Jeff Springer

Ascension Shared Services

Manager, Procure to Pay Initiatives

What Piloting Machine Learning Means for Shared Services

Wield the Power of Near Real-Time Prescriptive Analytics

By attending this session, you will better understand:

• How Machine Learning (ML) drives continuous optimization and reinvention in SSOs

• Why SSOs are in an optimal position to embrace ML

• What it takes to run an ML Pilot: Lessons learned from Ascension’s journey

• The Data imperatives to avoid being fooled by your Pilot

Jeff Springer Ascension Shared Services Manager, Procure to Pay Initiatives

Based in Indianapolis, Jeff joined Ascension’s Ministry Service Center (SSO) in April 2012 during the early stages of the healthcare Provider’s shared services transformation. With an early focus on Accounts Payable and match exception management, Jeff gained immense experience and lessons learned while leading a diverse workforce through technology, policy and process standardization, and continual improvement initiatives. Jeff has been acutely involved as a business owner and product owner across numerous RPA and RDA implementations while working on the team that initiated a cognitive automation / machine learning program in partnership with CognitiveScale. Jeff recently rejoined Ascension following a stent with Deloitte Consulting as a Manager in their Enterprise Operations offering portfolio, Healthcare - Provider sector. This experience supplied him with broad exposure to industry clients seeking to maximize investment into business operations, most notably through digital transformation. Outside the office, Jeff resides in Carmel, IN and enjoys spending time with family and friends, playing tennis and golf, passionately following his Indy sports teams, and traveling. Above everything, raising with his wife 3 young boys is the time most cherished and enjoyable.

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Ascension + MSC

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Ascension at a Glance

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• Founded in 2011

• To scale and standardize Services

for Health Ministries

• $3B in savings by 2018

• Ground Zero for “One Ascension.”

Ascension’s Ministry Service Center

The MSC

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• Associate Payroll

• Accounts Payable

• Accounting

• Benefits

Provide 20+ Business Services:

What We Do

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• Processed $12B in payroll

• Managed 83,000 clinical licenses

for 75K clinicians

• Fulfilled $8B in PO lines to 3,200

locations

• Processed $13.3B in payables

• Administered benefits for 156K

Associates

In FY18 the MSC…

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Labor Productivity 2018 vs. 2014: “Same Store”

Indexed to 2014 = 100 “While the volumes of

transactions has

increased significantly

since 2014, there has

been only marginal

change in FTE count.

These marginal changes

have been driven by

Ascension’s process

improvements,

automation and RPA

initiatives.” - ISG

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Ascension’s Journey to ML

Launched first small scale IPA projects via RPA

2012 2013 2014 2015 2018 2016 2017

COE completed product selection process (March/April)

Launched second RPA solution

(August)

Trained first business-unit associate via COE (March)

Cognitive and ML

70 in-service automations, 50 point automations; 200+ opportunities in queue

Established “Collaboratory” w/industry leaders (March)

Created Process Excellence team

(December)

Automated “swivel chair” activities (July)

Established Center of Expertise (CoE) for Automation

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ML for Shared Services

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Embracing Machine Learning in Shared Services

AI-powered cognitive solutions such as Machine Learning have the ability to boost efficiency, improve productivity, provide essential analytics, and free up human work-force time to focus on value-adding activities like analysis, planning, and decision making.

Machine Learning Pilots require dedicated multi-year investment of resources and a considerable financial investment against a business case riddled with assumptions and factored benefit.

So, what does Piloting Machine Learning mean to Shared Services?

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Transcend the Big Shift

Moving from the rearview mirror to the windshield

• SSOs are no longer focused on purely serving clients

• Satisfaction and Experience of the clients’ customers are in the driver’s seat

• Global Process Owners & Clients are looking for someone to take the lead

SSOs will thrive by offering prescriptive solutions that proactively meet clients and customers at every point in their journey

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SSOs are Especially Prepared for ML

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• Operations at scale

• Use Case definition

• Knowledge Domain definition

• Project Management capability

• Process knowledge

• Familiarity with technology and people

• Close relationship with IT (typically)

• Already using RPA and RDA

• Existing analytics capability

• Data, Data, Data

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Taking Inventory of How We Got Here

Shared Services was born from centralization, consolidation, and standardization

Over time, straight through processing neared 100% digital

Portal / EDI > Interface > SaaS > ERP > Interface > Vendor / Bank > Portal

Exceptions, however, drove headcount up if not forecasted and budgeted

In part, SSOs became exception handling houses, so we built RPA to control headcount, absorb new volume, and maintain SLAs

SSOs now “gifted” other problem processes, in large part due to established automation programs and focus on continual improvement

Current Mood: Enable an endless supply of juice no matter how hard the squeeze

Augmented Intelligence / ML are big bang (& big buck) enablers

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The ML Pilot

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Key Tenets of a Machine Learning Pilot

Use Case: Identify an objective within a strong knowledge domain

Team: Business led, but don’t go it alone

Investment: Time, money, resources, and more time

Data: SSO’s are data rich, but take a closer look

Strategy: Give consideration to the lever you are trying to pull, and where

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Align to your Organizational Strategy

Start with RPA - Get some wins, the savings and learnings are best to invest

What is the initiative in response to?

• Cost pressure, demand management, M&A, disruption, CX

Where is the strategic consideration?

• Service line, SSO, business, IT, sales

What other considerations are there?

• Funding sources

• Roadmap

• Coalition support

• Priority – not just for your group

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Critical to Quality: The Use Case

This is a PILOT - Use Case must have attainable & measurable exit criteria

• Knowledge domain

• Data (more on this later)

• Learnability

• Defined outcomes and end point

• Actionable results

• Beyond rules-based

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Ascension’s Production ML

The Use Case

• Knowledge domain

• Data (more on this later)

• Learnability

• Defined outcomes and end point

• Actionable results

• Beyond rules-based

Purchasing & Payables

Structured, Collectively Exhaustive

Consistent Feedback Loop

Confidence + Accuracy Thresholds

Task Resolutions – Handoff to RPA

Subjective Complexity

Match Exception Prescriptive Fix

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The Team: Much Assembly Required

Business takes lead, but must secure • Executive Sponsorship • Governance • IT • Analytics / Business Intelligence • Cognitive Partner • Task Automation Partner (or Internal team) • Audit • Operations – Management + SMEs

Define Roles - Everyone has a place, and in their place they must go

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The Team

Cog Partner Utilized Governance and IT to select the right vendor through capability vetting: • POT/POC for ML only

• Separate workstream • KPIs over 3 tollgates: Assisted -> Unassisted • Data selection

• Included our universe for control group and Type 1 / Type 2 Error discovery

• Use Case evaluation and POV • Data Modeling and solution design • Dashboarding and executive updates

Ascension’s Production ML

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The Team

IPA/RPA Partner Engaged our automation and six sigma experts to: • Drive POT/POC workstream • Provide POV & recommendation on vendor

selection • Work with IT and CognitiveScale to

integrate platforms and environments • Enable end of line task resolution with RPA • Design and build human feedback loop

workstation • RADILO™ integration (User UI

workstation & automation engine)

Ascension’s Production ML

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Plan for Impact – Beyond Monetary Investment ML Pilots have a longer runway than what you’ve experienced with RPA and traditional IT.

Time Potentially >2 years. Don’t abandon exit criteria that may trigger your backout plan. Continually evaluate performance & learnability.

Resources SMEs need to participate and contribute to the feedback loop. Adoption planning is an ongoing challenge.

Productivity You’re training your workforce of the future. The cycle of refinement involves humans, so expect productivity slowdowns.

Patience, Boss - Executive sponsors need to accept the time horizon

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ML is a Veracious Data Beast

Data is the kingpin of piloting ML in Shared Services. Without the right data your ML Pilot will fool you into thinking it’s working when it’s not, or not working when it is.

• Availability • Veracity • Cleanliness • Structured (Un) • Consumable • Relevant

ML Requires “Learnable” Data That is, data that is known to contribute to inference

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Why is Machine Learning finding a home in SSO?

• For the last three decades, companies have spent billions centralizing, and standardizing transactional and rule based work. Now we are leading with RPA.

• The first enterprise wide applications were all aimed at enabling core business processes. ERP and SSO became almost synonymous.

• Large enterprises expect SSOs to have data to serve the enterprise. IT and SSOs have partnered for 20 years to enable analytics.

So why does this matter? Because SSOs have….

Data

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Prescriptive Analytics Driving Service Orchestration

We spent millions building our data warehouse and data-marts for reporting They have value but are largely useless in the probabilistic world

Prescriptive analytics answers the question of “what is about to happen and - what should I do about it?”

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Communication is key. In the absence of facts, stories will be created.

Address the fears. This is not about job elimination.

Personify the new digital teammates.

Involve everyone. Participation leads to support.

Celebrate and recognize the behavior you want.

This is a change program. Human emotion MUST be embraced!

Human–Bot Harmony: Change Management

4 Key Ingredients to Feed your ML Pilot

1. Don’t Go it Alone: Find the Right Partner(s)

2. Prepare Leaders and Stakeholders: ML takes TIME and INVESTMENT

3. Success Hinges on Data and the Data Model

4. Lessons Learned Across Change Management Imperatives will be Plentiful: Be Ready to ACT and ADAPT

Questions?