Reflection Paper
Baba0914
AC21620-Chapter8.pptxA Practical Approach to Analyzing Healthcare Data, Fourth Edition Chapter 8, Exploratory Data Applications
Susan White, PhD, RHIA, CHDA
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Learning Objectives
Illustrate case mix index analysis techniques
Compare and contrast case mix measurement in outpatient vs inpatient setting
Explore relative value unit analysis
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Exploratory Data Analysis
AKA Data Mining
Using statistical techniques to find patterns in data
Typically, a mixture of graphical displays and descriptive statistics
Many practical applications in improving healthcare operations
HIM professionals are uniquely positioned to perform this analysis because they understand the data and the underlying operational and reimbursement implications of patterns
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Case Mix Analysis
Case Mix Index (CMI) – average MS-DRG weight for all patients
May be calculated for subsets of patients such as Medicare/Medicaid/selected MS-DRGs
May exclude portions such as transplants (very high weight MS-DRGs) or transfers (reduced payment and short stays)
Single number that may be used as a proxy for measuring the resource intensity of a hospital’s patients
Medicare CMI is the primary driver of the inpatient Medicare revenue
Frequently a key performance indicator for a hospital and a key driver of the revenue budget
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Case Mix Index Example
Multiply the number of cases in each MS-DRG by the relative weight
Sum the values from #1
Sum the number of discharges
Divide total relative weights by the number of discharges
Note: This is the weighted average of the relative weights for each MS-DRG.
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MS-DRG Families
MS-DRGs may be broken into families with two or three members:
No CC
CC (not present in all families)
MCC
The MS-DRG weight system is designed to assign higher weights to MS-DRGs that require a higher resource intensity
MCC MS-DRGs are assigned higher weights than no CC MS-DRGs in the same family
COPD
Pacemaker Replacement
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CC/MCC Capture Rates
Example:
This value can be compared to HCUP data using a z-test for proportions to determine if the sample rate is higher/lower than the national rate
In general, hospitals with higher CC/MCC capture rates have higher CMI
A unusually high CC/MCC capture rate may be indicative of a compliance issue (over-coding) and should also be investigated
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CMI Shifts
Significant shifts in CMI should be investigated to determine the root cause
Potential causes:
New service lines
Surgeon vacation schedules
Holidays
Natural disasters (hurricanes, tornados, etc.)
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Other DRGs Systems
AP-DRGs
All-patient DRGs
AKA “New York Grouper”
Three character, numeric
Weights are calibrated for all patients and not only Medicare
APR-DRGs
All patient refined DRGs
3M proprietary grouping system
3 character, numeric followed by digit (1-4) for severity and (1-4) for risk of mortality
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Ambulatory Patient Classifications (APC)
CMS uses APCs to pay for services in the hospital outpatient and ambulatory surgery settings.
Challenges of APCs
Claim may have more than one payable APC
Assignment of CPT/HCPCS codes to APCs may change each year
More of a fee schedule than a true prospective payment system
Can use APC weights to calculate a service mix index (SMI)
Note that this measures the average resource intensity for the services provided and not for the typical case
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Methods of Analysis
Validation of utilization patterns
Specialty specific codes
Comparison to hospitals with like service mix (trauma center, transplants, etc.)
RVU Analysis
Work RVUs may be used to benchmark physician productivity
Part of the CMS Physician Fee Schedule
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RVU – Physician Productivity
Dr. Kana billed the lowest number of wRVUs during July
When productivity is adjusted for the fact that Dr. Kana is a 40% FTE (cFTE = 0.4), she is actually the second most productive physician
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RVU – Other Uses
Average cost per RVU
Physician compensation per work RVU (wRVU)
Malpractice expense per Malpractice RVU (mRVU)
Overhead or practice expense per Practice Expense RVU (peRVU)
Break-Even Conversion Factor (BECF)
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