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.

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.

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

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

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

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.

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.

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.

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.

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.

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

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

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