Improving Outcomes with an NLP -Enabled Provider Risk ... · Natural Language Processing (NLP) for...
Transcript of Improving Outcomes with an NLP -Enabled Provider Risk ... · Natural Language Processing (NLP) for...
Improving Outcomes with an NLP-Enabled Provider Risk Adjustment Workflow
NAACOs Webinar — October 16, 2019
Robin Lloyd, Chief Commercial Officer
Chief Commercial [email protected]
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
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
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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?