Using easy-to-learn six sigma techniques, you can make your hospital more error-free, safe, and profitable.

1. Get a Cheaper
(More Profitable)
Hospital in Five Days
A Special Report by Jay Arthur
It should come as no surprise that a faster, better hospital will be cheaper to operate and more
profitable. When you’re not dealing with all of the delays in the ED-Admission-Discharge
process, fewer patients will be boarded in the ED, reducing diversion and LWOBS. More
patients can be seen more quickly, increasing revenue. When you’re not dealing with the extra
costs of preventable falls, infections and medication errors, it will make the hospital more cost
effective and profitable.
Faster + Better = Cheaper and More Profitable!
And there are other opportunities. Most hospitals have too many problems with rejected,
appealed and denied claims costing millions! Lean Six Sigma can help reduce billing problems
among other operational problems. And the process is simple.
To reduce rejected, appealed and denied claims, use Six Sigma tools to focus the improvement
effort.
1. Analyze Claims using control charts and pareto charts
• Rejected
• Appealed
• Denied
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2. 2. Analyze the Root Causes using the “Dirty 30 process”
3. Implement Countermeasures
4. Track Results
Reducing Denied Claims In Five Days
Denied claims mean no money for services rendered because the billing process failed in some
way. Non-payment drives up the cost of healthcare and pushes many hospitals toward
bankruptcy. In this case study, monthly denials were over $1 million (XmR chart).
Charges Coded as Denials
$3,000,000
UCL $2,552,122
$2,500,000
$2,000,000
$1,500,000 Denials (E)
UCL
CL $1,071,509 +2 Sigma
Charges
+1 Sigma
$1,000,000 Average
-1 Sigma
-2 Sigma
$500,000 LCL
$-
LCL $(409,103)
$(500,000)10/02 11/02 12/02 01/03 02/03 03/03 04/03 05/03 06/03 07/03 08/03 09/03
$(1,000,000)
2002-2003
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3. Using Pareto Charts of Denials
Using Excel PivotTables and the QI Macros, it was easy to narrow the focus to a few key areas
for improvement: Timely Filing (61%) and one insurer (67% of Timely Filling denials):
Denial-No Appeal Charges
n=12858108.93
$12,858,109 99% 100% 100%
97%
91% 90%
$11,250,845
81% 80%
$9,643,582
70%
$7,849,569
$8,036,318 61% 60%
Amount
$6,429,054 50%
40%
$4,821,791
30%
$3,214,527 $2,516,508
20%
$1,607,264 $1,295,032
$750,766 10%
$336,270 $109,813 $150
$- 0%
Timely Filing Medical No Auth Partial Auth Invalid Auth ET ES
Necessity
Memo Code
Denials for Timely Filing by Insurer
n=778
778 99% 99% 100% 100% 100% 100%
96% 98%
94%
93%
90% 90%
680.75 87%
84%
81% 80%
583.5 75%
70%
501
64%
Number of Denials
486.25
60%
389 50%
40%
291.75
30%
194.5
20%
97.25 81
45 10%
27 24 24 18 15 14 11 7 4 4 1 1 1
0 0%
Ins 1 Ins 2 Ins 3 Ins 4 Ins 5 Ins 6 Ins 7 Ins 8 Ins 9 Ins 10 Ins 11 Ins 12 Ins 13 Ins 14 Ins 15 Ins 16
Payer
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4. Analyze Root Causes and Initiate Countermeasures
• In a half-day root cause analysis session, the team identified ways to change the process
to work around the denials and change the contract process to 1) reduce delays that
contribute to timely filling denials and work with the insurer to resolve excessive denials.
Verify Results:
After implementing the process changes the following Monday, denied claims fell by $380,000
per month ($15 million/year). XmR chart below shows denials before and after improvement.
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5. Reducing Rejected Claims In Five Days
In software we have a saying that “finding a bug in a computer program is like finding a
cockroach in your hotel room. You don’t say: Oh, there’s a bug. You say: The place is infested.”
The same is true of rejected claims. Start with a line or control chart of rejects:
Use a series of Pareto charts to narrow your focus:
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6. Rejected claims are the frequent type of error; appeals tie up accounts receivable, and denials
result in lost revenue. How can we use Lean Six Sigma? Start with rejected claims.
Categorize Rejected Claims
Rejects by Type
n=152268067.28
$152,268,067 99% 99% 100% 100%
96% 97%
93% 95%
90% 90%
$133,234,559 86%
80% 80%
$114,201,050
70%
67%
$95,167,542
60%
Amount
53%
$76,134,034 50%
40% 40%
$57,100,525
$40,418,050 30%
$38,067,017 27%
20%
$20,435,973
$20,410,036
$20,135,403
$19,993,417
$19,033,508 $9,999,612 10%
$5,440,543
$4,163,173
$3,098,585
$2,266,610
$1,941,177
$1,789,756
$917,622
$893,878
$364,234
$- 0%
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Duplicate claims accounts for 27% of rejected claims. The next four bars of the pareto chart
combined with duplicate claims accounts for 80% of all rejected claims. Each of these five bars
of the pareto chart is an improvement story requiring root cause analysis. Let’s take duplicate
claims down to the next level of pareto chart.
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7. Duplicate Claims Verification
n=72
72 99% 100%
96% 97%
93% 94%
90% 92% 90%
63 60
83%
80%
54
70%
45
60%
Claims
36 50%
40%
27
30%
18
20%
9 5 10%
1 1 1 1 1 1 1
0 0%
Medicare PTBAL billed for pt billed Denied IC second bill UK Unknown
Forward to portion after secondary being sent
Secondary insurance again after for pt portion
Carrier pmt primary
payment
In this example, secondary payments for Medicare patients accounts for 83% of the duplicate
claims. The team investigated 72 of these secondary payments and found that they had been
paid, but incorrectly coded in accounting. Simple process changes reduced duplicate claims by
$24 million.
Teams Continued With the Other Four “Big Bars”
No Coverage turned out to be caused by charges after policy termination (44%).
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8. No Coverage by Code
n=460
460 98% 99% 100% 100% 100%
97% 98%
95% 96%
92%
89% 90%
402.5
85%
80% DZ Codes 80%
345 74% CAT Charges after Policy Termination
CIP Cannot Identify Patient 70%
67% COV Coverage
287.5 COV Treatment not Covered 60%
DEP Dependent
Claims
IAP Invalid Alpha Prefix
230 50%
202 IP# Invalid Policy Number
44% LIM Limits Exceeded
NCD No Coverage for DOS 40%
172.5
NF No Fault
NSC No Service Coverage 30%
115 106
O Other
PE Presumptive Eligibility 20%
57.5 34 26 22 21 10%
14 10 5 5 5 3 3 2 1 1
0 0%
SC
T
V
CD
O
P
P
F
#
T
M
S
EP
U
PE
D
IP
IA
N
CI
RE
CB
CA
CO
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Invalid Insurer info led to SSN incorrect and wrong primary insurer (51%).
Invalid Insurer Info
n=257
257 100%
89% 90%
224.875 86%
83%
80% 80%
192.75 76%
68% 70%
160.625
60% SSI - Social Security Incorrect 60%
OIN - Other Insurer Primary
Claims
128.5 51% VOU - Voucher 50%
CAT Charges after Policy Termination
CIP Cannot identify Patient 40%
96.375 NAME - Correct Pts Name
77 ADR - Incorrect Ins Address
30% NCD - Treatment not Covered 30%
64.25 54 PTB - No MCR Part B
20%
28
32.125 23 22 20 10%
9 8 8 8
0 0%
SSI OIN VOU CAT CIP NAME ADR NCD PTB OTHER
Code
Patient Info rejects led to analysis of Other Insurance (41%) and students missing from parent’s
insurance (39%).
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9. Patient Info Required Rejects
n=153
153 100%
98%
96%
92%
90%
133.875 86%
80% 80%
114.75
70%
95.625
60%
Claims
76.5 50%
63
41% 59 40%
57.375
30%
38.25
20%
19.125
10 9 10%
6
3 3
0 0%
Other Insurance Students Other Other Babies Auto Demographic
Dependents
Results
Half-day root cause analysis sessions for each of the “big bars” on these pareto charts and
subsequent improvements resulted in dramatic improvement in “first pass yield” of insurance
claims.
• 72% reduction in ED billing errors
• 60% reduction in impacted charges
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10. Reducing Appealed Claims in Five Days
Delayed payments caused by appealed claims can put a hospital in a financial crunch. In 2003,
appealed claims spiked due to Medicare Part B changes. The recent healthcare reform legislation
and subsequent changes will most likely cause further spikes.
Reject Appeals Chart
$17,681,040
$15,681,040 UCL $15,045,583
$13,681,040
$11,681,040 Reject Appeals
Reject Appeals
UCL
+2 Sigma
+1 Sigma
$9,681,040
Average
CL $8,363,312 -1 Sigma
-2 Sigma
$7,681,040 LCL
$5,681,040
$3,681,040
LCL $1,681,040
$1,681,040
10/02 11/02 12/02 01/03 02/03 03/03 04/03 05/03 06/03 07/03 08/03 09/03
Date/Time/Period
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11. Use Pareto Charts to Analyze Appealed Claims:
There is a number of ways to analyze appeals data: by patient and appeal type:
Reject Appeals
n=100359738.510001
$100,359,739 99% 100% 100% 100% 100% 100%
96% 98%
95%
90%
$87,814,771
$81,343,021
81% 80%
$75,269,804
70%
$62,724,837
60%
Charges
$50,179,869 50%
40%
$37,634,902
30%
$25,089,935
20%
$13,997,941
$12,544,967
10%
$1,361,097$1,346,122$1,077,005 $906,352 $243,241 $33,063 $32,456 $19,441
$- 0%
ED- 1 2 3 4 5 6 7 8 9
InPatient
FC
Reject Appeals
n=100359738.51
$100,359,739 100% 100%
92%
90%
$87,814,771
80% 80%
$75,269,804
70%
$64,562,998
$62,724,837 64%
60%
Amount
$50,179,869 50%
40%
$37,634,902
30%
$25,089,935
$15,341,703 20%
$12,302,541
$12,544,967 $8,149,240
10%
$3,256
$- 0%
Auth Precert Medical Necessity No Cert/Recert for days Timely Filing FC
Notification
Type
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12. Auth/Precert Appeals by Patient Type
n=5498
5498 100% 100%
98%
93%
90%
4810.75 88%
80% 80%
4123.5
70%
Number of FB Memos
3436.25
60%
2839
2749 52% 50%
40%
2061.75
1543 30%
1374.5
20%
687.25 429
309 266 10%
105
7
0 0%
In Out S V R E F
Patient Type
From these pareto charts, authorization and pre-certification of admissions from the ED are the
most common and costly appeals. Root cause analysis required team members from the ED and
admissions to identify and reduce Auth/Precert appeals.
Reducing Appealed Claims Cycle Time
Appeals Delays
40000
35000
Number of Appeals
30000
25000
20000
15000
10000
5000
0
0- 180 - 360 - 540 - 720 - 900 - 1,080 1,260 1,440 1,620 1,800 1,980 2,160 2,340 2,520
180 360 540 720 900 1,080 - - - - - - - - -
1,260 1,440 1,620 1,800 1,980 2,160 2,340 2,520 2,700
Days
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13. Using the simple tools of Lean (Post-it Notes), it was possible to redesign the appeals process to:
• Reduce touches per account from 21 down to 11
• Minutes per touch (16 minutes)
• 2.97 hours saved per account
• Accelerate payment by 50 days
Other Examples
In 2002, rural hospital, Thibodaux Regional Medical Center, used Lean Six Sigma to reduced
“discharged not final billed” from $3.3 million to $600,000. They also reduced net accounts
receivable from 73 days to 62 days resulting in an increased cash flow of $2 million per year. A
second wave of projects saved an addition $489,000 per year in inventory costs.
In 2006, North Shore Long Island Jewish Health System used Six Sigma to reduce oncology
billing errors (missing charges) from 50 percent to only 2.5 percent. This resulted in increased
revenue of $4 million per year. They also reduced turnaround time for charge entry from 3.7 to
2.4 days and DOS-to-billing from 13.6 days to 6.8 days. The biggest factor in timely filing of
bills: missing information. Biggest culprit, pharmacy:
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14. How to Get a Cheaper Hospital in Five Days
From working with teams in various industries, I’ve developed a simple method for achieving
breakthrough improvement on transactional processes like billing. I call it the Dirty Thirty
Process. I used it in the case study presented in this white paper.
The Dirty 30 Process for Better Billing
The secret is to:
1. Quantify the cost of correcting these rejected, appealed and denied transactions
2. Understand the pareto pattern of rejected, appealed and denied transactions
3. Analyze 30-50 rejected, appealed or denied transactions to determine the root cause
4. Revise the process and system system to prevent the rejected, appealed or denied claim.
Process: Typical root cause analysis simply does not work because of the level of detail required
to understand each error. Detailed analysis of 30 errors in each of the top error “buckets” (i.e.,
The Dirty Thirty) led to a breakthrough in understanding of how errors occurred and how to
prevent them. Simple checksheets allowed the root cause to pop out from analysis of this small
sample. As expected, the errors clustered in a few main categories. The Dirty Thirty process has
four steps:
1. Focus: Determine which rejected, appealed or denied error buckets to analyze first for
maximum benefit. (This analysis takes 2 to 3 days.)
2. Improve: Use the Dirty Thirty approach to analyze root causes (4 hours per error type—
facilitator with team) and determine process and system changes necessary to prevent the
problem.
3. Sustain: Track the rejected, appealed and denied claims after implementation of the changes.
4. Honor: Recognize and reward team members
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15. Insights
Using the basic tools of Six Sigma, anyone can learn to use what I call The Dirty Thirty Process
in a day or less to find the root causes of transaction errors. Once a team has found the root
causes of these errors, it’s just a matter of changing the processes and systems to eliminate these
errors.
Hundreds of people spend their lives fixing the fallout from these rejected, appealed and denied
claims. And they all think they’re doing meaningful work, not just fixing things that shouldn’t be
wrong to begin with.
Conclusion
Until you get to where you can prevent errors, every system could benefit from a simple, yet
rigorous approach to analyzing and eliminating errors. The Dirty Thirty process is ideal because
the data required to implement it is collected by most systems automatically. Then all it takes is 4
to 8 hours of analysis to identify the root cause of each error.
Need Guidance?
The first project may seem scary, but we can facilitate your improvement teams to achieve
breakthroughs in patient flow. Once you’ve learned how, you’ll find it easy to continue. Haven’t
you waited long enough to get a faster hospital in five days or less?
Jay Arthur, the KnowWare Man, works with hospitals that want to get faster, better and cheaper
in a matter of days using the proven methods of Lean Six Sigma. Jay is the author of Lean Six
Sigma Demystified and the QI Macros SPC Software for Excel. Jay has worked with healthcare
companies to reduce denied claims by $3 million per year, appealed claim turnaround time and
lab turnaround times by 30-70 percent.
To get a faster hospital in five days, call: Jay Arthur at 888-468-1537
Email: jay@qimacros.com
Web: www.qimacros.com
Mail: KnowWare, 2253 S. Oneida St. Ste 3D, Denver, CO 80224
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