Ingenix Hc Conference Pres 042108

CONFIDENTIAL & PROPRIETARY

Impact Pro HMGs: Using information to further describe population health

Presentation at the Ingenix Health Care ConferenceMichael Manocchia, Ph.D. Director, Outcomes and [email protected](work)781-419-8427

May 15, 2008 ● Orlando, FL

mailto:[email protected]

© Ingenix, Inc. 2CONFIDENTIAL & PROPRIETARY

Agenda Topics

Introduction and IPRO refresher Self Report Domains Current Self Report IPRO incorporation

Impact Pro Active Filtering Case Definitions and Business rules Limitations in current self report IPRO approach

Introduction of HMG Making the business case HMG introduction Self Report standardization HMG testing with obesity, smoking and nutrition

Additional Questions


Impact Pro – Features

Impact Pro is a multi-dimensional, episode-based predictive modeling suite

Accepts readily available information, including medical claims, pharmacy claims, and lab test results for modeling

Leverages Symmetry’s ETGs to build clinically homogenous markers of future risk

Predicts both future expenditures and calculates the probability of a hospitalization

Multiple predictive horizons available based upon available data and business needs

Transparent – allows users to easily understand the clinical and utilization factors affecting a member’s risk

Characterizes members along a number of dimensions, including clinical details and gaps in care


Business Applications for Impact Pro

Medical Management Identify and manage the right patients, at the right time, with the right

intervention

Identify members at greatest risk for future healthcare problems Understand key clinical drivers of risk – support steerage to appropriate

programs Identify care opportunities – members with gaps in care, complications and

co-morbidities

UnderwritingSet the right premium rate, attract and retain good business, promote stability

and profit

Historically, underwriters have used experience and other factors (age/gender, geographic and industry factors) to set healthcare premiums for individuals & groups

Information on health risk for groups and individuals is used to enhance the underwriting process


Impact Pro Suite


Clinical Indicators– Disease prevalence– Disease stage, advanced markers– Significant medical and pharmacy utilization events– More than 1,000 markers available– Rules engine to support custom rules

Care Opportunities– Gaps in care based on comparisons with evidence-

based medicine and guidelines– Future – supplemented by Symmetry’s EBM

Connect– Rules engine to support custom rules

Impact ProActive – Features


Care Teams– Identify key providers for a patient based on

observed interaction with the healthcare system

– In addition to PCP, Team defined for selected conditions, including diabetes, cardiovascular, and cancer

Case Definitions– Create any cohort of patients, using all Impact Pro

outputs and member attributes

– Actionability and savings Assumptions

Standard Rules and User-Defined Utilities

Impact ProActive – Features (cont)


Agenda Topics

Introduction and IPRO refresher Introduction to Self Report Domains Current Self Report IPRO incorporation

Impact Pro Active Filtering Case Definitions Limitations in current self report IPRO approach




Introduction to Self Reported MeasurementSurvey Domains


Standardized survey tools

HRAs Industry tools (dozens) and CDC’s BRFSS

HRQOL Generic Assess the health related quality of life of a population

– SF-36, NHP, SIP, Dartmouth COOP charts Disease-specific HRQOL measures

– St. George’s Respiratory Questionnaire– MOS-HIV instrument

Productivity Gold Book (2001): covered a number of instruments IHPM leads the way today in this regard today

Behavioral Health Clinical diagnostic assessments used in behavioral health that are administered via survey Psychological assessments, including substance use and abuse

Patient Activation measures Prochaska TTM; Hibbard’s Activation measures

Non-standardized tools Home-grown assessments when working with patients

Values in health care CAHPS and home grown satisfaction surveys Medical decision making surveys


Challenges on assessing Self Report

Descriptive statistics (ceiling/floor effects and missing data) Test-Retest Reliability Item Internal consistency Equality of Item-Scale Correlations Item Discriminant Validity Reliability (Cronbach’s alpha) Construct Validity Respondent burden Languages

Various tests based on type of items present in your self report measure database

MAP-R for Likert-based scales is a useful tool (Ware, Hays have versions available)


Strength of self reported measurement

Highly reliable instruments are used Valid with clinical and cost measures Measure domains not accessible by administrative data

Health behaviors and attitudes Health states, pain and function and disease severity Mental health indicators Perceptions of productivity impacted by health Values around medical decisions Satisfaction with health care Social Support

Validating your measurement See predicted response and benchmark with norms


Agenda Topics






Current Self Report IPRO incorporation

Today self report data (examples of HRA data) is “passed through” Impact Pro

However, IPRO can organize self report survey questions, which can then be used to View, select or report on a population

There is a defined HRA or survey table structure Clients have to prepare their self-report survey

questions into meaningful categories to filter on or create further case definitions

Following key principles to validate your data – (notes validation slides)


Seeing Self Reported Data in IPRO 5.0 – member list


Seeing Self Reported Data in IPRO 5.0 – HRA tab


Filtering on HRA data

Can also create case definitions using HRA data and Impact Pro


Filtering can take a few forms

To include or exclude members with all HRA responses for a particular survey All members that also took an HRA

To include members with a sub-set of responses to particular survey questions All members that reported missing work days frequently do to health

To create AND statements within the entries within the Included or Excluded boxes, or to create a combination of AND/OR statements between selections within the Included or Excluded boxes

Advanced Filters allows you to create AND statements or a combination of AND/OR statements between selections within the (Included or Excluded) boxes

Filtered records can also be reported on through customized reports within the IPRO reporting engine.


Case Definitions

Rules to identify sub-sets of members who meet certain criteria

Allow you to see how many different sets of criteria an individual member meets

Allow you to create a hierarchy or prioritization of criteria (Impact Score)

Allow you to pre-determine anticipated savings per criteria set (Predicted Savings)


• Unlike Filters which query data that has already been processed, creating a Case Definition is creating a Rule to be run through the Processing Engine – results will not be seen until the next data run

NOTE: This is true for ALL rules – Clinical Indicators, Care Opportunities and Case Definitions

• Once the Rule has been created though, it will be persistent for every future data run AND it can be used as a Filter itself.

Creating a Case Definition


Using Impact Score to Stratify Disease Management

Moderate Impact Diabetes Risk Category = Diabetes…and Risk Score = 7-24….and Care Opportunity

– New Insulin Rx…..or– New oral Hypoglycemic Rx…..or– Gaps in refills or no refills for oral hypoglycemic agents…….and

Case Definition– Exclude members who meet Case Definition of “High Impact – Diabetes”– Include members with stages of change self report question related to

improvement of diet

37 Ingenix created Case Definitions “out of the box” 37 Ingenix created Case Definitions “out of the box” for demonstration purposes onlyfor demonstration purposes only


Limitations of existing HRA functionality in IPRO

No standardization of HRA data from various sources Clients are left to create their own standards

No use of national standards with some self-report concepts that are widely understood and can add further depth to understanding health (e.g. CDC definitions) Similar to an EBM type model

The processing engine does not use the data for further calculation of cost and risk in a precise way

No current way to reconcile contradictions between administrative claims and self report data

No current way to build multi-dimensional variables with distinct levels that combine HRA, lab and administrative claims presently

Leads us to address many of these limitations


Agenda Topics



Introduction of HMG Making the business case Self report standardization HMG introduction HMG testing with smoking, physical activity, nutrition and obesity



HMGs – Making the business case

Information tools and methodologies to support care and health management – current state: Primary focus is on disease populations or individuals of

moderate to higher risk Clinical information and concepts supported by administrative

medical and pharmacy claims, some use clinical data Outputs include measures of risk, some add gaps in care Many tools add reporting and some cohort modeling capabilities Limited use of alternative sources of data today Include alternative sources for identification and stratification

methods, prediction and risk adjustment– How can data from various sources be combined into meaningful

building blocks of information?


HMGs – Making the business case

Health care organizations and employers are increasingly interested in focusing on healthier members in a population, or members of emerging risk Extend interventions to the lower end of the risk spectrum Improve wellness, healthy behaviors and lifestyle Improve attitudes on health Intervene in a more pro-active way, e.g., pre-diabetes, and

diabetes-risk


Segmenting Populations

Maintain Health Manage HealthImprove Health

• 57% of population has claims under $1,000

• 8% of total medical expenses are spent by this group

• 41% of population has claims between $1,000 and $20,000


• 2% of population has claims greater than $20,000


Moving Forward: Enhanced Information for Health Solutions

Source: National Health Plan Fully Insured Book of Business, 2004

Current Focus

Acute Conditions

Moderate Risk High Risk Catastrophic Illness

Low Risk Chronic Disease


Change of Focus and Requirements

Support analysis of healthier populations and patients of emerging risk

Leverage existing and new sources of data, including HRA/self report, biometric data and socio-economic data

Integrate these different sources of data in innovative ways: Improve on existing concepts, e.g., measures of future risk Support new domains of measurement, including behaviors, attitudes,

and social context Accommodate different data scenarios – consistent data

availability unlikely across and within populations Create a useful context for analysis

We are pulling together a larger number of concepts and variables than are currently available in Impact Pro

Add value for customers by developing a context – organize information for analysis, presentation, and operations – in a flexible way


Health Management Group Concepts and Domains

Ingenix HMG will provide methodologies and outputs to measure individuals along different domains that describe health

HMGs provide a context for integrating five domains of health

Domains support both identification/stratification and further segmentation

Risk and Severity

Health Management

Groups (HMGs)

Clinical

Health Behaviors

Social Context

Health Attitudes


HMG Concepts and Domains

Information and domains to support identification and stratification: Clinical

– A clinical description of an individual, based on diagnostic, drug and procedural claims available in Impact Pro and through HRA self report

– Examples – diabetes, CHF, depression, sleep disorder, obesity

Risk or Severity– Impact Pro risk, ETG severity, Self report of severity

– Examples – future risk, condition episode severity, health status

Behavior (Healthy behaviors)– HRA and EBM measures of behavior, behaviors inferred from

consumer data (future)

– Examples – smoking, physical activity, compliance with EBM chronic and preventive rules



Further segmentation can occur with: Attitudes and ratings related to health

– Readiness to change, activation and perceived social support– Productivity: absenteeism and presenteeism impacted by health

Social Context– Ascribed and achieved status found on HRA and administrative data– Examples – Age, gender, race ethnicity, education, income, SES

HMGs will provide a context for organizing patient information around the five domains

HMGs are being built for specific conditions and behavioral concepts HMG Levels will be defined to support identification and

stratification



The HMG methodology will: Leverage existing Ingenix technologies (Impact Pro, ETG,

EBM Connect), but expand their focus to new populations and types of measures

Introduce new sources of data and methods to improve on existing concepts and support new concepts

Integrate data from multiple sources to describe and categorize a patient along each domain

Define key building blocks and categorizations within each HMG

– Includes frequency of events and actions, timing (associated with different data sources) and depth of HMG level


Key Advance: HMG Standardizes Alternative sources of data

The HMG methodology will: Standardize self report and biometric data found on HRAs and

other surveys into meaningful sets of building blocks– Smokers: current, quitting, former never– BMI: underweight, normal weight, overweight, obese and morbidly

obese– We call these standardized measures dimensional concepts

Will require the user to map their items through a standardized common HRA map with leading HRA tools in the field

– Also maintain flexibility for the user to develop meaningful HRA categories or build case definitions present in Impact Pro Active

We will use a scoring framework to map variable attributes into consistent scores and indicators of risk


Key Advance: HMG Standardizes self-report data

Standardized self-reported concepts include: Condition, treatment, utilization via self report Health behaviors: lifestyle, preventive testing, immunizations,

safety and other forms of risk aversion Health attitudes: readiness, activation and awareness HRQOL: physical, emotional, bodily pain, role function Productivity: absenteeism and presenteeism Social Support Social Context: race, ethnicity, education, income Biometric markers: cholesterol, BMI, BP, etc.

This data could extend the risk spectrum for those that answer an HRA and some variables could be used to tailor communication and intervention


Examples of HMGs under development

Disease Management Wellness-Related

Asthma/COPD Smoking

CAD Physical Activity

Diabetes Safety

Back Problems Stress

Depression Alcohol Abuse

Obesity Sexual Risk Activity

Sleep Problems Nutrition

Migraine Headaches *preliminary results to be presented


Current HMG Development

Scope out identification and stratification algorithms for HMGs Solicit clinical input and review

Apply algorithms to current analytic data warehouse that has claims, lab and HRA for seven large employers Interested in other clients participating in this process

Test hypotheses related to expected levels associated with HMGs to see if levels are distinct and valid

Begin to look at emerging risk associated with certain behaviors or conditions


Smoking-Related HMG

HMG Levels Current Quitting Former smoker Never smoked

Information used to identify and stratify HRA: smoking status, quantity of use, quitting stage Medical and pharmacy: diagnoses, drug therapies, procedures indicating

quit attempts, quit treatments and counseling Map relevant clinical and family history to further define levels

Ask ourselves: If I run a smoking cessation program what would I want to understand about my members? Levels of smoking use How does smoking relate to cost and risk? What does the emerging risk across smoking groups look like?


Smoking Results

Smoking Distribution

62.7%

15.8%

3.9%

17.6%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Never Former Quitting Current


Smoking Results by Cost and Risk

Cigarette Use by Cost, Imputed

$276$317

$656

$578

$0.00

$100.00

$200.00

$300.00

$400.00

$500.00

$600.00

$700.00

$800.00


Cigarette Use by IPRO Risk

1.21

1.41

2.59

2.00

0.00

0.50

1.00

1.50

2.00

2.50

3.00



Tobacco Use w ithin Risk Groups

36%29%

19%11% 9% 8%

9% 21% 35%41%

38%

51%

33%28%

21%14% 10% 10%

30%

43%35%

21.8%

25%

22%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<0.5 -1.5 -3.0 -5.0 -10 >10

Never Former Quitting Using

Smoking Results – Emerging Risk


Smoking Results by Health Status and Lost Work Days

Self-Reported Health by Cigarette Use

77.12% 77.14%

69.73% 69.28%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%


Average Days Absent by Cigarette Use

1.6

1.8

0.8

2.3

0.00

1.00

2.00

3.00



Physical Activity HMG

HMG Levels Active, complete – goes beyond CDC definitions Active, sufficient – meets CDC definitions Active, light – falls below CDC definitions Active, insufficient – falls far below CDC definitions Inactive

Information used to identify and stratify HRA: many physical, exercise items to standardize Medical and pharmacy: limited set of procedures to increase or counsel

on PA

Ask ourselves: If I run a PA program, program what would I want to understand about my members? Levels of activity How does PA relate to cost and risk? What does the emerging risk of inactivity look like?


Physical Activity - Identification

Physical Activity Levels

28.9%

19.0%

37.6%

9.6%

5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Complete Activity Sufficient Activity Light Activity Insufficient Activity Inactive


Physical Activity – Cost and Risk

Physical Activity by Cost, PMPM

$230

$265 $275

$239

$394

$0.00

$50.00

$100.00

$150.00

$200.00

$250.00

$300.00

$350.00

$400.00

$450.00

$500.00


Physical Activity by IPRO Risk

1.08

1.47

1.30

1.42

1.79

0.8

1.0

1.2

1.4

1.6

1.8

2.0



Physical Activity – Emerging Risk

Physical Activity w ithin Risk Groups

26%20% 16% 12% 10% 9%

19%

20%21%

22%21% 20%

21%

20% 19%17%

17% 19%

20%

20%20%

21% 25% 21%

14%21% 24% 28% 27% 30%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<0.5 -1.5 -3.0 -5.0 -10 >10

Complete Suff icient Light Insuff icient Inactive


Physical Activity – Health Status and Work Absence

Self-Reported Health by Physical Activity

79.0%72.6%

69.6%64.3%

60.9%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

Complete Activity Sufficient Activity Light Activity InsufficientActivity

Inactive

Average Days Absent by Physical Activity

1.9

2.32.1

2.3

3.7

0.0

1.0

2.0

3.0

4.0



Nutrition HMG

HMG Levels Severe nutrition problems Moderate nutrition problems Low nutrition problems No nutrition problems

Information used to identify and stratify Medical and pharmacy: nutrition and weight management counseling

appointments and obesity HMG (claims and biometric data) HRA: items related to nutrition behaviors and choices

Ask ourselves: If I run a nutrition behavior change program what would I want to understand about my members before enacting programming? Levels of nutrition problems How do nutrition problems relate to cost and risk? What does the emerging risk across nutrition groups look like?


Nutrition Problems - Identification

Nutrition Problems

10.1%

50.9%

33.7%

5.4%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

None Low Medium High

Nutrition Problems Identification

47.78%43.30%

35.19%

100.00%

52.22%56.70%

64.81%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

1 2 3 4

Claims

HRA


Nutrition Results – Cost and RiskNutrition Problems by Cost

$289.68$260.41

$309.93

$576.80

$0.00

$100.00

$200.00

$300.00

$400.00

$500.00

$600.00

$700.00


Nutrition Problems

Nutrition Problems by IPRO Risk

1.19 1.22

1.36

1.78

0.8

1

1.2

1.4

1.6

1.8

2


Nutrition Problems


Nutrition Results – Emerging Risk

Nutrition Problems by IPRO Risk Score

9.80% 11.00% 9.00% 7.00% 8.00% 10.00%

53.50% 52.00%46.00% 45.00% 41.00% 40.00%

32.40% 32.00%38.00% 39.00%

38.00% 36.00%

4.30% 5.00% 7.00% 9.00% 13.00% 13.00%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-.5 .501-1.5 1.501 - 3.0 3.01 - 5.0 5.01 - 10.0 10.01

High

Medium

Low

None

Nutrition Problems by PMPM Ranges

10.00% 10.00% 11.00% 10.00%

53.00% 51.00% 49.00% 45.00%

33.00% 34.00% 36.00%36.00%

5.00% 5.00% 5.00% 9.00%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$0 - $100 $101 - $200 $201 - $350 $351+

High

Medium

Low

None


Nutrition Results – Health Status and Work Absence

Nutrition Problems Related to Health Status

8478

7166

0

10

20

30

40

50

60

70

80

90

100


100 =

best

healt

h

Absent Days by Nutrition Problems

2.01

1.64

2.23

3.58

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00



Obesity HMG

HMG Levels Morbidly Obese Obese Overweight Normal weight Underweight

Information used to identify and stratify Medical and pharmacy: BMI, obesity diagnoses, obesity

procedures, complications associated with obesity, obesity DCC’s.

HRA: BMI and Waist circumference


Obesity Problems - IdentificationFinal obesity levels (n=26184)

1.0%

34.2%

38.1%

23.1%

3.6%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Underw eight Healthy w eight Overw eight Obese Morbidly Obese

Obesity levels

Obesity Levels Identification

12.63%

29.09%

100.00% 100.00% 100.00%

87.37%

70.91%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Underweight Healthyweight

Overweight Obese MorbidlyObese

Claims

HRA


Obesity Results – Cost and Risk

Obesity levels by IPRO Risk

1.11.17 1.18

1.52

2.16

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4


Obesity levels

Obesity levels by Cost

$230.00

$284.00$254.00

$347.00

$632.00

$0.00

$100.00

$200.00

$300.00

$400.00

$500.00

$600.00

$700.00


Obesity levels


Obesity Results – Emerging Risk

Obesity Problems by PMPM Ranges

35.22% 35.01% 32.99% 30.59%

40.71% 37.01% 36.12%33.06%

20.79% 23.69% 26.06%28.11%

2.23% 3.50% 3.94% 7.14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$0 - $100 $101 - $200 $201 - $350 $351+

Morbidly Obese

Obese

Overweight

Healthy weight

Underweight

Obesity Problems by IPRO Risk Score

36.93% 35.81%28.39% 26.24% 27.98% 30.09%

42.13%38.06%

35.03% 33.90% 27.98% 27.43%

18.36%22.08%

30.10% 31.11%31.35% 30.09%

1.44% 3.01% 5.75% 7.66% 12.10% 11.50%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-.5 .501-1.5 1.501 - 3.0 3.01 - 5.0 5.01 - 10.0 10.001 +

Morbidly Obese

Obese

Overweight

Healthy weight

Underweight


Obesity Results – Health Status and Work Absence

Obesity level related to health status

7581

77

68

60

20

30

40

50

60

70

80

90

100

Underweight Healthyweight

Overweight Obese MorbidlyObese

Obesity levels

Absent days by obesity level

21.8 1.7

2.4

3.9

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Underweight Healthy weight Overweight Obese Morbidly Obese

Obesity levels


Obesity Results – Diabetes and Obesity

Proportion of diabetics by obesity HMG level

0.33%

12.38%

25.36%

46.95%

14.98%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

Underweight Healthy weight Overweight Obese Morbidly Obese

Obesity HMG level


HMG Summary

Provide a context to organize and focus information in a way that is consistent from both a clinical perspective and also from a health management operational perspective

Describe both clinical and wellness intervention opportunities Have defined levels – that map to potential cohorts for intervention –

e.g., level of acuity; categories of smoking status; level of physical activity

Have rules and algorithms that assign an individual to a clinical or behavioral status (HMG) and levels of problems within an HMG (reconcile data sources, conflicts, timing, frequency) Allow users to further define or combine levels that fit program goals

using Impact Pro Active

Incorporate methods to accommodate different data availability scenarios for each individual


Questions/Comments


Appendix

HRA Input Slides from Impact Pro manual


Assessing and Validating HRA Inputs - appendix

Validity and reliability of input data is key to its use not only with claims data, but also with self report

With self report, you want to see key elements that are included in the input tables, pass through properly, and can be used for filtering and selecting patients Answer_Desc: Each answer should be unique, and should provide

information about the question the answer responds to. For example, an appropriate answer description is “Yes, I am a smoker.”

Quest_Desc: Each answer should be unique, and should provide information about the question topic. For example, an appropriate question description is “Do you smoke?” An inappropriate response description is “Question 1.”

Impact Pro uses the MEMBER field to uniquely link information regarding each member to the Impact Pro output datamart. MEMBER should uniquely define an individual. Every member ID in the HRADATA table must also have an entry in the MEMBER table.

Lastly, check to see number of questions and members is correct and duplicates and invalid records indicated in the validation output.


HRA Table Structure - appendix








Input data steps to include HRA data - appendix


Input and Output Data Steps – HRA considerations - appendix

Documents

Ingenix Hc Conference Pres 042108