1. Clinical Documentation Excellence ICD 10 conversion
H. Louis Harkey, M.D.
Department of Neurosurgery
MSHIMS Annual Conference
June 22, 2016
In anticipation of ICD 10 conversion, HIM developed an action plan was data driven and focused upon provider engagement.
HIM at UMMC identified Neurosurgery as one risk/reward area.
There was both risk of loss of revenue and potential for additional revenue.

2. Program Fundamentals Data-Driven Engagement No Data
Actionable Knowledge
Engagement
None
Engaged
In anticipation of ICD 10 conversion, HIM developed an action plan was data driven and focused upon provider engagement.
HIM at UMMC identified Neurosurgery as one risk/reward area. There was both risk of loss of revenue and potential for additional revenue.

3. Drive improvement in quality outcomes measures and cash collections
Key Components
Engage physicians in initiatives to improve pay-for-performance and revenue cycle metrics
Create solutions that support physicians in real-time within existing work flows
Improve inpatient and outpatient documentation accuracy, completeness, and timeliness
Drive improvement in quality outcomes measures and cash collections
The key aspects of the plan attempted to Engage providers in the revenue cycle process. This was made easier by the fact that all UMMC providers are employees.
They attempted to Support the providers by building solutions into their existing workflows as much as possible.
The Target was to improve documentation which would in turn drive improvement is outcome metrics

4. Documentation Maturity Model
Stage 1
No data sharing
Limited queries
No education or templates
Poor delinquency rate
Stage 2
Historical data
Queries in all settings
Some structured templates
30-50% Delinquency Rate
Stage 3
Descriptive analytics
Education and queries
Templates and work flows
10-30% Delinquency Rate
Stage 4
Predictive analytics
Multi-faceted communications
Optimized work flows
< 10% Delinquency Rate
Components
Data-Driven
Complete
Accurate
Timely
At the beginning of the CDE program about 2 ½ years after Epic golive we were between Stage 2 & 3. We had collected historical data for one year. We had a robust query process, or at least thought we did. Templates were is place for basic documentation. We also had some Descriptive analytics in place.
Epic Go live
CDE

5. Neurosurgical CDE Program
Team included:
department chairman / residency program director
CDI specialist
pro fee coder
project manager
Initiated November 2013
Met monthly
Actively supported rounding with physicians and educational sessions
To address Neurosurgery in particular, a team was put together.
We met monthly to plan our tactics, review metrics and look for opportunities for process improvement

6. Neurosurgery Plan Focus on inpatient documentation
Complete list of diagnoses that drive patient complexity
Deficiencies and delinquency rate
Timely query responses
Case Mix Index, MC & MCC capture rates
Unspecified diagnoses
Application of the overall strategy to Neurosurgery focused specifically on inpatient documentation.

7. A weekly list of deficiencies preventing coding was circulated to the departmental chairs. I made sure that case specific data was forwarded to the providers including the expected charges.

8. Queries were an interesting challenge
Queries were an interesting challenge. HIM had developed a methodology relying on the Epic patient list to indicate a query. There were two problems here, Neurosurgery did not tend to use the patient list AND the query was only visible on the list if the provider’s patient list had been customized to include the query column.

9. 2013 2014 Difference CMI 2.97 3.16 At base rate $20,365 $21,690
$1,325/case
By focusing on the diagnosis codes that drive complexity in Neurosurgery, we were gradually able to improve our CMI.
Given the number of admissions for 6 surgeons over a year, this amounts to significant contribution to margin. Not by working harder, just documenting better.
At 1200 cases per year + $1.5M added to margin.
If we are able to reach an average of 3.5 CMI, that will add another $2.5M to margin
FY 14 NS contributed $7.5M to margin with 6 surgeons.

10. Everything did not prove to be effective
Everything did not prove to be effective. We never significantly impacted unspecified coding. Nor did we ever make CC/MCC capture rates a useful metric.

11. UHC Neurosurgery Data CMI Expected LOS Expected Mortality
Mortality Index
FY14
3.5441
8.02
7.32%
0.83
FY15
3.7492
8.5
8.09%
0.89
Rank
62/122
10/122
36/122
11/123
10/123
8/123
52/123
Overall, we were successful in improving CMI which had a couple of interesting byproducts. Using UHC data, UMMC Neurosurgery better than average mortality in a medium complexity population to better than average mortality in top 10% complexity population
Index is Actual/Expected

12. Documentation Maturity Model
Stage 1
No data sharing
Limited queries
No education or templates
Poor delinquency rate
Stage 2
Historical data
Queries in all settings
Some structured templates
30-50% Delinquency Rate
Stage 3
Descriptive analytics
Education and queries
Templates and work flows
10-30% Delinquency Rate
Stage 4
Predictive analytics
Multi-faceted communications
Optimized work flows
< 10% Delinquency Rate
Components
Data-Driven
Complete
Accurate
Timely
On the maturity model we are now somewhere between stage 3 and 4. We have robust and improving descriptive analytics and developing predictive analytics. We initiated a targeted provider optimization project based upon efficiency profiles and collection rates. Neurosurgery rarely has more than a couple of deficiencies preventing coding > 7 days.
Epic Go live
CDE

13. Neurosurgery Accuracy
NEUROSURGERY CASE MIX INDEX
 Month
2013
2014
2015
January
2.94
3.37
3.62
February
3.25
3.19
3.91
March
3.22
3.11
3.13
April
2.53
3.71
2.85
May
3.36
3.04
 3.37
June
2.82
 2.93
July
3.03
3.06 
August
2.87
2.74
2.81 
September
2.57
3.12
 3.28
October
2.96
3.09
3.30 
November
2.78
2.98
3.34 
December
3.27
3.28
3.75 
 Yearly Average
2.97
3.16
3.30
Postscript: continued improvement in CMI through 2015.
In 2016, UMMC redirected their focus to other departments and reallocated resources to quality projects. We have seen a subsequent decline in CMI stimulating a return to our original
2013
2014
2015
 Yearly Average
2.97
3.16
3.30