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2014 Annual Conference

Engaging the IPA Physician

Jessica Kwon, Pharm.D.

2014 Annual

Conference

ABC Physician Association IPA

100,000 patients, 18,000 senior lives

1,000 independent physicians 180 PCPs

90% PCPs are exclusive

10 year history of EMR strategy 75% of patients are on EMR

2014 Annual

Conference

HCC Department Resources

Varied Resources• 5 coders

• Clinical RAF Educator

• 2 Home Visit Coordinators

• 1 Clerical

• Data Analytics

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2014 Annual

Conference

Then and Now:

Irritated FrustratedConfused Silent Disengaged Disdain

Engaged EducatedQuestions IdeasInput Experts

2014 Annual

Conference

RAF Score Increase

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2008 Score 2009 Score 2010 Score 2011 Score

14%

Audit Scores: 2009: 69%2010: 78%2011: 84%2012: 91%2013: 92%

2014 Annual

Conference

The IPA Physician

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Conference

OpportunitiesCharacteristic Opportunities

Self-motivated Identify what motivates them

Entrepreneur Engage them in providing own solutions and be innovative

Competitive Provide numbers and data

Individualized workflows Provide support that will be flexible for the physician to adopt into their own office

Fairly engaged Get information to them accurately and timely

Resistant to change Provide habits and workflows that optimize RAF and become second nature

2014 Annual

Conference

Challenges

Not enough time

This is a game – it is not clinical and does not improve care

Patients don’t come in

I need tools

My office staff is the problem

2014 Annual

Conference

Build a Reputation Physicians trust is our greatest asset

“Trust is like a eraser. It gets smaller and smaller after every mistake.”

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EngagementIdentify what motivates them

Give them a platform to provide input

Provide support but Teach to Fish

2014 Annual

Conference

MotivationClinical Value Population

management Continuity of care Financial resources

Incentives Specific actions Consistent and clear Providers have direct

control

Reports and Data Negative Lists Scorecards

2014 Annual

Conference

Motivation: Incentives

Examples Chronic Condition Recapture

See Senior Panel Before Deadline

Increase PCP Clinical RAF Score (improvement)

Program Components Spaced throughout year

Audit Adjusted

Consistent

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Motivation: Reports and Data

2014 Annual

Conference

Chronic Codes Not Captured

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Conference

Seniors Not Seen

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Conference

2013 Mid-Year Scorecards

2014 Annual

Conference

Engagement: Physician Input

Provide clinical input on gray area codes

Gives program feedback

Provide ideas on incentives

Become “HCC Advocates” among physicians

2014 Annual

Conference

Engagement: Teach to Fish

More Consistent Scores

Build on Previous Years

More Efficient

Better Documentation and Fewer Missed Opportunities = Increased Revenue

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Conference

Engagement: Support

Enhance EMR to “make it easy”

Provide solutions

• Home Visit Program

Mentorship Programs

• Physician Mentorship

• Hospitalist/Coder Feedback

2014 Annual

Conference

Master PCP LYNTY

2014 Annual

Conference

Key Learning'sMake it easy

Make it relevant

Make it worth their while

“Already know you that which you need” -- Yoda

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HPMS Memos / HCC RerunsBottom Line Implications

----------RADV

R U Prepared?

Prepared by:Karen Bach

Pam Klugman

2014 Annual

Conference

HPMS MEMOsCMS Communications – HPMS Memo

Health Plan Management System (HPMS)

Plan Guidance

Manual Chapters

Our Focus: Risk Adjustment Related

• 2014 Risk Score Reruns for Purposes of Payment Recovery (March 25, 2014)

Payment Years impacted: 2006-2009, 2012

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Purpose: All deletions of diagnoses for these payment years are able to be submitted in the Risk Adjustment Processing System (RAPS) at this time. • CMS will sweep diagnoses submitted for a prior

payment year as of the deadline (communicated by CMS).

• MA Organizations should look to the monthly payment letters to determine when adjustments will be applied to payments.

2014 Risk Score Reruns

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What we know ….

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CMS is in the process of rerunning risk scores during calendar year 2014 for the following payment years:• 2006, 2007, 2008, 2009, 2012

2014 Payments Months affected:• March, July, August, October

Deletes only• New codes not accepted

2014 Annual

Conference

May be precursor to the overpayment collection rules inside ACA

Reruns for deletes will include all FFS information

EDPS training – insights gained

• Unpredictable bottom line impact

CPT drives Fee for Service (FFS) submissions

FFS overlays diagnosis information

• Your risk is proportional to the new members coming from FFS during those years

Why is this happening?

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2014 Annual

Conference

Transition to next topic

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Conference

Risk Adjustment Data Validation

While you can’t plan which charts CMS will select, you can prepare….

RADV – Are you Ready?

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2014 Annual

Conference

CMS conducts Medicare Advantage (MA) risk adjustment data validation activities for the purpose of ensuring the accuracy and integrity of risk adjustment data and MA risk adjusted payments.

Risk adjustment data validation (RADV) is the process of verifying that diagnosis codes submitted for payment by an MA organization are supported by medical record documentation for an enrollee.

What is RADV

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2014 Annual

Conference

READINESS SCALERegret ReviewAgony AssessDefeat DevelopVolatile Verify

Where do you want to be?

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PLAN PREPARE PROSPER

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Conference

Project Manage

• CMS Selects Plan for Audit

• Determine best record (Provider, Inpatient)

• Communicate to impacted providers

• Create process for handling receipt

• Medical Record Review

• Report on findings

• Appeal process

Major components of audit process

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HOW TO SELECT

Project Management

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Follow CMS sampling selection criteria

Refer to Notice of Final Payment Error Calculation Methodology for Part C Medicare Advantage Risk Adjustment Data Validation Contract-Level Audits (http://www.cms.gov/Medicare/Medicare-Advantage/Plan-Payment/PaymentValidation.html)

2014 Annual

Conference

First, CMS identifies all beneficiaries under each MA contract who are “RADV-eligible” because they meet the following criteria:

Enrolled in an MA contract in January of the payment year

Continuously enrolled from January of the data collection year through January of the payment year;

Sampling Method

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Conference

Non-End Stage Renal Disease (non-ESRD) status

Non-hospice status

Enrolled in Medicare Part B coverage for all 12 months during the data collection year

Had at least one risk adjustment diagnosis (ICD-9-CM code) that led to at least one CMS-Hierarchical Condition Category (HCC) assignment for the payment year.

Sampling Method

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2014 Annual

Conference

Section of up to 201 enrollees for medical record review

Divide into three equal groups• 1st – highest risk scores

• 3rd – lowest risk scores

• 2nd – the rest of them

Sample Size and Strata

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2014 Annual

Conference

Random select 67 from each group

The corresponding stratum-based enrollee weights will be computed as the number of RADV-eligible enrollees in the population grouping (or stratum) divided by the number of enrollees selected from that grouping for the sample, i.e., Nh/nh, where h represents the corresponding stratum.

Sample Size and Strata

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Conference

Treat the RADV process as a project

• Project Manager, project team (risk adjustment team, clinical)

• Certified coders, staff with HCC experience, Medical Directors

Conduct Mock Audits and/or Targeted Audits

Review / train on CMS Requirements at least annually

Review CMS Sampling methodology for eligibility

Conclusion - PLAN

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2014 Annual

Conference

Ongoing provider education ‘snippets’ in newsletters or provider communications

• Importance of proper and accurate documentation

• Medical record must support HCC

Identify IT resources and software (PDF reader/professional)

Ensure document retrieval options are viable (paper, fax, electronic)

Conclusion - PLAN

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Conference

Manage the Process and Flow

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Identify Providers

Request Records, Call Providers and Send Letters

Record Retrieval

Image and Index in system

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2014 Annual

Conference

Manage the Process and Flow

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Review Records

Follow Up Requests (2nd and

3rd)

Determine Best

Record

Submit Record to

CMS (Secure)

2014 Annual

Conference

While you can’t plan for which charts CMS will select, you can prepare by performing an internal mock audit using CMS methodology.

Last thought….

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Adjustment Analytics: Measuring Illness Burden to Optimize Health Plan Payment

Presented by: Richard Lieberman

Mile High Healthcare Analytics, LLC

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You need to understand how risk adjustment models identify and quantify member illness burden

You want to learn how health insurance issuers need to incorporate risk adjustment into their care delivery systems

You need to appreciate the similarities and differences between risk adjustment approaches used by different insurance sponsors

You want to predict how issuer and member behavior will change because of the ACA’s reliance on risk adjustment

A compare and contrast of the various revenue optimization strategies would be peachy!

WHY ARE YOU HERE?

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Because, I have been actively involved in the development of risk adjustment systems for over 20 years

• Johns Hopkins ACG Development Team, 1991-2005

• Designed the risk-adjusted payment system for Maryland Medicaid

• Worked with CMS on development of the Medicare risk adjuster

One of the nation's leading experts on financial modeling, risk adjustment, and quality measurement in the managed care industry

I combine a unique array of expertise in provider profiling, risk adjustment, case-mix measurement, and provider reimbursement strategies

WHY AM I HERE?

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Prior to the 2004 advent of risk adjustment in Medicare-Advantage risk adjuster, health plans cherry-picked healthier-than-average members

The limited deployment of risk adjustment in Medicaid managed care prevented expansion of Medicaid managed care to higher risk populations

Community-rating in small-group/individual products requires risk adjustment

Risk adjustment provides a limited-response to the, “my patients are sicker” retort

THE PURPOSE OF RISK ADJUSTMENT

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According to the Society of Thoracic Surgeons, as of 2014, “…A relatively new term being discussed among those in the health care field is "risk adjustment.“ (http://www.sts.org/patient-information/what-risk-adjustment, accessed on 10/11/2014)

The B-52’s SAID IT BEST!

• Certain state Medicaid programs starting using risk adjustment in 1997

• Medicare implemented risk adjustment in 2004

• Commercial issuers in the small-group and individual markets are subject to risk adjustment beginning in 2014

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Risk adjustment models organize diagnosis codes, and sometimes prescription drug claims, into discrete categories• Relatively homogenous with respect to cost

and utilization

• Category groupings need to be clinically meaningful to minimize opportunities for gaming or discretionary coding

• Condition categories should have adequate sample sizes

RISK ADJUSTMENT MODELS

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Understanding the risk adjustment model is only the beginning

The risk adjustment methodology incorporates

• The risk adjustment model

• Define how the payment weights are constructed

• Calculation of plan average actuarial risk

• Risk adjustment data collection approach

• The schedule for the risk adjustment program

• Payment integrity provisions

RISK ADJUSTMENT MODEL vs. METHODOLOGY

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Sponsors that have adopted risk adjustment have designed their methodologies very differently:

• Medicare Advantage: Assigns risk scores to individual members and pays for each member individually; prospective model is used

• Health Benefit Exchanges: Will pay at the plan level, with no payment lag; a concurrent model is used

• Medicaid: Most states pay at the plan level with a multi-year lag; others for each member individually; a mixture of prospective and concurrent models are used

Currently 24 states use risk adjustment in their Medicaid programs

• Dual-Eligible Financial Alignment “Demonstrations”

Typically parallel Medicare and Medicaid risk adjustment schemes

Medicaid risk adjustment is typically rating categories: institutional vs. LTSS

DESIGN VARIATIONS BY LINE OF BUSINESS

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Both Medicare and Commercial issuers are subject to a single risk adjustment model (models have enrollment-sensitive variations)

Medicaid states that use risk adjustment, have selected from a variety of different models• Most Medicaid states use Chronic Illness and Disability and Payment

System, CDPS (http://cdps.ucsd.edu/)

• The Johns Hopkins ACGs are used by three states

• Arizona uses the Episode Risk Groups (ERGs), derived from Episode Treatment Groups. The ERG model assigns each member to one or more of the 167 ERGs based on diagnostic and procedural information available on medical and pharmacy claims

• New York State uses the 3M CRGs (Clinical Risk Groups)

THE ARRAY OF RISK ADJUSTMENT MODELS

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Some analysts argue that prescription drug claims represent actual treatment; diagnosis codes are subject to reporting bias• But prescription drugs fail to capture treatment not accomplished with

drugs, e.g., chronic renal failure

Diagnosis-based risk adjustment sometimes engenders perverse responses by health plans and risk-bearing entities

The explanatory power of prescription drugs alone is rarely better than diagnosis codes; Rx data doesn’t improve the explanatory power when combined with diagnosis code data

WHY DON’T WE RELY ON PHARMACY DATA?

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AGGREGATING DIAGNOSIS CODES FOR LINEAR MODELS

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AFFORDABLE CARE ACT VS. MEDICARE RISK ADJUSTMENT

Category ACA Risk Adjustment Medicare

Plan Benefits

Benefit tiers based on actuarialvalue; benefit structure varieswithin tiers

Plans provide, at a minimum, Medicarebenefits

Plan-level premiums

Can vary based on age,geography and family size of subscriber unit

Uniform plan premiums

Monetary basis fortransfers

Based on premiums seen in market Standardized bid

Transfer of funds

Charges assessed at issuer level;lower risk plans are charged andhigher risk issuers make payments after the benefit year

Prospective payment adjustments (up ordown) to individual standardized bid

Budget Budget-neutral Not budget-neutral

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RISK ADJUSTMENT IN THE HEALTH BENEFIT EXCHANGES

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AFFORDABLE CARE ACT VS. MEDICAID

Category ACA Risk Adjustment Medicaid

Plan BenefitsBenefit tiers based on actuarial value; benefit structure varies within tiers.

Plans provide, at a minimum, federally or state-mandated benefits

Plan-level premiums

Premiums paid by enrollees can varybased on age, tobacco use, geographyand family size.

Premiums paid to plans typically vary by eligibility category, age, gender, and geographic area

Model estimation ConcurrentProspective or concurrent (more states use concurrent)

Lag Period None Typically 1 – 2 years

Transfer of funds

Charges assessed at issuer level; lower risk plans are charged and higher risk issuers makepayments after the benefit year.

Prospective adjustment for relative risk based on historical plan-level average; a few states employ individual level risk adjustment

Budget Budget-neutral Budget-neutral

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RISK ADJUSTMENT DATA FLOW (MEDICARE & MEDICAID)

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Risk Adjustment Administrator (CMS or

State Medicaid)Pharmacy Data

Medical Claim Data

Eligibility Data

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RISK ADJUSTMENT DATA FLOW (EXCHANGES)

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Risk Adjustment Administrator (Exchange)

Issuers’ Edge Server

Pharmacy Data

Medical Claim Data

Eligibility DataDe-identified Risk AssessmentScores/Prevalence Data


De-identified data: do the XML files going to the EDGE server accurately represent the utilization documented in the data warehouse?

Risk score calculation is very tightly tied to enrollment status

• Metal level (5 different models, with 4 models blended together)

• Cost-Sharing Reduction (CSR) status

• Diagnosis codes must occur within the enrollment window

Diagnosis code acceptance for risk adjustment is tied to paid claims

COMPLEXITY OF EXCHANGE DATA FLOW

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BASIC FORM OF THE PAYMENT TRANSFER CALCULATION

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ZERO-SUM TRANSFER AT THE ISSUER LEVEL

Plan 1Average risk score = 0.9

Exchange Plan 2Average risk score = 1.1

Average pmpm premium = $400Plan A pays Plan B: $40 pmpm


Data files processed by the EDGE servers by December 5th

Intends to generate issuer-specific risk scores in December

No state-wide average risk scores will be generated

Many issuers are still struggling with EDGE server provisioning yet

No one has visibility into the payment transfer formula

Zero-sum financial settlement of risk-adjusted 2014 premiums is slated for June 2015

A comprehensive audit regimen begins in 2015, plans must select an auditor by early 2015

Open enrollment began in November 2015; how will that go?

Most states have significant new issuer entrants; increased competition

CONCERNS OF EXCHANGE ISSUERS

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Quality measurement and reporting requirements begin next year

• Initial year (2015) is a beta test

Many issuers are eager to pursue revenue management strategies, but have been consumed by program implementation tasks

The impact of the “transitional policy” phase-outs and the “private option” Medicaid expansions vary by state

MORE CONCERNS…..

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There is some actuarial evidence that the profits lie in the sicker than average enrollees, not the healthier-than-average ones

• ACA risk adjustment may turn much of the predictions about age distribution among enrollees on its head

• Relatively few diseases in the model; very large coefficients

• Issuers are concerned about the financial viability of the bronze products

WHAT IS DIFFERENT ABOUT COMMERCIAL RISK ADJUSTMENT?

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There is also substantial evidence that many morbidities in this population are not represented by diagnosis codes transmitted by paid claims and these members do not have valid office visits during the risk assessment year

• Several “targeting runs” for in-home assessments reveal that up to half of all risk score augmentation requires bringing the members into care

WHAT ELSE IS DIFFERENT ABOUT COMMERCIAL RISK ADJUSTMENT?

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In-home assessments may be valuable for selected members, but there is a greater need for bringing these members into treatment

If a member has significant morbidities, as evidenced by prescription drug use, clinical care needs to be deployed.

In-office health risk assessment programs are likely to be appropriate for the commercial population

REVENUE MANAGEMENT STRATEGIES FOR COMMERCIAL ISSUERS

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Most Medicare-Advantage plans have a very narrow view of risk adjustment

• They view it primarily as a way to increase revenue

In-home “health risk appraisals”

Retrospective medical record reviews

Very high probability that CMS policy will change in 2015

• CMS will be changing the rules around the provision of in-home assessments

Health plan concerns about the recalibration of models based on encounter data

MA PLAN RESPONSES TO RISK ADJUSTMENT

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Linking relative risk scores to the provision of health benefits

• If risk scores go up resulting from a data collection intervention, then there need to be clinical interventions associated with the increase

Using risk adjustment models to identify patients with particular co-morbidity vectors

CMS is now ready to calibrate risk adjustment models from encounter data, instead of Medicare fee-for-service data

Extracting data from electronic medical records

• Hampered by limited inoperability by EMR vendors

“NEXT GENERATION” RESPONSES

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CONTACT INFORMATION

Richard Lieberman

[email protected]

720-446-7785

www.healthcareanalytics.expert

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HCC Diagnosis ReconciliationsAre you sure everything is working?

2014 Annual

Conference

Our Journey

Supposed Path of a Diagnosis

Real-world Paths

HCC Reconciliations

Technical Processes

Business Processes

Key Points

Q&A

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2014 Annual

Conference

Simple Path of a Diagnosis

Providers Send Claims

Health Plans Adjudicate Claims

Health Plans Transmit Diagnoses to CMS

CMS Stores the Diagnoses

CMS Calculates Risk Scores

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Conference

Simple path of a diagnosis

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2014 Annual

Conference

More than Providers, Health Plans & CMS

Lots of Systems, Not Just Claims

Data Quality

Data Transfers

Encounter Filtering

Miracle

5010 837s

The Real Path of a Diagnosis

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2014 Annual

Conference

Reality… a subway system

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2014 Annual

Conference

Reconciliations

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Missing Diagnoses

2014 Annual

Conference

The RAPS Record

HICN… the Member ID

Provider Type… IP, OP and Prof

From Date & Thru Dates

Diagnosis Code

New, Home Code

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2014 Annual

Conference

Business Knowledge

Data Quality

Systems Come in all Flavors

People… Business & Technical

Data Transfers

Flawed Filtering Logic

Differing Opinions

Common Problems

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2014 Annual

Conference

Acquisition

Data Discovery

Encounter Filtering

Reconciliation Granularity

Venn Diagram, SQL OUTER JOIN

Audit Reports

837s Solution

Technical Processes

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2014 Annual

Conference

Internal Data Acquisition… and Discovery

External Data Acquisition… and Discovery

External Acquisitions Issues

Internal Resources

Commitment from the Top

Business Understanding

Encounter Acquisition

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2014 Annual

Conference

Data Distributions

Encounter Counts for Focus

Comes in all Flavors

Decimals

Member IDs

More to come on this

Data Discovery

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2014 Annual

Conference

Inpatient, Outpatient and Professional

Under vs Over Filtering

Filtering Matching the Data

Institutional vs Professional Claims

Codes that Count

Provider Specialty

Federal Exchange Procedure Codes

Encounter Filtering

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2014 Annual

Conference

Encounter Level

Member Diagnosis Level

Member HCC Level

Pros and Cons

No Choice

Reconciliation Granularity

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2014 Annual

Conference

This is the easy part

Think Venn Diagram

Tools to Use

Outer Join & Granularity

What to do w/Fall Outs

Reconciliation

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2014 Annual

Conference

Small Audit Samples

Tools to Generate

Provider Specialty

Paid Date

Provider

Audit Tools & Processes

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2014 Annual

Conference

Initial Contact w/Business Unit

Business Units Stretched

Listen, Ask to Show You

Beauty of Routine

Patterns

Findings… All Kinds

Getting Business Involved

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2014 Annual

Conference

Finding the Right Person

Small, Solid Examples

Make Routine

Technical vs Business Involvement

Finding Solutions

Finding Root Cause

Plan B

Health Plans & Submitters

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2014 Annual

Conference

Things Break

Slow to Start, Takes Support of Leadership

Audit, Audit, Audit

Problems Everywhere

Small, Golden Examples

Make Routine

Key Points to Remember

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2014 Annual

Conference

Q & A

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HCC Risk Adjustment Reimagined

Accuracy. Brought to You By Data Science

Overview

• HCC risk adjustment accuracy challenges

• The HCC risk adjustment process

• Can data science help?

• How to apply what we’ve learned

About Bob

Chief ScientistApixio

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Poll

Are you involved in:

A. Coding

B. MA risk adjustment

C. Commercial risk

D. Healthcare IT

E. Other?

Another Poll

Is your organization a:

A. Provider Group?

B. Plan/Payor?

C. Vendor?

D. Other?

HCC Is Challenging

CodingDocumentation

ACCURACY!

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The Risk Adjustment Process

Documentation Coding Submission

Documentation Gaps

What conditions does each member have?

Coding Gaps

Is each condition properly coded?

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Coding Errors

Is each code properly documented?

RAPS Gaps

Did CMS actually accept each code?

Accuracy at Scale?

DATA

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What Does a Plan Member Look Like To A Data Scientist?

Structured Data

Text

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Scanned Documents

How Can We Access ALL the Data?

Knowledge Graph

Knowledge Graph

Glucose

Hemoglobin

A1c

Retinal Eye

Exam

Echo

Diabetes

Type 1

Diabetes

Type 2

Glucose

A1c

Retin

al Eye Exam

Echo

Diab

etes Typ

e 1

ICD 250.xx

Knowledge Graph

Glucose

Hemoglobin

A1c

Retinal Eye

Exam

Echo

Diabetes

Type 1

Diabetes

Type 2

Glucose

A1c

Retin

al Eye Exam

Echo

Diab

etes Typ

e 1

ICD 250.xx

NLP & Machine Learning

Pattern Analysis

Flexible Ontology

Endocrine andmetabolic disorders

Endocrine andmetabolic disorders

DM w/o complication

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Endocrine & metabolic disorders

DM w/o complication

Encounter Note

Endocrine & metabolic disorders

DM w/o complication

Encounter Note

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Coding Gaps

Is each condition properly coded?

Manual chart audits…

Manual Chart Audit25,000 Medicare Advantage Lives

4 charts per hour = 3.1 coder‐years

10 coders will take almost 4 months

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A Study To Measure Accuracy

• 14 organizations• 2.1 million MA patient‐years• 2011‐2014 dates of service• 239,000 annotations

Study Procedure

Automate chart review

Validate all codes

Measure:• Efficiency• Accuracy• Documentation quality

Charts Per Hour

134

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UnderstandingChart Audit Accuracy

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Manual vs Automated

10 results!

Coder Performance

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High Error Rate Means High Audit Risk

X

Fast Coders are Accurate Coders!

Multiple Coding Reduces Error Rates

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+

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Coders Disagree On Some HCCs More Than Others

Which HCCs Have Gaps?

Machine Learning Helps Close Documentation Gaps


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“Heart Failure”in EHR problem list

Is it Heart Failure?

Heart Failure No Heart Failure

… or Chart Failure?

Machine Learning

Heart failure

NOHeart failureM

easurement 1

Measurement 2

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Annotation Data Helps Close Documentation Gaps


Identify Key Areas For Improvement

Providers Struggle To Document These HCCs

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Provider Scorecard

Best Practices

• Use All Data Types• Automate HCC Identification• Measure Coder Accuracy• Tailor Education to Specific Gaps• Use Measure‐Based Incentives for Providers

The Old Risk Adjustment Process

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The NEW Risk Adjustment Process

Thanks, Data Science!

Thank you!

Bob [email protected]@scientistBob

Labs

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Lab Details

Tables

Claims

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Problem List

Progress Note

Discharge Summary

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Consult Letter

Text Mining Finds New Codes Even After A Manual Audit

% of accepted codes

HCC Category Description

How Long To Review Each HCC?

Confident Decision More Uncertainty

Time on page

% of documents (findings)

Distribution for HCC 19 (Diabetes)

Time on page

% of documents (findings)

Distribution for HCC 83 (Angina Pectoris)

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QA Oversight

Distribution of HCC Codes per Patient Among Patients with at Least One New Code

# of Codes per Patient with at Least One New Code

Distribution of New HCC Codes for PY2013 model Found from 2013 DOS


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Distribution of New HCC Codes for PY2014 model Found from 2013 DOS


What Are My Coders Doing?

What Are My Coders Doing?

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The Risk Adjustment Process

Documentation Coding RAPS