Assessing Risk of Readmission. NoCVA Preventing Avoidable Readmission Collaborative Laura Maynard, MDiv, NCQC Amanda Hobbs, NCQC July 31, 2013

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Assessing Risk of

Readmission

NoCVA Preventing Avoidable Readmission Collaborative Laura Maynard, MDiv, NCQC

Amanda Hobbs, NCQC July 31, 2013

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Collaborative Goals

• Reduce readmission rates by 20%

• Increase the number of patients in the pilot unit or population who undergo assessment for risk of readmission to 95% • Increase the number of patients in the pilot unit or population

who are assessed to be at high risk of readmission

whose primary care physician is informed of their hospitalization within 48 hours of admission to 80%

• Increase number of patients in the pilot unit or population

who are assessed to be at high risk of readmission who

are scheduled for a follow-up physician visit within 7 days of discharge from hospital to 80%

• 10% improvement or national 25th_{percentile in scores on}

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Why assess risk of readmission?

• Limited resources, time and financial, available for

care transitions interventions

• Reduce readmissions to improve population health,

care experience, and costs (Triple Aim)

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Poll / chat

Does your hospital use a tool to assess

risk of readmission?

a. Yes, hospital-wide

b. On some units

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Poll / chat

What types of factors does your readmission risk

assessment consider?

a. Clinical factors only

b. Psychosocial factors only

c. Both clinical and psychosocial factors

d. N/A (no readmission risk assessment)

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Poll / chat

Please chat in some of the predictors

(clinical, psychosocial, or both)

considered in your hospital’s

readmission risk assessment.

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Multiple, complicated risk factors

• “inconsistencies regarding which characteristics are

most predictive” – STAAR How-to Guide, IHI

• “Efforts are needed to improve the ability to identify the

likelihood of readmission…” – RED Toolkit, AHRQ

Readmission <30-Days

Diagnosis and Comorbidities

Post-hospital

Care Access Patient Activation

Psychosocial Support

Step 4: Identify Which Patients Should Receive the RED. Tool 2: How to Begin the Re-Engineered Discharge Implementation at Your Hospital RED Toolkit. Agency for Healthcare Research & Quality. 2013.

http://www.ahrq.gov/professionals/systems/hospital/ toolkit/redtool2.html#Step4

STAAR How-to Guide: Transitions from the Hospital to Community Setting to Reduce Avoidable Rehospitalizations. Institute for Healthcare Improvement. 2012.

http://www.ihi.org/knowledge/Pages/Tools/HowtoGu ideImprovingTransitionstoReduceAvoidableRehospi talizations.aspx

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Multiple, complicated risk factors

Do not let perfect

be the enemy of good.

• “high- and low-risk scores were associated with

clinically meaningful gradient of readmission rates”

• up to an 0.83 C-statistic (proportion of time a model

correctly discriminates a pair of high- and low-risk

patients, thus 0.50 is no better than chance)

Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306(15):1688-98.

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Characteristics of “good”

readmission risk assessments

• Good predictive ability

o Considers relevant predictors related to both clinical and psychosocial factors of readmission

• Easily interpreted results

o Stratification of risk groups o Targeted interventions

• Time-manageable

• Cost-manageable

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Stratification of outcomes

• Each tool analyzes differently

o Some have two strata, high-risk or not o Some use stoplight, three-strata system

using “scores”

• Beneficial to identify high-risk patients

o Include in intensive programs i.e. home visits

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How have these been measured?

Readmission <30-Days

Diagnosis and

Comorbidities

Post-hospital

Care Access

Patient Activation

Psychosocial

Support

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Variables considered

• Specific medical diagnosis or comorbidity index • Mental health comorbidities

o Mental illness

o Alcohol or substance abuse

• Illness severity

o Severity index (acuity of admission) o Lab results

• Prior use of medical services

o Hospitalizations (non-elective) or ED visits o Clinic visits or missed clinic visits

Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review.

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Variables considered (cont.)

• Overall functional status

o Functional status, ADL dependence, and mobility o Cognitive impairment

o Visual or hearing impairment

o Self-rated health or quality of life

• Sociodemographic factors

o Age o Sex

o Race/ethnicity

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Variables considered (cont.)

• Social determinants of health

o SES, income, and employment status o Insurance status

o Education

o Marital status or number of people in home o Caregiver ability/other social support

o Access to care

o Discharge destination (home, nursing home, SNF, etc.)

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Interrelation of factors

• Some predictors used are affected by multiple factors

o Polypharmacy and problem medications o PCP access, relationship, or history

o Patient or caregiver activation o Length of stay Readmission <30-Days

Clinical

Factors

Psychosocial

Factors

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LACE tool

Readmission Risk Calculator. Modified LACE tool. Mobile application, 2011. RAADplan.com

• Length of Stay

• Acuity of Admission

o Inpatient or outpatient

• Comorbidity

• ER Visits

o Previous 6 months

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HOSPITAL score

Donze J, Aujesky D, et al. Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients: Derivation and Validation of a Prediction Model. 2013.

• Hemoglobin level

• Oncology

• Sodium level

• Procedure

• Index admission Type

• no. of Admissions in

past year

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8Ps

Risk Assessment Tool: the 8Ps. BOOSTing Care Transitions. Society of Hospital Medicine. 2008.

http://www.hospitalmedicine.org/ResourceRoomRedesign/R R_CareTransitions/html_CC/06Boost/03_Assessment.cfm

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8Ps

• Problem medications (warfarin, digoxin, insulin, etc.)

• Psychological (depression)

• Principal diagnosis (cancer, stroke, DM, HF, COPD)

• Polypharmacy (>5 medications)

• Poor health literacy (inability to TeachBack)

• Patient support (caregiver absence)

• Prior hospitalization (non-elective, prior 6 months)

• Palliative care (progressive serious illness)

Risk Assessment Tool: the 8Ps. BOOSTing Care Transitions. Society of Hospital Medicine. 2008.

http://www.hospitalmedicine.org/ResourceRoomRedesign/ RR_CareTransitions/html_CC/06Boost/03_Assessment.cfm

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STAAR

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PAM

Patient Activation Measure. Insignia Health. 2012.

http://www.insigniahealth.com/solutions/pati ent-activation-measure

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Developing new approaches

Discharge Risk Assessment. EQ Health Solutions: The Medicare QIO for Louisiana. 2011.

http://louisianaqio.eqhs.org/PDF/Care%20Transition s/DxRiskAssessment_Rev5_12.pdf

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Developing new approaches

Discharge Risk Assessment. EQ Health Solutions: The Medicare QIO for Louisiana. 2011.

http://louisianaqio.eqhs.org/PDF/Care%20Transitions/DxRiskAssessment _Rev5_12.pdf

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New Hanover Regional Medical Center

Readmission Risk Assessment Scale

July 31

st

_{, 2012}

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PDSA: Develop a Readmission Risk Assessment Scale

Cycle 1:

• Retrospective Study

– 90% success at predicting readmission

• Concurrent study

– 91% accurate with clinical estimate

– Almost all patients ruled in high risk Cycle 2:

– More Selective Cycle 3:

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PDSA Cycle 4: Test Readmission Risk Assessment Scale

on Other Units

Nephrology 3rd_Floor:

– Percent of patient population identified as high risk 65% Cape Fear 2nd_Floor:

– Percent of patient population identified as high risk 37%

CMTU 8th_Floor:

• Concurrent Study

– Percent of patient population identified as high risk 69%

Staff Feedback:

•Psychosocial is the biggest factor for readmissions

•Consider using a Likert Scale •Add ESRD to scale

•Add Smoking and diet issues to psychosocial as it plays a big part in CHF patients

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Common pitfalls

• Unnecessary length / complication – is not feasible

to incorporate into existing workflow

• Inappropriate stratification, too high or too low – too

few or too many patients categorized as “high risk”

• Deficient criteria – does not cover both clinical and

psychosocial factors

• Vagueness in definitions of predictors – affects

repeatability and reproducibility

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Suggested methods

• Work within feasibility constraints

o Time, cost, existing workflow for assessment

o Resources available for interventions from

results of assessment

• Include both clinical and psychosocial factors

• Clarify definitions of predictors

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Going forward…

• Choose, create, and/or reevaluate the pilot unit’s risk assessment tool to identify high-risk patients using the suggested methods

• Apply risk assessment and appropriate process methods (follow up with primary care doc, etc.)

Remember:

• “best choice of model may depend on setting and the population…” (Kansagra et al)

• “assessment… is an ongoing process that requires the

multidisciplinary team” (STAAR) Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA

2011;306(15):1688-98.

http://www.ihi.org/knowledge/Pages/Tools/HowtoGuideImprovingTrans itionstoReduceAvoidableRehospitalizations.aspx

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Resources

• Coleman E, Min S, Chomiak A, et al. Posthospital Care Transitions: Patterns, Complications and Risk

Identification. Health Service Research. 2004.

• Discharge Risk Assessment. EQ Health Solutions: The Medicare QIO for Louisiana. 2011.

http://louisianaqio.eqhs.org/PDF/Care%20Transitions/DxRiskAssessment_Rev5_12.pdf

• Donze J, Aujesky D, et al. Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients: Derivation

and Validation of a Prediction Model. 2013.

• Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic

review. JAMA 2011;306(15):1688-98.

• Patient Activation Measure. Insignia Health. 2012.

http://www.insigniahealth.com/solutions/patient-activation-measure

• Readmission Risk Calculator. Modified LACE tool. Mobile application, 2011. RAADplan.com

• Risk Assessment Tool: the 8Ps. BOOSTing Care Transitions. Society of Hospital Medicine. 2008.

http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/html_CC/06Boost/03_Assess ment.cfm

• STAAR How-to Guide: Transitions from the Hospital to Community Setting to Reduce Avoidable

Rehospitalizations. Institute for Healthcare Improvement. 2012.

http://www.ihi.org/knowledge/Pages/Tools/HowtoGuideImprovingTransitionstoReduceAvoidableRehospitalizati ons.aspx

• Step 4: Identify Which Patients Should Receive the RED. Tool 2: How to Begin the Re-Engineered Discharge

Implementation at Your Hospital RED Toolkit. Agency for Healthcare Research & Quality. 2013. http://www.ahrq.gov/professionals/systems/hospital/toolkit/redtool2.html#Step4

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Contact

For more information, contact:

Laura Maynard, Director of Collaborative Learning, [email protected]

Amanda Hobbs, Collaborative Learning Intern, [email protected]