Get a Cheaper (More Profitable) Hospital in Five Days

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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 EDAdmissionDischarge 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.. Analyze Claims using control charts and pareto charts Rejected Appealed Denied 200 KnowWare International Inc

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. Nonpayment drives up the cost of healthcare and pushes many hospitals toward bankruptcy. In this case study, monthly denials were over $ million (XmR chart). Charges Coded as Denials $3,000,000 $2,500,000 UCL $2,552,22 $2,000,000 Charges $,500,000 $,000,000 $500,000 CL $,07,509 Denials (E) UCL +2 Sigma + Sigma Average Sigma 2 Sigma LCL $ LCL $(409,03) $(500,000) 0/02 /02 2/02 0/03 02/03 03/03 04/03 05/03 06/03 07/03 08/03 09/03 $(,000,000) KnowWare International Inc

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 (6%) and one insurer (67% of Timely Filling denials): DenialNo Appeal Charges n= $2,858,09 $,250,845 9% 97% $9,643,582 $8,036,38 $7,849,569 6% 8% Amount $6,429,054 $4,82,79 $3,24,527 $2,56,508 2 $,607,264 $ Timely Filing Medical Necessity $,295,032 $750,766 $336,270 $09,83 $50 No Auth Partial Auth Invalid Auth ET ES Memo Code Denials for Timely Filing by Insurer n=778 Number of Denials % 75% 8% 84% 87% 93% 94% 96% 98% Ins Ins 2 Ins 3 Ins 4 Ins 5 Ins 6 Ins 7 Ins 8 Ins 9 Ins 0 Ins Ins 2 Ins 3 Ins 4 Ins 5 Ins 6 Payer KnowWare International Inc

4 Analyze Root Causes and Initiate Countermeasures In a halfday root cause analysis session, the team identified ways to change the process to work around the denials and change the contract process to ) 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 ($5 million/year). XmR chart below shows denials before and after improvement. 200 KnowWare International Inc

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: 200 KnowWare International Inc

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 Amount n= $52,268,067 $33,234,559 $4,20,050 $95,67,542 $76,34,034 $57,00,525 $40,48,050 $38,067,07 27% $9,033,508 $ Dup Claim 53% 67% $20,435,973 $20,40,036 $20,35,403 $9,993,47 No Coverage Add't Info Req'd Provider Info Req'd Incorrect Ins Info Rejects by Type 86% 93% 95% 96% 97% $9,999,62 $5,440,543 $4,63,73 $3,098,585 $2,266,60 $,94,77 $,789,756 $97,622$893,878$364,234 D6 D7 D8 Type D9 D0 D D2 D3 D4 D5 2 Duplicate claims accounts for 27% of rejected claims. The next four bars of the pareto chart combined with duplicate claims accounts for 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. 200 KnowWare International Inc

7 Duplicate Claims Verification n= % 92% 93% 94% 96% 97% 45 Claims Medicare Forward to Secondary Carrier 5 PTBAL billed for pt portion after insurance pmt billed secondary again after primary payment Denied IC second bill being sent for pt portion UK Unknown 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%). 200 KnowWare International Inc

8 No Coverage by Code Claims n= % 06 67% 74% 85% 89% 92% 95% 96% 97% 98% 98% DZ Codes CAT Charges after Policy Termination CIP Cannot Identify Patient COV Coverage COV Treatment not Covered DEP Dependent IAP Invalid Alpha Prefix IP# Invalid Policy Number LIM Limits Exceeded NCD No Coverage for DOS NF No Fault NSC No Service Coverage O Other PE Presumptive Eligibility CAT CIP COV NCD IP# PE IAP RES DEP LIM O DEPEND NSC CBT NF STU Invalid Insurer info led to SSN incorrect and wrong primary insurer (5%). Claims n= % 23 Invalid Insurer Info 22 68% 20 SSI OIN VOU CAT CIP NAME ADR NCD PTB OTHER Code 76% % 8 86% SSI Social Security Incorrect OIN Other Insurer Primary VOU Voucher CAT Charges after Policy Termination CIP Cannot identify Patient NAME Correct Pts Name ADR Incorrect Ins Address NCD Treatment not Covered PTB No MCR Part B 8 89% 28 2 Patient Info rejects led to analysis of Other Insurance (4%) and students missing from parent s insurance (39%). 200 KnowWare International Inc

9 n=53 Patient Info Required Rejects % 92% 96% 98% Claims % Results Other Insurance Students Other Other Dependents Babies Auto Demographic Halfday 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 reduction in impacted charges 200 KnowWare International Inc

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 $7,68,040 $5,68,040 UCL $5,045,583 $3,68,040 Reject Appeals $,68,040 $9,68,040 $7,68,040 CL $8,363,32 Reject Appeals UCL +2 Sigma + Sigma Average Sigma 2 Sigma LCL $5,68,040 $3,68,040 LCL $,68,040 $,68,040 0/02 /02 2/02 0/03 02/03 03/03 04/03 05/03 06/03 07/03 08/03 09/03 Date/Time/Period 200 KnowWare International Inc

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= $00,359,739 $87,84,77 $75,269,804 $8,343,02 8% 95% 96% 98% $62,724,837 Charges $50,79,869 $37,634,902 $25,089,935 $2,544,967 $ ED InPatient $3,997,94 $,36,097$,346,22$,077,005 $906,352 $243,24 $33,063 $32,456 $9, FC 2 Reject Appeals n= $00,359,739 $87,84,77 92% $75,269,804 $62,724,837 $64,562,998 64% Amount $50,79,869 $37,634,902 $25,089,935 $2,544,967 $5,34,703 $2,302,54 $8,49,240 2 $ Auth Precert Notification $3,256 Medical Necessity No Cert/Recert for days Timely Filing FC Type 200 KnowWare International Inc

12 Auth/Precert Appeals by Patient Type n= % 93% 98% Number of FB Memos % In Out S V R E F Patient Type From these pareto charts, authorization and precertification 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 Number of Appeals ,080,080,260 Days,260,440,440,620,620,800,800,980,980 2,60 2,60 2,340 2,340 2,520 2,520 2, KnowWare International Inc

13 Using the simple tools of Lean (Postit Notes), it was possible to redesign the appeals process to: Reduce touches per account from 2 down to Minutes per touch (6 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 DOStobilling from 3.6 days to 6.8 days. The biggest factor in timely filing of bills: missing information. Biggest culprit, pharmacy: 200 KnowWare International Inc

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:. Quantify the cost of correcting these rejected, appealed and denied transactions 2. Understand the pareto pattern of rejected, appealed and denied transactions 3. Analyze 3050 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:. 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 200 KnowWare International Inc

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 3070 percent. To get a faster hospital in five days, call: Jay Arthur at jay@qimacros.com Web: Mail: KnowWare International, Inc S. Colorado Blvd., Suite 555 Denver, CO KnowWare International Inc