FutureDocs Forecasting Tool An Open Source Physician Projection Model

FutureDocs Forecasting Tool

An Open Source Physician Projection Model

Erin P. Fraher, PhD, MPPwith G. Mark Holmes, PhD and Andy Knapton, MSc

Cecil G. Sheps Center for Health Services ResearchUniversity of North Carolina at Chapel Hill

July 29, 2014

This project is funded by a grant from The Physicians Foundation.

In case your editor calls, here’s why our model is innovative in 2 slides: Slide 1

Starts with different question-what services will patients need versus how many doctors will we need?

Uses different geography-state and sub-state data highlight differences between “haves” and “have nots”

Displays data in interactive format-model is web-based, open source and designed to be customized, challenged and improved

Seeks different outcome-designed to educate and engage stakeholders in redesigning system


In case your editor calls, here’s why our model is innovative in 2 slides: Slide 2

Mindset — it’s a tool, not an answer. Tool allows user to choose from:

3 models — supply, utilization, relative capacity (a.k.a. “surplus/shortage”)

3 types of visualizations — maps, line charts and population pyramids

3 geographic views — national, state and sub-state level 5+ alternate futures — “what if” scenarios regarding ACA

implementation, physician FTEs, retirement, use of NPs and PAs, and redistributing graduate medical education (GME)


Presentation Overview

Highlight why our model is a disruptive innovation Explain (at ~60,000 feet!) methodology Demonstrate sample findings Answer your questions


Our model is a disruptive innovation that challenges traditional workforce modeling

Silo-based projections by physician specialty No “what if” scenarios Proprietary (read: black box) &

uncustomizable models Not regularly updated Lack friendly and interactive user interface


https://www2.shepscenter.unc.edu/workforce




Innovations in modeling physician supply

Supply Side Innovations Modeled patient care hours (patient care FTE) First model to include detailed GME training pathways,

including sub-specialization trends Includes scenario that redistributes GME slots to states and

specialties where greatest need


Created Clinical Service Areas (CSAs) to capture why and where people seek care 19 Clinical Service Areas (e.g., respiratory conditions, circulatory

conditions, endocrinology, mental health, preventative care, etc.) Modeled use of health care in 3 settings:

outpatient (including physician offices and hospital outpatient settings)

inpatient settings emergency departments

Innovations in modeling patient use of health care services


Developed sub-state unit of geography, Tertiary Service Areas (TSAs)

To capture sub-state variation, created TSAs Based on Dartmouth’s Hospital Referral Regions

TSAs are markets that encompass primary and specialty care services

Health system consolidation and ACOs and ACO-like structures create need for region-based data


Plasticity matrix brings supply and service use together by mapping physicians to services

Starting question: what health services will patients need?

Next question: which physician specialties can provide those services?

Innovation: plasticity matrix maps services provided by physicians in different specialties to patients’ visits


Plasticity—Providers and Services: A sample matrix for outpatient settings

Number of outpatient visits, select specialties and CSAs



Within a CSA, how are outpatient visits are distributed across specialties?

Number of outpatient visits, select specialties and CSAs



Number of outpatient visits provided per FTE per year, select specialties and CSAs



Within a specialty, how are visits distributed across CSAs?

Number of outpatient visits provided per FTE per year, select specialties and CSAs


These Innovations turn workforce modeling upside down

Model does not produce estimate of counts of physicians needed by specialty

Instead, it asks: what are patients’ needs for care and how can those needs be met by different workforce configurations in different geographies?


“Relative Capacity”: Indicator of how well physician supply matches use of servicesModel calculates “relative capacity”—a measure for each clinical service area in each geography

= supply of visits physicians in that TSA/State can provide utilization of visits needed by population in TSA/State

<.85=shortage .85-1.15=in balance >1.15=surplus


You end up with a picture that shows capacity of workforce to meet demand for

different types of health servicesShortage/Surplus for Outpatient Circulatory Visits by TSA, 2014

Washington, DC

Boston, MA

New York, NY

Atlanta, GA

Raleigh-Durham, NC

Miami-Fort Lauderdale, FL

Dallas-Fort Worth, TX Houston, TX

San Francisco, CA

Los Angeles, CA

Slidell, LA

Rochester, MN

Boulder, CO

Huntington, WV

Bangor, ME


Just a few interesting findings to show model capacity

FutureDocs Forecasting Tool


Supply model: Pediatric surgical FTEs double between 2011 and 2030

699

1,402


Supply model: 12% decline in general internal medicine FTEs

51,553

58,849


Not much change in shortage/surplus for all visits at the national level

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But looking at national data conceals broad variation between geographies

Shortage/Surplus for All Visits, All Settings by TSA, 2014Aurora, IL

New Orleans, LA

Washington, DC

Boston, MA

New York, NY

Atlanta, GA

Raleigh-Durham, NC

Miami-Fort Lauderdale, FL

Dallas-Fort Worth, TX Houston, TX

San Francisco, CA

Los Angeles, CA

Slidell, LA

Rochester, MN

Boulder, CO

Huntington, WV

Bangor, ME

Melrose Park, IL


Health system is rapidly changing.Need scenarios to model “what ifs”

Model includes scenarios for: Baseline model (2011) assumes ACA not implemented Utilization side — implement exchanges and different

Medicaid assumptions On supply side — change retirement, FTE, use of

NPs/PAs, and redistribute GME


What would be effect on shortage/surplus if all states expanded Medicaid?

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Shortage/surplus for all visits in all settings if all states expand Medicaid


The model highlightsdifferential effect between states:

Medicaid expansion has larger effect in Texas…


…And a smaller effect in Massachusetts


We modestly redistributed GME slots to specialties/states where demand > capacity in 2030

State Number of slots received

Nevada 88

Tennessee 50

Florida 47

Montana 36

Utah 16

Georgia 10

New Mexico 7

Total 254

State Number of slots removed

New York 101

Michigan 73

Washington, DC 63

West Virginia 7

Rhode Island 6

Massachusetts 4

Total 254

Specialty that loses the most slots: Emergency medicine, 60 slots Specialty that gains the most slots: General internal medicine, 61 slots

(Note that family medicine gains 55 slots.)


Example: GME scenario removes 40 emergency medicine slots from New York, reducing emergency

medicine physician supply in that state

3,785 patient care FTEs under baseline, 20303,499 patient care FTEs under GME redistribution, 2030-7.6% difference


But New York still has surplus capacity for ED visits, even when 40 residency slots are removed

2.58 supply/visits under baseline, 20302.41 supply/visits under GME redistribution, 2030-6.6% difference

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GME redistribution has differential effect by state: Nevada benefits but IM still declines

171 patient care FTEs under baseline, 2030189 patient care FTEs under GME redistribution, 2030+10.5% difference


Now let’s field some questions…

We’ll take the next 10 minutes for any questions For interview requests please contact

[email protected]

mailto:[email protected]

FutureDocs Forecasting Tool An Open Source Physician Projection Model

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