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

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

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

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

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 * **

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

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

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

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

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

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

Current Emergency Room Process Admissions Get Demographics Verify Insurance Authorize Patient Documentation Manual Discharge Wait Until Midnight For List Release NICKA Coding (2 days) Charge EntryAuditingFinal DRGBill Insurance

Proposed Emergency Room process Admissions Get Demographics Verify Insurance Authorize Patient Documentation * Increase Accuracy * Reduce Lag Time Manual Discharge Auditing *Time Reduced *Fewer mistakes to correct Bill Insurance

Comparison of Processes Admissions Get Demographics Verify Insurance Authorize Patient Documentation * Increase Accuracy * Reduce Lag Time Manual Discharge Auditing *Time Reduced *Fewer mistakes to correct Bill Insurance This new process reduces both time and error increasing revenue

Sage Intergy EHR system

Largest AR issues Average % of AR

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

Comparison

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 at admissions Send out an 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

Automated Solution Example XactiMed Revenue Cycle Solutions MedAssets’ Solutions Automated System *DHMC: Dartmouth-Hitchcock Medical Center; NH **JPS: John Peter Smith Hospital; Fort worth, TX Decreased A/R Times Medical FacilityOriginalNowReduction *DHMC: Medicare271930% **JPS: third party AR302227% **JPS: Commercial ARFell an average of 90.4 days44% Fox Chase Cancer Center % Average:39% Target for Gritman:40%

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: 10Time: 6+ days New Process with automated billing and coding Admission Patient Documentation Manual Discharge Billed to Payee Steps: 4Time: 3 days* *This minimum time is designated by Federal regulation and is out of the scope of this project to reduce

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