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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!