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

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

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

Day 2 Agenda

• Care Algorithms / Standardization (Evie Alessandrini)

• Breakout Sessions

– Diagnostic Evaluation

– Anticipatory Guidance

– Transition

– Parent Training

• Closing Comments

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

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

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

Ad

dit

ion

al

Bio

stat

isti

cs

Eric Macklin Senior statistician, contact on study design and analysis

James Chan Statistician

Stu

dy

Mgm

t

Hilda Gutierrez Project manager for AIR-P, AS ATN Registry and AS studies, study management, regulatory

Frances Lu Data manager

Info

rmat

ics

Richard Morse Data systems developer

Adrian Lagakos Data management

Ad

min

Carolyn Hintlian Grants manager and senior administrator

Jenna Pedrin Research coordinator

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

Network Accomplishments Dan Coury Karen Kuhlthau Donna Murray

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

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

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

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

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

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

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

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.

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.

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

Questions?

MCHB Updates

Romey Azuine

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

Presentation Outline

• Introduction

• MCHB transformation and research networks

• MCHB and its research networks

• Emerging issues

• Demonstrating and communicating impact

• Discussions

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

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.

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.

Transformation & MCHB RNs

• Strategic relationship

• Impact and outcome-driven

• Flexibility in outlook

• Collaboration across other RNs

• Adaptability to address emerging MCH policy and practice

Emerging Issues – US Preventive Services Task Force Report

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.

Contact Information

Romuladus (Romey) E. Azuine Email: RAzuine@hrsa.gov

Tel: 301-443-2410

Autism Speaks

Thomas Frazier

Network of Networks

Peter Margolis

Panel Discussion: Future Directions

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

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

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.

Transition

• The big T transition concept

– Health care

– Other transitions

• School

• Employment

• Community

• Adulthood

The Network • How will it have impact across these transition areas?

• How can families be an integral part of this process?

Questions?

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?

Day 1 Adjourn

Please join us for a preview of the film, “The Family Next Door”

National Ballroom

5:30pm – 7pm

Day 2 Agenda

• Care Algorithms & Standardization

• Breakout Sessions

– Diagnostic Evaluation

– Anticipatory Guidance

– Transition

– Parent Training

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

ATN AIR-P Meeting

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

ATN AIR-P Meeting

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.

ATN AIR-P Meeting

N Engl J Med 2003;348:2635-45

ATN AIR-P Meeting

55%

ATN AIR-P Meeting

N Engl J Med 2007;357:1515-23

ATN AIR-P Meeting

67%

ATN AIR-P Meeting

67% 53%

ATN AIR-P Meeting

67% 53% 41%

ATN AIR-P Meeting

Healthcare errors result in 98,000 deaths per year….

One 747 crashes every day for a year 1999

ATN AIR-P Meeting

? %

ATN AIR-P Meeting

Autism surveillance study identified 1 in 68 children as having ASD

• 1 in 42 boys and 1 in 189 girls

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

ATN AIR-P Meeting

Appreciation

of a system

Understanding Variation

Theory of Knowledge Psychology

Deming’s System of Profound Knowledge

14

Knowledge for Improvement

Profound

Knowledge

Subject Matter

Knowledge

Improvement

ATN AIR-P Meeting

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”

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

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

Networks are the exemplars in standardizing to improve health

and healthcare

ATN AIR-P Meeting

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!

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!

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

Reducing Unintended Variation is a Strong Foundation for Research

Minimal

Variation

Enhances

Statistical

Power,

Detects

Impact of

New

Discoveries

Readily

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

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

ATN AIR-P Meeting

ATN AIR-P Meeting

ATN AIR-P Meeting

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

ATN AIR-P Meeting

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

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

Where Should We Start?

Importance, Amount of Evidence

Pra

ctice o

r P

rocess V

ariation

Make it

meaningful

to you!

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

ATN AIR-P Meeting

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

PRAM Score and Asthma Algorithm

PRAM Score and Asthma Algorithm

Consensus Methods Examples • Thumbs • Nominal Group • Delphi

“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

EPIC Integrated Documentation of PRAM

Algorithm Link Embedded in Asthma Order Set

Asthma Order Set Segmented by Risk, Weight

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

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

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

Clinician Report Card

Provider

performance

Clinician Report Card

Site

performance

Clinician Report Card

Overall network

performance

Clinician Report Card

Achievable benchmark

of care

Clinician Report Card

Variability of

providers within site

Clinician Report Card

Trend of single

provider over time

Trends of site and

network over time

Clinician Report Card

Site Results

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

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

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

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

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

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

CCHMC approach – evidence and consensus

CCHMC approach – evidence and consensus

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

• 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

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

ATN AIR-P Meeting

The

Learning

Healthcare

System

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

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

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

Questions?

Day 2 Adjourn