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V2 HIF410 Week 11, Assignment 2 Operational Process Improvement in Coding after Internal Benchmarking
Domain VI.3 (4)
This assignment is worth 50 points, up to 20 points for the graph showing current staffing/needed staffing, and up to 30 points for your approach of addressing the staffing issues.
As you have demonstrated in Assignment 1 of this week, coding productivity tends to fluctuate, based upon many factors. Another factor to influence coding departments is benchmarking work measurement and productivity so managers have maximally efficient staffing rates.
Assignment Instructions & Deliverable Information
Using the data in Table 1 below and the calculations in the AHIMA Benchmarking Coding Productivity on slides 25 and 26 located here, develop a graph demonstrating the differences in current coding staffing and expected coverage required.
Then write a recommendation, from the perspective of the HIM Director, providing information including the completed graph, as to how you would approach the problem in the scenario below. Graph tips:
- You’ll need to do some calculating to complete the “You must determine” column in table 1. The steps on how to calculate the new FTEs and information needed are located on the assignment page and slides 25 and 26.
- Compare the # of FTEs.
- Create a graph of information gathered in step 2, then analyze the data to create a recommendation for the problem in the scenario.
Scenario:
Your hospital recently became a corporate entity by buying 5 surrounding small hospitals in your area. You have been promoted to corporate director of HIM. You have been tasked by your CIO to evaluate inpatient coder productivity and coverage in all 6 hospitals in your corporation and make recommendations for any changes to staffing for inpatient coding that may be necessary. You know that hospital #1 has the least amount of coder training, needs to consolidate some scanned documentation through barcoding in their EDMS so coders can have access to the documentation in one EDMS location, and also consistently has the highest rate of deficient charts.
What you know about the hospitals, work volumes and the inpatient coders working in them are provided in Table 1.
Table 1
| Known Details | You must determine | |||||||||
| Inpatient | Inpatient | Total volume | Average | Inpatient coders | How many inpatient coders are | |||||
| coding | coder hours | of charts to be | coding time | currently present | needed in the department? | |||||
| spent | coded | per chart: | in the | |||||||
| coding: | (discharges): | departments: | ||||||||
| Hospital #1 | 2000 | 20,000 | 30 min. | 4 | FT | |||||
| Hospital #2 | 1500 | 15,000 | 15 min. | 4 | FT | |||||
| Hospital #3 | 1000 | 10,000 | 10 min. | 1 | FT | |||||
| Hospital #4 | 1200 | 12,000 | 12 min. | 1 | FT | |||||
| Hospital #5 | 1600 | 16,000 | 16 min. | 3 | FT | |||||
| Hospital #6 | 1400 | 14,000 | 14 min. | 2 | FT 1 PT | |||||
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Example interpretation for Hospital #3
To help with understanding the calculation on slides 25 and 26, the calculation for Hospital #3 (note the example on slide 26 is for four different coding volumes, but we are only doing inpatient discharges for volume of charts in our scenario):
Coding time:
- 10,000 discharges X 10 min. per chart = 100,000 minutes
- 100,000 minutes / 60 min = 1667 (rounded) hours coding time
FTE calculation (how many are needed):
- 1667 hours coding time / 1000 hours spent coding per coder = 1.6 FTEs
