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This is the Full Title of a Session

Exploring Mortality Scores: How Mortality Scores Improve Quality Data

Pam Hess, MA, RHIA, CDIP, CCS, CPC AHIMA‐Approved ICD‐10‐CM/PCS TrainerVice President, Strategy & OperationsMedASTUTE Consulting, LLCPhoenix, AZ

Susan Schmitz, JD, BSN, RN, CCS, CCDS, CDIPRegional Director‐Clinical Documentation ImprovementKaiser PermanentePasadena, CA

2018 Copyright, HCPro, an H3.Group division of Simplify Compliance LLC. All rights reserved. These materials may not be copied without written permission.

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Mortality Scoring DefinedSystems & Dashboards


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Learning Objectives

• At the completion of this educational activity, the learner will be able to:– Understand mortality scoring and why it is important– Discuss expected versus observed mortality calculations– Differentiate between various scoring systems– Explain the importance of public mortality scorecards– Discuss how to develop a program for your CDI Team


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Mortality Scoring Systems

• Mortality rate is a measure of the frequency of death occurrence in a defined population during a specified interval of time (CDC, 2018).

CDC (2018). Epidemiology Glossary. Retrieved from https://www.cdc.gov/reproductivehealth/data_stats/glossary.htmlAHRQ. (2017). Inpatient Quality Indicators Technical Specifications. Version v7.0 Retrieved from http://www.qualityindicators.ahrq.gov/Modules/IQI_TechSpec_ICD10_v70.aspx

• Mortality scoring system (methodology) refers to a variety of severity scales used to predict hospital mortality using a complex set of criteria for risk adjusted mortality rates.

• The AHRQ’s inpatient quality indictors includes mortality rates for specific procedures and medical conditions.


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Example APACHE II Scoring Criteria

Rapsang, A. & Shyam, D. (2014). Scoring systems in the intensive care unit: A compendium. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4033855/


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Observed to Expected Calculation

• Observed mortality: The number of patients who died in the hospital

• Expected mortality: The expected average of hospital patient deaths if provider case‐mix is the same as the reference population

• Risk adjusted rate: The rate the hospital would have if its case mix were the same as the case mix in the reference population

• O/E mortality ratio: Observed deaths/ expected deaths – 0.5 mortality rate is 50% lower than expected– 1.0 mortality rate is equal to expected– 1.5 mortality rate is 50% higher than expected

AHRQ (2018) AHRQ Quality Indicators Toolkit. Retrieved from https://www.ahrq.gov/professionals/systems/hospital/qitoolkit/index.html


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Inpatient Quality Indicator 12: Coronary Artery Bypass Graft (CABG) Mortality Rate

• Description: – In‐hospital deaths per 1,000 discharges with coronary artery bypass graft

(CABG), ages 40 years and older. Excludes obstetric discharges and transfers to another hospital.

• Numerator: – Number of deaths (DISP=20) among cases meeting the inclusion and exclusion

rules. • Denominator:

– Discharges, for patients ages 40 years and older, with any‐listed ICD‐10‐PCS procedure code for CABG.

• Exclusions: – Transferring to another short‐term hospital (DISP=2).– MDC 14 (pregnancy, childbirth, and puerperium).– With missing discharge disposition (DISP=missing), gender (SEX=missing), age

(AGE=missing), quarter (DQTR=missing), year (YEAR=missing) or principal diagnosis (DX1=missing).

AHRQ. (2017). Inpatient Quality Indicators Technical Specifications. Version v7.0 Retrieved from http://www.qualityindicators.ahrq.gov/Modules/IQI_TechSpec_ICD10_v70.aspx


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Importance of Mortality Scoring

• Medical decision making based on objective criteria and not subjective judgements

• Refined scores for comparison purposes with various groups of patients

• Clinically relevant outcomes and high predictive power• Decreased hospital costs• Statistical formulas to compare hospitals against peers• Severity scores transformed into a hospital death probability using logistic regression equations

Rapsang, A. & Shyam, D. (2014). Scoring systems in the intensive care unit: A compendium. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4033855/


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Mortality Rates in Quality Measurement

• Challenges to accurate quality measurement – Clinical record does not reflect severity of illness; secondary

diagnoses may be other than MCC/CC.– Diagnoses codes impacting severity of illness scores are not

intuitive; there is no list.– Risk factors may be under or over reported causing biased

reporting.– Statistical model bias without adjustment for over or under risk

factor estimation.• Solutions

– “Hospitals should carefully evaluate coding consistency, completeness and accuracy, including the coding of UB‐04 fields like admission source and admission status, as well as ICD‐10‐CM diagnosis and procedure coding.”

Meurer, S. (2008). Mortality Risk Adjustment Methodology for University Health System’s Clinical Data Base University Health System Consortium. Retrieved from https://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf.


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Mortality Scoring Methodologies

1. HSMR2 (Hospital Standardized Mortality Ratio v2) 2. ACA (Acute Care Admission Mortality Ratio) 3. UHC (University Hospital Consortium) 4. APACHE (Acute Physiology, Age and Chronic Health

Evaluation Systems)5. SAPS (Simplified Acute Physiology Score)6. MPM (Mortality Prediction Models)7. POSSUM (Physiological and Operative Severity Score

for the enumeration of Mortality and Morbidity)


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Polling Question 1

• Which of the following mortality scoring methodologies does your facility use?– HSMR2 (Hospital Standardized Mortality Ratio v2) – UHC (University Hospital Consortium) – APACHE (Acute Physiology, Age and Chronic Health Evaluation Systems)

– Other– I don’t know


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Mortality Scoring Methodology: Drill Down HSMR@

• Logistic regression model to calculate the risk of death for each inpatient stay

• Adjusted for age band, sex, admission type, comorbidity, transfer in, diagnosis subgroup, admission quarter, and an interaction between age band and comorbidity

• Risks summed together to derive the total expected number of deaths at a hospital in a specified period of time

• HSMR2 = Observed number of inpatient deaths/expected number after adjustment

• Restricted to a maximum of 33 CCS diagnosis groups that together contribute to 80% of in‐hospital deaths in all payer, all ages of the US Healthcare Cost and Utilization Project (HCUP) data in 2012


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Mortality Scoring Methodology: Drill Down UHC

• Assignment of a Severity of Illness (SOI) and Risk of Mortality (ROM) level to each case.– SOI is defined as the extent of organ system loss of function (3M, 2016).– ROM is the likelihood of dying in the hospital (3M, 2016). The ROM class

is used for the evaluation of patient mortality.• Selection of a patient population to serve as the basis of the

model to provide norms. • Use of regression techniques to predict probability of mortality

based on the normative patient population.• Assignment of expected mortality probability to every patient in

UHC’s clinical database.

3M. (2016). 3M All Patient Refined Diagnosis Related Groups (APR DRGs). Retrieved from https://www.forwardhealth.wi.gov/kw/pdf/handouts/3M_APR_DRG_Presentation.pdfMeurer, S. (2008). Mortality Risk Adjustment Methodology for University Health System’s Clinical Data Base University Health System Consortium. Retrieved from https://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf


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UHC – Independent Variables for Regression Model

Independent Variables

• ROM subclass for mortality, and SOI subclass for LOS and costs

• Patient age• Patient sex• Admit source = transfer from another

acute care hospital• Admit source = transfer from skilled

nursing facility• Admit source = long term care facility• Low SES (based on Medicaid, self‐pay,

charity as primary payer)• Admit status = emergency• Patient Race

Example Comorbid conditions defined by the AHRQ

• Valvular disease• Pulmonary circulation disorders• Peripheral vascular disorders• Hypertension (complicated and

uncomplicated)• Paralysis• Other neurological disorders• Chronic pulmonary disease• Diabetes (complicated and

uncomplicated)• Hypothyroidism• Renal failure • Liver disease • Peptic ulcer disease excluding

bleeding Meurer, S. (2008). Mortality Risk Adjustment Methodology for University Health System’s Clinical Data Base University Health System Consortium. Retrieved from https://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf


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Public Scorecards – Why Are They Important?

• Available to the public via the Internet• Reflects quality of patient care and is used by perspective

patients to shop for hospitals and physicians• Used by CMS, The Joint Commission, Healthgrades, and

Leapfrog Group to measure quality of care• Used by CMS for Value Based Purchasing benchmarks:

– Example Clinical Care Domain VBP Mortality Measures• MORT‐30‐AMI Acute Myocardial Infarction (AMI) 30‐Day Mortality Rate

• MORT‐30‐HF Heart Failure (HF) 30‐Day Mortality Rate• MORT‐30‐PN Pneumonia (PN) 30‐Day Mortality Rate

CMS. (2012). Hospital Value‐Based Purchasing Program. Retrieved from https://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/Value‐Based‐Programs/HVBP/HVBP‐FAQs.pdf.


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AHRQ Quality Indicator Toolkit Dashboard

AHRQ (2016) AHRQ Quality Indicators Toolkit. Retrieved from https://www.ahrq.gov/professionals/systems/hospital/qitoolkit/index.html


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CMS Hospital Compare

CMS. (2018) Hospital Compare Retrieved from https://www.medicare.gov/hospitalcompare/search.html?


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CMS Hospital Compare – COPD

CMS. (2018) Hospital Compare Retrieved from https://www.medicare.gov/hospitalcompare/search.html?

• In the public eye:– The death rates show whether patients died within 30 days of surgery after

being hospitalized for a specific condition.– Graph shows whether a hospital's death rate is better than, no different than, or

worse than the national rate. – Death rates provide information about important aspects of hospital care that

affect patients’ outcomes – like prevention of and response to complications, emphasis on patient safety, and the timeliness of care.


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CMS Variation in 30‐Day Risk Standardized Mortality Rates (RSMRs)

CMS (2018) Outcome Measures. Retrieved from https://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/HospitalQualityInits/OutcomeMeasures.html


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Initiating a CDI Mortality ProgramA Team Approach


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Polling Question 2

• How many of you have a CDI Mortality Program in place?– Yes– No– Sort of– I don’t know


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Where to Start

• Obtain approval– Executive leader– Steering committee– Direct manager

• Obtain buy‐in from those involved– HIM management– Billing

• Develop a mortality report


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Mortality Report

• Patient name• Patient MRN• Birthdate• Age at discharge• Gender• Hospital account number• Coding status• Coding date

• Medical center name• Attending provider name• Attending provider type• Admission date• Discharge date• SOI• ROM


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Designing Your Program

• Factors to consider– Average mortalities per day with SOI/ROM < 4– Resources available

• Experienced CDI specialist– Size of health system– Coding and billing cycles


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Pilot Program

• What medical center had the most opportunity?– Focused review

• Easily managed• Quickly added additional medical centers• Within 6 months all medical centers being reviewed


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Spreadsheet Example


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Case Study – PDX Opportunity

• Initial SOI/ROM 3/3• Patient’s initial PDX was Acute Respiratory Failure however patient was also admitted with marked leukocytosis with WBC > 200. Bone marrow biopsy confirmed Chronic Myelogenous Leukemia (CML). Request was made to coding supervisor to resequence PDX to CML. MS‐DRG 840, Lymphoma and non‐acute leukemia with MCC was coded and billed.

• Subsequent SOI/ROM 3/4


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Case Study – Query Opportunity

• Initial SOI/ROM 4/2• Patient admitted with Chron’s disease and initial MS‐DRG was 386, Inflammatory Bowel Disease with cc. Noted in ED notes recent pulmonary embolism with current heparin administration. Physician queried and agreed patient had acute PE. Added to coding, PDX changed to MS‐DRG 385, Inflammatory Bowel Disease with MCC.



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Case Study – Query Opportunity

• Initial SOI/ROM 1/1• Patient admitted through ED after fall. Had nausea and started vomiting. Became hypotensive. Systolic pressure in the 60’s. Also c/o chest pain. EKG abnormal. CT head normal. Documentation of hypoxemia, O2 sat’s in the 80’s. Became bradycardic and nonresponsive in ICU. Went into asystole.

• Queries for Acute respiratory failure and acute MI. Physician agreed and placed addendum in the discharge note.



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Case Study – Coding Opportunity

• Initial SOI/ROM 3/3• Patient admitted with stroke. MS‐DRG 065, Intracranial hemorrhage with CC. Glasgow Coma scale documented separately on neuro nurses flow sheet. Coma can be added to coding. Final MS‐DRG 064, Intracranial hemorrhage with MCC.



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2018 Official Guidelines for Coding and Reporting FY 2018 (October 1, 2017‐September 30, 2018)


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Individual Coma Score

• For Traumatic Brain Injury, if it is the PDX individual GCS scores are not an MCC

• For CVA, individual GCS scores are MCC• If only the total GCS score is available, query for coma

(R4020) • Total GCS score when coded does not have an impact if

CVA is the PDX• Unconsciousness codes to unspecified Coma (R4020)• If the documentation reflects Persistent Vegetative State

(CC) and the GCS score falls between 3‐8, need to clarify for Coma (MCC)


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Results Example


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AppendixWorkflow Design Example


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Design Example


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Thank you. Questions?

[email protected]@kp.org

In order to receive your continuing education certificate(s) for this program, you must complete the online evaluation. The link can be found in the continuing education section at the front of the program guide.


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