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©2015 CHESS Proprietary and Confidential

CONTROLLING COST AND QUALITY IN POST-

ACUTE CARE Lisa P. Shock, MHS, PA-C Director of Care Transformation, CHESS Keith Thompson, MS, MA Data Scientist

©2015 CHESS Proprietary and Confidential 2

©2015 CHESS. All rights reserved. WARNING: This document/presentation is protected by federal copyright law and is provided for education on behalf of CHESS ONLY. Copying, reproduction, modification, distribution, display, or transmission of any portion of this document for any purpose is strictly prohibited without the express written consent of CHESS.


• Defining the Post-Acute Care Management Mission and Purpose

• Clinical Outcomes and Drivers • Defining the Research Questions • Analytics Methodologies • Discussion of Data-Driven Clinical Change

OBJECTIVES




OBJECTIVES


“The health outcomes of a group of individuals, including the distribution of such outcomes within the group” - Kindig and Stoddart, 2003

POPULATION HEALTH


POST ACUTE CARE NETWORK MANAGEMENT


TRANSFORMATION IN REVENUE

7

Revenue from Pay for Value

Revenue from Fee-For-Service (FFS)

2013 2023

FFS FFV

Revenue from Fee for Service (FFS) and from Pay for Value as % of total provider revenue

PROVIDER REVENUE WILL BE PRIMARILY DRIVEN BY PAY FOR VALUE IN THE NEXT DECADE ….

... ACCORDING TO LEADING INDUSTRY OBSERVERS

National Commission on Physician Payment Reform called for a phase out of the FFS model within 5 years

Partnership for the Future of Medicare (PFM) believes the FFS

payment model should be phased out over the next 5-7 years

Harvard Business Review called for a shift from the volume and

profitability of services provided to the patient outcomes achieved




OBJECTIVES


Anecdotal

Patient satisfaction

Functional assessment and quality of life surveys

Patient engagement

Standard process and outcomes measures

Utilization and financial measures

MEASURING SUCCESS


FACTORS DRIVING THE PUSH

10

Regulation

Healthcare Spend

Technology

Outcomes

•ACO •Medicare Advantage

•Pressure from employers •Pressure from consumers

•EHR adoption •HIE and interconnectivity

•US versus Western/OECD (Org for Economic Co-op and Dev) nations on various health indicators

MOVE FROM FFS TO FFV


JAN 26, 2015


CHESS CLIENT – CORNERSTONE Observed Medicare Expense

20%

60% 28%

Population Distribution Cost Distribution

28%

1.7%

70% 20%


TOTAL COST OF CARE - CHC Cornerstone

35%

16% 15%

9% 6% 5% 4% 3% 3% 2% 2% 1% 0%

0%

5%

10%

15%

20%

25%

30%

35%

40%


QUALITY AND POPULATION HEALTH

TIMELY

EQUITABLE

EFFECTIVE

EFFICIENT

SAFE

Quality Measures

and Utilization

Review




OBJECTIVES


WHICH FACILITIES?

Lowest readmit rate?

Lowest cost?

Fewest publicly reported (survey) deficiencies?

Relative to specific diagnoses?


WHICH PATIENTS?

Highest medical cost?

Medication spend?

Most office visits?

Most ED visits?

Most hospitalizations?

Specific diagnoses?




OBJECTIVES


ANALYTICS TEAM • Generalist vs. Specialist • Generalist – subset of skills in 2 or more domains • Specialists – assembly line approach • 2:1 or 3:1 ratio of generalists to specialists


TYPES OF ANALYTICS

Descriptive

Predictive

Prescriptive


POST-ACUTE DASHBOARD

View Dashboard Demo Tableau

SQL

Visual Studio SSIS TOOLS

Python


DEALING WITH (MISSING?) DATA

PROVNUM: 345115 BRIAN CTR HEALTH & REHAB/SALISBURY

635 STATESVILLE BOULEVARD SALISBURY, NC 28144

NPI: 1295856151 BRIAN CENTER HEALTH AND REHABILITATION – SALISBURY

635 STATESVILLE BLVD SALISBURY, NC 28144

SIMILARITY: 0.78 CONFIDENCE: 0.99 NAME SIMILARITY: 0.77 ADDRESS SIMILARITY: 0.80 CITY SIMILARITY: 1.0 ZIP CODE SIMILARITY: 1.0 CLASSIFICATION: MATCH

TRAIN DECISION TREE CLASSIFIER


SIMPLE SNF SCORECARD 𝑋1 = 𝐿𝐿𝐿 = 30.2 𝑋2 = 𝐶𝐶𝐶𝐶 = 10658 𝑋3 = 𝑅𝑅𝑅𝑅𝑅𝑅𝐶𝐶 = 0.175 𝑋4 = 𝐸𝐸 𝑉𝑅𝐶𝑅𝐶𝐶 = 379 𝑋5 = 𝐸𝑅𝐷𝑅𝐷𝑅𝑅𝐷𝐷𝑅𝑅𝐶 = 13

Scale

𝑋𝑋1 = 𝐿𝐿𝐿 = 0.593 𝑋𝑋2 = 𝐶𝐶𝐶𝐶 = 0.350 𝑋𝑋3 = 𝑅𝑅𝑅𝑅𝑅𝑅𝐶𝐶 = 0.405 𝑋𝑋4 = 𝐸𝐸 𝑉𝑅𝐶𝑅𝐶𝐶 = 0.547 𝑋𝑋5 = 𝐸𝑅𝐷𝑅𝐷𝑅𝑅𝐷𝐷𝑅𝑅𝐶 = 0

Wei

ght

Score = 0.376 Rank = 7th


TYPES OF ANALYTICS

Descriptive

Predictive

Prescriptive


READMISSIONS LITERATURE

• Kansagara et al., 2011 - JAMA • Identified 30 studies who reported both training metrics and test validation metrics • C-statistics between 0.55 and 0.83

• Only 6 studies above 0.7 • 2 of these were for disease-specific models • Best performing model used survey / chart review data (e.g., functional status)


PREDICTIVE MODELING

Goal: • Predict 30-day all cause readmissions for general patient population Variable Types: • Claims data – disease states / comorbidities, SNF readmit rates, DRG values • Clinical data – medication classes, demographics (will add lab values, vitals later) • Survey data – staff-to-patient ratios, deficiencies Missing data • Inferred missing (quality) values based on averages Types of Models Considered: • Logistic regression • Random forest


LOGISTIC REGRESSION

Variable P-Value

CHF < 0.0001

Renal Disease < 0.0001

Readmit Rate < 0.0001

Peripheral Vascular Disease

0.008

Age 0.009

DRG Weight 0.009

Antipsychotic 0.031

IP Visit Count 0.033

Antidepressant 0.045 C-statistic (AUC) = 0.71 Sensitivity = 0.65 Specificity = 0.68 Precision = 0.33


RANDOM FOREST

Charlson Score IP Visit Count ED Visit Count DRG Weight Readmit Rate

AUC = 0.67 Sensitivity = 0.66 Specificity = 0.61 Precision = 0.29


ENSEMBLE CLASSIFIER

• Different models have different strengths and weaknesses • Ensemble classifiers can draw on the strengths of multiple approaches while mediating

the weaknesses


TYPES OF ANALYTICS

Descriptive

Predictive

Prescriptive


OUR GOAL

𝐸 𝐷𝐶𝐶𝐶 𝐶𝐷 𝑝𝑅𝐶𝑅𝑅𝐷𝐶 𝐴 𝐷𝑅𝐷𝑅𝑓𝑅𝐶𝑓 𝑋)

= 𝐿𝑆𝑆 𝑋 𝐷𝐶𝐶𝐶 + 𝑅𝐷𝑝𝑅𝐶𝑅𝑅𝐷𝐶 𝐷𝐶𝐶𝐶 𝑝 𝑟𝑅𝑅𝑅𝑅𝑅𝐶 𝐴,𝑋) + 𝐿𝑆𝑆 𝑋 𝐷𝐶𝐶𝐶 (1 − 𝑝 𝑟𝑅𝑅𝑅𝑅𝑅𝐶 𝐴,𝑋))

argmin𝑋

𝐸 𝐷𝐶𝐶𝐶 𝐶𝐷 𝑝𝑅𝐶𝑅𝑅𝐷𝐶 𝐴 𝐷𝑅𝐷𝑅𝑓𝑅𝐶𝑓 𝑋)

Choose:

Goal: Build stochastic models that allow us to estimate …


NEXT STEPS • Add EMR data (labs, vitals, etc.)

• Lack of clinical data from inpatient and SNF setting

• Partnering with area hospitals • Partnering with area SNFs • Getting access to MDS / Oasis data

• Risk / case-mix adjustment for metrics

• Build disease-state specific models

• Play more with data



• Defining the Research Questions • Clinical Outcomes and Drivers • Analytics • Discussion of Data-Driven Clinical Change

OBJECTIVES

©2015 CHESS Proprietary and Confidential 34 34 Proprietary and Confidential

Claims

Demographics

Clinical (EMR) Data

Quality Metrics

Risk Scoring

Patient Registries

Identify patients with health challenges

Intervene to support better outcomes

Measure success

Types of Data Using the Data for Transformation

USING DATA TO DRIVE RESULTS


PREDICTIVE ANALYTICS AND RISK SCORING

Predictive analytics allows resources to

be targeted to prevent a health

crisis.

Risk

Sco

re

Diagnoses

Medications and Compliance

Recent care history


CHESS CARE PARTNER RELATIONS

Consult & Understand

Execute Strategy

Develop Terms of

Engagement

Define Scope

Define Strategy Needs

Assessment

• Build? • Buy? • Collaborate?


NETWORK MANAGEMENT


• Must be committed to the Triple Aim • Agree to shared accountability for whole-

person care delivery • Agree to Continuous Quality Improvement • Agree to share data in usable formats

PREFERRED PROVIDERS - CHESS ACO


THANK YOU!!!

• Not everything that counts can be measured

• Not everything that can be measured counts

[email protected] [email protected]

mailto:[email protected]

mailto:[email protected]

CONTROLLING COST AND QUALITY IN POST-ACUTE CARE › wp-content › uploads › 2015 › 03 ›...

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