Payment Assurance for Hospitals

Applying [Data] Science to the Art of Patient Access:

How artificial intelligence and machine learning is improving Patient Access Performance, Net Revenue and Patient Experience

Speakers: aul Shorrosh

WA AK HFMA May 1-3, 2019

About Our Speaker

Paul Shorrosh

Founder and CEO, AccuReg- Payment Assurance For Hospitals

Former Revenue Cycle Management Operations

HFMA Member and Sponsor >20 years

Former Board and Committee Chair, NAHAM

Today’s Discussion Points

• What is Patient Access?

• What is Data Science?

• Can Data Science apply to Revenue Cycle Performance?

• Can Data Science apply to Patient Financial Engagement?

• Partnering to bring science and prediction to the revenue cycle

Patient Access is Front-End RCM

Revenue Cycle Management

Front (FERC)

Order Schedule Pre-Reg Arrival Registration

BackMiddle

Revenue Capture Management

Patient Access Objectives

• Patient experience• Speed - efficiency• Accurate data capture• Collect patient liabilities• Offer financial assistance• Prevent denials:

• Eligibility (COB)• Authorizations• Medical Necessity• Data Quality

5 OPPORTUNITY POINTS:1. Ordering2. Scheduling3. Pre-Registration4. Arrival5. Registration

Patient Access – Multiple Complex Processes and Tasks

Patient Access - Financial Goals

Collect Cash

Prevent Denials

Improve Net

Revenue

What is Data Science?

Data Science Terms

• Artificial Intelligence- the development of computer systems to make decisions and perform tasks that normally require human intelligence.

• Predictive Analytics- the practice of analyzing data to identify past patterns to create algorithms that reliably predict the future.

• Algorithms- a logical rule that can be used to identify a current or future situation that meets that same pattern from the past.

• Machine Learning- the systematic process of identifying new patterns and algorithms that constantly improve a system’s ability to make predictions and decisions.

• Learning Rules Engine- a software system with rules (algorithms) that are constantly updated based on new input.

Artificial Intelligence –Computers with the ability

to reason as humans

Machine Learning –Computers with the ability to learn without being explicitly

programmed

Deep Learning –Network capable of

adapting itself to new data

Can Data Science Apply to Revenue

Cycle Performance?

Predictive Intelligence in PA Workflow

Data Analysis

Patterns

RulesRCM Data Sources:• Provider Claims• Payer Remits• Patient Payments• Payer EV/Benefits• Physician Orders• Scheduling• Registration• Propensity• Errors• Charge Post

Data Science - Impact on Net Revenue in PAS

• Patient Revenue – POS Collections:• Predictive Estimation & Accuracy Validation (CQI)• Scheduled Patient Rate• Complete Order Rate (CPT present)• Complete Pre-Reg Rate (Tier 4)• Estimate to Registration Rate

• Payer Revenue – Denials Prevention:• Payer denial patterns -> rules engine -> real time alerts -> intervention• Predictive Authorization• Changing Payer Requirements;

• Eligibility and benefit limitations, COB complexities• Medical Necessity requirements

• Outsmart payer tactics in the reg booth

Collect Cash

Prevent Denials

Improve Net

Revenue

Can Data Science Apply to Patient Financial Engagement?

Source; HFM Nov 2018Survey on

Patient Financial Engagement

Source; HFM Nov 2018Survey on

Patient Financial Engagement

Data Science Improves Patient Estimates

• Accuracy Measurement and Improvement Process

• Accuracy defined: estimates within 10% of remit

• Variance analysis: good 85% / better 90% / best 95%

• CQI improvement process

• Product

• Provider

• Predictive Estimates based on order patterns

• To increase POS Collections, increase the Estimate to Registration Rate

• To increase the Estimate to Registration Rate, automate estimate generation

• To automate estimate generation, capture CPT codes and partner carefully

Predicting the Affordability Tipping Point

0

25

50

75

100

125

150

175

200

225

250

275

300

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

FPL

Account Patient Balance Due

Predictive Charity Qualification

20

FPL

Credit Score

3D Financial Assistance Policies

Data Science Improves Financial Counseling

• Redesign FAPs based on payment patterns and consumer data

• New FAPs can be built into a rules engine which;• Matches up patients to FAPs in pre-service or point of service• Prompts registrars when a patient qualifies• Scripts the registrar to offer the right FAP to the right person

• Allowing registrars to do financial counseling!

• FAPs are prioritized and sequenced

• Patient Financial Communication is standardized

• Financial Assistance options are personalized AND consistent• Treat people the same – depending on individual situation: balance due,

ability and willingness

Data Science andPatient Access Partners

4 Front-End RCM Models

Intelligence in PA - Tech Partnerships

• Does the company specialize in Patient Access?

• What is their vision and strategy for Patient Access?

• Do they have a dedicated team for data analytics/validation?

• Do they have an adaptive learning rules engine?

• Do they maintain your payer rules?

• Do they service and train well – and often?

• How engaged will they be with optimization and customization?

Patient Access Intelligence

An analytics team and a dynamic rules engine that brings intelligence into the Patient Access workflow to improve Net Revenue and Patient Experience.

Shared Vision

www.accuregsoftware.com

251-338-4443

Founder & CEO, AccuReg

Paul Shorrosh

251-209-0110

paul@accuregsoftware.com

Payment Assurance from the Front-End

Thank You!Questions?