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

Faculty Disclosure No financial disclosures to report

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

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

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

UK’s Comparator Group

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

FYTD 17 UK Expected Mortality Chandler only Ranking: 1/135 AMCs Hospital/ Hospital System Cases % Deaths (Obs) % Deaths (Exp) Mortality Index 180067 UKCHANDLER 21,118 3.65 4.74 0.77 500064 UW_HARBORVIEW 11,402 3.62 4.49 0.81 360242 JAMESCANCER 9,322 3.10 3.87 0.80 210002 MARYLAND 19,061 3.31 3.81 0.87 150056 IU_HEALTH-MEDICALCENTER 25,238 3.42 3.52 0.97 220171 LAHEY_HEALTH_LHMC 15,401 2.82 3.49 360003 CINCINNATI_UCMC 18,832 2.93 3.45 0.85 050696 USC_KECK 7,802 3.38 1.02 050660 NORRIS 1,095 4.57 3.29 1.39 320001 NMEXICO-UNIVERSITY 14,814 2.96 3.26 0.91 520177 FH_FROEDTERT 20,794 1.94 3.25 0.60 390256 PENNSTATE 19,286 2.13 3.24 0.66 280013 NEBRASKA 17,795 2.52 3.20 0.79 050262 UCLA-RONALD_REAGAN 15,857 2.67 3.17 0.84 360180 CLEVELANDCLINIC 34,308 2.43 3.14 160058 IOWA 23,597 2.64 3.13 450068 HERMANN 29,311 2.56 3.12 0.82 340047 WFBH_NCBH 25,980 2.39 3.09 010033 ALABAMA 35,102 2.97 3.07

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

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

Documentation Quality Metrics eLOS & eROM

How are These Expectations Determined? Each DRG has Risk Models

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

eROM Hospital Medicine Example

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%

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

What is expected?

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

eROM Neurology Example

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%

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

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

Surgery eROM Example

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%

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

“CHF” eROM changes from 1.9% to 4.3%

eLOS examples Each DRG has Risk Models

UHC/Vizient Risk Calculator: LOS

Hospital Medicine eLOS Example

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

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

“Suspected MDR Bacterial Infection” eLOS changes from 8. 34 to 11 “Suspected MDR Bacterial Infection” eLOS changes from 8.34 to 11.75 days

Neurology eLOS Example

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

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

“Encephalopathy” eLOS changes from 5.4 to 7.7 days

Surgery eLOS Example

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

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.

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

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Hospital/ Hospital System Reputation & Revenue Reputation Hospital/ Hospital System Cases % Deaths (Obs) % Deaths (Exp) Mortality Index 180067 UKCHANDLER 21,118 3.65 4.74 0.77 500064 UW_HARBORVIEW 11,402 3.62 4.49 0.81 360242 JAMESCANCER 9,322 3.10 3.87 0.80 210002 MARYLAND 19,061 3.31 3.81 0.87 150056 IU_HEALTH-MEDICALCENTER 25,238 3.42 3.52 0.97 220171 LAHEY_HEALTH_LHMC 15,401 2.82 3.49 360003 CINCINNATI_UCMC 18,832 2.93 3.45 0.85 050696 USC_KECK 7,802 3.38 1.02 050660 NORRIS 1,095 4.57 3.29 1.39 320001 NMEXICO-UNIVERSITY 14,814 2.96 3.26 0.91 520177 FH_FROEDTERT 20,794 1.94 3.25 0.60 390256 PENNSTATE 19,286 2.13 3.24 0.66 280013 NEBRASKA 17,795 2.52 3.20 0.79 050262 UCLA-RONALD_REAGAN 15,857 2.67 3.17 0.84 360180 CLEVELANDCLINIC 34,308 2.43 3.14 160058 IOWA 23,597 2.64 3.13 450068 HERMANN 29,311 2.56 3.12 0.82 340047 WFBH_NCBH 25,980 2.39 3.09 010033 ALABAMA 35,102 2.97 3.07

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

How do expectations affect your bottom line?

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

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

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)