Using CDI and CAC to Improve Quality and Reimbursement

Anne Robertucci, DirectorUPMC Corporate Coding

Conflict of Interest Disclosure

Anne Robertucci (UPMC)Has no real or apparent conflicts of

interest to report.

UPMC has a financial interest in the Optum Clinical Documentation Improvement Module

Learning Objectives• Discuss how physician documentation provides the only

source for hospital reimbursement and quality metrics• Explore how ignored, rejected or inadequately answered

physician queries limit the data integrity of the patient record and the revenue integrity of provider organizations

• Outline how natural language processing enables more comprehensive medical record reviews

• Describe how integrating physician queries within electronic medical record interface can improve physician response

• Demonstrate how NLP technology can improve Physician Documentation, Quality measures and Hospital Reimbursement

UPMC Snapshot

UPMC Prior State:

The CDI Challenge

What is Clinical Documentation Improvement? Why is it Important?

– Clinical documentation created by physicians & allied health providers is the only source used to...

• Capture quality metrics• Determine hospital reimbursement

– Serves as proof of the care provided– Increasingly audited by payors and regulators– Lack of sufficient specificity results in a

query to establish the diagnosis– Query process can be cumbersome and

time consuming for HIM departments and physicians

– ICD-10 will require more specific clinical documentation by providers

6

Documentation Gaps in the EMR• Cut & Paste Phenomenon

– new information often buried

• When Doctors Type – not much information is

provided• Symptoms…not diagnoses…

are documented• Doctors can’t find correct

diagnosis from pick-list• Communication must be

within physician workflow

COMMUNICATION

Common Ways Physicians Downplay Severity of Illness

Unable To Code Acceptable to CodeCHF, Give IV Lasix Acute systolic (or diastolic) CHFUrosepsis Sepsis due to UTI350 lb female, ordered large bed Morbid Obesity, or obesityRLL infiltrate on CXR Possible Aspiration PneumoniaHgb 7.2/Hct 22.1; Transfuse 2 units PRBC’s Acute Blood loss Anemia/Post-op

Blood Loss AnemiaCachectic, Prot/Alb 5.2/1.8, Nutrition consult

Severe Protein-Calorie Malnutrition

ABG 7.22/68/44; FiO2 increase to 100% NRB

Acute Respiratory Failure, Respiratory Acidosis

Hx CHF Chronic Systolic heart failureBP 70/40, start Dopamine @ 3mcg/kg/min Shock+ Troponin, ST changes Inferior Leads Acute MI or NSTEMIDry, Renal Insufficiency Acute Kidney Injury, DehydrationNurses document Stage 3 pressure changes

Physician/CRNP/PA must document pressure ulcer

Unable To Code Acceptable to Code

CHFgive IV Lasix

Acute systolic (or diastolic)

CHF, give IV Lasix

Revolutionary Changes with ICD-10

NLP Technology at UPMC• UPMC Technology Development Center

– Makes strategic investments in technology to aggregate and translate data into knowledge

• Co-developed first inpatient CAC solution– Launched in 2008– Improved CMI by 8%– External audits decreased by 50%– Saved more than $500K per year– Increased coder productivity by 20-22%– Decreased overtime by 84%

• No concurrent CDI program in place• 100% retrospective focus• Average 550 inpatient medical

records coded per day• 5% of the total discharges result in a query

with revenue impact of $1M per month

• Creating, distributing, monitoring and resolving physician queries is labor intensive

• Queries that are not resolved quickly impact the DNFB

• This data is inclusive of 20 UPMC hospitals

CDI at UPMC: The challenge

CONCURRENTCONCURRENT

100% RETRO

5% = $1M/mo.

550 records/day

UPMC Prior State:

Drivers to Change

Why a technology solution is needed for CDI and ICD-10

Balancing Organizational Approach with Physician Needs

ICD-10 Impact on Coder Productivity and Hospital Revenue

CAC KeyCAC Key CDI KeyCDI Key

Content derived from The Advisory Board Company. 2013

Staffing per Financial Benchmarking Report

Source: “Best-in-Class Clinical Documentation Improvement Programs.“ Financial Leadership Council - The Advisory Board Company. 2010.

Assumptions:•Total Beds Provided from UPMC Finance•Average Salary for CDIS $28.84/hr per Indeed.com•Salary marked up 22% to add benefit costs to total salary $35.18/hr•FTE of 2,080 hours per year

UPMC Current State:

Using Technology to Create an Electronic Concurrent Coding and CDI System

• Natural language processing (NLP) is transforming HIM and coding with computer-assisted coding (CAC) solutions

• Benefits: Productivity, accuracy, efficiency, transparency, manageability

• CDI programs shares these same goals

• However CAC is not the same as CDI

• Not limited to finding only “code-able” facts, but clinically significant facts that are evidence of an information gap

Transformation opportunityfor CDI with NLP

Two Types of CDI Opportunities Test NLP Capability

Example 1: Specificity

Physician documents “CHF improving.”

Example 2: Clinical Clarity

Physician documents “fluid retention and shortness of breath improving.”

NLP Identifies •“CHF” in History and Physical •“CHF” in progress note•Suggests code for Unspecified CHF

NLP Identifies•Pulmonary Vascular Congestion in CXR•Ejection Fraction of <30% in Echo•BNP of 700•IV Lasix in MAR

Approach to Query•Engage physician to provide specificity in CHF diagnosis

– Acute vs Chronic– Diastolic vs Systolic– Acute on Chronic

Approach to Query •Engage physician to clarify clinical facts•Ascertain if there is a diagnosis that could be added to reflect the clinical picture and rationale for treatment of this patient•Subsequent query for specificity in diagnosis if indicated

Easy to Moderate High Difficulty

Discrete Data

O D B 6 8 Z X

Med-Surg

Gastrointestinal

Excision

Stomach None

TransorificeIntraluminalEndoscopic

Diagnostic

CDI for ICD-10: Using PCS Structure to Identify Gaps in Procedure Documentation

????

System-built System-built queries vs. queries vs. manually-builtmanually-built

Dear Dr.

What kind of

CHF is being

treated?

CDI Module TechnologyHow it works

Concurrent CDI Case Finding

Continuous processing of the EMR data through NLP to both code and apply case-finding rules to each admission

If a case is marked for CDI, ensure that it conforms to business rules for presentation to a user:•Financial class•Revenue code•Physician service•Location

How should it be routed•Direct to physician•Peer advisor•CDI specialist•CDI manager•Specific user coder

Business Rules Logic

Query passively built with minimal (if any) additional editing and update required by CDIS

Presentation to physician either interfaced to EMR or Inbox or via PQRT Portal

Passive Query Building

Query response returned to NLP

Example of Nonspecific Physician Documentation

22

Electronic Query Using NLP

23

Physician Selects Appropriate Dx

24

Physician Adds Supporting Statement

25

UPMC NLP CDI Outcomes

Types of Queries Sent

Physician Query Response RatesComparing paper query process to NLP CDI query process

Physician Query Average Turnaround TimeComparing paper query process vs. NLP CDI query process

Coding TAT to Final BillCases with a Query 2012 vs. 2013 (average, days)

Queries Yielding ROI

UPMC CC/MCC Capture Improvement

Presbyterian / Shadyside St. Margaret

• 3% Improvement CC/MCC at Presbyterian/Shadyside Hospitals• 4% Improvement CC/MCC at St. Margaret Hospital

Physician Query Related DRG ShiftDRG shift related to queries that changed the MSDRG

CDI Audit of Discharged Patients

271 Concurrent

• A CDI audit was completed on queries that were generated between November 10, 2013 and December 8, 2013 of all 5,359 discharges

• 847 queries were generated for 814 patients or 15% of discharges

• The results found a total of $1.32M in value from the combination of concurrent (32%) and discharge (68%) queries

Projected Value of Automated CDI to UPMC

$2.1M$2.6M

$3.4M$3.6M

$5.8M

$7.7M

$3.9M$3.7M

$32.8M*Projected Annual Revenue UPMC: All Hospitals $32.8M

PresbyterianMcKeesport/Horizon/

Northwest

MercyHamot/Bedford

St. Margaret Passavant/East

Magee Shadyside All UPMC

Outcomes of NLP Driven CDI• NLP identifies more cases with missing or

nonspecific documentation than manual review• Query volume increased by 5 fold• Physicians answer queries more timely and

accurately when integrated with the electronic record

• Time to final bill improved for cases with query• MCC/CC most frequent reason to query

– Principal Dx and procedure codes also queried• Services lines most queried for MCC/CC

– Cardiology, Medicine, Oncology• ROI: MS-DRG as well as APR-DRG (SOI, ROM)• Second level peer review can yield additional ROI

UPMC Future State:

Preparing Physicians for ICD-10 and Beyond

Educational Videos

CDI Performance MetricsMetrics Groups

Volume of Queries CDI Specialist

Query response rate Coder

CC/MCC capture Physician

DRG Service line

SOI, ROM Unit/Facility

CMI Insurance

Productivity

Future State• Make current process more automated

– Examples: BMI, pathology reports– Add parameters to business rules to determine course of action– Determine queries that are always clinically relevant

to prompt. May not always affect reimbursement or SOI/ROM– Weigh automated decisions with the “Physician Annoyance Factor”

• Apply a second level of physician review– Improve education, documentation and

response to queries• Apply CDI to ICD-10 and assess risk

prior to October 1, 2015

Questions?

Thank You!