
– Developing a plan for increased collaboration across network for FAC development
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Transition
• Beyond the registry….we need access to care NOW!
• Continued development of the ATN Care model
– Dissemination
– Communication to and with families
• 25% 12 – 17 years old have special health care need…thus each year an estimated 1 million youth with special heath care needs NEED transition support.
• 60% of these are NOT receiving needed transition support.
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Transition
• The big T transition concept
– Health care
– Other transitions
• School
• Employment
• Community
• Adulthood
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The Network • How will it have impact across these transition areas?
• How can families be an integral part of this process?
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Questions?
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Group Discussion Facilitators: Susan Levy, Micah Mazurek, Kristin Sohl
Time Keeper: Bob Parker
TOPIC #1: The Registry – Future Data Collection & Analyses of Registry Data
– Identifying the cohorts moving forward and rethinking the assessment battery – what is for clinical best practice? What is for research?
– Rethinking data collection – can we identify specific topics that need additional research and tailor data collection to these? Should measures be added?
– What additional analyses can be done using existing registry data?
TOPIC #2: Future Network Signature Projects
– Given our past and current Network research studies, what research areas of importance remain?
– Do we have the capacity to do research in these areas?
TOPIC #3: Dissemination to the Network and Beyond
– What are strategies to better disseminate Network findings and products internally and externally?
– How can we utilize social media to reach wider audiences?
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Day 1 Adjourn
Please join us for a preview of the film, “The Family Next Door”
National Ballroom
5:30pm – 7pm
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Day 2 Agenda
• Care Algorithms & Standardization
• Breakout Sessions
– Diagnostic Evaluation
– Anticipatory Guidance
– Transition
– Parent Training
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Evie Alessandrini, MD, MSCE
Professor and Associate Chair of Outcomes
Department of Pediatrics
James M. Anderson Center for Health Systems Excellence
The Benefits of Standardization in Improving Health and Healthcare
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Agenda and goals for the session
• Review the rationale for practice standardization • Understand how networks are the exemplars in standardizing
to improve health and healthcare • Case studies to apply practical tools and sustain the gains
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Institute of Medicine Committee on Quality of Healthcare in America
The purpose of the healthcare system is to reduce continually the burden of illness, injury, and disability, and to improve the health status and function of the people in the United States.
In its current form, habits, and environment, American healthcare is incapable of providing the public with the quality healthcare it expects and deserves.
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N Engl J Med 2003;348:2635-45
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55%
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N Engl J Med 2007;357:1515-23
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67%
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67% 53%
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67% 53% 41%
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Healthcare errors result in 98,000 deaths per year….
One 747 crashes every day for a year 1999
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? %
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Autism surveillance study identified 1 in 68 children as having ASD
• 1 in 42 boys and 1 in 189 girls
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IOM Framework of Improvement: Four Levels
A: Experience of Patients
B: Functioning of Microsystems
C. Functioning of Organizations
D. Environment of Policy, Payment, Regulation and
Accreditation
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Appreciation
of a system
Understanding Variation
Theory of Knowledge Psychology
Deming’s System of Profound Knowledge
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14
Knowledge for Improvement
Profound
Knowledge
Subject Matter
Knowledge
Improvement
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Deming’s System of Profound Knowledge
“If I had to reduce my
message to management to
just a few words, I would say
it all had to do with reducing
variation”
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Understanding Variation
Unintended variation is due to changes introduced into healthcare processes that are not purposeful, planned or guided
• The changes can come from decisions made or through equipment, supplies, environment, measurement, and management practices • This is the variation that creates inefficiencies, waste, rework, ineffective care, errors, and injuries in our healthcare system
Intended variation is an important part of effective, patient-centered care • also called purposeful, planned, guided, or considered variation
Reducing unintended variation nearly always results in improved outcomes and lower costs
Berwick, Donald M, Controlling Variation in Health Care: A Consultation with
Walter Shewhart, Medical Care, December, 1991, Vol 29, No 12, page 1212-1225
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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Networks are the exemplars in standardizing to improve health
and healthcare
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Standardization Improves Outcomes: Data from the Improve Care Now Network
IBD remission rates go from 50%
to 80% by applying evidence and
standardizing care:
No new discoveries!
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Standardization Improves Outcomes: Data from the Improve Care Now Network
Strengthens
ability to identify
gaps in outcomes
requiring research
/ discovery IBD remission rates go from 50%
to 80% by applying evidence and
standardizing care:
No new discoveries!
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Outcomes Improvement for Inflammatory Bowel Disease
21
• Building registries for population management with clinical and functional outcomes
• Healthcare teams reliably delivering evidence/consensus-based care
• Co-producing with patients to self-manage their disease
• Integrating research into improvement of clinical care
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Reducing Unintended Variation is a Strong Foundation for Research
Minimal
Variation
Enhances
Statistical
Power,
Detects
Impact of
New
Discoveries
Readily
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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Solutions for Patient Safety
• Network of 100+ Children’s Hospitals that share the vision that no
child will ever experience harm while we are trying to heal them
• Developed and rapidly adopted standard definitions of pediatric
hospital acquired conditions
• Standardizing, developing and implementing bundles in pediatric
care delivery to generate the evidence for pediatric prevention
standards
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Solutions for Patient Safety
• Standardizing practices and processes has
• Reduced harm including hospital acquired conditions and
readmissions
• Saved 6,944 children from serious harm
• Estimated $130 million of healthcare costs avoided
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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SP20: Changing the Outcome Together
Care PLAN
SP20
Care Algorithms
Care Algorithms are plans that detail essential steps, decisions and actions in the care of patients with a specific clinical problem, making the right care easier to provide.
Care Algorithms standardize the “practice” of healthcare, or “what we do”.
The “processes” of healthcare are “how we do it”.
Standardizing practices and processes reduces unintended variation
Facilitators of success
• Continuous pursuit of improving outcomes
• Commitment to evidence – both generation and implementation
• A culture of data-driven decision-making
• Commitment to assuring the voice of clinicians and other stakeholders, including patients and families, contribute to decisions that require consensus when evidence is lacking
• Transparently sharing performance at the level of individual sites and clinicians so we can learn faster from the best performers
• Accountability to make care more affordable by understanding how our decisions impact healthcare costs
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Where Should We Start?
Importance, Amount of Evidence
Pra
ctice o
r P
rocess V
ariation
Make it
meaningful
to you!
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0%
20%
40%
60%
80%
100% P
25 (
n=
05)
P26 (
n=
05)
P27 (
n=
06)
P28 (
n=
06)
P29 (
n=
06)
P30 (
n=
06)
P31 (
n=
06)
P32 (
n=
07)
P33 (
n=
07)
P34 (
n=
07)
P35 (
n=
07)
P36 (
n=
08)
P37 (
n=
08)
P38 (
n=
08)
P39 (
n=
08)
P40 (
n=
08)
P41 (
n=
08)
P42 (
n=
08)
P43 (
n=
08)
P44 (
n=
08)
P45 (
n=
09)
P46 (
n=
09)
P47 (
n=
09)
P48 (
n=
09)
P49 (
n=
10)
P50 (
n=
10)
P51 (
n=
10)
P52 (
n=
11)
P53 (
n=
11)
P54 (
n=
11)
P55 (
n=
12)
P56 (
n=
12)
P57 (
n=
12)
P58 (
n=
12)
P59 (
n=
12)
P60 (
n=
12)
P61 (
n=
13)
P62 (
n=
13)
P63 (
n=
14)
P64 (
n=
14)
P65 (
n=
14)
P66 (
n=
15)
P67 (
n=
15)
P68 (
n=
15)
P69 (
n=
15)
P70 (
n=
16)
P71 (
n=
16)
P72 (
n=
16)
P73 (
n=
17)
P74 (
n=
18)
P75 (
n=
18)
P76 (
n=
18)
P77 (
n=
18)
P78 (
n=
19)
P79 (
n=
19)
P80 (
n=
19)
P81 (
n=
19)
P82 (
n=
19)
P83 (
n=
20)
P84 (
n=
20)
P85 (
n=
21)
P86 (
n=
21)
P87 (
n=
22)
P88 (
n=
22)
P89 (
n=
22)
P90 (
n=
22)
P91 (
n=
22)
P92 (
n=
22)
P93 (
n=
23)
P94 (
n=
23)
P95 (
n=
23)
P96 (
n=
23)
P97 (
n=
24)
P98 (
n=
24)
P99 (
n=
24)
P100 (
n=
25)
P101 (
n=
25)
P102 (
n=
25)
P103 (
n=
26)
P104 (
n=
26)
P105 (
n=
27)
P106 (
n=
30)
P107 (
n=
31)
P108 (
n=
33)
P109 (
n=
35)
P110 (
n=
38)
P111 (
n=
43)
Pro
po
rtio
n o
f A
sth
ma
Vis
its A
dm
itte
d
Provider
Proportion of Asthma Visits Admitted by Provider Average Proportion of Asthma Visits Admitted by Provider Control Limits
Funnel Plot: Emergency Medicine Provider Admission Rates for Asthma, Fall 2015
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Start with the 7 steps Step
Number
Step Description
Asthma
1 Identify the condition or area for Care Algorithm development
“What is the scope of the problem to be addressed?” Try and right-size it!
Treatment of an acute asthma exacerbation
2 Define the main “outcomes” for improvement
“What am I trying to accomplish? How will I know that reliable implementation of my Care
Algorithm is an improvement?”
Decrease the variation in rates of admission for children with acute asthma exacerbations across providers
Decrease the cost of a 30-day asthma episode of care
3 Define the key decision points within the Care Algorithm
“Where do we know (or suspect) we have variation and/or don’t apply evidence, and that if we
reduce that variation and/or apply evidence we will improve our outcomes? Where are we going
to focus our improvement and measure its impact?”
What systemic corticosteroid should be used in an acute asthma exacerbation, including dose and duration?
What is the ideal process to follow to assure discharged patients are able to leave the ED with their asthma
exacerbation medications in hand?
What are the criteria for hospital admission for an acute asthma exacerbation?
4 Define decision methods for key decision points in the Care Algorithm
“Is evidence available to inform this decision or do we need to garner consensus with our peers
and/or key stakeholders?”
1. Corticosteroid – apply evidence
2. Discharge meds – apply evidence
3. Standardized admission criteria – group consensus
a. Nominal Group Technique
b. Delphi surveys of all clinicians
5 Define key process measures
“How do we measure that our activities reflect our Care Algorithm key decision standards?”
1. Percent of children receiving dexamethasone for those requiring systemic corticosteroids
2. Percent of children discharged with “all meds in hand”
3. Percent of children whose decision to admit meets standardized criteria
6 Define potential unintended consequences
“When we implement this Care Algorithm, what could happen that is untoward and/or
unanticipated?”
Rate of return ED visits
Rate of return ED visits resulting in hospitalization
Rate of hospital readmission
7 Recruit key content experts/stakeholders and define leaders of Care Algorithm development
“Who do we need to lead this work in order to be successful? Think physicians, nurses and
business representation!”
Emergency Medicine, Hospital Medicine, Pulmonary, Allergy, General Pediatrics, Adolescent Medicine; consider
utilization team in pharmacy
8 Draft the algorithm These are tbd
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PRAM Score and Asthma Algorithm
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PRAM Score and Asthma Algorithm
Consensus Methods Examples • Thumbs • Nominal Group • Delphi
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“Tollgates before technical solutions”
PEOPLE, PROCESS, TECHNOLOGY CHECKLIST
Clinical providers “weighed in” on the Care Algorithm
We have achieved ___% of consensus of all eligible clinicians on our critical practice
decision(s)
We have tested our Care Algorithm enough to have allowed us to “get most of the kinks out” of
it and its implementation
We have identified the team members who are responsible for collecting the data and where
data collection occurs in the workflow.
We have identified the team members who are responsible for following/acting upon the care
decisions and where the clinical action of the care decisions occurs in the workflow.
We have taken what we have learned from the above 5 steps and used that information to
improve our Care Algorithm and better understand when it is and is not applicable/relevant
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EPIC Integrated Documentation of PRAM
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Algorithm Link Embedded in Asthma Order Set
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Asthma Order Set Segmented by Risk, Weight
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0%
20%
40%
60%
80%
100%
P20 (
n=
05)
P21 (
n=
05)
P22 (
n=
06)
P23 (
n=
06)
P24 (
n=
06)
P25 (
n=
06)
P26 (
n=
06)
P27 (
n=
06)
P28 (
n=
06)
P29 (
n=
07)
P30 (
n=
07)
P31 (
n=
08)
P32 (
n=
08)
P33 (
n=
08)
P34 (
n=
08)
P35 (
n=
08)
P36 (
n=
08)
P37 (
n=
09)
P38 (
n=
09)
P39 (
n=
09)
P40 (
n=
09)
P41 (
n=
09)
P42 (
n=
09)
P43 (
n=
10)
P44 (
n=
10)
P45 (
n=
10)
P46 (
n=
11)
P47 (
n=
11)
P48 (
n=
11)
P49 (
n=
11)
P50 (
n=
11)
P51 (
n=
12)
P52 (
n=
12)
P53 (
n=
12)
P54 (
n=
12)
P55 (
n=
12)
P56 (
n=
12)
P57 (
n=
12)
P58 (
n=
13)
P59 (
n=
13)
P60 (
n=
13)
P61 (
n=
13)
P62 (
n=
13)
P63 (
n=
13)
P64 (
n=
13)
P65 (
n=
13)
P66 (
n=
13)
P67 (
n=
13)
P68 (
n=
14)
P69 (
n=
14)
P70 (
n=
14)
P71 (
n=
15)
P72 (
n=
15)
P73 (
n=
16)
P74 (
n=
16)
P75 (
n=
16)
P76 (
n=
16)
P77 (
n=
16)
P78 (
n=
17)
P79 (
n=
17)
P80 (
n=
17)
P81 (
n=
18)
P82 (
n=
18)
P83 (
n=
18)
P84 (
n=
18)
P85 (
n=
19)
P86 (
n=
21)
P87 (
n=
21)
P88 (
n=
21)
P89 (
n=
21)
P90 (
n=
21)
P91 (
n=
21)
P92 (
n=
22)
P93 (
n=
22)
P94 (
n=
22)
P95 (
n=
22)
P96 (
n=
22)
P97 (
n=
22)
P98 (
n=
22)
P99 (
n=
22)
P100 (
n=
23)
P101 (
n=
24)
P102 (
n=
24)
P103 (
n=
25)
P104 (
n=
25)
P105 (
n=
26)
P106 (
n=
27)
P107 (
n=
27)
P108 (
n=
28)
P109 (
n=
30)
P110 (
n=
33)
P111 (
n=
36)
P112 (
n=
41)
Pro
po
rtio
n o
f A
sth
ma
Vis
its A
dm
itte
d
Provider
Proportion of Asthma Visits Admitted by Provider Average Proportion of Asthma Visits Admitted by Provider Control Limits
Funnel Plot: Emergency Medicine Provider Admission Rates for Asthma, Fall 2016
ATN AIR-P Meeting
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Metric – E. Alessandrini Faculty Your Performance
Overall PEM Faculty Performance
Goal Performance
PEM Faculty Currently at Goal Performance
Proportion Asthma Visits given steroids who receive them within 60 minutes of arrival
67% (6/9) 50% (438/881) 80% 1/46
Proportion Asthma Visits Admitted 10% (1/10) 32% (342/1063) 20% 7/46
Proportion Asthma Visits with Mild PRAM given MDI Only
50% (1/2) 49% (193/392) 80% 3/46
Proportion Asthma Visits with Severe PRAM given IV Magnesium
100% (1/1) 66% (65/99) 80% 15/40
Below you will see a table with your individual performance on the asthma
metrics for all visits with diagnosis of asthma (age >=2 years at time of visit)
from 9/2016 – 2/2017 that we are tracking moving forward as a division.
ATN AIR-P Meeting
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Methodology: Staggered
Interrupted Time Series Trial
of Audit and Feedback
Audit and feedback may be most effective when
• the health professionals are not performing well
to start out with
• the person responsible for the audit and
feedback is a supervisor or colleague
• it is provided more than once
• it is given both verbally and in writing
• it includes clear targets and an action plan
Cochrane Database of Systematic Reviews
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Clinician Report Card
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Provider
performance
Clinician Report Card
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Site
performance
Clinician Report Card
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Overall network
performance
Clinician Report Card
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Achievable benchmark
of care
Clinician Report Card
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Variability of
providers within site
Clinician Report Card
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Trend of single
provider over time
Trends of site and
network over time
Clinician Report Card
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Site Results
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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Syncope/Dizziness Local Consensus Guideline Inclusion Criteria: No previous cardiac diagnosis Presenting complaint of dizziness or syncope (new visit)
Standard workup includes: Situational History Family History Physical exam
ECG
Any Red Flags?
Goal: To Minimize testing
Vasovagal / Neurocardiogenic Initial Treatment:
― Start hyperhydration with 100 oz/day H20; daily exercise routine; increase Salt intake (3-5gm/day)― No F/U; Return if symptoms worsen
If failed hydration & syncope >1 per week then consider medication ― Fludrocortisone 0.2 mg/daily or midodrine 10 mg tid while awake― Follow up in 2 months
― BP check 1-2x/week for 2 weeks
Testing shown to be generally unhelpful for initial workup of pediatric syncope:
Holter monitor Event monitor Tilt Table Test CT scan
Patient Presents
Red Flags: (Any of the following)
Demographics: Syncope(not dizziness) in Age < 8 years
Goal: To identify those patients at risk of having pathology
Echo for: Age <8 yrs of age DURING exercise, preceded by chest pain, or accompanied with physical injury from sudden fall Family history of sudden death or cardiomyopathy ECG w/ Abnormal Voltage or ST segment or T-wave changes Exam abnormal
GXT for: Age 5 – 8 w/ normal Echo DURING exercise, preceded by chest pain, or accompanied with physical injury from sudden fall with a
normal echo Refer to Electrophysiology Near drowning Syncope DURING exercise Family History of channelopathy or pacemaker/defibrillator ECG w/ QTc interval > 470ms , first degree AV block w/ a PR interval >250, pre-excitation or Brugada pattern
Refer to Cardiomyopathy FH of cardiomyopathy or sudden death <50 years of age
Refer to Channelopathy FH of sudden death < 50 years of age with a negative autopsy
Refer to Neurology Seizure activity with post ictal state Focal neurological finding after the event
Yes
No
HPISyncope (not dizziness) that occurs: DURING exercise Preceded by chest pain Accompanied by significant physical
injury from sudden fall Near drowning Seizure activity with post ictal state Focal neurological finding after event
Family HistoryFirst degree family history of: Cardiomyopathy Sudden death <50 y/o Channelopathy Pacemaker or Defibrillator
ECG QTc interval > 470ms First degree AV block w/ a PR
interval > 250ms Pre-excitation Brugada Pattern Abnormal voltage T-wave inversion Pathological ST segment changes
Exam Pathologic murmur Hepatosplenomegaly Loud S2 Abnormal cardiac/neuro exam
finding
If Work Up is Negative
Algorithm
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Algorithm Introduced
0
500
1000
1500
2000
2500
3000
3500
4000
11/0
1/2
015 (
N=
13)
11/0
8/2
015 (
N=
19)
11/1
5/2
015 (
N=
10)
11/2
2/2
015 (
N=
16)
11/2
9/2
015 (
N=
9)
12/0
6/2
015 (
N=
11)
12/1
3/2
015 (
N=
9)
12/2
0/2
015 (
N=
8)
12/2
7/2
015 (
N=
5)
01/0
3/2
016 (
N=
18)
01/1
0/2
016 (
N=
20)
01/1
7/2
016 (
N=
9)
01/2
4/2
016 (
N=
19)
01/3
1/2
016 (
N=
12)
02/0
7/2
016 (
N=
15)
02/1
4/2
016 (
N=
12)
02/2
1/2
016 (
N=
18)
02/2
8/2
016 (
N=
24)
03/0
6/2
016 (
N=
27)
03/1
3/2
016 (
N=
12)
03/2
0/2
016 (
N=
6)
03/2
7/2
016 (
N=
10)
04/0
3/2
016 (
N=
14)
04/1
0/2
016 (
N=
14)
04/1
7/2
016 (
N=
15)
04/2
4/2
016 (
N=
17)
05/0
1/2
016 (
N=
10)
05/0
8/2
016 (
N=
16)
05/1
5/2
016 (
N=
20)
05/2
2/2
016 (
N=
16)
05/2
9/2
016 (
N=
6)
06/0
5/2
016 (
N=
11)
06/1
2/2
016 (
N=
5)
06/1
9/2
016 (
N=
15)
06/2
6/2
016 (
N=
8)
07/0
3/2
016 (
N=
9)
07/1
0/2
016 (
N=
11)
07/1
7/2
016 (
N=
20)
07/2
4/2
016 (
N=
17)
07/3
1/2
016 (
N=
15)
08/0
7/2
016 (
N=
27)
08/1
4/2
016 (
N=
22)
08/2
1/2
016 (
N=
17)
08/2
8/2
016 (
N=
21)
Av
era
ge T
ota
l C
harg
e
Week
Average Total Patient Charge by Week X-bar Chart
Mean Centerline (Xbar) Control Limits K Simon, AC Analyst Data Source: Financial
Desired
Direction
Syncope Algorithm – a Focus on Reducing Waste
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Algorithm Introduced
0
500
1000
1500
2000
2500
3000
11/0
1/2
015 (
N=
13)
11/0
8/2
015 (
N=
19)
11/1
5/2
015 (
N=
10)
11/2
2/2
015 (
N=
16)
11/2
9/2
015 (
N=
9)
12/0
6/2
015 (
N=
11)
12/1
3/2
015 (
N=
9)
12/2
0/2
015 (
N=
8)
12/2
7/2
015 (
N=
5)
01/0
3/2
016 (
N=
18)
01/1
0/2
016 (
N=
20)
01/1
7/2
016 (
N=
9)
01/2
4/2
016 (
N=
19)
01/3
1/2
016 (
N=
12)
02/0
7/2
016 (
N=
15)
02/1
4/2
016 (
N=
12)
02/2
1/2
016 (
N=
18)
02/2
8/2
016 (
N=
24)
03/0
6/2
016 (
N=
27)
03/1
3/2
016 (
N=
12)
03/2
0/2
016 (
N=
6)
03/2
7/2
016 (
N=
10)
04/0
3/2
016 (
N=
14)
04/1
0/2
016 (
N=
14)
04/1
7/2
016 (
N=
15)
04/2
4/2
016 (
N=
17)
05/0
1/2
016 (
N=
10)
05/0
8/2
016 (
N=
16)
05/1
5/2
016 (
N=
20)
05/2
2/2
016 (
N=
16)
05/2
9/2
016 (
N=
6)
06/0
5/2
016 (
N=
11)
06/1
2/2
016 (
N=
5)
06/1
9/2
016 (
N=
15)
06/2
6/2
016 (
N=
8)
07/0
3/2
016 (
N=
9)
07/1
0/2
016 (
N=
11)
07/1
7/2
016 (
N=
20)
07/2
4/2
016 (
N=
17)
07/3
1/2
016 (
N=
15)
08/0
7/2
016 (
N=
27)
08/1
4/2
016 (
N=
22)
08/2
1/2
016 (
N=
17)
08/2
8/2
016 (
N=
21)
Std
Dev
To
tal C
harg
e
Week
Standard Deviation Total Patient Charge by Week S-Chart
Mean Centerline (Sbar) Control Limits K Simon, AC Analyst Data Source: Financial
Desired
Direction
Syncope Algorithm – a Focus on Reducing Waste
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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Domain Leader(s):
Two Hospitalists +
Inpatient RN
Domain Leader(s):
Two PICU Docs +
ICU RN
Domain Leader(s):
Two Oncology Docs
+ Heme-Onc RN
General Care
Units
PICU
CICU
Heme-Onc-
BMT
Emergency
Medicine
Domain Leader(s):
Two ED docs + ED
RN
Acute Care / Inpatient Care
CHA Improving Pediatric Sepsis Outcomes (IPSO) National Collaborative
Collaborative Leadership: Drs. Brilli, Macias, Niedner, and Other Domain Leaders & Disciplines (TBD)
Future Domains: Community EDs, Ambulatory Clinics, NICUs
Where Possible: Joint data analysis; common interventions; shared data; MOC
Domain Leader(s):
PMR Doc + PMR RN
Specialty Care
Hospital or
Unit(s)
Pre-
Hospital
Care
Domain Leader(s):
(ED doc+ paramedic
or EMT or clinic RN)
**National Expert Advisory Committee (includes non-geographic specific disciplines: Ped Surgery /Inf Disease /Ancillary Services (pharmacy, RT, etc.)/Parents
Initial Focus on Acute / Inpatient Care
Typical Continuum of Care
Decrease mortality from Severe Sepsis by 75% in US Pediatric Acute Care
Settings from a baseline of ~10% to 2.5% by 12/2017
Decrease the incidence of hospital-onset Severe Sepsis
in US Pediatric Acute Care Settings by 75% from ~2% to 0.5% by 12/2017
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CCHMC approach – evidence and consensus
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CCHMC approach – evidence and consensus
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Yes
Unsure
Ongoing resuscitation – Sepsis order set
□ Need for ongoing resuscitation should be driven by perfusion or BP /MAP concerns
□ 2nd and 3rd boluses given rapidly □ If patient not responding to 1st and 2nd bolus at all, consider other causes of
tachycardia/shock □ Investigate/treat potential sources of infection □ Stress dose steroids in at risk populations □ Address electrolyte deficiencies (Na+, glucose, Ca++) □ Clinician reassessment and discussion of next steps within 15 minutes of each
intervention □ Recommended additional labs and imaging (guided by clinical
situation/population) □ Plan for disposition
Evaluation □ Place on monitors □ Clinician assessment focused on perfusion □ Initial huddle: discuss whether there are signs of shock, initiation of
sepsis pathway, plan for reassessment (and MRT for floor patients)
Screen positive
Usual care/ reassessment
Yes
20
-60
min
No
Probable septic shock?
0-1
0 m
in
0-2
0 m
in
Initial diagnosis and management – Sepsis order set
□ Establish IV access □ Administer O2 □ Place patient on monitors: vitals at least q15 minutes including BP □ Administer 20 ml/kg NS via rapid infuser or push/pull unless
contraindicated □ All patients: CBC, blood culture, blood gas, lactic acid, BMP □ Order antibiotics (see recommended antibiotic list) □ Recommended additional labs and imaging (guided by clinical
situation/population) □ Clinician reassessment/discussion of next steps within 15 min of intervention
Modified pathway
Frontline provider
concern for septic
shock
Usual care/ reassessment
and disposition
Evidence-Based Care Algorithm for the Management of Septic Shock
Watcher/SA
concern for septic
shock
Ongoing signs of shock?
No
Recommended antibiotics
Rapid fluid administration / contraindications
Recommended labs/imaging
MRT considerations
MAP for age
Stress dose steroid recommendations
Recommended labs/imaging
Disposition considerations
Signs of altered perfusion
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• Decreased average time to first bolus from 92 minutes to 33 minutes (among screen-positive patients treated for presumed septic shock)
• Decreased days between sepsis-related preventable deterioration from 13 to > 50
61
Results at CCHMC
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Why Standardize & Reduce Unintended Variation?
Promotes
Efficiency by
Reducing
Waste &
Costs
Improves
Research Facilitates
Customization
Improves
Experience
by Allowing
Prediction
Clarifies
Roles: “Top
of our
Licensure”
Reduces
Errors &
Harm
Improves
Outcomes
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ATN AIR-P Meeting
The
Learning
Healthcare
System
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Logic Model
Tool Development
Analytics
PEOPLE:
Leadership team with vision and
expertise
Development team with expertise and
timeEvidence,
Measurement,Analytics,Process
Improvement,Informatics
Clinical teams with will, ownership and
time
Resources Activities Outputs
Short Term Long Term
Outcomes
TECHNOLOGY
DATAClinical
Financial
DISSEMINATION
Marketing/communication
campaign
Current State and Variation in:
CostsPractice
Outcomes
Care AlgorithmsEvidence evaluationConsensus methods
Cost tool kit
FY16Number of care
algorithms developed;Number of cost metrics
delineated;Evaluation of Learning
Sessions and Care Algorithm Methodology
and Tools
FY17Percent of algorithms
with reliable implementation* of
standard practice decision;
Percent of algorithms measure charges at the
“episode” level, and understand variation in
charges;Division/institute
capability understood in measuring and
improving the value equation
FY19 and 20OUTCOME FOCUSED:
Improved “VALUE” of care
Improved outcomes,
Decreased costs
IMPACTDeliver Exceptional, Safe and Affordable Care for every child
and every family, every day
FY17 and 18PROCESS FOCUSED:
Increased use of evidence and
consensus-based care practices
Decreased variation in care practices
Increased use of more affordable care options
Increased division/institute capability in
measuring and improving the value
equation
Team Building and Training
Algorithm Sustainability
Train teams on using tools and reports
Collaborative methodIndividual team training
Responsible personnel;
Reliable processes;Information-enabled
solutions for tool implementation
LOGIC MODEL FOR CARE ALGORITHMS & COST REDUCTION – May 24th, 2016
CURRICULUM &METHODOLOGY
Information Dissemination
Create email account for
questions; develop communications
documents
Intermediate Term
Reports
Accurate and standardized data
reports:Practice variation
CostsOutcomes
Tools
Care algorithm development
manual;Paper algorithms;
Algorithms hardwired by technology;
Visualization tools for outcomes,
practice variation, costs
Teams
Team Charter;Roles and
responsibilities of team members;
methods of incorporating results
into daily work
Communication Materials
SharePoint site;CenterLink space;
Email blasts;Print documents
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Step Number Step Description Asthma
1 Identify the condition or area for Care Algorithm development
“What is the scope of the problem to be addressed?” Try and right-size it!
Treatment of an acute asthma exacerbation
2 Define the main “outcomes” for improvement
“What am I trying to accomplish? How will I know that reliable implementation of my Care Algorithm is an improvement?”
Decrease the variation in rates of admission for children with acute asthma exacerbations across providers
Decrease the cost of a 30-day asthma episode of care
3 Define the key decision points within the Care Algorithm
“Where do we know (or suspect) we have variation and/or don’t apply evidence, and that if we reduce that variation and/or apply
evidence we will improve our outcomes? Where are we going to focus our improvement and measure its impact?”
What systemic corticosteroid should be used in an acute asthma exacerbation, including dose and duration?
What is the ideal process to follow to assure discharged patients are able to leave the ED with their asthma
exacerbation medications in hand?
What are the criteria for hospital admission for an acute asthma exacerbation?
4 Define decision methods for key decision points in the Care Algorithm
“Is evidence available to inform this decision or do we need to garner consensus with our peers and/or key stakeholders?”
1. Corticosteroid – apply evidence
2. Discharge meds – apply evidence
3. Standardized admission criteria – group consensus
a. Nominal Group Technique
b. Delphi surveys of all clinicians
5 Define key process measures
“How do we measure that our activities reflect our Care Algorithm key decision standards?”
1. Percent of children receiving dexamethasone for those requiring systemic corticosteroids
2. Percent of children discharged with “all meds in hand”
3. Percent of children whose decision to admit meets standardized criteria
6 Define potential unintended consequences
“When we implement this Care Algorithm, what could happen that is untoward and/or unanticipated?”
Rate of return ED visits
Rate of return ED visits resulting in hospitalization
Rate of hospital readmission
7 Recruit key content experts/stakeholders and define leaders of Care Algorithm development
“Who do we need to lead this work in order to be successful? Think physicians, nurses and business representation!”
Emergency Medicine, Hospital Medicine, Pulmonary, Allergy, General Pediatrics, Adolescent Medicine; consider
utilization team in pharmacy
8 Draft the algorithm Follow guiding principles
Revise and update existing or other organization’s algorithms as available and relevant
9 Apply evidence or consensus-based decisions to the algorithm as determined in step 4 These are tbd
1. Use dexamethasone at 0.6mg/kg to max of 16mg q24h x 2 doses
2. Meds in hand
3. Standardized admission criteria
10 Test algorithm in a small sample Who – lead clinician, QIC?
What – algorithm on paper, space for written feedback included
When – include all relevant times of day
Where – include all relevant contexts and locations
11 Study preliminary results to inform algorithm revisions
a. Key process measures
b. Main outcome measures
c. Unintended consequences
Per steps 2, 5 and 6
12 Revise algorithm per test samples (PDSA) See steps 11 and 12
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Care Algorithm Example: Diagnosis of Suspected Appendicitis Proportion of patients undergoing CT scanning: high suspicion
Proportion of patients undergoing CT scanning: equivocal
Total average charges: CT scan of abdomen/ pelvis $5,316 Ultrasound, single quadrant $ 995 No change in negative appendectomy or missed appendicitis rates
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Questions?
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Day 2 Adjourn