04_KPMG_MKT PHL Presentation.pptx … · 243 Number of engagements that leverage the stack. Including internal engagements USERS 1.29 PETABYTES Total capacity of the stack 45 Number

Care ContinuumOptimization

Data enabled analytics

August 10, 2017

2© 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International

Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS 690650

These pressures are affecting every player in healthcare



Drivers of change

Advances in

technology

Healthcare

reform

Financial

pressures

Recent advances in healthcare

technology include full integrated EMRs,

decision support systems, and BI

software

PPACA and HITECH have proven to be

market disruptors that have forever

changed healthcare operations

Many organizations are facing

decreasing operating margins

Market shift towards high deductible

health plans, risk-based and value

reimbursement models and increase

in self-pay population

Changes in

payment model

Data and Analytics can

help healthcare entities

through this time of

change, and drive

improved care while

reducing costs



Analytics value acceleratorV

AL

UE

DIFFICULTY

Descriptive

Analytics

Diagnostic

Analytics

Predictive

Analytics

Prescriptive

Analytics

What happened?

Why did it

happen?

What will

happen?

How can we make

it happen?

Standardized Analytical Templates, Dynamic Filtering

Capabilities, and Internal/External Benchmarking

Advanced Analytics for Root

Cause and Effect Analysis

Determining Readmission and

Complication Risks

Template From GARTNER

Changing Clinical

Practice Patterns



Information extraction & retrieval

Document Clustering

Fuzzy Matching and Term Weighting

Link Analysis

Web Crawling

Web Scraping

Data mining & machine learning

Dimension Reduction

Feature Selection

Large-Scale Machine Learning & Algorithms

Social Network Analysis and Mining

Ensemble Methods and Random Forests

Supervised Techniques: SVM

Supervised Techniques: Neural Networks

Supervised Techniques: Decision Trees

Supervised Techniques: Logistic Regression

Unsupervised Learning & Clustering: (K-Means,

LDA)

Unsupervised learning & clustering

Unsupervised Learning & Clustering

K-Means

LDA

Big data architecture

Cloud Computing

MapReduce

Stream Processing

Natural Language Processing

(NLP)

Machine Translation

Named Entity Recognition (NER)

Part-of-Speech Tagging

Sentence Parsing and Chunking

Sentiment Analysis & Text

Classification

Topic Modeling & Keyphrase

Extraction

Word Sense Disambiguation

Statistics & modeling

Bayesian Data Analysis

Discrete Choice Models

Exploratory Data Analysis

Linear Regression

Monte Carlo Methods

Panel data/longitudinal data

Statistical Estimation Adaptive Filtering

Survival Analysis

Time series analysis

Advanced Regression

Supervised techniques

Decision Trees

Ensemble Methods, Random Forests

Logistic Regression

Neural Networks

SVM

Decision science/ops research

Decision Theory

Linear Programming/Mixed Integer Programming

Stochastic Processes & Queuing Theory

Leverage the right advanced analytics approach to solve complex issues



KPMG D&A Light House technology stack by the numbers

1,994Number of

unique users

across all

technologies

offered on

the

technology

stack

253Number of

unique

departments

across the

firm using the

technology

stack

243Number of engagements that

leverage the stack. Including

internal engagements

USERS

1.29 PETABYTESTotal capacity of the stack

45Number of

open source

and

commercial

applications

in the stack.

Includes

internally built

applications

60Physical

Servers

132Virtual

Machines

192Total

number of

servers

165Number of external facing

client engagements

54Number of servers on AWS

TECHNOLOGY

150+ Client

engagements

Internal Use Only



Data & Analytics Can Drive Performance Improvement in a Number of Areas

Growing market share of populations served

Improving clinical operations

Improving reimbursement

Evaluating the most appropriate and cost effective

site of care

— Develop tiered or narrow

network structure

— Identify gaps in clinical care

within the System causing

volume leakage (patient access

opportunities)

— Identify market changes that

open the door for new service

lines that demonstrate superior

value

— Identify inpatient, outpatient,

physician and post-acute rate

opportunities (e.g. fee-schedule

increases)

— Restructure contracts to

maximize value over the total

patient lifecycle

— Analyze and evaluate various

alternative payment approaches

— Identify variations in cost and

quality of care provided

— Identify duplication of testing

and diagnostic activities

— Use of medical devices

— Use of post-discharge care

management

— Modifying care delivery

protocols to consider the full

continuum of care within a

Clinically Integrated Network

(CIN)

— Facilitate discussions regarding

appropriate sites of service such

as free-standing ambulatory

centers, sub-acute providers,

use of telemedicine and digital

health platforms, or perhaps at

home



A sustainable VBC operating model requires new analytical capabilities to identify areas for operational excellence

Emerging Themes Informing Our Perspective on Service Line Analytics

Providers are using total cost of care analytics as a starting point for understanding the populations

they are serving, and the effort required to achieve operational excellence and drive performance.

Total Cost

of Care

Outpatient

Diagnosis &

evaluation

Treatment

Follow-up &

readmission

Outpatient drugs

Properly analyzed, data enables providers to

focus on costs of care and incentives for the

entire episode and for each participating

provider

Analyzing the total cost of care at this level allows

health systems to understand care delivery across

the care continuum.

Ind

ivid

ual

Sil

oes o

f C

are

Patie

nt-C

en

tere

d P

ers

pectiv

e

1 2



Total Cost of Care – Thoracic Cancer



Market Analysis and Volume Leakage



Volume Leakage – Physician Analysis



Optimizing Post-Acute Patient Flow

▪ 90 day cost: 5K

▪ Readmission rate: 9%

Home Health

▪ 90 day cost: 11K


SNF

▪ 90 day cost: 20K


Rehab Hospital

Care Continuum Optimization (CCO)



Reducing clinical/operational variance

Variation is found in all organizations and generally derived from a variety of factors:

Keys to managing variation:Different

medical and

nursing training

Different clinical

experiences and

biases

Clinical

complexity of

patients

Different

preferences and

practices

Technological

advancement,

research

requirements, or

vendor biases

Evolving

intricacies of

healthcare

systems

Data and analytics are key to

identifying variation

Building a culture open to

learning, innovation, and new

practices

Overcoming historical deference

with individual decision-making

and clinician discretion

Aligning to leading-practice,

evidence-based standards of care



Business support functions

— Revenue Cycle

— Supply Chain

— Services (Centralized/Purchased)

Clinical governance and structures

— Medical Staff, Nursing and Ancillary

Accountability Models

— Population Health Models of Care

— Culture and Change Management

— Leadership, Talent and Training

Process of care through the continuum

— Care Management

— Transitions in Care

— Patient Placement

— Patient Flow Management

Site of service

— Emergency Department

— Perioperative Services

— Treatment and Recovery Services

— Inpatient Care (CC, Telemetry,

M/S, IPR)

— Hospice/Palliative Care

— Skilled Nursing Facilities

— Primary Care/PCMH

— Specialty Care/PCSP

— Home Care

— Physician Practice

— Finance/Accounting/Tax

— Compliance/Legal

— Labor/HR

PQMO

Process of care

through the

continuum

Site of

service

Information

technology

and business/

clinical

intelligence

External

environment

Business

support

functions

Clinical

governance

and structures

— Multidisciplinary Care

Coordination

— Clinical Variation/Consistency

in Care Delivery

— Enterprise Scheduling

— Clinical Documentation

— Quality

Outcomes/Management

— Clinical Supply Utilization

Information technology and

business/clinical intelligence

— Concurrent and Retrospective

Reporting and Monitoring

— EHR Optimization

— Data Governance

— Support Existing/New Business

Systems/Infrastructure Upgrades

— Appropriateness Analysis for

Clinical Decision-making

— Clinical Decision Support

— Predictive Modelling

— HIPPA and Security Compliance

External environment

— Business Strategy and Planning

— Service Line Strategy and Growth

— Alternative Payment Models

— Partnerships, Networks and

— Alliances (e.g., Accountable Care

Organizations)

— Mergers and Acquisitions

CCO

Develop the broad set of

capabilities required for

this transformation

CCO-based approach helps organizations…

Internal Use Only



CCO data driven approach

Develop hypothesis

Solution helps client determine areas of process and outcome improvements by

answering questions such as:

— Are there particular discharge units that are taking longer to discharge patients?

— Are there any service charges that are getting applied unnecessarily?

— Has there been incorrect/missing clinical documentation to support accurate health

conditions?

— What is driving variation in care?

Improve business outcomes

Process and work-stream optimizations will help client measure and track the impact on

key strategic measures by

— Reduced avoidable costs

— Drive quality metrics

— Increased reimbursement

— Optimized use of resources



Reporting and performance management processThe dashboard reports

incorporate expansive dynamic

filtering to drill down and identify

root causes for unexpected

clinical variation.

Length of Stay

(LOS)

LOS by DRG

LOS by DRG + Dx Code

LOS by DRG + Px Code

LOS by DRG + Unit

LOS by DRG + Facility

LOS by DRG + Intensive Care

LOS by DRG + Service Line

LOS by DRG + Admission Source

LOS by DRG + Discharge Disposition

LOS by DRG + Payer

LOS by DRG + Discharge Month

LOS by Day of Week

LOS by DRG + Time of Day

LOS by DRG + Unit + Dx CodeLOS by DRG + Unit + Px Code

LOS by DRG + Unit + Facility

LOS by DRG + Unit + Intensive Care

LOS by DRG + Unit + Intermediate Care

LOS by DRG + Unit + Service Line

LOS by DRG + Unit + Admission Source

LOS by DRG + Unit + Discharge Disposition

LOS by DRG + Unit + Payer

LOS by DRG + Unit + Discharge Month

LOS by DRG + Unit + Day of Week

LOS by DRG + Unit + Time of Day

LOS by DRG + Facility + Dx CodeLOS by DRG + Facility + Px Code

LOS by DRG + Facility + Intensive Care

LOS by DRG + Facility + Intermediate Care

LOS by DRG + Facility + Service Line

LOS by DRG + Facility + Admission Source

LOS by DRG + Facility + Discharge Disposition

LOS by DRG + Facility + Payer

LOS by DRG + Facility + Discharge Month

LOS by DRG + Facility + Day of Week

LOS by DRG + Facility + Time of Day

LOS by Facility

LOS by Unit

LOS by Service Line

LOS by ...



Benchmarking for opportunity identification



Opportunity analysis including quality measures



Resource utilization



Clinical documentation integrity



Benchmarking



Level-of-care benchmarking



Cost opportunity benchmarking



Quality measure benchmarking



Executive dashboard

Advanced analytics

Variation of resource usage by provider



Understanding drivers of length of stay variation

LOS is subtracted

from benchmark value

for each record

Multiple factors are fit

to a regression model

The effect of each

variable is multiplied

by its volume to

measure annual

impact

Challenge: What is driving outcomes like

LOS variance? What is the total economic

impact in terms of patient-days to the

institution?

Analysis: Use the outcome as the

dependent variable in a regression model

testing the effects of the drivers



Select dashboards: excess LOS driver analysis

• Compares actual vs expected

(benchmark) LOS

• Uses regression to find drivers of

LOS variance

• Shows how much factors contribute

while controlling for other factors

(marginal effect)

• Returns reliable results based on

statistical significance

• Shown left:

- Temporal effects on LOS variance

- Red line is no effect, values to right

indicate drives longer stays

- Blue bars represent confidence in

estimate (one and two std errors)



Select dashboards: temporal effects

Detailed view of temporal effects. The “weekend effect” is real and measurable. Seasonality

can be controlled for in the model.



Usage indexing to the charge-item level

See which facilities or attending physicians over-index compared to internal peers

on a risk-controlled basis



Value of excess usage reduction

— By facility and APR-DRG for any charge item, see the potential savings realized by

reducing utilization to index levels

Diagnostic and predictive modeling for outcomes



Readmission: Example Insight

• In the above illustration, a machine learning model was fit to predict readmissions of

patients with Acute Myocardial Infarction. The marginal effects plot of a predictor

variable, time since previous admission, is shown.

• Controlling for other variables, there is an uptick in the likelihood to readmit based on

the time since a patient’s previous admission at the 80-100 day period

• This suggests cardiac patients need additional follow-up 3 months after discharge



Q & A

AskHow

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The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide

accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one

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Documents

04_KPMG_MKT PHL Presentation.pptx … · 243 Number of engagements that leverage the stack. Including internal engagements USERS 1.29 PETABYTES Total capacity of the stack 45 Number