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Building Local Capacity for Data Analysis and Use. Sharon Walsh , Data Accountability Center (DAC) Mary Anne White , Infant & Toddler Connection of VA Haidee Bernstein , Data Accountability Center ( DAC) Beverly Crouse , Infant & Toddler Connection of VA. Data Accountability Center Goal.

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building local capacity for data analysis and use

Building Local Capacity for Data Analysis and Use

Sharon Walsh, Data Accountability Center (DAC)

Mary Anne White, Infant & Toddler Connection of VA

Haidee Bernstein, Data Accountability Center (DAC)

Beverly Crouse, Infant & Toddler Connection of VA

data accountability center goal

Data Accountability CenterGoal

Form partnerships in states that join state and local agencies in the use of data to drive improved results

premises
Premises

Data Use Involves:

  • Working through a Collaborative Team approach.
  • Engaging Team in a Continuous Improvement Process.
  • Relating the Data to specific Problem/Issue.

Using Data is an Iterative Process!

important points for helping local data teams be successful
Important Points for Helping Local Data teams Be Successful

How do you maximize data you already collect and collect what you need?

How do you organize your staff and your agency around ongoing data use?

Its all about continuous improvement

Use data to determine priority for focus

It is important to “drill down” to understand performance to identify meaningful solutions

slide6

Action

1. Identify relevant data

2. Conduct data analysis to generate hypothesis

5. Evaluate Progress

Data Analytics

Preparation

3. Test Hypothesis to determine root cause

4. Plan for Improvement

Inquiry

DATA ACCOUNTABILITY CENTER

DATA ANALYTICS

historical perspective
Historical Perspective

Historical Perspective

historical perspective1
Historical Perspective

Historical Perspective

Leadership Academy April 2010

Two Sessions Held: Plenary & Breakout

    • Plenary:

overview of use of quality of data

    • Breakout sessions:

Use of actual local data

Results:

  • Positive feedback from meeting evaluations
  • Wanted more time to spend on the activity
  • First activity in all CAP or SEP’s developed requires a data analysis be completed
haidee s stuff
Haidee’s stuff

From Notes to PowerPoint

slide13

Local Lead Agencies

Infant & Toddler Connection of

1 Alexandria 11 Danville-Pittsylvania 21 the Highlands 31 Prince William, Manassas and Manassas Park

2 the Alleghany Highlands 12 Dickenson 22 Loudoun 32 Rappahannock-Rapidan

3 Arlington 13 Crater District 23 Middle Peninsula-N Neck 33 Rappahannock Area

4 the Roanoke Valley 14 the Eastern Shore 24 Mount Rogers 34 the Blue Ridge

5 Central Virginia 15 Fairfax-Falls Church 25 the New River Valley 35 Richmond

6 Chesapeake 16 Goochland-Powhatan 26 Norfolk 36 the Rockbridge Area

7 Chesterfield 17 Hampton-Newport News 27 Shenandoah Valley 37 Southside

8 Williamsburg * James City * York * Poquouson 18 Hanover 28 the Piedmont 38 Valley

9 Planning District 14 19 Harrisonburg-Rockingham 29 LENOWISCO 39 Virginia Beach

10 Cumberland Mountain 20 Henrico-Charles City-New Kent 30 Portsmouth 40 Western Tidewater

Local Lead Agencies

Infant & Toddler Connection of

1 Alexandria 11 Danville-Pittsylvania 21 the Highlands 31 Prince William, Manassas and Manassas Park

2 the Alleghany Highlands 12 Dickenson 22 Loudoun 32 Rappahannock-Rapidan

3 Arlington 13 Crater District 23 Middle Peninsula-N Neck 33 Rappahannock Area

4 the Roanoke Valley 14 the Eastern Shore 24 Mount Rogers 34 the Blue Ridge

5 Central Virginia 15 Fairfax-Falls Church 25 the New River Valley 35 Richmond

6 Chesapeake 16 Goochland-Powhatan 26 Norfolk 36 the Rockbridge Area

7 Chesterfield 17 Hampton-Newport News 27 Shenandoah Valley 37 Southside

8 Williamsburg * James City * York * Poquouson 18 Hanover 28 the Piedmont 38 Valley

9 Planning District 14 19 Harrisonburg-Rockingham 29 LENOWISCO 39 Virginia Beach

10 Cumberland Mountain 20 Henrico-Charles City-New Kent 30 Portsmouth 40 Western Tidewater

possible reactions
Possible Reactions

Negative Reactions

Positive Reactions

Potential Facilitators

In the long run this will save time

I didn’t know this was possible

This information will help me do my job better

This information will help families

  • Potential Roadblocks
    • I do not have time for this
    • I already know this
    • I know the problems
    • I have the solutions
what is your purpose
What is Your Purpose

Reactive

Proactive

Example: Conduct quality review or assessment to determine areas of need

Purpose: To Proactive look at the quality of data

  • Example: Responding to an issue such as monitoring results
  • Purpose: To address monitoring results that are below the state target

Good Idea

Good Idea

pre on site visit
Pre On-Site Visit

With Local System Managers

  • Discuss purpose of data analysis process
  • Discuss potential data team members
  • Identify ITOTS reports to be reviewed
  • Identify data from other sources that need to be reviewed
    • Pull three years worth of data

Desk Audit

  • Review and analyze same data as local system
  • Formulate questions about data
  • Identify additional data that may need to be collected
first on site visit

Define and Articulate the Problem

  • Define the Problem/Issue

Preparation

Inquiry

  • . Identify Relevant Data
  • . Conduct Data Analysis to Generate Hypotheses
  • . Test Hypotheses to Determine Actionable Causes
First On-Site Visit
beginning the journey
Beginning the Journey
  • Complete the Preparation Phase and part of Inquiry Phase

2. Review the data reports

    • “What does the data tell you?”
      • What are the good things the data is telling you?
      • What surprises you about the data?
      • What questions strike you as you look at the data?
      • What data appears to be missing?
    • What are the good things the data is telling you?
    • What data appears to be missing?
slide25

Infant & Toddler Connection of Playground City

Referral Outcome by Referral Source

7/1/09 – 7/30/10

slide26
Infant and Toddler Connection of Playground CityReferral Outcome by Referral Source (7/01/09-7/30/10)

1. Information Local System Gathered through this report:

53% of all referrals are evaluated;

47% are not evaluated

  • 46% of all referrals will receive services
  • 6% of all referrals were evaluated ineligible
  • 11% of all referrals were lost to contracts
  • 9% of all referrals declined screening

26% of all referrals declined an evaluation

2. Physician Referrals: 26% of all referrals

3. Parent Referrals: 19% of all referrals

4. Health: 15% of all referrals

5. Dept. of Social Services: 14% of all referrals

A. Physician Referrals: 39% were evaluated; 69% were not evaluated. Of those not evaluated, 39% declined either screening or evaluation.

B. Family Referrals: 65% were evaluated; 35% were not evaluated. Of those not evaluated, 100% declined screening or evaluation.

C. Health Referrals: 56% were evaluated; 44% were not evaluated. Of those not evaluated, 43% were lost to contact and 57% declined screening or evaluation

D. DSS Referrals: 40% were evaluated; 60% were not evaluated. Of those not evaluated, 22% were lost to contact and 78% declined screening or evaluation

additional data needed
Additional Data Needed
  • What is the average age of referrals?
  • Which physicians are referring?
    • Specific name versus name of practice
    • What is the average age of the physician referral?
  • How do families hear about Part C services?
  • Why are families declining Part C services?
    • At what point in the process are families declining Part C services?
second on site visit

Inquiry

  • . Identify Relevant Data
  • . Conduct Data Analysis to Generate Hypotheses
  • . Test Hypotheses to Determine Actionable Causes
Second On-Site Visit
data collection 8 1 10 11 30 10
Data Collection (8/1/10 – 11/30/10)
  • Average age of referral: 16 months
  • 10 referrals received from physicians:
    • Average age of referral: 14 months
      • Dr. Swingset: 0 referrals received < 18 months
      • Dr. Sandbox: 0 referrals received < than 24 months
      • Dr. Bottle: average age of referral is 9 months; 50% of declined a screening
  • No referrals from the NICU at the ABC hospital
data collection 8 1 10 11 30 101
Data Collection (8/1/10 – 11/30/10)
  • 12 referrals received from family’s:
    • 7 Families declined services:
      • 57% of families felt their child was developing at age level
      • 43% of families wanted to receive services through a private agency
  • 5 families declined a developmental screening
  • 2 families declined Assessment for Service Planning
itc playground city hypotheses
ITC Playground City Hypotheses
  • Physicians are not referring children at very young ages
  • Physicians are not providing families with a complete explanation of early intervention and reason for referral
  • Hospitals are not referring premature babies
final on site visit

Inquiry

  • . Identify Relevant Data
  • . Conduct Data Analysis to Generate Hypotheses
  • . Test Hypotheses to Determine Actionable Causes
Final On-Site Visit

Action

  • Develop and Implement Improvement Plan
  • 7. Evaluate Progress
final on site visit1
Final On-Site Visit

Moving from inquiry to action

  • Review Hypotheses
    • Were we correct?
    • Do we need to re-look at data to formulate new or additional hypotheses?
  • Improvement Planning and Evaluating Progress
    • Consider priorities
    • Sphere of Influence
    • Use data to determine if moving in right direction
itc playground city improvement plan
ITC Playground City Improvement Plan

Plan to address increase in referrals of premature babies from NICU:

  • Identify Discharge Social Workers, Nurses or Therapists responsible for referrals
  • Meet with individuals
  • Gather data from hospital (# of premature births residing in their community, where are referrals being made)
  • Provide information about EI in Virginia
  • Collaboratively develop mechanism to meet with family prior to NICU discharge
things to remember
Things to Remember

States can assist local agencies/programs to remember:

It is all about improved quality of services for children and families

Hard to let go of traditional improvement planning

Hard to let go of your own sense of what the problem/solution is

Follow the data where it leads you

Ask the difficult questions

Create an environment where solutions are generated