PDGM: Relating Analytics to Operational Performance · 2019. 10. 10. · 10/10/2019 1 PDGM: Relating Analytics to Operational Performance M. Aaron Little, CPA Karen Vance, BSOT BKD,

PDGM: Relating Analytics to Operational Performance

10/10/2019

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PDGM: Relating Analytics toOperational Performance

M. Aaron Little, CPA Karen Vance, BSOTBKD, LLP BKD, LLP

[email protected] [email protected]

Today’s Objectives

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Identify PDGM operational performance management KPIs

Apply benchmarks for PDGM KPIs based on historical performance data

Relate KPIs & best practice concepts to PDGM readiness

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Is PDGM really budget neutral?

Missouri Estimated PDGM Financial Impact(before behavior adjustments)

4 Note: Per CMS 2018 LDS data for all Missouri home health agencies

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-3.2%

• Based on proposed 2018 CMS data

• Negative impact of 10% or more

• 45 agencies

• Negative impact of 5% to 10%

• 27 agencies

• Negative impact less than 5%

• 32 agencies

• Zero or positive impact

• 51 agencies



-3.2%

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-3.2%



-3.2%

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-3.2%



• 48% Community, late• 29% Institutional, early

• 21% MS Rehab• 13% QEs

• 2.0 average functional impairment grouping

• 0.5 average comorbidity adjustment score

• 1.0803 average case-mix weight (non-LUPA)

• 10% LUPAs

• 39 days average episode length

• 10 average visits per payment period

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So…what’s the difference between

agencies?

What’s the Difference?

12 Note: Per CMS 2018 LDS data

• Freestanding, health system owned

• Non-profit

• Urban

• Missouri• $9 million annual Medicare

revenues

• 9% estimated payment increase under PDGM

Agency A

• Freestanding, part of multi-state organization

• For profit

• Urban

• Missouri• $2 million annual Medicare

revenues

• 9% estimated payment decrease under PDGM

Agency B

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KPI Agency A Agency B

Overall estimated financial impact 9% increase 9% decrease

Average case‐mix weight* (*non‐LUPA) 1.1819 0.9682

Community, late* 45% 69%

Institutional, early* 32% 13%

Top clinical grouping* (MS Rehab) 18% (MS Rehab) 26%

Total QEs 9% 19%

Average functional impairment grouping* 2.6 2.0

Average comorbidity adjustment* 0.7 0.5

LUPAs 17% 6%

Average episode length 37 days 52 days

Average visits per payment period 9 10

What’s the Difference? MS Rehab(Agency A = top row, Agency B = bottom row)


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What’s the Difference? MS Rehab(Agency A = top row, Agency B = bottom row)


What’s the Difference? MMTA – Cardiac (Agency A = top row, Agency B = bottom row)


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What’s the Difference? MMTA – Cardiac(Agency A = top row, Agency B = bottom row)






Average case‐mix weight* (*non‐LUPA) 1.1819 0.9682



Top clinical grouping* (MS Rehab) 18% (MS Rehab) 26%

Total QEs 9% 19%

Average functional impairment grouping* 2.6 2.0


LUPAs 17% 6%



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Umm…OK? Nice data …but how is

it useful?

Where is Your Influence of Control?(Agency A = top row, Agency B = bottom row)


MS Rehab MMTA – Cardiac

• Chronic• Revolving door pts?

• Chronic care management?

• Acute pts• CJR/ACO impact on health system?

• Acute pts• Access to institutional cases?

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•Pursue as much info as possible for:

• Admission source

•Diagnoses confirmation

• FTF Encounter Document

• ‘Pull’ info from portals rather than requiring them to push referral documents to you

•Balance pesky pursuit of info with keeping referral sources happy

•Pursue as much info as possible for:

• Admission source

•Diagnoses confirmation

• FTF Encounter Document

• ‘Pull’ info from portals rather than requiring them to push referral documents to you

•Balance pesky pursuit of info with keeping referral sources happy

Intake

•Confirm with patient/caregiver the source of referral

•Document accurately and in a consistent location in record for easy handoff of info to billing

•Primary reason for home care is derived from a thorough Comprehensive Assessment

•Confirm with patient/caregiver the source of referral

•Document accurately and in a consistent location in record for easy handoff of info to billing

•Primary reason for home care is derived from a thorough Comprehensive Assessment

Admission•Confirm accurate episode timing on CWF or other website as needed

•Compare with other clinical record documentation

•Code accurately on claim:

•Admission source

•Admission timing

•Diagnoses codes

•Confirm accurate episode timing on CWF or other website as needed

•Compare with other clinical record documentation

•Code accurately on claim:

•Admission source

•Admission timing

•Diagnoses codes

Billing

Where is Your Influence of Control? (Agency A = top row, Agency B = bottom row)



What is the difference between these two agencies & their OASIS reviewing model? Do they:• Collaborate?• Outsource?• Individual “QA”?• None at all?

This is an opportunity for lots of influence of control

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Functional Scoring Accuracy

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Collaborate on data accuracy for all new episodes

Consensus discussion on discrepancies

(observation or interview?)

Assessing functional tasks in isolation limits the picture of

the patient’s routine

Consider how time of day effects performance

Patients living alone are not necessarily performing ADLs safely just because they have

no assistance

Be VERY aware of the response item in which assistive devices are

introduced

Practice among therapists and nurses to be very familiar with how “25%” physical assistance really feels

Remember dressing items include getting things out of closets and drawers (and letting go of the walker?)

Some ADL items are best scored starting from the bottom up to capture the

most accurate response item




What are the coding practices here? Do they:• Outsource?• Borrow hospital coders?• In house?• Collaborate?• Pre‐code?

This is also great opportunity here for influence of control

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Unacceptable Primary Diagnoses

M54.5 Low back pain

M62.81 Muscle weakness (generalized)

R26.2 Difficulty in walking, not elsewhere classified

R26.81 Unsteadiness on feet

R26.89 Other abnormalities of gait and mobility

R26.9 Unspecified abnormalities of gait and mobility

R29.6 Repeated falls

R53.1 Weakness

Z48.89 Encounter for other specified surgical aftercare

9 of the top 50 primary diagnoses used from 2015 –2017 are not on the acceptable list




10.2 avg visits

10.4 avg visits

9.8 avg visits

10.8 avg visits

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Interdisciplinary Care Management

Patient

• Get patient participation & engagement in POC

• Primary goal is for patient to manage own condition

Team

• Coordinate care for effective use of each discipline

• Coordinate care for efficient use of visits

Outcomes

• Focus on goal of patient self management

• Taper frequency in response to patient progress to outcomes

Patient Participation with Tapered Frequency

Clinician frequency

Patient engagement

Patient engagement

Clinician frequency

Beginning of episode End of episode

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Managing LUPAs with Tapered Frequency

Analyze data1 | | | 5 | | | | 10 | | | | 15 | | | | 20 | | | | 25 | | | | 30 1 | | | 5 | | | | 10 | | | | 15 | | | | 20 | | | | 25 | | | | 30

1 | | | 5 | | | | 10 | | | | 15 | | | | 20 | | | | 25 | | | | 30 | | | | 35 | | | | 40 | | | | 45 | | | | 50 | | | | 55 | | | | 60

PDGM

Front‐loaded visitsFront‐loaded visits Tapered visitsTapered visits

Full 30‐day payment

LUPA or managed utilization?

Where is Your Influence of Control?

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Avg case‐mix weight* (*non‐LUPA) 1.1819 0.9682



Top clinical grouping* (MS Rehab) 18% 26%

Total QEs 9% 19%

Avg functional impairment grouping* 2.6 2.0


LUPAs 17% 6%



Data accuracy collaboration

Care & Visit Management

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Interdisciplinary Case Conferencing

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Begin of Episode

• OASIS/diagnosis collaboration

• Most effective/ efficient POC

• Care coordination

• Best skill mix

• Tapered frequency?

30 Day Review

• Inpatient facility admissions?

• Change in primary diagnosis?

• Documentation to support the change in diagnosis

End of Episode

• Challenge planned recerts & planned discharges for appropriateness

• Identify outcomes that are unexpected

• Does it change your plan?

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Wrap it up Karen!

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Summary

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Identify KPIs most relevant to your PDGM operational performance

Apply benchmarks to your PDGM KPIs based on your historical performance data

Manage & monitor your operational practices based on KPIs

PDGM: Relating Analytics toOperational Performance

M. Aaron Little, CPA Karen Vance, BSOTBKD, LLP BKD, LLP

[email protected] [email protected]

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Glossary

• ADL Activities of daily living

• CMS Centers for Medicare & Medicaid Services

• CWF Common working file

• FTF Face-to-face

• GI/GU Gastrointestinal/genitourinary

• KPI Key performance indicator

• LDS Limited data set

• LUPA Low utilization payment adjustment

• MMTA Medication management,

teaching & assessment

• MS Musculoskeletal

• PDGM Patient Driven Groupings Model

• POC Plan of care

• QE Questionable encounter

Documents

PDGM: Relating Analytics to Operational Performance · 2019. 10. 10. · 10/10/2019 1 PDGM: Relating Analytics to Operational Performance M. Aaron Little, CPA Karen Vance, BSOT BKD,