Improving Outcomes with an NLP-Enabled Provider Risk Adjustment Workflow

NAACOs Webinar — October 16, 2019

Robin Lloyd, Chief Commercial Officer

Chief Commercial OfficerRobin.Lloyd@healthfidelity.com

Robin leads Health Fidelity’s go-to-market strategy and the sales, marketing, professional services, and product teams. Robin brings more than 25 years of experience building and leading organizations through rapid growth and transformation. He has led teams throughout the globe and is fanatical about developing effective, customer-driven leaders who can scale and adapt along with dynamic business needs.

Over the past decade, Robin has delivered a series of market-leading solutions to the healthcare provider market. Prior to joining Health Fidelity, Robin was VP/GM of the Clinical Documentation business unit of Nuance Healthcare where he led the transformation of the market-leading provider speech recognition product from desktop to SaaS, growing subscription revenues tenfold in a three year period.

Robin holds a Bachelor’s degree in Economics from Williams College. He advises several early-stage technology companies, when not exploring the trails of Northern California and beyond.

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Moderator

Adele L. Towers, MD MPH FACP

Director of Risk Adjustment

UPMC Enterprises

towersal@upmc.edu

Dr. Towers is the Director of Risk Adjustment for UPMC Enterprises, a practicing physician, and a professor of medicine. She is directly involved in the development of healthcare related technology, with emphasis on use of Natural Language Processing (NLP) for Risk Adjustment coding and use of Clinical Analytics to optimize clinical performance. Prior to this role, she has served as the Medical Director for Health Information Management at UPMC with responsibility for CDI and inpatient coding denials.

Dr. Towers has been on the faculty in the Division of Geriatric Medicine at the University of Pittsburgh for over 25 years, and continues to see patients at the Benedum Geriatric Center in UPMC.

Dr. Adele Towers is an employee of University of Pittsburgh Physicians, which is an affiliate of UPMC. UPMC has a financial interest in Health Fidelity.

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Speaker

Today’s Agenda

1. The importance of Risk Adjustment for ACOs2. The "ideal state" where technology is deployed to optimize risk adjustment activities:

– Record Retrieval– NLP analysis – Payer and Provider workflow

3. UPMC’s Risk Adjustment Transformation – UPMC Overview– Risk Adjustment Journey – from Health Plan to Provider– Going Forward: UPMC Provider Workflow– Outcomes and Lessons Learned

4. Questions/Answers

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The Current Challenge

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In 2018, >100 Million* U.S. Lives Were Managed Under a Risk-Based Payment System

Enrollment (Millions of Lives)

Medicare Advantage

ACA

Managed Medicaid

Medicare ACO

The number of risk-adjusted lives is growing 15 – 20% annually

*AIS Health Directory of Health Plans

0 10 20 30 40 50 60

What is Risk Adjustment?

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Member 166 years old

HTN

Member 287 years oldCHF + DM

Risk ScoreAccounts for cost difference

Factors Contributing to Risk Score

$ $$

Diagnoses on Claims

Age Gender

Risk Score = 1.0 (Average Cost)

Risk Score > 1.0 (Patient is likely to have higher costs)

Risk Score < 1.0 (Patient should have costs that are

less than average)

Provider Documentation

is critical to accurate Risk Adjustment

Risk Score as a Measure of Cost Efficiency

• Reviewed 2018 Performance of 548 MSSP ACOs

• 37% ACOs generated a savings over the minimum savings rate (MSR)

• Not surprisingly, the higher the risk score, the higher the savings

Risk Score Avg. Per Beneficiary Generated SavingsGreater than 1.1 $4111.0 – 1.1 $159Less than 1.0 $135

https://data.cms.gov/Special-Programs-Initiatives-Medicare-Shared-Savin/2018-Shared-Savings-Program-SSP-Accountable-Care-O/v47u-yq84

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A Better Way

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Utilize All Available Information for Analysis Most organizations lack an automated method to analyze vast amounts

of unstructured clinical documentation to extract valuable insights

NLP allows organizations to take large amounts of clinical information and:

1. Analyze2. Organize3. Contextualize4. Prioritize

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Clinical Evidence Interpretation Example 1. NLP Analysis:

E11.9: Type 2 diabetes mellitus without complicationsI10: Essential (primary) hypertensionZ85.3: Personal history of malignant neoplasm of breastN18.3: Chronic kidney disease, stage 3 (moderate)Z68.37: Body mass index (BMI) 37.0-37.9, adultNegation correctly suppresses CHF-related suggestions

2. Coding Rules:E11.9 + N18.3 = E11.22 Type 2 diabetes mellitus with chronic kidney disease

3. Suspect Diagnosis:Z68.37 + E11.9 = E66.01 Severe obesity

Physician Note:

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Ms. Taylor is an 82-year-old patient with a past medical history of diabetes, HTN, breast ca. She recently progressed to CKD3.

She had some chest pain and we were worried about CHF, but we've since ruled that out.

VitalsBMI 37

LabsHer GFR test showed a value of 46.6 ml/min.

Assessment and Plan1. Diabetes - continue Metformin2. CKD3 stage 3 - continue Lisinopril 3. HTN - continue managing with diet

Step 1: Automate Chart Retrieval

Solving for optimal medical record retrieval with technology

Without Technology

Phone calls

Emails

Fax

Snail-mail

On-site abstractions

Etc.

Manual Process

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Chart Retrieval Automation Far Superior

Across multiple factors, manual retrieval methods fail to deliver

Manual Retrieval Automated Extraction

Expense Variable, High Predictable, Low

Quality Inconsistent, Low Uniform, High

Completeness Partial Complete

Timeliness Long lead time Continuous, 24/7

Friction High, Complex Passive, Simple

100%

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Step 2: Apply NLP to Centralized Data Repository

Admin. / Claims Data

CMS Files

Eligibility / Enrollment

Claims / Billing Data

EHR Data

Lab & Test Results

Provider Notes

Patient History

NLP and Inference

Generate clinical evidence from historical data

Apply machine-learned algorithms

to identify gaps

Data Warehouse

Create a complete and

holistic member profile registry

for analysis

Documentation and Coding Gaps

Compliance Risks

Suspected Conditions

Physician Engagement and Education Opportunities

Member Stratification

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Step 3: Deliver NLP Insights via Care Workflow

Pre-Visit Prep

Point of Care Post-Visit Review

Retrospective Review

Patient Prioritization

Physician / Provider Encounter

Claim Adjudicated

Encounter / Chart Prep

Patient Encounter Lifecycle

Population Health Clinical Mgmt. > Optimization Administrative Program > Recalibration

Patient Population Management / Monitoring

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UPMC’s Risk Adjustment Transformation

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Life Changing Medicine at UPMC

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$20 billion integrated provider and insurer system closely affiliated with the University of Pittsburgh

2007 2012 2013 2015 2018 2019

Inpatient Computer Assisted Coding

Inpatient Clinical Documentation Improvement

Retrospective Risk Adjustment Coding, UPMC Health Plan ACA plans

Provider Facing Risk Adjustment Post-Encounter

Retrospective Risk Adjustment Coding, UPMC Health Plan Medicare Advantage

UPMC use of NLP

Provider Facing Risk Adjustment POC

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Risk Adjustment Journey started with NLP-enabled Retrospective Review at Health Plan

2013Health Plan implements NLP-powered retrospective review for MA members

Educate Providers and

Review Records

Prospective Campaign

Clinical Services Rendered/

Claim Submitted

Claims sent to HCC Scout

Abstracted and Integrated

Records are transferred to

NLP

NLP suggests codes to add

Coder accept/ rejects codes

QA

Codes sent to CMS

Model and NLP inputs

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Tackling Data Acquisition

2013Health Plan implements NLP-powered retrospective review for MA members

2014Health Plan adopts technology-enabled data acquisition strategy

• Medical Claims• Medications• Lab Values• Medical Record Documentation

– PCPs, Specialist, Hospitals, etc. • Care Management Assessments• Enrollment & Demographics Data• Health Risk Assessments

(self-reported)

A unique patient story can be found in disparate data sources:

GOAL: Develop centralized registry of

member clinical presentation and

lifestyle profiles for clinical analysis

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Streamlining efforts across multiple lines of business

2013Health Plan implements NLP-powered retrospective review for MA members

2014Health Plan adopts technology-enabled data acquisition strategy

2015Health Plan adopts NLP-enabled retrospective review for ACA members

Health Plan’s Risk Adjustment operations are fully streamlined across MA and ACA Single coding platform Increased coder productivity ~75% of medical records retrieved automatically Ongoing coding throughout the year vs. batched sweep

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Moving upstream for more comprehensive risk adjustment strategy

2013Health Plan implements NLP-powered retrospective review for MA members

2014Health Plan adopts technology-enabled data acquisition strategy

2015Health Plan adopts NLP-enabled retrospective review for ACA members

2018-2019Provider adoption of NLP-enabled workflow for comprehensive risk capture

“Developing an accurate portrayal of our patient population’s disease burden is a key organizational goal for our health system. We were looking for tools to standardize risk capture across our patient population and do so without burdening our physicians and clinical staff.”

- Dr. Francis Solano, President, Community Medicine Inc. at UPMC

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Payer-Provider Collaboration

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For providers, there’s still more opportunity for improvement

MA ACA MedicaidOther at-risk lives

Addressed by UPMC HP

retrospective risk adjustment

Goals:

• Address lines of business other than MA & ACA

• Utilize NLP for improvement in prospective risk adjustment

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Immediate opportunity – Engage Providers

An embedded tool for point-of-care display and documentation of chronic conditions and suspected diagnoses, followed by a post-encounter review.

< 70% MA capture rate for

annual chronic condition reconfirmation

HISTORICAL EFFICACY: PERFORMANCE GOAL:

+ 15% increase in chronic reconfirmation rate

POTENTIAL UPSIDE:

+ $19M increase in annual

revenue, not including non-MA opportunities

EQUIP PROVIDERS WITH NEW TECHNOLOGY:

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Providers Play a Vital Role in Risk Adjustment

• Medical record documentation must support each coded diagnosis and adhere to coding guidelines.

• Document and Code all conditions that coexist at the time of the encounter and require or affect patient care treatment or management.

• Cannot code from documentation that is only in the Problem List or Past Medical History.

• Documentation must show MEAT – Monitor, Evaluate, Assess or Treatment.

• Code to the highest level of specificity.

• Do not code conditions that were previously treated and no longer exist.

Undercoding or underdocumentation results in a lower risk score

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Diagnoses coded by physician

“Missing” Diagnosis – CKD Stage 4

Example: Physician Undercoding

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Underdocumentation – Specificity Matters!

“Mrs. Jones is depressed”➟ F32.9 ✗ NOT a CMS-HCC

“Mrs. Jones has major depression, recurrent”➟ F33.9✔ This is a CMS-HCC

How do I remember all these rules?

It isn’t possible!

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Three interventions for NLP-enabled Provider Risk Capture

Point-of-care Post-EncounterPre-encounter

Leverage NLP to analyze historical medical record data in addition to claims 10-15% increase in prospective

opportunity identified Substantiation of gaps using

clinical evidence -> increases physician trust

Helps prioritize members that need to be seen

Push identified gaps to the physicians at the point of care through the EHR Gaps show up as flags within

the EHR workflow Provide clinical evidence and

documentation tools Add diagnosis to claim and

problem list Analytics

Leverage NLP to streamline pre-bill coding prior to claim submission Identify documented but

missed codes Decrease number of

encounters requiring human review

Improve compliance Implement a query workflow

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Pre-encounter

• Identify the HCC opportunities of patients who have been scheduled

• Identify patients that need to be scheduled for a visit

• Use NLP to identify gaps from medical records data (not just claims data)

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Goal: Find more accurate and complete set of gaps to address

NLP-powered Prospective Gap Identification

Incorporation of Medical and Pharmacy Claims

Machine Learning and Manual Curation

Refinement based on Business Rules

Campaign Suspect

REVEAL ICD Output

• Campaign suspects draw from multiple sources of data: documents, claims and data correlations

• Refinement based on business rules and PCP feedback

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Point-of-care

• Current methods are manual, paper-based– Insufficient information regarding source of the suspected

diagnoses• Provide the clinical evidence of the suspected condition• Provide compliant documentation and allow providers to add

their own documentation• When approved, the diagnosis is added to the claim and

problem list

Goal: Enable physicians to close verified gaps during the visit

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Point-of-care

EHR Integration

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Present the Facts

• Last billed claim• NLP-found clinical evidence • Last seen clinician

Make it Easy

Post-encounter

• Add and/or remove diagnoses from bill– Use NLP to increase review efficiency– Only review encounters with opportunity

• Low hanging fruit– Conditions have already been documented– No physician abrasion– Offers the quickest ROI

• Optimizes Medicaid and Medicare ACO performance

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Goal: Close coding gaps prior to submitting bill

HCC Description % of Population Reconfirmation Rate

Chronic Obstructive Pulmonary Disease 15.67% 85%

Vascular Disease 15.43% 46%

Diabetes with Chronic Complications 15.06% 89%

Specified Heart Arrhythmias 14.12% 86%

Congestive Heart Failure 12.08% 77%

Diabetes without Complications 10.36% 91%

Morbid Obesity 8.55% 76%

Provider Population Risk

Dr. Adele Towers 1.612

Colleague 1 1.784

Colleague 2 1.0384

PA AVG 1.246

US AVG 1.0

Analytics to Manage Provider RA Performance

Assess Prevalence and Reconfirmation Rates Provider Risk Score Comparison

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UPMC Results

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UPMC MA Results with NLP and Retrospective Coding

Coding Application: Uses NLP to identify diagnoses in physician documentation

UPMC Health Plan: ~$200M in additional revenue from Nov 2013 - July 2018

Coders: Productivity increase 4X

Physician Shared Savings: NLP captured ~12% of shared savings revenue for providers

Technology Platform

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UPMC Pilot Results with NLP-enabled Post-encounter and Point-of-Care Workflow

Medicare Advantage

$452K

HHS/ACA

$110K

Medicaid

$461K

©2016 UPMC Health Plan: PROPRIETARY

Small pilot, 37 Doctors from August - December 2018, resulting in: >$1M

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Post-Encounter and Point-of-Care Improvements

19,789 Codes through 9/30/19 Point-of-Care• Improved physician engagement• Increased chronic reconfirmation rate• Net new conditions documented

Post-Encounter Review• Increased HCC capture• Coder productivity gains• Decrease in encounters requiring coder

review• Removed unsubstantiated codes

39©2016 UPMC Health Plan: PROPRIETARY

852 316

8594

5902

25771548

0

2000

4000

6000

8000

10000

Point-of-Care Post-Encounter

Total Codes Captured by LOB

ACA MA MCAID

Individual Physician Performance Report

Proprietary & Confidential 40

Report Takeaways• High volume + high

utilization = high performer

• Data can be shared in peer group or by practice, depending on adoption goals / incentives

Primary Care Practice Performance Report

Proprietary & Confidential 41

Report Takeaways• >100 encounters considered

high volume• High volume + >70%

adoption considered excellent

• Reveals high potential groups for establishing best practices

QUESTIONS?