# Presentation: 130622

## Using Medical Billing Data For Physician And Clinic Profiling

- Ginny Sharp
- Center for Children with Special Needs &
- Chronic Health Conditions
- Children’s Hospital & Regional Medical Center, Seattle, WA

## “Identification and Tracking of Children with Special Health Care Needs in Managed Care Using the NACHRI Classification of Congenital and Chronic Health Conditions”  

- “Identification and Tracking of Children with Special Health Care Needs in Managed Care Using the NACHRI Classification of Congenital and Chronic Health Conditions”
- Funded by Maternal and Child Health Bureau SPRANS Grant
- Partners: Northwest Washington Medical Bureau, National Association of Children’s Hospitals & Related Institutions, Washington State Department of Health Maternal and Child Health

## Presentation Overview

- M records
- -Procedures
- -Diagnoses

- 3M CRG
- Software

- Enrollment
- Data
- Claims
- Data

- Clean &
- Reformat
- Data

- CRG &
- 3 ACRGs
- EDCs,EPCs &
- MDCs, errors
- & code use

**Presentation Overview**

- Analyze
- Grouper
- Results

## Classification Using CRGs

- Data
- Claims
- Data

**Classification Using CRGs**

## Health Plan Databases

- Enrollment/eligibility files
- Member and all dependents
- Basic demographic data
- Claims data
- Hospital claims--UB92
- Other claims--HCFA1500
- Both contain data on procedures performed and related diagnoses

## Why Use Health Plan Data?

- It’s already there--low cost to use
- Collected unobtrusively
- Longitudinal
- Comprehensive
- Relatively consistent
- Known population coverage
- Process oriented

## Issues Associated With Using Claims Data

- Accuracy of unique identifiers, especially for children
- Coding accuracy
- Clinical meaning of codes
- Completeness
- Comparability
- Lack of uniform provider identifiers
- Amount and accuracy of patient demographic information

**Notes:**
Unique IDs for kids complicated by being included on multiple policies by parents & step-parents, sometimes with different surnames
Coding affected by service site, who does coding
Clinical meaning affected by provider type; ex. chiropractic codes
Provider IDs being dealt with by HCFA

## Evaluation of Current Coding Practices Among NWMB Pediatricians

**Evaluation of Current Coding Practices Among NWMB Pediatricians**

- Collected sample “face sheets” from 9 clinics in NWMB service area
- Wide variety in number & organization of codes, code specificity (digits)
- No. of codes ranged from 0 to 179
- 3236 different ICD-9-CM codes observed
- For CY96-97, 220 codes covered 80% of all pediatric diagnoses

**Notes:**
One very large clinic did not actually have ANY codes on it's face sheet, but provided 5 pages of hand-written notes (crib sheet) on codes used by their clinic coders.

Only 3 specific ICD-9 codes were found on the 8 face sheets:
462 = Acute Pharyngitis
5990 = Urinary Tract Infection, NOS
684 = Impetigo

## Pediatric Coding Evaluation

- Focused on several frequent diagnoses:
- Otitis media
-  43 possible ICD-9-CM codes to describe, of which 26 occurred in CY96-97 encounter data
-  10 of these included on clinic face sheets
-  “Unspecified otitis media” included on 7 face sheets & accounted for 42% of total OM codes
- “Acute suppurative otitis media without spontaneous rupture of eardrum” ONLY choice on largest pediatric clinic face sheet & accounted for 28% of total OM codes

## Pediatric Coding Evaluation

- Asthma
-  14 possible ICD-9-CM codes to describe, of which 12 occurred in CY96-97 encounter data
-  3 of these included on clinic face sheets
-  Most frequently used codes:
-  “Asthma, unspecified type, without mention of status asthmaticus”  39% of asthma Dx
-  “Asthma, unspecified”  21% of asthma Dx
-  “Extrinsic asthma without mention of status asthmaticus”  18% of asthma Dx

## Coding Issues Identified by NWMB Pediatricians 

**Coding Issues Identified by NWMB Pediatricians**** **

-  Incomplete Accounting of Diagnoses
- Misuse of Rule Out Diagnoses
- Co-morbidity and chronic illness diagnoses are often omitted in claims submissions
- Diagnoses omitted if services not provided
- Reluctance to use some mental health or cognitive/behavioral diagnoses

**Notes:**
Rule Out misuse esp. obvious with PKU test for newborns.  In earlier work we found one pediatrician who was using the PKU CONDITION code for every PKU test performed!  Also an issue with scoliosis.

Service provision an issue with HCFA1500 form; BUT you can include additional ICD-9 codes that are NOT linked to a specific procedure.

## Solutions Proposed by NWMB Pediatricians

**Solutions Proposed by NWMB Pediatricians**

- Education in appropriate use of diagnostic codes
- Frequent feedback to practice groups & physicians
- “Reminder” flag to code chronic conditions
- Require that the same diagnosis appear twice, separated by at least one month, for identification of a condition as chronic
- Use at least one year of continuous data

**Notes:**
mention: 
1.   Muldoon/Neff training & Tracy's on-site consultations
2.   post-its attached to face sheets
3.  last two are essentially requirements of the CRG software

## Presentation Overview

- Data
- Claims
- Data

- Clean &
- Reformat
- Data

**Presentation Overview**

## Data Cleaning Issues

- Identify unique individuals
- Not as obvious as it sounds, especially for children
- Variables used: patient ID, date of birth, gender, surname, given name, address, claims data, original enrollment records
- Rules for handling “questionable” individuals

## NWMB 1999 Database

- Initial enrollment file indicated 48,013 children ages 0-18
- 11,408 removed for multiple coverage
- After screening for eligibility requirements
- 		(3 mo.<1 yr; 6 mo.>1 yr.), N= 34,549
- Of these children, 27,771 had one or more medical claims in CY99
- 6,778 children (19.6% of eligibles) had NO claims in calendar year 1999

**Notes:**
Lack of encounters in this database does not necessarily mean that these children do NOT have chronic health conditions.  E.g., many of these children may be covered by other health plans and their encounters may never be captured by NWMB.  OR they may be receiving services through  public health or school-based providers/programs and not be billed to NWMB.

## From the Enrollment Data File...

- Create* *PATIENT FILE:* enrollee demographic and coverage information:*
-   Each enrollee uniquely identified
-   Date of birth
-   Gender
-   Period of eligibility

## From the Claims data file....

- Create* *CLAIMS FILE:* Enrollee encounter data for all services over a defined period of time* *including:*
-  Date of service
-  Site of service
-  Provider type
-  Diagnosis (ICD-9-CM) codes
-  Procedure codes and type (ICD-9-CM, CPT, HCPCS)
-  Principal diagnosis flag

**Notes:**
CRGs have only 4 provider type codes (institutional, physician, allied health professional, other) and 9 service site codes (clinic, home, hospice, inpatient fac., nursing fac., office, outpatient, skilled nursing facility, other)

## Presentation Overview

- M records
- -Procedures
- -Diagnoses

- 3M CRG
- Software

- Enrollment
- Data
- Claims
- Data

- Clean &
- Reformat
- Data

**Presentation Overview**

## What are CRGs?

- Clinical Risk Groups (CRGs) are a categorical clinical model which uses claims data to assign each patient to a single mutually exclusive risk category.
- Each CRG is clinically meaningful and provides the basis for the prediction of future health care utilization and costs.

## Design Objectives for CRGs	

- Categorical clinical model
- Severity adjusted
- Based on standard claims information
- Separate methodology for computation of payment weights
- Separate payment adjustments for non-clinical factors
- Three tiers of CRG aggregation

**Notes:**
Main point is that this tool is specifically designed to place patients in CLINICAL CATEGORIES.

## Risk Adjustment Design

- Classify patients according to their health status
- range from Healthy to Catastrophic
- Differentiate patients by their illnesses
- e.g., diabetics are different from patients with heart disease who are different from patients with both diabetes & heart disease
- Stratify patients in each group by the severity of their illness
- early stages less intensive than later stages

## Overview of CRG Clinical Logic

- Phase I	Creation of a Disease Profile and History of Past Medical Interventions
- Phase II	Selection of Primary Chronic Disease(s) and Assignment of Severity Level to Each Primary Chronic Disease
- Phase III	Determination of the Base CRG and Severity Level for the Individual
- Phase IV	Consolidation of CRGs into Three Successive Tiers of Aggregation

## Phase I: Creation of Disease Profile and History of Medical Interventions 

- Each disease is classified into one of 533 Episode Diagnostic Categories (EDCs)** **
- Dominant chronic (diabetes) - 60
- Moderate chronic (asthma, history of CABG) - 65
- Minor chronic (migraine headache) - 40
- Chronic manifestations of a chronic disease (diabetic neuropathy) - 99
- Significant acute (pneumonia) - 151
- Minor acute (upper respiratory infection) - 118
- All chronic EDCs in an MDC are ordered hierarchically  in terms of their clinical significance

## Episode Procedure Categories (EPCs)

- All procedures are subdivided into 605 EPCs
- 58 EPCs are used to create history of significant procedure EDCs (e.g., history of heart transplant)
- Performance of a procedure is infrequently used for severity adjustment (e.g., respiratory therapy for COPD)
- Only 75 EPCs are actually used for severity adjustment

## Phase II: Selection of Primary Chronic Disease (PCD) and Assignment of Severity Level to Each Primary Chronic Disease

- When there is more than one chronic EDC present in an MDC, one of the chronic disease groups is selected as the PCD based on
- Hierarchy of EDCs in each MDC
- Site of treatment for EDC (Hospital)
- Treatment within latest year
- Duration of treatment for EDC (90 days)
- The PCD identifies the most significant chronic disease in each organ system (i.e., MDC**)**

## Phase II cont’d: Assignment of Severity Level to each PCD 

- Each PCD is assigned a severity level
- Clinically meaningful
- Describe extent and progression of disease
- Differentiate future levels of resource use
- Individual chronic EDCs are assigned two or  four severity levels depending on PCD type

## Phase III: Determination of Base CRG and Severity Level for the Individual

- Based on the patient’s PCDs and severity levels, each patient is assigned to one of nine CRG  statuses
- The CRG status provides a general categorization of the patient’s clinical condition
- The CRG status is assigned based on the CRG status hierarchy

## CRG Status Hierarchy

- Catastrophic conditions
- Subdivided into 4 severity levels
- Dominant and metastatic malignancies
- Subdivided into 4 severity levels
- Dominant chronic disease in 3 or more organ systems
- Subdivided into 6 severity levels** **
- Significant chronic disease in multiple organ systems
- Level 1 minor chronic PCDs not considered significant
- Subdivided into 2, 4 or 6 severity levels

## CRG Status Hierarchy (cont’d)

- Single dominant or moderate chronic disease
- Subdivided into 2 or 4 severity levels** **
- Minor chronic disease in multiple organ systems
- Subdivided into 4 severity levels
- Single minor chronic disease
- Subdivided into 2 severity levels
- History of significant acute disease
- Healthy

## Phase IV: Consolidation of CRGs into Three Successive Tiers of Aggregation

- The 1081 CRGs are consolidated into three tiers of aggregation
- Each successive tier of aggregation has fewer base CRGs
- CRG	1081	(full CRG)
- ACRG1	413	(Body Systems)
- ACRG2	149	(Super Body Systems)
- ACRG3	37	(Core Health Status)
- Severity levels are maintained within each tier

**Notes:**
Severity levels are NOT comparable ACROSS grouping levels.

## CRG Algorithm Evaluates...

- Quality of coding
- evaluates each code for validity
- weights for code source
- Quantity of similar codes
- multiple diagnoses required for classification, but not necessarily the same specific code
- Timing between occurrences of similar codes

## Issues Associated with Using Claims Data...Revisited

- Accuracy of unique identifiers, especially for children--handle in data cleaning process
- Coding accuracy		)  CRG software ignores
- Clinical meaning of codes  )  erroneous codes,
- Completeness			)  groups codes by Dx,
- Comparability			)  weights code source
- Lack of uniform provider identifiers
- Amount and accuracy of patient demographic information

## Presentation Overview

- M records
- -Procedures
- -Diagnoses

- 3M CRG
- Software

- Enrollment
- Data
- Claims
- Data

- Clean &
- Reformat
- Data

- CRG &
- 3 ACRGs
- EDCs,EPCs &
- MDCs, errors
- & code use

**Presentation Overview**

## CRG Grouper Output

- Grouping Results:  Base CRG (5 digits), ACRG1 (6 digits), ACRG2 (4 digits), ACRG3 (2 digits)
- Information on Grouping summarized by Patient
- CRG patient errors
- Medical code use report
- CRG medical code errors
- Generated EDCs, EPCs and MDCs

## CRGsCore Health Status Groups

**CRGs****Core Health Status Groups**

- 1.  Healthy (includes Non-Users)
- 2.  Significant Acute Diagnoses
- 3.  Single Minor Chronic
- 4.  Multiple Minor Chronic Pairs
- 5.  Single Dominant or Moderate Chronic
- 6.  Multiple Significant Chronic Pairs
- 7.  Chronic Triplets
- 8.  Dominant/Metastasized/Recurrent Malignancies
- 9.  Catastrophic

## CRG Severity Levels

- Defined with respect to expected complexity and costliness of health care services in upcoming 12 months
- Each chronic condition category has either 2 or 4 severity levels, depending on type of condition
- Chronic condition pairs and triplets have either 4 or 6 severity levels, depending upon type of chronic conditions

## CRG Severity Levels

**CRG Severity Levels**

** **Range from 0 for non-chronic (Healthy & Signif. Acute) to 6 (highest severity level)
-  Patient Severity is computed from:
** **specific form of chronic disease
-  comorbid secondary chronic conditions
-  acute-complicating conditions
-  procedure/treatment status indicators
-  patient age, site of service (e.g., inpatient)
-  timing (times, frequency, lapse & reoccurrence)

## Presentation Overview

- M records
- -Procedures
- -Diagnoses

- 3M CRG
- Software

- Enrollment
- Data
- Claims
- Data

- Clean &
- Reformat
- Data

- CRG &
- 3 ACRGs
- EDCs,EPCs &
- MDCs, errors
- & code use

**Presentation Overview**

- Analyze
- Grouper
- Results

## Northwest Washington Medical Bureau

- 4 northwestern counties in Washington
- Total Area population  347,900
- Total Area children ages 0-18  103,400
- Total NWMB members  121,000
- Total NWMB members 0-18  46,600
- Multiple lines of business, including fee for service & Medicaid managed care

## NWMB Service Territory

**NWMB Service Territory**

## 3M CRG Classification of NWMB Children

-  Healthy (includes non-users)	       85.2%  29,446
-  Significant Acute				5.2%    1,807
-  Single Minor Chronic			4.4%    1,510
-  Multiple Minor Chronic			0.2%         75
-  Single Dominant/Moderate Chronic	4.5%    1,546
-  Multiple Significant Chronic 2s & 3s	0.4%       126
-  Malignancies (Metastatic)			0.05%       17
-  Catastrophic					0.05%       17

**Notes:**
CRGs identified 9.6% of these children as CHRONIC based on the one year of data.
Earlier version run on two years of data (1996-97) in same plan IDed 11.2% of children as chronic.

## Distribution of NWMB Children by ACRG3 Status & Level, CY99

- Level

**Status**** **

- 0

**1**

**2**

**3**

**4-6**

- Healthy

- 29,451

- Significant Acute

- 1,807

- Minor Chronic

- 1,345

- 165

- Mult Min Chronic

- 52

- 18

- 3

- Dom or Mod Chr

- 1,010

- 435

- 80

- 21

- Pairs & Triplets

- 70

- 24

- 14

- Malignancies

- 11

- 4

- Catastrophic

-  6

- 3

- 7

- 1

- Totals

- 31,258

- 2,484

- 640

- 123

- 44

- 1

- 1

- 18

- 2

## Distribution of NWMB Unique Encounters by ACRG3 Status & Level, CY99

- Level

**Status**** **

- 0

**1**

**2**

**3**

**4-6**

- Healthy

- 101,560

- Signif. Acute

- 21,202

- Minor Chronic

- 14,141

- 2,998

- Mult Min Chronic

- 859

-  408

- 103

- Dom/Mod Chr

- 13,569

- 8,018

- 1,960

-  710

- Pairs & Triplets

- 1,873

-  880

-  727

- Malignancies

-  848

-  221

- Catastrophic

-  103

- 129

-    293

-    34

- Totals

- 122,762

- 30,567

- 12,915

-  3,609

- 2,413

- 22

-   179

- 1,387

- 42

- 59.0%

- 12.3%

-  9.9%

-  0.8%

- 14.1%

-  2.8%

-  0.7%

-  0.3%

- Total unique encounters = 172,266

**%**

## Distribution of Pediatric Encounters by Provider Type

## CRG Status by Provider Type

- All NWMB pediatric encounters, CY99

## Pediatric PCP Comparisons

- Providers with >500 Pediatric encounters only.

## Comparison of 2 Pediatricians

## Encounters by Service Site & Status

## Using CRGs for Coordination of Care Decision-making

- Evaluate individuals within study cells for:
- number of providers seen by each child
- number of medical encounters during year
- specific chronic health condition
- Use CRG Status & Severity (ACRG3) with cost data to define patients for study

## NWMB CY96-97 ACRG3 Classification Results

**Severity**

**Status Level**

**0**

**1**

**2**

**3**

**4-6**

- Healthy

- 18,124

- Significant Acute

- 1,075

- Minor Chronic

- 944

- 62

- Mult Min Chronic

- 28

- 6

- 1

- Dom or Mod Chr

- 902

- 308

***34***

***12***

- Pairs

***67***

***18***

***2***

- Malignancies

***16***

***3***

- Catastrophic

***11***

***4***

***1***

***1***

- Totals

- 19,199

- 1,952

- 408

- 46

- 14

## NWMB Focus Group

***169 ***children in focus group for potential coordination of care
- 8780 CY96-97 claims for these ***169*** children
- Of these 8780 claims, 1612 were for Inpatient encounters ( 23% of ALL CY96-97 inpatient encounters <1% of the children!)

## Diagnosis-based Groups--Diabetics

- 32 diabetics within our group of ***169*** focus group kids
- by ACRG3 status & severity:

**Status**** ****Level**

**1**

**2**

**3**

**4-6**

- Dom/Mod Chr

- 168 (902)

- 76 (308)

- 3 *(34)*

- 3 *(12)*

- Pairs

- 21 *(67)*

- 5 *(18)*

- 0 *(2)*

- Numbers in parentheses are NWMB totals.

## Diagnosis-based Groups--Diabetics

-  Claims by ACRG3 and Place of Service:
- 				   no.	  	Total		Inpatient
- Status  	Sev.    kids	 	Claims  	  Claims
- Dom/Mod  	3	    3	     	   49	       	      3
- Chronic     	4	    3	     	   62		      9
- Signif.      	1	  21	   	 958	             168
- Pairs        	2	    5	   	 583               250

## Summary

- Clinical Risk Groups are clearly a useful tool for classifying individuals with respect to medical complexity
- Health Plans and large provider groups may find CRGs useful for profiling providers and clinics
- CRGs can be a useful tool for identifying appropriate sub-populations for case management