Current Topics in Obstetrics

BUMEDAltarum Institute

Kennel and Associates

FOR OFFICIAL USE ONLY1

Projects Completed/In Progress:

Context

GIS

Locati

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1. Purpose:1. Outline how this work has been done 2. Describe how analysts can participate3. Define how this work impacts Navy

Medicine

2. Outcome:1. A better understanding of the analytical

work in OB2. Apply or request these techniques to be

used at your MTF or area of interest

FOR OFFICIAL USE ONLY3

Overview

1. GIS Location Analysis for Outpatient Perinatal Services• Altarum Institute and Kennel & Associates

2. Adverse Outcome Report• Altarum Institute

FOR OFFICIAL USE ONLY4

GIS Location Analysis for Outpatient Perinatal

Services

Navy BUMED – Timothy LinkAltarum Institute – John

HuffordKennell & Associates – Geof

Hileman

Agenda

FOR OFFICIAL USE ONLY6

Problem Statement

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Where We Left Off …

The next slide combines two FY 2010 measures for each ZIP Code in the NH Camp Pendleton catchment area on one map:

Travel-time appears to be an important factor in OB

provider choice.

Travel-time appears to be an important factor in OB

provider choice.

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Purchased Care Non-Active Duty OB Admission Counts and Percent of Total Deliveries by Beneficiary Residence ZIP Code Camp Pendleton Catchment Area, FY

2010

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A Retail Example

• In the retail sector, store and other facility location site location decision support techniques are well-established.

• The following, hypothetical and simplified, example (for instance, for a toy store) is based heavily on material obtained from Spatial Insights, Inc. (spatialinsights.com)

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Proximity Map showing population within 5 miles of each Census Block• In this case, retailer

“knows” that right radius

Thematic “Quilt” Map showing 2010 population by Census Block Group• Difficult to make overall sense

of area

A Retail Example

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Proximity Map showing average household income within 5 miles of each Census Block

Thematic “Quilt” Map showing 2010 average household income by Census Block Group

A Retail Example

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A Retail Example

Our retailer “knows” that they are looking for areas having at least 150K population within 5 miles and median household income of at least $100,000. Overlaying (or spatially joining) these proximity maps yields the few locations with that combination

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Proximity Maps for NH Camp Pendleton

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Purchased Care Non-Active Duty OB Admissions by Beneficiary Residence ZIP Code Camp Pendleton Catchment Area, FY 2010

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Purchased Care Non-Active Duty OB Admissions from Patient ZIP Codes within 30 minutes of each ZIP Code within Camp Pendleton Catchment Area, FY 2010

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Purchased Care Non-Active Duty OB Admissions from within 30 minutes of each ZIP Code within NH Camp Pendleton Catchment Area ((excluding those within 30 minutes of NH CP) , FY 2010

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We need more information to make the best choice: we need an OB provider choice

model to estimate to tell us which factors matter, and by how much.

We need more information to make the best choice: we need an OB provider choice

model to estimate to tell us which factors matter, and by how much.

The Rest of the Story

In our retail example, the planners “knew” the basic criteria and parameters driving sales, and therefore, optimal site selection. In our case, we don’t know: •Whether all purchased care admissions are created equal (i.e., that all are equally recoverable), nor •how many will ultimately be recoverable (i.e., will return be worth the facility investment involved).

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Objective: Maximize direct care share of deliveries in the San Diego / Pendleton area by varying the location of a potential new pre-natal clinic.

To Be Continued …

Step 1: Develop regression model to estimate the probability of delivering in the direct care system.

• Travel times to direct and purchased care inpatient and outpatient locations will be among independent variables.

• Note: This step uses the current NH Camp Pendleton location.

Step 2: Apply model to various candidate clinic locations and determine optimal location(s) by observing projected workload.

• First: Use new NH Camp Pendleton location to identify recapture

• Second: Identify optimal clinic locations relative to new MTF

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Referring back to our proximity maps, we are taking the retail example to a higher degree of sophistication. We will use the model to predict the number of OB dispositions recoverable by a clinic placed in each ZIP Code, factoring in not only:

To Be Continued …

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Volume of workload originating in

surrounding ZIP Codes; but

Volume of workload originating in

surrounding ZIP Codes; but

Care that is demographically

recoverable from those ZIP Codes, and also

Care that is demographically

recoverable from those ZIP Codes, and also

Distance from those ZIP Codes.

Distance from those ZIP Codes.

Demographically, the model will tell the proportion of care that can be recovered from each ZIP Code, given the demographic profile of that ZIP Code, if a clinic were located in that ZIP Code.

Geographically, the model will tell how that proportion declines as the clinic is placed in more distant ZIP Codes.

To Be Continued …

Proposed Data ElementsIn developing the model, we need to identify those

factors that influence whether a mother delivers in an MTF or in the civilian sector. These are the independent variables. Those that we have identified so far are in the following general categories:

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To Be Continued …

Data Elements ProposedData about the mother:

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To Be Continued …

Data about the delivery:

Data Elements Proposed

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To Be Continued …

Data about geography/travel times:

Data Elements Proposed

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Questions?

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John Hufford

Altarum Institute

734-302-4614

John.Hufford@altarum.org

Tim Link

Geof Hileman

Program Analysis and Evaluation (M81)

Bureau of Medicine and Surgery571.309.9824 cell

202.762.3543 worktimothy.link@med.navy.mil

Kennell & Associates

7919-424-7465

ghileman@kennellinc.com

Projects Completed/In Progress:

Context

GIS

Locati

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An

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sis

Ad

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utc

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BUMED Perinatal Advisory Board Adverse

Outcome Report

Navy BUMED – Timothy LinkAltarum Institute – Christina Ciucci

Agenda

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What is our goal?

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To provide decision makers with metrics which offer:•Prospective view of quality to address issues before they happen•Insight into the private sector•Usable information to make decisions

BUMED contracted with Altarum Institute to develop a report which could provide the Perinatal Advisory Board and the individual MTFs with actionable data.

BUMED strives to provide world-class Perinatal Care to its beneficiaries in all care environments.

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In the absence of a nationally accepted set of quality indicators for obstetrics, these metrics were developed with input from the following:

What are the Adverse Outcome Indices?

What are the Adverse Outcome Indices?

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The 10 adverse outcome measures and their weighted values are used as input to the AOI metrics:

What are the Adverse Outcome Indices?

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Why are these modified?

A modified version of the AOI was defined for reporting after discussions with BUMED, Navy personnel, and Altarum.

Indices are modified due to the following:

FOR OFFICIAL USE ONLY33

How is the data developed?

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MDR/Service Node

No PHI/PII

How is the data reported?

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Perinatal Advisory Board/BUMED Report

Individual MTF Report

*Note: Data shown are not representative of actual scores.

How is the data distributed?

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Specific i

nquiries

Monthly

reports

and inquiry

resp

onses

What makes these reports great?

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How do the pieces fit together?

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What is the road ahead?

The goal is to transform data into information, and information into insight

~ Carly Fiorina, Former President Hewlett Packard

If you want to know your past, look into your present conditions. If you want to know your future, look into your present actions.

~ Buddhist Saying

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Questions

FOR OFFICIAL USE ONLY40

Program Analysis and Evaluation (M81)

Bureau of Medicine and Surgery571.309.9824 cell

202.762.3543 worktimothy.link@med.navy.mil