Identifying Patients at High Risk for Hospitalization Brooke Salzman, Rachel Knuth, Elizabeth Gardner, Marianna La Noue, Amy Cunningham Department of Family & Community Medicine Thomas Jefferson University April 26, 2015

Disclosures No conflicts of interest HRSA Geriatric Academic Career Award,

Background: Hospitalizations Hospitalizations –Potentially avoidable –Harmful –Costly Patients age 65 and older –At higher risk for hospitalization and readmission –Occupy ½ of hospital beds in the US –Have greater lengths of stay

Care Management Programs Variety of interventions designed to reduce avoidable admissions/readmissions –Team-based care  SW, PharmD, health coaches –Care managers –New care delivery models  PCMH, Grace, Guided Care, PACE, etc. –New payment models/penalties  Care coordination codes, ACO’s, value-based care

Identifying patients Target interventions for those who are at the highest risk for high health care utilization

Factors Predicting Hospitalization Patient factors –Conditions –Medications –Prior use of services –Demographics –Illness severity –Self-rated health –Functional performance –Cognition/mental health –Vision/hearing impairments –Socioeconomic status –Marital status –Caregiver, social support –Health literacy/health beliefs –Substance abuse –Housing stability –Food access –Childcare –Transportation Provider factors –Diagnostic uncertainty –Risk aversion –Lack of time –Communication/trust Practice-based factors –Limited appointments or access during off hours –Sub-optimal communication between providers –Lack of continuity of care Health system factors –Insurance –Access to care –Availability of beds –Transitional care –Quality of inpatient and/or outpatient care –Number of different providers

Current Strategies Predictive modeling examples –Adjusted Clinical Groups (ACG) –Hierarchical Condition Categories (HCC) –Elder Risk Assessment (ERA) –Chronic Comorbidity Counts (CCC) –MN Tiering –Charlson Comorbidity Index Not real time, use administrative data, often involve calculations, not used or validated in primary care setting, modest predictive value

Potential Methods to Predict Hospitalization in Primary Care Setting Probability of Repeated Admission (Pra) Vulnerable Elders Survey (VES-13) Physician Selection Other variables: previous hospitalization in last year, self-reported health, number or type of conditions, number of medications

Background Need for more clinically based, practical, validated case finding methods that don’t rely on complex administrative data or calculations Is there a simple tool that can be used in a primary care setting to identify older patients for care management in real time?

Project Description Compare 3 methods in the primary care setting to help identify patients who are at highest risk for hospitalization –Pra (Probability of Repeated Admission) –VES-13 (Vulnerable Elders Survey) –PCP survey with estimation of risk for hospitalization

Methods: Patient Surveys From VES-13 From PRA From Both Difficulty level with completing six physical activities Sex Age Difficulty level with completing 5 IADLs and ADLs Number of hospitalizations in previous year Self-rated health status Number of visits to a clinic or doctor's office in previous year Presence of diabetes History of CAD Presence of an informal caregiver

Physician Survey How likely is it that this patient will be hospitalized in: –The next 6 months? –The next year? –Very unlikely, somewhat unlikely, somewhat likely, very likely

Project Description Chart review –High risk conditions: CHF, CAD, COPD, diabetes, OA, dementia, active cancer –Number of hospitalizations in the last year –Number of medications

Methods Included: Patients 65 and older Excluded: Moderate to severe dementia or on hospice 2 tools (Pra and VES) administered by research assistant when patient roomed Provider filled out brief survey – blinded to results of 2 tools Chart review at baseline Patients called at 6 months and 1 year Hospitalization/ED visits  verified in chart

Research Questions Best predictor of hospitalization/s and ED visits at 6 months and 1 year Do the PRA, VES-13, and physicians identify the same patients as high risk? Are these tools useful & feasible in a primary care setting with patients ages 65 and older ?

Patient Demographics Sample included 60 patients Age –44 females (73.3%), 16 males (26.6%) –Minimum age: 65 –Maximum age: 94 –Average age: (std. deviation: 7.801) Patients with hospitalization in previous year: 29 (48.3%) Number of Medications –Range: 0-19 –Average number: 8.57 (std. deviation 4.84)

Patient Demographics  Number of Selected Chronic Conditions  5 with no chronic conditions (8.3%)  24 with one chronic condition (40%)  12 with two chronic conditions (20%)  17 with three chronic conditions (28%)  2 with four chronic conditions (3.3%)  Average number of conditions: 1.78 (std. deviation: 1.05)

Results – at 12 months or 1 year Patients with ED visit in 1 year –12 patients (20%) –Range: 0-5 Patients with Hospitalization in 1 year –20 patients (33%) –Range: 0-5

Predicting Hospitalizations Probability of Repeated Admissions (Pra) –High score associated with increased risk for hospitalization (OR 6.00; p<.01) Hospitalization in prior year –Associated with increased risk for hospitalization (OR 3.89; p<.05)

Predicting Hospitalization Not associated with hospitalization –Age –Self-rated health –VES-13 (Vulnerable Elders Survey) –Number of conditions –Specific conditions –Number of medications

Probability of Repeated Admissions (Pra) Sensitivity 40% [95% CI ] Specificity 90% [95% CI ] Positive predictive value: 66.7% Negative predictive value: 75.0%

Prior Hospitalization in past year Sensitivity: 70.0% [95% CI ] Specificity: 65.0% [95% CI ] Positive predictive value: 50.0% Negative predictive value: 81.25%

Physician Survey Physician estimate of likelihood of hospitalization –Categories: Very unlikely, somewhat unlikely, somewhat likely, very likely –Associated with hospitalization OR 4.8 at 6 months OR 2.3 at 1 year

Physician Survey Sensitivity: 43.75% [95% CI ] Specificity: 78.57% [95% CI ] Positive predictive value: 70.00% Negative predictive value: 55.00%

Pra + Physician Rating Sensitivity: 58.33% [95% CI ] Specificity: 84.62% [95% CI ] Positive predictive value: 63.6% Negative predictive value: 81.5%

Discussion Feasible and easy to administer in primary care setting Strongest predictors: Pra, hospitalization in previous year, and PCP rating Combining tools may enhance predictive value Negative predictive value is high— identification of low-risk patients is more accurate

Limits Small sample size Relatively sicker, urban population Pra—copy-righted Tools don’t include measures of willingness to participate in care management interventions

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