Welcome

Creating a User-Determined Analytics Experience

Kaci Dominguez, PharmD

Director of Analytics

McKesson Corporation

# T C 1 8

Patrick Whalin

Senior Architect

McKesson Corporation

Agenda

• Background

• Meeting customer needs

• Utilization solution approach

• Utilization analytics live demo

• Technical challenges and lessons learned

• Summary and future directions

National Trends in Pharmacy Expenditures

• Total healthcare spending has grown substantially in the last 20 years

• Outpacing the growth of the US economy

• Accounts for ~ 17% GDP

• Pharmaceutical spending outpaced total healthcare spending growth over the last few years

• Driven by new drugs and higher prices on brand name drugs

Schumock G, Et al. National trends in prescription drug expenditures an projections for 2018. Am J Health-Syst Pharm 2018;75:3353073.

• 2017 had lower growth in spending than expected for pharmaceutical expenditures

• 1.7% increase for pharmaceutical expenditures

• Driven by growth in clinics

• Driven by increase in use of new agents and

higher prices of existing agents

• Forecasted 3-5% increase in pharmaceutical spending during 2018 driven by spend in outpatient clinics

National Trends in Pharmacy Expenditures

Schumock G, Et al. National trends in prescription drug expenditures an projections for 2018. Am J Health-Syst Pharm 2018;75:3353073.

Setting Projected Increase in Drug Expenditures

Hospital 0-2%

Clinics 11-13%

10.90%

-2.20%

-0.07%

-4.00% -2.00% 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00%

Growth in Spending by Sector - 2017

Nonfederal Hospitals Retail Clinics

Financial Stability for Hospital Pharmacies

Inpatient Drug Usage Outpatient Drug Usage

Reduce drug costs

Optimize drug

contracts

Use lowest cost

drug for indication

Increase profitability

Restrict drugs by

locations

Optimize drug use

guidelines

Ensure appropriate

billingNegotiate payer

contracts

Use more profitable

drug for indication

Meeting Customer Needs

Analyzing Pharmacy Purchase Data

• Existing customer facing analytics solution

• Includes pharmacy purchase data only

• Analyzes trends in purchase data • Identify opportunities to reduce costs

• Explain variances

• Track cost savings

Case Study – Spend Analytics

The customer:Large health system on East Coast

• Serving Virginia and North Carolina

• 12 hospitals, 12 long-term care facilities,

10 retail pharmacies and 1 specialty pharmacy

The situation:Sentara wanted to reduce annual drug

spend and replace its time-consuming

manual drug spend–management process

The results:• Reduced manual work, saving 520 labor hours per year

• Confirmed a formulary change from IV Ofirmev to oral equivalent that

reduced spend from $2M to $1,053

• Helped accounting department easily research financial variances

Changes in Healthcare

• Changes in Reimbursement• Reduction in 340b reimbursement by 28.5%

• Value based payment models

• Many other proposals pending such as site neutral payments

• Move Toward More Outpatient Care• Largely seen a positive move for both patients and payers

• Reimbursement models differ depending on where the drug is administered which increases complexity

• Market Consolidation• Steadily increased since 2009

• Driven by financial factors and move towards value based care

Banker E. How Consolidation is Reshaping Health Care. Apr 2017. http://www.hfma.org/Leadership/E-Bulletins/2017/April/How_Consolidation_Is_Reshaping_Health_Care/

LaPointe J. Hospital Groups Decry Proposed Outpatient Reimbursement Cuts. Jul 2018. https://revcycleintelligence.com/news/hospital-groups-decry-proposed-outpatient-reimbursement-cuts

Challenges for Pharmacies

Changes in

Reimbursement

Move Toward More

Outpatient Care

Market Consolidation

Analyze Profitability of Drugs Quickly

Assess Usage of Drugs

Consolidate Data from Many Sources and Disparate Systems

Market Changes Pharmacy Needs

Meeting the Needs of Hospital Pharmacies

Purchase Data

• Cost of drug

• Volume purchased

Usage Data

• Usage by diagnosis

• Usage by location

• Compare prescribers

• View dosage and length of therapy

Reimbursement

• Reimbursement amounts for outpatient drugs

• Multiple payers

• Compare prescribers

Customer facing analytics solution that combines many sources of

clinical data to reduce inpatient drug costs and optimize outpatient

drug profitability.

Utilization Solution Approach

Combining Data from Multiple Sources

Purchase PaymentUsage

McK

Direct

FDB

Pt

Accounts

Dispens

e data

835

Payer 1

835

Payer 2

Pt

Accounts

Actionable Insights

Identify opportunities

to reduce IP costs

Identify Drugs with Sub-

Optimal ReimbursementView Prescriber

Variance

Solution Architecture

Data Sources

Patient

Accounting

System

Pharmacy

System

Wholesaler

Purchase

History

Data Transformation

Layer

First Data

Bank835 Files

Customer

Interface Usage by

DiagnosisUsage by Drug Reimbursement

Data WarehouseStaging TablesPricing

Algorithm

Validation/Rules

Table

Utilization Solution Approach

• Dashboard Goals• Simplify vast amounts of data

• Quick method for identifying areas of opportunity worth investigating

• Intuitive Interface

• Approach• Created 3 summary level dashboards

• Used tiles on each dashboard to summarize key points

• User determined path to more detailed data

Diagnosis

Summary

Drugs for

Selected

Diagnosis

Prescriber

Variance

Live Demonstration

Utilization by Diagnosis - Summary

Summary Tiles

Identify High-

Cost

Diagnoses

Identify Impact

of Drugs on

LOS

Determine

High-Cost vs

High-Volume

Diagnoses

LOS = length of stay

Impact: Determine areas of opportunity to reduce costs

Utilization by Diagnosis – Drug Level

Identify Drugs

Used for a

Diagnosis

Determine if

LOT is

Appropriate

Compare

Prescribing

Patterns

LOT = length of therapy

Impact: Determine drugs and prescribers to target for

reducing spend

Utilization by Diagnosis – Prescriber Level

Identify

Prescribing

Variance

Identify LOT

Variance by

Prescriber

LOT = length of therapy

Impact: Identify prescribers to notify of high cost trends

Utilization by Drug - Summary

Visualize

Where Drugs

are Used

Compare Cost

per Day by

Location See Price

Changes and

Impact on

Budget

Summary Tiles

Impact: Determine drugs and locations to target to reduce

costs

Utilization by Drug – Prescriber Level

Visualize Which

Services are

Using a Drug Visualize Which

Prescribers are Using a

Drug and for Which

Diagnosis

Impact: Determine services and prescribers to target for

reducing spend

Outpatient Reimbursement - Summary

Summarize

Profitability

Identify Locations and Payers with

Sub-Optimal Reimbursement

Identify

Prescribers

with Low

Profit

Identify

Drugs with

Low Profit

Impact: Identify locations, drugs and prescribers with sub-optimal

reimbursement to further investigate

Reimbursement – Drug Level

Identify When

Reimbursement

Decreased or Costs

Increased

Impact: Determine when reimbursement or costs changed to

identify appropriate methods of correction

Reimbursement – Prescriber Level

Identify Which Drugs

have Sub-Optimal

Reimbursement for a

Prescriber

Impact: Determine which drugs have sub-optimal reimbursement by

prescriber to identify appropriate methods of correction

Technical Challenges and Lessons Learned

Technical Challenges & Lessons Learned

Navigating between

levels using Javascript API

Dynamic Color

Changes

Development Efficiencies

Maximize Performance

Development Efficiencies

• Dividing work: Multiple developers can’t work on the same workbook at the same time

• Excessive complexity: The more complex a workbook is the more difficult it is for a new developer to pick it up and understand it

• Unintended consequences: More streamlined workbooks have less of a chance of a change having an unintended effect somewhere else in the functionality

• Split up levels: Using a different workbook for each level allows more developers to work on Tableau at the same time while also simplifying each one

Maximizing Performance

• Worksheet count: In our experience, having too many worksheets in one dashboard noticeably slows down Tableau’s performance

• Auto-updates: Pausing auto-updates is an effective way to prevent excessive Tableau calculations

• Minimized updates: Since only one level is visible to the user at any given time, only that level needs to go through the work of updating itself

• Combined webpage: So although all the levels are present on a single webpage, the ones currently not visible will pause auto-updating until the user looks at them

Navigating Between Levels

• When to update: The Javascript API allows us to programmatically pause or un-pause workbooks as needed

• Pass along selection: It also allows up to let the next level down know what the user selected to drill down on

• Keep filters synced: Finally, we can use it to asynchronously pass along filter changes the user makes to the workbooks that aren’t currently visibleThird Level

Second

Level

First Level

Dynamic Color Changes

• The limitation: Tableau assigns colors during development rather than dynamically

• Our design: We only show the top of various categories in our workbooks and which ones are at the top can change dynamically based on filters or drill down selections

• Resulting problem: Since what’s shown changes dynamically but the colors don’t, users can sometimes see two categories with the same color in a graph

Summary and Future Direction

Summary of Benefits

*Projections based on existing proprietary data from current customers

Profitability of

Outpatient Drugs

Project 0.5-1% of outpatient drug spend in additional revenue*

Inpatient Drug Spend

Project 0.5-1% of inpatient drug spend in

savings*

Staff time and IT

resources

Less time needed to manually manipulate and combine multiple sources of data into one

Future Directions

• Launch Plan

• Roadmap of Future Features• Based on customer input and interviews

• External benchmarking

Q4 18 Q1 19 Q2 19 Q3 19

• Complete

Development

• Beta testing

• Post-Beta

testing

development

• Launch

solution

• New customer

onboarding

• New customer

onboarding

• Plan next

feature

development

Thank you!

#TC18

Kaci.Dominguez@mckesson.com

Appendix

Layering of Tableau Instances

• <div id="tableauViz1" class="tableauViz" data-x="0" data-y="0" style="position: absolute; z-index:3; width: 1017px; height: 2030px;"></div>

• <div id="tableauViz2" class="tableauViz" data-x="0" data-y="0" style="position: absolute; z-index:2; width: 1017px; height: 2030px;"></div>

• <div id="tableauViz3" class="tableauViz" data-x="0" data-y="0" style="position: absolute; z-index:1; width: 1017px; height: 2030px;"></div>

• …

• viz1 = new tableauSoftware.Viz(document.getElementById("tableauViz1"), url, options);

• viz2 = new tableauSoftware.Viz(document.getElementById("tableauViz2"), url, lvl2Options);

• viz2.pauseAutomaticUpdatesAsync();

• viz3 = new tableauSoftware.Viz(document.getElementById("tableauViz3"), url, lvl3Options);

• viz3.pauseAutomaticUpdatesAsync();

Third Level

Second Level

First Level

Move Between Levels

• } else if ($('#hdnDrillDownLevel').val() == "2") {

• if ($('#tableauViz2').css('z-index') != '3') {

• $('#tableauViz2').css('z-index', 3);

• $('#tableauViz1').css('z-index', 1);

• $('#tableauViz3').css('z-index', 2);

• workbook = viz2.getWorkbook();

• activeSheet = workbook.getActiveSheet();

• viz2.resumeAutomaticUpdatesAsync().then(function () {

• activeSheet.getWorksheets()[0].applyFilterAsync(

• "Primary Diagnosis Code Desc", $('#hdnDiagnosis').val(),

• tableauSoftware.FilterUpdateType.REPLACE).then(function () {

• viz.pauseAutomaticUpdatesAsync().then(function () {

• finishUpTableauChange(viz2);

• waitDialogBox(false); }); }); });

• } else {

• finishUpTableauChange(viz2);

• waitDialogBox(false); } }

Third Level

First Level

Second Level

Flip z-order

Keep Tableau Instances in Sync

• if ($('#hdnDrillDownLevel').val() == "1") {

• applyFilterChangeTo2(viz2.getWorkbook().getActiveSheet(), viz3.getWorkbook().getActiveSheet(), filterEvent);

• } else if ($('#hdnDrillDownLevel').val() == "2") {

• applyFilterChangeTo2(viz1.getWorkbook().getActiveSheet(), viz3.getWorkbook().getActiveSheet(), filterEvent);

• } else {

• applyFilterChangeTo2(viz1.getWorkbook().getActiveSheet(), viz2.getWorkbook().getActiveSheet(), filterEvent);

• }

• . . .

• function applyFilterChangeTo2(sheet1, sheet2, filterEvent) {

• filterEvent.getFilterAsync().then(function (filter) {

• var appliedValues = filter.getAppliedValues().map(function (a) { return a.formattedValue });

• sheet1.getWorksheets()[0].applyFilterAsync(filter.getFieldName(), appliedValues,

• tableauSoftware.FilterUpdateType.REPLACE).then(function () {

• sheet2.getWorksheets()[0].applyFilterAsync(filter.getFieldName(), appliedValues,

• tableauSoftware.FilterUpdateType.REPLACE).then(function () {

• currentlyBeingFiltered = ‘’; }); }); }); }

Third Level

Second Level

First Level

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