HFMA Data Mining for Revenue Cycle Gold!

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May 2019

HFMA Data Mining for Revenue Cycle Gold!

Presented by:

Philip Roudabush UIHC AVP, Revenue Cycle Management


The University of Iowa Hospitals and Clinics

Iowa’s Only Comprehensive Academic Medical Center

• 3 Hospitals (UIHC, Children’s, Psychiatric) – – – –

811 beds Inpatient Admissions 46,325 Emergency Department 66,732 Outpatient Claims Billed 865,882

• 300 Clinics – UI Clinic Visits 762,463 – Outreach and UI Community Medical Services Visits 173,277

• 11,837 Staff – Nurses 1,972 – Physicians and Dentists 1,653



UIHC Data in this Presentation… The data/numbers which you will be viewing are fanciful, perplexing, horrific… and possibly pithy. They are also wholly fake and filtered such to the point that they will have whimsical meaning to the topics and screen shots I am discussing.

The Revenue Cycle - PFS Analytics


The Revenue Cycle and the Importance of Data and Reporting

Key Metrics and Actionable Data “In regards to reporting on the revenue cycle for any large hospital setting, the best method is full transparency.



Epic Reporting!


Tableau Reporting!

Patient Financial Services (PFS)– Tableau


Epic and Tableau Integration

Epic (Chronicles)

Epic Clarity (SQL Server)

Microsoft SQL Server Management Studio

Tableau Desktop

Tableau Server

 Epic Clarity SQL Server is refreshed with current data on Monday, Tuesday, and Thursday

 PFS Analytics writes SQL based statements in Microsoft SQL Server Mgmt Studio  PFS Analytics adds the custom SQL statements into Tableau Desktop  Once data is sourced into Tableau Desktop the creative visualization process begins  Completed dashboards are published to Tableau Server  End users are then able to access and interact with the published dashboards

Phil’s Data Gold Mine



Answering Revenue Cycle Questions?

Tableau Report – Denials Example



Denial Category


Filters Plan

Denial by Remit Code


CPT Code

Possible Filters/Parameters Department Grouper: Professional billing grouper used to isolate distinct areas of business for reporting purposes Example: Community Clinics, Student Health, etc.

Post/Service Period: Allows users to view data summarized by post day or service date CBU (Combined Business Unit) Category: Revenue allocation grouper by physical location site Example: IRL CBU category is comprised of IRL Anesthesia, IRL Dermatology, IRL Diabetes Center, IRL Family Practice, etc. financial divisions

CBU (Combined Business Unit) Name: Revenue allocation grouper by specialty discipline across multiple physical locations Example: Otolaryngology CBU is comprised of IRL Otolaryngology and Otolaryngology financial divisions


Possible Filters/Parameters Financial Division: Professional billing revenue allocation grouper by specialty discipline and physical location, comprised of one or more financial subdivisions Example: Otolaryngology financial division is comprised of OTO General and Rhinology, OTO Head and Neck, OTO Otology/Neurotology, OTO Speech & Hearing, etc. financial divisions

Financial Subdivision: Professional billing revenue allocation grouper by specialty and physical location, comprised of one or more bill areas Example: OTO Speech & Hearing financial subdivision is comprised of OTO Hearing Aid Center and OTO Audiology OP Clinic bill areas

Bill Area: Professional billing revenue allocation grouper by subspecialty and physical location Example: OTO Hearing Aid Center bill area

Billing Provider: Supervising provider on the service Service Provider: Performing provider on the service


Possible Filters/Parameters Account Type: Guarantor account type used to identify specific visit classifications Example: Personal/Family, Behavioral Health, Cosmetic, etc. Account Base Class (HAR): Hospital account class derived from the patient class which determines level of care as inpatient, outpatient or emergency Account Class (HAR): Hospital account class derived from the patient class Example: Observation, Newborn, etc. Charge Cd/Name: Allows users to view matched transactions, outstanding AR amounts and payor mix for selected charge codes Financial Class: Grouping of similar payors and benefit plans Example: Commercial, Medicaid, Medicare, Self Pay, etc. Payor Name/ID: The organization to which claims are sent and from which remittance is received Example: Cigna, Medicaid of Iowa, United Healthcare, etc.


The Revenue Cycle - PFS Analytics

Revenue Cycle KPI’s


The Revenue Cycle - PFS Analytics


Revenue Cycle Review (RCR) - Tableau

Core Metrics Templated Automated

Simple Actionable

RCR mantra The RCR is not designed to answer every question, rather, it should spawn questions and conversational topics while identifying areas of interest and potential development




Choose FY or FYTD trending

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RCR – Charge Dash

Total and trended charges by department, specialty, physician, payor, plan, CPT code


RCR – Adjustments/Payment Dash

Total and trended adjustments/payments by department, specialty, physician, payor, plan, CPT code Focus on controllable adjustments and/or payments for improvement


RCR-Denial Rates

Denials – First Pass Review


Charge Mix Rank versus Denial Mix Rank


The Revenue Cycle - PFS Analytics

Evaluation and Management Bell Curves


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The Revenue Cycle - PFS Analytics

Account Receivables


Accounts Receivable

Let’s Talk A/R Classic Department Chair Question • My total A/R for last month was $6 million. • This month it is at $9 million! It rose by $3 million!


Account Receivables


Aged A/R

+ $3 million

+ $3 million

Account Receivables


Trended Good and Bad A/R A Desirable Divergence

“Good A/R” 0-60 A/R $

“Bad A/R” > 90


A/R – How to Know Aging and Trending?

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The Revenue Cycle – Overview


Anatomy of Gross Account Receivables (A/R)

Service Provided

Charge Lag

Charge Entered

Claim Edits

Unbilled A/R

Gross A/R

Physician RVU Generated

Claim Dropped

Billed A/R

Claim Adjudication


The Revenue Cycle - PFS Analytics

Payor Mix


Payor Mix

Important profit indicator? Can be controlled! – Advertising – Referrals – Word of mouth – Etc..

A change in payor mix does not mean that you are seeing less patients in any payor category.


Payor Mix Cash Impact Analysis

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The Revenue Cycle - PFS Analytics

Collection Rates


Collection Rates - Phil’s Phavorite!!!!!!

P a y o r

P l a n

B i l l i n g

D o c t o r


C o d e

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The Revenue Cycle - PFS Analytics

Reporting Need – Case Example Hospital Late Charges


Charge Lag Understanding Late Charges

Charge Lag – Days between service date/discharge and the date the charges post (Hospital & Professional)

Charge Lag — 37 —

Professional Charge Lag

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Professional Charge Lag

Service Date

Post Date


Hospital Late Charges Understanding Late Charges

Charges are “late” when they: 1. Drop after min days • ED/IP charges: Holds for 3 days • OP Charges: Holds for 7 days

2. Drop after a claim/HAR is in billed status Original Hospital Claim Inpatient Eligible to Drop Day After Hold Date of Service / Discharge Date

2nd Hospital Claim

Outpatient Eligible to Drop Day After Hold

ED/Inpatient Late Charges Outpatient Late Charges Problem Late Charges — 40 —

The Revenue Cycle – Claim Submission


All 3 Physician claims created before: Hosp Min Days Hold Hospital Pre Billed Outpatient: 7 days Hospital Claim

Physician Claim

Physician Claim

Physician Claim

The Revenue Cycle – Claim Submission


2 Physician Claims Drop before Replacement Hosp minClaim days Hold Hospital Pre Billed Payment or Denial Received Hospital Claim Hospital

Replacement Claim

Physician Claim

Physician Claim

Physician Claim

The Revenue Cycle – Claim Submission

Why are late charges a problem? • Late charge claims cause unnecessary extra work – Potential change in primary payment - rework – Potential change in secondary payment - rework – Potential change in patient liability – rework – Credit work – Potential payer recoups – Statement cost to mail multiple statements

• Patient Receives Multiple EOB’s and UIHC Statements – Confusing to patient to receive multiple EOB for same service – Patients do not like to have to pay multiple times for the same service • Timely filing ugliness!


Hospital Late Charges


Hospital Late Charges Created From Practitioner Charge Lag

Which departments and practitioners are creating late charges due to documentation occurring past hospital min days


The Revenue Cycle - PFS Analytics

Under Construction Cost to Collect



Healthy Revenue Cycle - Cost to Collect Measures Expense against Revenue

Revenue – Expense -

Easy-peasy Tricky

Benchmarking expense definition can be complicated and can include: Activities/Departments

– Vendors, Systems, Scheduling, Registration, Check-in & Check-out, Admissions, Prior Authorizations, Coding, Payment Posting, Claims Management, Denials Management, Customer Service, Patient Billing, etc… To build – Allow for filters of above – Allow for percentage dialer of above


Many, many more

Dashboard for… • various audiences (Executive, Supervisor, Staff, etc…) • • • • • • • • •

staff productivity and workqueues referral payor mix procedure mix (trending on procedure usage…) net collection rates (variability in formulas) Medicaid MCO specific data Self Pay vendor management payor report cards daily, monthly, yearly cash tracking


The Revenue Cycle - PFS Analytics


Data Success


The Revenue Cycle - PFS Analytics


Lessons of Reporting Successes • Communicate with your customers • Present meaningful and actionable data • Keep the reporting simple • Allow ad hoc reporting • Do NOT try to get it “right the first time”… evolve!

• Great reports drive great results!


The Revenue Cycle - PFS Analytics


Data Failure


The Revenue Cycle - PFS Analytics


Lessons of Reporting from Failures • Good data, which may be actionable, can be abandoned if not backed by unified “administrative will” • Accept that some data is just good data and not, necessarily actionable • The executive belief of “more data = better decisions” can be debilitating • Don’t get cute! Produce data which “answers the question”


Phil’s Most Favoritist Meme!!

What we suspect payors are doing!


Mining for Data Gold

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