1. How Documentation Affects Your Reputation & the Reputation of UK
Clifford Kaye MD
Medical Director of Clinical Documentation Integrity
September 2017

2. Faculty Disclosure
No financial disclosures to report

3. Two Codes Generated from our Documentation
ICD Codes
E&M codes
Facility Fees
&
Professional Fees
Quality Metrics
CDI works to assure that the codes accurately reflect the complexity of care we’re delivering…and our outcomes
CDI
Clinical Documentation Integrity Service
KMSF

4. Goals By the end of this session you will understand:
How our documentation affects quality metrics
The role of CDI in maintaining the integrity of our quality metrics

5. Objectives Describe UK’s cohort Explain the impact of Quality metrics
Review case examples demonstrating documentation effects on:
eROM
eLOS
Describe CDI’s role in this process

6. UK’s Comparator Group

7. Vanderbilt Medical Ctr University of Tennessee Mass General
We are a voluntary member of the UHC….ssembled to develop the Quality & Accountability Study. Goal: objective data-driven measure to compare systems.
Cleveland Clinic
Vanderbilt Medical Ctr
University of Tennessee
Mass General
Johns Hopkins
Duke Univ. Health System
Temple
U Penn
All Mayo Clinics
Stanford Health Care
Emory University Hospital
Northwestern Memorial
Loyola University Medical Center
University of Colorado

8. FYTD 17 UK Expected Mortality Chandler only Ranking: 1/135 AMCs
Hospital/ Hospital System
Cases
% Deaths (Obs)
% Deaths (Exp)
Mortality
Index
UKCHANDLER
21,118
3.65
4.74
0.77
UW_HARBORVIEW
11,402
3.62
4.49
0.81
JAMESCANCER
9,322
3.10
3.87
0.80
MARYLAND
19,061
3.31
3.81
0.87
IU_HEALTH-MEDICALCENTER
25,238
3.42
3.52
0.97
LAHEY_HEALTH_LHMC
15,401
2.82
3.49
CINCINNATI_UCMC
18,832
2.93
3.45
0.85
USC_KECK
7,802
3.38
1.02
NORRIS
1,095
4.57
3.29
1.39
NMEXICO-UNIVERSITY
14,814
2.96
3.26
0.91
FH_FROEDTERT
20,794
1.94
3.25
0.60
PENNSTATE
19,286
2.13
3.24
0.66
NEBRASKA
17,795
2.52
3.20
0.79
UCLA-RONALD_REAGAN
15,857
2.67
3.17
0.84
CLEVELANDCLINIC
34,308
2.43
3.14
IOWA
23,597
2.64
3.13
HERMANN
29,311
2.56
3.12
0.82
WFBH_NCBH
25,980
2.39
3.09
ALABAMA
35,102
2.97
3.07

9. Quality Metrics Build a Reputation
Your hospital’s reputation
Your reputation
Your job security

10. O/E index = Observed/Expected expected is determined by what you write
What do we see compared to what we’re expecting?
UK Healthcare Sets Goals Performance Bonus

11. Documentation Quality Metrics eLOS & eROM

12. How are These Expectations Determined? Each DRG has Risk Models

13. It’s all about what’s POA
CDI Nurses comb through the chart looking for documentation of diagnoses and evidence of undocumented diagnoses

14. eROM Hospital Medicine Example

15. Hospital Medicine: eROM model
Admitted due to ESRD & volume overload (missed HD)
This commits her to a specific DRG
Mean expected ROM for this population is 1.3%
Because the following were documented as POA:
Shock
Malnutrition
Thrombocytopenia
Transfer from Acute Care Facility
Her eROM adjusted to 19%

16. CDI Query: “You documented Hep C. Was this acute or chronic Hep C?”

17. What is expected?

18. “Chronic Hep C” eROM changed from 19.1% to 47.5%

19. eROM Neurology Example

20. Because the following were POA:
Neurology: eROM model
85yo admitted with acute CVA manifesting with new aphasia & right arm weakness
Because the following were POA:
Age >= 85 years
Chronic AFib
His eROM = 0.8%
Mean expected ROM for this population is 9.2%

21. CDI Query: The CT head demonstrated “diffuse hypodensity in the right lentiform nucleus & mass effect upon the right lateral ventricle” Which statement accurately describes this patient: 1) Brain compression is evident 2) No evidence of brain compression
Mass Effect & Midline Shift can be chronic.
Cerebral Edema & Brain Compression imply acuity

22. “Brain Compression” eROM changes from 0.8% to 1.8%

23. Surgery eROM Example

24. Because the following were POA:
Surgery: eROM model
64yo s/p right periprosthetic distal spiral femur fracture returns with surgical site infection.
Mean expected ROM for this population is 1.6%
Because the following were POA:
Sepsis
Protein Malnutrition
Hyperkalemia
Her eROM adjusted to 1.9%

25. CDI Query: A TTE notes an EF of 35% and the patient is on chronic Lasix. Does this patient have: 1) Acute systolic CHF 2) Chronic systolic CHF 3) Acute on chronic systolic CHF 4) None of the above

26. “CHF” eROM changes from 1.9% to 4.3%

28. eLOS examples Each DRG has Risk Models

29. UHC/Vizient Risk Calculator: LOS

30. Hospital Medicine eLOS Example

31. Hospital Medicine: eLOS model
Same patient with ESRD and volume overload (missed HD)
This commits her to a specific DRG
Mean expected LOS for this population is 3.7 days
Because the following were POA:
Malnutrition
Coagulopathy
Diabetes with CC
Liver Disease
Deficiency Anemia
Depression
Her eLOS adjusted to 8.34 days

32. CDI Query: You treated the patient with Vanc/Zosyn until the time of her death. Were you suspecting a MDR bacterial infection?

33. “Suspected MDR Bacterial Infection” eLOS changes from 8. 34 to 11
“Suspected MDR Bacterial Infection” eLOS changes from to days

34. Neurology eLOS Example

35. Neurology: eLOS example
85yo admitted with acute CVA manifesting as new aphasia & right arm weakness
Mean expected LOS for this population is 5.3 days
Because the following were POA:
Protein Malnutrition
Dementia
Chronic CHF
Chronic AFib
Her eLOS adjusted to 5.4 days

36. CDI Query: You stated that the patient had AMS on admission
CDI Query: You stated that the patient had AMS on admission. Does this patient have: 1) Toxic Encephalopathy 2) Metabolic Encephalopathy 3) Encephalopathy related to acute CVA 4) No evidence of encephalopathy

37. “Encephalopathy” eLOS changes from 5.4 to 7.7 days

38. Surgery eLOS Example

39. Her eLOS adjusted to 23.3 days
Surgery: eLOS example
64yo s/p right periprosthetic distal spiral femur fracture returns with surgical site infection.
Mean expected LOS for this population is 9.5 days
Because the following were POA:
Sepsis
Non-excisional debridement
Deficiency anemia
Hyperkalemia
CHF
Her eLOS adjusted to 23.3 days

40. CDI Query: Nutrition noted that the patient meets the ASPEN criteria for mild protein calorie malnutrition. Which statement is correct: 1) This patient has mild protein malnutrition. 2) This patient does not have malnutrition.

41. “Mild Protein Malnutrition” eLOS changes from 23.3 to 33.6 days

42. Deloitte PowerPoint timesaver – September 2011

43. Hospital/ Hospital System
Reputation & Revenue
Reputation
Hospital/ Hospital System
Cases
% Deaths (Obs)
% Deaths (Exp)
Mortality
Index
UKCHANDLER
21,118
3.65
4.74
0.77
UW_HARBORVIEW
11,402
3.62
4.49
0.81
JAMESCANCER
9,322
3.10
3.87
0.80
MARYLAND
19,061
3.31
3.81
0.87
IU_HEALTH-MEDICALCENTER
25,238
3.42
3.52
0.97
LAHEY_HEALTH_LHMC
15,401
2.82
3.49
CINCINNATI_UCMC
18,832
2.93
3.45
0.85
USC_KECK
7,802
3.38
1.02
NORRIS
1,095
4.57
3.29
1.39
NMEXICO-UNIVERSITY
14,814
2.96
3.26
0.91
FH_FROEDTERT
20,794
1.94
3.25
0.60
PENNSTATE
19,286
2.13
3.24
0.66
NEBRASKA
17,795
2.52
3.20
0.79
UCLA-RONALD_REAGAN
15,857
2.67
3.17
0.84
CLEVELANDCLINIC
34,308
2.43
3.14
IOWA
23,597
2.64
3.13
HERMANN
29,311
2.56
3.12
0.82
WFBH_NCBH
25,980
2.39
3.09
ALABAMA
35,102
2.97
3.07

44. How do Expectations Affect Your Bottom Line?
= $444,330

45. How do expectations affect your bottom line?

46. How do Expectations Affect Your Bottom Line?
1 more point meets Quality & Safety Target
= 0.25% of 44M
= $111,082 more in the pool

47. In Summary: When CDI Calls, Help Them Improve: - UK’s reputation - Your reputation - Your bottom line

48. Special Thanks to these CDI Specialists: Lisa Kingsely R. N
Special Thanks to these CDI Specialists: Lisa Kingsely R.N. (Neuro) John O’Hair R.N. & Kathy Tevis R.N. (Surgical) Pam Florence R.N. (Hospital Med/Mortality)