Gritman Medical Center

1. Gritman Medical Center Ben Wood Tessa Scholl Marc Boisvert Mimi Sproul Darren Schnider

2. Problems • Delays • Internal • Common among users of manual systems • Every time someone comes in contact with a process, there is a 5% chance that an error will occur within the step • Unneeded queues build up • Charts are not taken by staff frequently because it reduces productivity • Every process that has a 95% chance of success (1- 5% of error) requires additional time in auditing to fix • Up to 10 minutes hands on time in system • Days out of system if returned by payee • External • People do not pay

3. Problems (continued) • Faster collections from insurance and payee • Problems: • Extended time for bill insurances • Non clean bills • Ideal scenario • Collections would take place immediately post treatment • Problems solved • Possible error processing claims • Delay before Final DRG can be sent • Improved personnel allocation • Can be done with automated coding and billing system

4. Scope Statement • Modeling current billing process to determine accounts receivable days and chart processing times • Examining possible reengineering and mistake proofing of system • Determining NPV’s of implementing new systems

5. Objectives • Reducing AR time • Reducing delay of collections from insurance and self pay account to increase TVOM • Emergency Room Coding & Record Keeping • Finding the best solution to decrease lag time • Five Day Window • Reducing it to 3 days • Additional Improvements • Institutional clean claims increased 10%* • Increased billing productivity by 70%** • Decreased error leads to less auditing time *http://www.medassets.com/casestudies/pages/foxchasecancercenter.aspx **http://www.medassets.com/casestudies/pages/wellmonthealthsystems.aspx

6. Findings • Apparent long wait times in 6.5 day window processes • Enough errors in billing to make AR age 57.37 days average • Industry findings that AR can be reduced dramatically, to at most 30 days

7. Recommendations • Automate billing system • Add a new financial councilor • Decrease self-pay time • If the system is unable to be automated, use improved business rules • Able to reduce queue times and improve throughput

8. Road map of remaining presentation • Model Description • Model Inputs • Sensitivity Analysis • Additional improvements • Emergency room • Current process and improvement • New billing program example • Accounts to concentrate on

9. Model Description • Gathered Information and assumptions • Inputs • Hours worked, chart processing times, staffing schedules, average payee payment periods and 90 patient types • Information collected from actual Gritman medical staff member • Model Purpose • Accurately model current billing cycle through Gritman medical center • Sensitivity Analysis • Individual changes made and reviewed for improvement

10. Sensitivity Analysis • Our purpose is to reduce Gritman’s AR by 40% to improve costs • Changed general business rules reducing process time while keeping old system • We are also investigating 30%, 20%, 10% and comparing each NPV to find the break even point

11. Model Inputs • 5 Types of Patients • Out-patient Sleep Study: OP Sleep Study • Out-patient Laboratory/Out-patient Radiology: OPL/OPR • In-patient/Out-patient Surgery/Observation: IP/OPS/Observation • Emergency Room: ER • Therapy/Series/Adult Day Health: Therapy/Series/ADH • 3 Categories of Payment Amounts • >$1,500 • $1,500 - $7,500 • <$7,500 • 6 Payment Options • Medicare • Medicaid • Blue Cross • Commercial • Self-Pay • Charity

12. Current Emergency Room Process

13. Proposed Emergency Room process

14. Comparison of Processes This new process reduces both time and error increasing revenue

15. Sage Intergy EHR system

16. Largest AR issues • Average % of AR

17. Largest AR issues • The highlighted percentages are how many more days each of the corresponding insurances are taking over the average

18. Comparison

19. Pro-Active Bill Collection Example • Different communication forms will reduce collection time of accounts • Increase the likely-hood of collection on a Self-pay Account • Students are less likely to receive/respond to letters • Should collect email at admissions • Send out an email on day 15 reiterating the 15% bill reduction if paid within the 20 days • On day 18 call and remind • Ask if bill can be paid by credit/debit card

20. Automated Solution Example • XactiMedRevenue Cycle Solutions • MedAssets’ Solutions • Automated System *DHMC: Dartmouth-Hitchcock Medical Center; NH **JPS: John Peter Smith Hospital; Fort worth, TX

21. 5 Day Window Reduction • Current Process for IP/OP Surgery Admission Patient Documentation Manual Discharge On Site or Off Site Coder if Off Site: Hold Charge Entry . Auditing Final DRG AR Queue Billed to Payee Steps: 10 Time: 6+ days • New Process with automated billing and coding Admission Patient Documentation Manual Discharge Billed to Payee Steps: 4 Time: 3 days* *This minimum time is designated by Federal regulation and is out of the scope of this project to reduce

22. NPV • Present Value with 40% reduction • $980,000 • NPV of -$1.89 million • About $400,000 per year reduction in costs • Factors to consider • Doesn’t account for savings of reducing employee costs • Reduction of employee time for billing none value added for other processes • Doesn’t account for additional financial counselor