Big Data/Little Data Big Team/Little...

41
Big Data/Little Data Big Team/Little Team Pat Teske, MHA, RN Cynosure [email protected] 1

Transcript of Big Data/Little Data Big Team/Little...

Page 1: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Big Data/Little Data

Big Team/Little Team

Pat Teske, MHA, RN

Cynosure

[email protected]

1

Page 2: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Do you operate like this?

2

Page 3: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Should you continue?

3

Page 4: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

This is NOT the answer?

4

Page 5: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

What would be better?

5

Page 6: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Big DATA + Little DATA = A better approach

BIG Data

• The entire readmissions population

• Dice and slice by payer, REaL, etc.

• Learn which groups are readmitted at a higher rate

• These are the groups you will TARGET with special effort

Little Data

• What you are learning on a day-to-day basis

• From patients, providers, case review

• Help you understand where the gaps are in your current processes and program

• Helps you decide WHAT to prioritize from a PI perspective

6

Page 7: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Why use big data?

• Medicare readmission penalties drive many approaches

• But these DRGs are not the top reasons for readmissions in all populations

• Focusing on these DRGs only will not reduce hospital-wide readmission rates and leaves many needs unmet

7

Page 8: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Let’s drill down

8

Page 9: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

9

Page 10: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

30-Day Potentially Preventable Readmission (PPR) Rates by Race and

Ethnicity

Page 11: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Little data provide a different perspective

• Why ask the patients and providers?

– Gain their perspectives

– Understand reasons

– Identify gaps

– Develop a better plan for the specific patient

– Design a more effective program

• Why do case reviews (focus on quick returns)?

– Determine care gaps

– Look at plans overtime

– Prioritize repeated issues

11

Page 12: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

“I RAN OUT OF LASIX”

51 year old male with 3 acute care admissions and 2 ED visits in the past 180 days.

When asked why he thought he was readmitted said…

12

Page 13: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Aggregate and prioritize

13

Page 14: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Ongoing data

• Monitor what matters to you

• If you aren’t where you need to be - adjust

14

Page 15: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Other data to consider

• Inventory your efforts

– Across departments

– Coordination of activities

– Check for duplication

– Look for gaps

• Inventory community resources

– Clinical

– Non-clinical

15

Page 16: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Framing or Reframing Your Approach

Care Continuum

Ris

k fo

r R

ead

mis

sio

n

Now that you’ve reviewed your data take a look at your current approach. One way to think about it is along the lines of risk of readmission and along the care continuum. Build out the blank slide to show a picture of your overall approach. Does the current approach match the needs you identified in your analysis? If not, how do you want to modify your approach?

Page 17: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Risk Community ED Hospital Based Immediate Post Hospitalization

Ris

k fo

r R

ead

mis

sio

n

Page 18: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Risk Community ED Hospital Based Immediate Post Hospitalization

Ris

k fo

r R

ead

mis

sio

n

Special programs such as:• Complex Care

Management (CCM)• Social programs

Highestutilizer care

plans

BASIC bundle +moderate to high bundle• Palliative care

BASIC post discharge bundle + moderate to high bundleANDstronger linkage with community programs

PCP/care team management per patient needs with prioritized post discharge visit or outreach• Disease specific

programs

BASIC bundle + moderate to high bundle:• Care transitions

nurse• Pharmacy

intervention

BASIC post discharge bundle + moderate to high bundle:• 7 day f/u

appointment• f/u call(s)/visits

Routine PCP/care team management per patient needs

Admit BASIC bundle:• Maximize PFE in

discharge planning• Inc. meds, f/u, signs

& symptoms and what to do

• Education using

BASIC post discharge bundle:• Referrals• Instructions

Example to get you started framing your

approach

Page 19: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

1. Partnering with other hospitals in the local area to reduce readmissions2. Tracking % of patients discharged with a follow-up appointment already scheduled within 7 days3. Tracking % of patients readmitted to another hospital4. Estimating risk of readmission in a formal way and using it to guide clinical care during hospitalization5. Having electronic medical record or web-based forms in place to facilitate medication reconciliation6. Using teach-back techniques for patient and family education7. At discharge, providing patients with heart failure written action plans for managing changes8. Regularly calling patients after discharge to follow up on post-discharge needs9. Discharging patients with an outpatient follow-up appointment already scheduled

Yale Global Health LD Institute

Page 20: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• Hospitals that took up any 3 or more strategies had significantly greater reductions in RSRR compared with hospitals that took up only 0-2 strategies.

• -93 different combinations of strategies

• High and low performing groups both used recommended clinical practices.

• Four specific approaches distinguished high performers– Collaboration across

departments/ disciplines

– Working with post-hospital providers

– Learning and problem solving

– Senior leadership support

What works?

Page 21: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Operationalizing your plan

Little team

• Individuals within the hospital who contribute to effective care transitions

• Who are they?– CM, SS, Nursing, IT, Physician,

Therapy Services, Pharmacy, Palliative Care, ED, navigators, a pt or family caregiver

• Work on highest priority issues– Basic services for all

– Enhanced services for selected populations

Big team

• Individuals across the continuum who receive/manage your patients

• Who are they?– SNFs, HH, LTAC, PCPs, BH

clinics, post discharge clinics, insurance providers, ssagencies, ACOs, medical homes, complex cm, EMS

• Work to understand and improve gaps in services for shared patients

21

Page 22: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• Get people in the same room

• Learn what everyone has to offer

• Learn what everyone's frustrations are

• Start with one issue and go from there

Simple but effective

Page 23: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• http://interact2.net/tools.html

– Free tools for:• Nursing homes

• Home health

• Assisted living

• LTAC

Encourage your partners to use

Page 24: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire
Page 25: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• Shadow program

• ED & SNF

• Experience a day in the life

• Stronger understanding and empathy

25

Walk a mile in my shoes

Page 26: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• http://ctb.ku.edu/en/table-of-contents/assessment/assessing-community-needs-and-resources/identify-community-assets/main

26

Community Asset Mapping

Page 27: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• At WVU Hospitals, in Morgantown, W.V., physicians and medical residents teamed up to see their patients at the hospital’s outpatient clinic, within 7 to 14 days after discharge. – A psychologist, pharmacist and nurse case manager soon joined the team.

– Medical residents talk with patients before discharge, explaining the follow-up process and ensuring patients have a pre-scheduled appointment.

– The nurse case manager tracks all appointments, contacting patients until they are seen.

– On clinic days, the team huddles in the early afternoon and sees patients afterward.

– With this team-based follow-up care, 80-85 percent of patients are seen within 14 days

of discharge.

• One additional benefit: discharge summaries have improved now that residents use their own summaries for the follow-up.

• Karen Fitzpatrick, M.D., quality director, WVU

Teams

Page 28: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Next Steps!

• Perform an analysis of your big data

• Aggregate your little data

• Evaluate your little and big team membership

• What changes are needed?

– Your approach

– Team(s)

28

Page 29: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Any questions?

29

Page 30: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Skill Building

CMS discharge planning checklist

Practice interviewing your readmitted patients

30

Page 31: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• Patients and caregivers

CMS Discharge Planning Checklist

Page 32: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Instructions

Page 33: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

What’s ahead

Page 34: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

• Download the CMS discharge planning checklist

• https://www.medicare.gov/Pubs/pdf/11376.pdf

• If you’re not already using it, make a plan to start

How are you using it?

Page 35: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Learning from our patients

35

Page 36: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Why & How

• To get the story behind the chief complaint

• Ask open ended questions

– Why do you think you were readmitted?

– What do you think needs to happen differently when you go home this time?

• Drill down with why, why, why to get to the causes

• Explore non-clinical reasons as well - $, access, housing, etc.

• Note all causes36

Page 37: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Listen for themes

• Leaving the hospital unprepared, or inadequately informed, without specific instructions on what to do

• A lack of coordination - Challenges in accessing services: appointments, transportation, medications, equipment

• Changing circumstances after discharge

• PCP instructions to return to the ED

• Readmissions are O.K., expected, or very frustrating

37

Page 38: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Barb-Interviewer, Pat-Pt., You-Observers

38

Page 39: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Time to practice

• Get into groups of three

– Interviewer

– Interviewee

– Observer

• Conduct a mock interview (5 minutes)

• Debrief

• Switch and repeat

• Discuss themes & observations

39

Page 40: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire
Page 41: Big Data/Little Data Big Team/Little Teamk-hen.com/Portals/16/Education/QualitySymposium2017/BigDataPresentation.pdfBig DATA + Little DATA = A better approach BIG Data • The entire

Pat Teske, RN, MHAImplementation Officer

Cynosure Health

[email protected]

41