Impact Of Change Forecaster Documentation

Impact of Change® Forecaster Product Documentation

SG-2 Forecasting Philosophy 1. Comprehensive (forecasts should incorporate a broad range of signficant drivers of utilization; e.g. a forecast that only focuses on one driver cannot reliably forecast total utilization) 2. Timely (forecasts should incorporate the latest information available about emerging technologies, population trends, etc. 3. Locally specific (forecasts should reflect local conditions as the practice of health care is highly regional) 4. Precise (Precision in the input data and empirical timings and impact factors is critical)

The Impact of Change™

About the Impact of Change® Forecaster The Impact of Change® Forecaster quantifies the impact of changes in the business and technology of health care on utilization of health care services. In addition to population changes, the IoC forecasts utilization based on technology, economic and sociocultural drivers as well.

Technology

Inpatient Discharges

Impact of Change® Database

Inpatient Days

Economy

Socio-cultural

2002 - 2010

Outpatient Services

Specific Examples of Factors

Technology Minimally Invasive Surgery Imaging Immune Modulation Targeted Drug Therapies Genetic Engineering Implantables Biologicals Medical Informatics

Economy

Socio-Cultural

Insurance Coverage Unemployment Total Employment Consumer Confidence Gross Domestic Product Cost Inflation

Obesity Smoking Physical Activity Lifestyle

The Impact of Change™

Data Sources Demographics Utilization Inpatient Outpatient

Mathematical Approach The most powerful approach to modelling changes over time, given initial conditions (e.g. initial population, use-rates/volumes), is to use differential equations. The time variable, however, is typically discrete (e.g. one-year intervals) so it is extremely common on economic and social sciences modelling to use what are termed difference equations.1 This is the basic approach used with the IoC. Essentially, one takes the initial conditions, along with factors (developing over time) that affect the initial conditions and generates a sequential evolution over time of utilization. Some details on how these drivers are constructed is described in the pages following.

1

Goldberg, S. "Introduction to Difference Equations," Dover Press, 1986. The Impact of Change™

Technology Impact

SG-2 Technical Brief

Assumptions 1. Most models of technology adoption involve a "logistic" or S-shaped diffusion & adoption curve. 2. "National" adoption curves are the "sum" of many individual regional/institutional curves and are hence "spread" out. By definition, then, the local adoption metrics will be different than the national one.

Approach: 1. Timing: Assign the following variables for each "technology item" a. ts = Start Year b. ti = Inflection Year c. td = Decline Year d. r = rate of growth e. Item scale factor 2. Mapping: Map all technologies to utilization variables 3. Impact Factors: Assign an impact factor (IF) for each mapping (most are zero). 4. Calculation parameters: Most importantly the probability scale factor.

Timing:

The Impact of Change™

The equation:

 Sgn( ( tb + t ) − ts ) + 1  − aPs (( Sgn( Sgn(ts − tb ))+ 1)(ts − tb ) ) T (t ) =  • e 2  

(

2

) (

 1 •  − r ( ( tb + t ) − ti ) 2  1+ e

)

  [ Sgn( td ) • Sgn( Sgn( ( tb + t ) − td ) + 1) ] •  − r ( t2b − th )    •e      

The curve: Timing Progression Chart 1.2

Fractional Impact

1.

.8

.6

.4

.2

. 2003

2004

2005

2006

2007

2008

2009

2010

2011

Year

The Impact of Change™

The curve (another example): Timing Progression Chart .7

.6

Fractional Impact

.5

.4

.3

.2

.1

. 2001

2002

2003

2004

2005

2006

2007

2008

2009

Year

The Impact of Change™

Impact Factors:

" Based on titrating to additional 800 procedures in 2002 (est.) and approximately 4000 on "waiting-list" with given national timings 0.06 IF was determined to be optimal"

The result: Yearly Discharge Growth Chart 45,000 40,000

Cumulative Percent Change

35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Year

The Impact of Change™

Economic Effect

SG-2 Technical Brief

Assumptions 3. Studies show a 25% reduction in utilization (inpatient) with an increase in unemployment. Effect is indirect via loss of employment determined health insurance benefits. 4. The effect is probably "across-the-board" – e.g. even non-discretionary utilization is affected. The only exception is probably acute, trauma-related care. 5. By corollary, does utilization increase by 25% if unemployment declines? Probably by not as much.

Approach: 5. Get change in unemployment forecasts from Bureau of Labor Statistics and SG-2 analysis. Need. This is δ. Increasing unemployment is a positive number; while decreasing unemployment is a negative number. 6. The "utilization depressor" is µ. Usually set to -0.25 7. The "negative bias factor" is κ. Default is 25. Using this in an exponential accentuates the change on the positive (or increasing unemployment) side.

∆ = δ • µ • e (κ • δ ) Current Defaults:

The Impact of Change™

δ: Our current national set looks as follows:

Eastern Mass, SSM/Cent. Missouri and ANOVA were all run with these national numbers. Unemployment Curve Chart

"Change in unemployment" Impact

.02

.015

.01

.005

.

-.005

-.01 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

The Impact of Change™

∆: Applying the above equation (with defaults for κ and µ) to the national δ's, we get the following results:

∆

δ

Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

No kappa .0042 .0167 -.002 -.004 -.002 0 0 -.004 -.001 -.001

(with Kappa

-0.00105 -0.00417 0.0005 0.001 0.0005 0 0 0.001 0.00025 0.00025

-0.00117 -0.00634 0.000476 0.000905 0.000476 0 0 0.000905 0.000244 0.000244

"Delta" Chart ∆ 0.02

Pure

0.015

Kappa No Kappa 0.01

0.005

0

-0.005

-0.01 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

These ∆ 's are the "changes in rate" that will be applied to the utilization volumes (non-Medicare component – see below).

The Impact of Change™

Implementation: These "rates" are then integrated and applied to the non-Medicare component (for DRGs) or total (for OPCs) utilization volumes. Currently the Medicare fractions are "hard-coded" (non time & space dependent) into the core DRG table. Example of DRG Medicare fractions: DRG

DRG Description

Medicare Fraction

1.00 Craniotomy >17 X Trauma

0.3429158

1.10 Craniotomy >17 X Trauma

0.3429158

2.00 Craniotomy For Trauma >17

0.5328467

2.10 Craniotomy For Trauma >17

0.5328467

3.00 Craniotomy Age 0-17

0

3.10 Craniotomy Age 0-17

0

4.00 Spinal Proc

0.1917476

4.10 Spinal Proc

0.1917476

The rates can be applied to: 1. initial utilization volumes or 2. "nonlinearly" multiplied against population-driven utilization.

Treatment of Medicare Fractions There are two sources for medicare fraction: a. "Hard-coded"in the DRG tables directly or b. derived directly from the age-group identifier of the relevant dataset. E.g. If the age group is greater than 65 then the medicare fraction is 1; other wise it is zero. If the age grouping spans 65 then the medicare fraction is some number between 0 and 1 – appropriately weighted at the time that the age group designation is defined. The default is to use option (2); if option (2) does not yield a medicare fraction (e.g. for undefined or global age group designations) then option (1) will be applied. Option (2) also allows for the medicare fraction to change over time as the proportion of the population over 65 likewise changes.

The Impact of Change™

Results: National data; linear run. Using κ, m defaults Economic Population

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

157,348

157,170

156,203

156,244

156,383

156,460

156,462

156,462

156,599

156,640

156,679

157,348

161,549

166,054

170,891

176,088

181,679

187,701

194,194

201,204

208,782

216,987

-0.4% 37.9%

Population / Economic "Wedge" NHDS National 2000 - 2010 SG-2 Forecast Economic Population

250 200 150 100 50

0 2000 2001 2002 2003 2004 2005 2006 2007

2008

2009

2010

Economic Effect Detail 157,600 157,400 157,200 157,000 156,800 156,600 156,400 156,200 156,000 155,800 155,600 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

The Impact of Change™

Consumer Effect

SG-2 Technical Brief

Assumptions 1. The consumer effect is closely related to the economic effect. 2. Applies to discretionary DRGs/OPCs only 3. Grows over the decade by an SG-2 determined curve

Approach: 1. Convolute D's from economic analysis with the SG-2 Consumerism curve. 2. These are the new rates 3. Apply only to discretionary DRGs (non-Medicare fraction) or discretionary OPCs (total fraction)

Consumerism Curve Chart 12.

Fractional Impact

10.

8.

6.

4.

2.

. 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

DRG

DRG Description

Discretionary DRG?

1.00 Craniotomy >17 X Trauma

N

1.10 Craniotomy >17 X Trauma

N

2.00 Craniotomy For Trauma >17 N 2.10 Craniotomy For Trauma >17 N 3.00 Craniotomy Age 0-17

N

3.10 Craniotomy Age 0-17

N

4.00 Spinal Proc

N

4.10 Spinal Proc

N

5.00 Extracranial Vascular Procs N 5.10 Extracranial Vascular Procs N 6.00 Carpal Tunnel Release

Y The Impact of Change™

DRG

DRG Description

6.10 Carpal Tunnel Release

Discretionary DRG? Y

The Impact of Change™

Results:

Population / Consumerism "Wedge" NHDS National 2000 - 2010 SG-2 Forecast

250 Consumerism Population

200

150

100

50

0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Consumerism Effect Detail 157,500 157,400 157,300 157,200 157,100 157,000 156,900 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

The Impact of Change™

Sociocultural Impacts

SG-2 Technical Brief

Assumptions 1. Many effects act roughly progressively over the forecast period (e.g. without a logistic curve, etc.). 2. These "meta-trends" often involve socio-cultural shifts within a population that are similar to demographic changes but involve factors beyond simply population change. Examples, include "obesity", increasing reliance of ERs for primary care, increasing acceptability of cosmetic surgery, etc. 3.

Approach: 1. Identify the sociocultural factor (potentially locally specific) 2. Identify which utilization parameters (DRG, OPC, etc.) are affected 3. Determine from the literature and/or "micro-model" an estimation of th percent yearly change in utilization attributable to that sociocultural factor. This number is termed the impact factor (IF).

Example sociocultural factors:

The Impact of Change™

Example Result: Effect of obesity on DRG 127 Heart Failure & Shock Yearly Discharge Growth Chart 1,050,000 1,040,000

Cumulative Percent Change

1,030,000 1,020,000 1,010,000 1,000,000 990,000 980,000 970,000 960,000 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Year

The Impact of Change™