consumer analytics for financial and clinical … analytics for financial and clinical engagement...

Proprietary and Confidential ©2015 Connance, Inc.

Consumer Analytics for Financial and Clinical Engagement

Fall 2015


Agenda

• Understanding patients

• Translating insight to value

• Principles for success

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New Patient-Centered Healthcare Environment

Healthcare rapidly transitioning to a patient-centered business model

0%

20%

40%

60%

80%

100%

2010 2015 2020

P4P/At-Risk

Fee-For-Service

Changing Provider Revenue Mix, 2010-2020

Source: Oliver Wyman

Non-Government HSA / HDHP Enrollment, AHIP January, (Millions Lives)

0

5

10

15

20

Indv,SmallGrp, Other

LargeGroup

Source: AHIP January Census

Bronze and Silver over 80% of state exchange enrollees

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Context of a Consumer’s Healthcare Purchase

– Everyone consumes something

– Not elective purchase

– Cannot be repossessed

– Weak sense of “value”

– Mixed view as Right or Privilege?

– Wide range of experience

– Limited understanding of insurance

– Limited insight to their financial exposure

– Fragmented relationship

– Mistrust of corporate incentives

– Assumed “error prone” process

– Significant Community, State and Federal government intervention

– ED is “the clinic”

– 70+% of patient collections come after discharge

– Patient-balances 3X more costly to collect than commercial and 10X more than government

– Majority of cash comes from accounts with balances over $500 but 70% of them pay nothing

– 30% of accounts assigned to bad debt qualify for some financial assistance program

– Of households with Income >$70k, BAI collections range from 33-50%

– 70% of patients experiencing poor billing experience will tell friends to seek different hospital and physician

– 35% of patients give business offices Top-Box score (5) and more than 50% are at-best satisfied (1-3)

Consumer Behaviors

Consumers purchase healthcare differently than other services, impacting business model end-to-end in many counterintuitive ways

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Patient Socio-Demographics and Behaviors Critical

HCIT Data History Physical Exam Allergies Medications Laboratory Data Imaging Studies Payor class / plan Amount Due

*Individual/Family Socio-Economics

Household structure

Household Income

Individuals education

Ethnicity

Head-of-household

Car ownership

Occupation

Change in employment

Rent or own house

Others…

*Community Context

Urban /rural/suburban

Education level

Income mix

Safety

Weather patterns

Cultural mix

Community centers

Resource(s)

Transportation

Others…

• According to the CDC, social and economic factors drive upwards of 40% of consumer health and behavioral elements account for another 30%.

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Socio-demographics and behavior core to how a consumer manages their health clinically and financially

• Risks, barriers, challenges and behaviors that weigh against the patient or household• Not structured data in the EMR, PAS or Claim


Effective Healthcare Consumer Analytics

• Healthcare specific

– Based on healthcare data in healthcare applications

– Credit Score: likelihood of ability to repay loan for elective purchase that can be repossessed

• Predictive

– Range of future outcomes on the individual account/patient

– Uncertainty is helpful because it implies ability to influence positively

• Leverage data outside the EMR

– Best: Socio-demographics + Prior Experience + Visit information

– Warning: Credit data

• Built backward from the application

– Who will use, with what resources and information available, at what time

– Pre-service vs. Post

– Collection vs. Financial Eligibility

• Informs workflow

– Cash Value vs. Propensity to Pay vs. Ability to Pay

– Segmentation and resource allocation

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Group accounts requiring similar collection effort and having similar cash opportunity so resources efforts impact cash upside

Patient Behavior

(=Cost to Collect)

Reluctant Payor

(Hi Cost to Collect)

Self-Directed

(Low Cost to Collect)

Expected Cash Value

Low High

“Wait and Watch”

“Conserve”

“Invest”

Patient-Pay Segmentation Logic

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Effective analytics separate accounts into segments with unique performance characteristics

Collection StrategyHighly

Automate(Lower Left)

Auto/reactive CSR

(Upper left)

Identify payor Early

(Mid-Bottom)

Self cure First

(Mid-Top)

Early intervention with

settlement(Right)

% of Accounts 20% 26% 37% 12% 5%

% of Collection 1% 11% 26% 25% 36%

Avg. Balance at Assignment $526 $64 $1,800 $318 $4,029

Unit Yield $7 $46 $78 $221 $754

% Accounts which Paid (Event Rate)

6% 73% 21% 79% 56%

% of Balance Paid if Pay 53% 97% 43% 89% 52%

Collection Rate 1% 72% 4% 70% 19%

Patient-Pay Segmentation

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Actual Connance Client Patient-Pay Portfolio


– Balance vs. uninsured/BAI– Everyone received same letter

sequence

– Calling program focused on higher balance accounts

Uninsured BAI

$

$$

$$$

Patient-Pay Collection Segmentation Case Study

Reluctant Payor

Self-Directed

Low Expected Value

High Expected

Value

– Cost to collect vs. Expected cash value

– Five segments, upside value whenever marginal effort applied

– Each segment a unique sequence of letters, calling and messages to match opportunity and need

Pre: Traditional Balance-Based logic Post: Collection ROI-Based Strategy

Deployed predictive technology to focus effort against high ROI opportunities

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Call Volume Letter Volume Staffing Level

Patient-Pay Collection Segmentation Case Study

2010-Q4 2011-Q4 2012-Q4

Average $ Collected Per Account

More than 30% Increase

Post-ProgramOne Year

Later

Post-ProgramTwo-Years

Later

Reduced 12%

Reduced 44%

Reduced 34%

Change in Operating Statistics

Pre-Program

Transformed collection performance by targeting activity against highest-value opportunities

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Underlying segment characteristics enable insourcing low-cost / high value segments and leveraging contingent resources on the rest

Collection StrategyHighly

Automate(Lower Left)

Auto/reactive CSR

(Upper left)

Identify payor Early

(Mid-Bottom)

Self cure First

(Mid-Top)

Early intervention with

settlement(Right)

% of Accounts 20% 26% 37% 12% 5%

% of Collection 1% 11% 26% 25% 36%

Avg. Balance at Assignment $526 $64 $1,800 $318 $4,029

Unit Yield $7 $46 $78 $221 $754

% Accounts which Paid (Event Rate)

6% 73% 21% 79% 56%

% of Balance Paid if Pay 53% 97% 43% 89% 52%

Collection Rate 1% 72% 4% 70% 19%

Patient-Pay Segmentation and Insource/Outsource Optimization

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Actual Connance Client Patient-Pay Portfolio

– 43% of accounts– 72% of cash– Event Rates >50%


Predicting Patient Poverty For 501(r) Compliance

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Leverage predictive technology to identify people living in poverty but non-responsive to outreach

– People living in poverty unique challenge

– Many non-responsive to outreach

– Lack traditional data profiles

– Significant oversight burden

– Expensive to process manually

– Predictive models specifically built to assess patient qualification for financial assistance

– Aligns with requirements for presumptive charity and community benefit per IRS 990

– Best predict poverty and check against predicted household income and assets

– Best calibrated to local market and facility-specific policies and procedures

– Deploy u front for eligibility prioritization or at Bad Debt for presumptive charity reclassification

=> Warning: “Low Propensity to Pay” is not the same as “Charity Qualified”

=> Warning: Estimated HHI error-rate makes it unreliable screen for eligibility

Accounts failing to document in

financial counseling

Accounts completingactive A/R

unpaid

Charity Analytic

Bad debt collection

Declarepresumptive

charity

Bad debt assignment

Presumptive Charity Deployment


– Single platform across vendors to ensure inventory integrity

– Rule-driven for policy and procedure compliance

– Account placement, recall, replacement

– Reconciliation

– Tracking

– Integrated work queue communication to coordinate hand-offs and special requests

– Comprehensive reporting: recoveries, internal activity, agency activity, exceptional issues

– Independent 3rd party commission calculations

– Integrates with predictive analytics

Advanced Vendor Management Technology Required

Applying predictive analytics in vendor placement requires superior underlying process technology

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Vendor Management Platform

Effective Vendor Management Technology


Visibility on apples-to-apples basis to performance and process information for internal as well as external resources

Collections Activity Issues• How much am I collecting –

by month? By vintage? By segment?

• How do my agencies compare on an apples-apples basis?

• How does my internal operation performance compared to agencies?

• What commissions do I owe?

• What is my net-yield by segment?

• What activities (letters, calls) havebeen performed on my accounts?

• Which accounts have no collection activity?

• Are internal teams working as well as external?

• Why have accounts been closed? Cancelled?

• How many exceptional requests are agencies requesting?

• Which accounts don’t reconcile? Why?

• Which accounts are overdue to be recalled?

• Which accounts failed to place? Why?

• Which accounts are being denied and why?

Advanced Vendor Management Performance Reporting and Analysis

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– Cost-focused strategy

– Small internal CSR team

– Skim low hanging fruit and move to contingent

– Simple vendor network for process control and reporting

Patient-Pay Segmentation Case Study

– Insource using Collection ROI-Based Segmentation

– Internal segments had specific duration and strategy

– Presumptive charity analytics deployed at bad debt assignment

– Expanded vendor network and vendors free to work accounts as they wish

– Vendor management platform for end-to-end control

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30-Day Internal

90-Day Early Out Vendor

Singe Primary B-D

Mixed In/Out Active A/R Process

Two Primary B-DSingle Secondary B-D

Pre: Cost and control focused Post: End-to-End ROI Optimized

End-to-end optimization including both work routines and in/outsource blending


Patient-Pay Segmentation Case Study

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0

20

40

60

80

100

120

140

2010 2011 2012 2013

Indexed 90-Day Patient-Pay Collections

Inhouse

Outsource

Area of Impact Annual Value

Incremental Internal-Team Collections $2.7 million

Commission Savings Through Insourcing

$0.6 million

External Active A/R Vendor Cash Increase

$5.4 million

External 1BD Collection Increase $1.9 million

External 2BD Cash Increase $1.0 million

Total $11.6 million

Increase in Presumptive Charity $58.7M

Dramatic change in cost and cash leading to superior overall performance as well as more control over patient experience for brand and loyalty building


Begin readmission reduction efforts in the emergency department

Contextualize the history of present illness within longitudinal utilization information

Specifically inquire about social and behavioral health needs

Conduct a Comprehensive Whole-Person Assessment

Effectively engage patients and caregivers

Use teach-back

Customize written information and write it at an elementary reading level

Clearly explain medication information

Provide an early post hospital point of contact

Connect patients to primary care, behavioral health, and social services as needed

Ensure that patients have or can obtain medication, supplies, and transportation

Provide real-time information to receiving providers and health plans

Use a checklist to ensure all transitional care elements are reliably provided

AHRQ lists 13 ‘key actions’ to reducing readmission through improved hospital-

based care transitions:

Clinicians recognize that socio-demographics critical to patient health and wellness

Socio-Demographics and Health and Wellness

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Connance Whole-Patient InsightTM and Readmissions

Patient : AOther Attributes: Household Size > 3 %Home Market Value to

Income < 150% Owns multiple cars Married over 5 years

…and other elements

Readmission Risk: 6%

Patient : BOther Attributes: Household size <=2 Balance On New Finance

Accounts > $500 Limited food access High crime area

… and other elements


Patient : AOther Attributes: Income ~$60k Household Size > 3 Credit Utilization <70% Multiple Generations in

home … and other elements


Patient : BOther Attributes: Income ~60k Rents home < 2 years Household size <2 Limited food access

… and other elements


Predictive Analytics Predictive Analytics

Patient (s)Age: 69

Condition: SepticemiaLength of Stay: 4 Days

Readmission Rate: 19%

Patient (s)Age: 48

Condition: COPDLength of Stay: 1 Day

Readmission Rate: 15%

Consider two patients with Sepsis… Consider two patients with COPD…

Underlying patient challenges predict different readmission experience

Source: Connance

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Connance Whole-Patient InsightTM and Readmissions

Stratify patients into readmission risk segments based using socio-demographics in the context of their clinical encounter

– Stand-alone socio-demographic measure separate from clinical measures

– Enables care team to think separately about clinical and socio-demographic challenges

– Not reliant on clinical documentation to process

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Source: Connance

0%

10%

20%

30%

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50%

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70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

% o

f To

tal R

ea

dm

iss

ion

s

% Sample

Perfect Random Predicted Readmissions

High Risk Segment

Low Risk Segment

Mid Risk Segment

AUC: 93.7%


Connance Whole-Patient InsightTM and Stressor Indices

Because the models process environmental information, risk-driving factors can be flagged as intervention guides

Stability Index Financial Index Food Access Index Transportation Index

SDRA

Socio-demographic Risk Assessment

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Moderate Risk

High Risk

Low Risk


Checklist for Successful Consumer Analytic Applications

1. The Right Analytics

2. Segment-Specific Workflow

3. Ongoing Performance Management

PatientSegmentation

Patient Experience

Performance Outcome

– Does model answer the question at hand and allow targeted activity?

– What data is used in the model? Prior experience? Claim? Credit? Clinical?

– Is the model predictive to identify uncertainty?

– What % of population is covered by the model?

– How will the insight convert to segments?

– What capabilities (internally and externally) are available to utilize?

– What is the workflow in each segment?

– Does the workflow deliver a different patient experience?

– Does the workflow match financial and care goals?

– How will impact be measured?

– Is activity and output being tracked?

– How will improvement opportunities be identified?

– What is the process for maintaining the model?

– What benchmarking can be done to understand absolute vs. relative performance?

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Questions?

Steve Levin

Chief Executive Officer

Connance

[email protected]

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mailto:[email protected]

consumer analytics for financial and clinical … analytics for financial and clinical engagement...

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