Identifying Patients at HighRisk 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,2010-2015
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 reduceavoidable 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 Carecoordination codes, ACO’s, value-based care
Identifying patients
•Target interventions for those who are atthe highest risk for high health careutilization
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 accessduring off hours
–Sub-optimal communicationbetween providers
–Lack of continuity of care
•Health system factors
–Insurance
–Access to care
–Availability of beds
–Transitional care
–Quality of inpatient and/or outpatientcare
–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 involvecalculations, not used or validated in primary caresetting, modest predictive value
Potential Methods to PredictHospitalization in Primary Care Setting
•Probability of Repeated Admission (Pra)
•Vulnerable Elders Survey (VES-13)
•Physician Selection
•Other variables: previous hospitalization inlast year, self-reported health, number ortype of conditions, number of medications
Background
•Need for more clinically based, practical,validated case finding methods that don’trely on complex administrative data orcalculations
•Is there a simple tool that can be used in aprimary care setting to identify olderpatients for care management in realtime?
Project Description
•Compare 3 methods in the primary caresetting to help identify patients who are athighest risk for hospitalization
–Pra (Probability of Repeated Admission)
–VES-13 (Vulnerable Elders Survey)
–PCP survey with estimation of risk forhospitalization
Methods: Patient Surveys
From VES-13
From PRA
From Both
• Difficulty level withcompleting six physicalactivities
• Sex
• Age
• Difficulty level withcompleting 5 IADLs and ADLs
• Number of hospitalizations inprevious year
• Self-ratedhealth status
• Number of visits to a clinic ordoctor'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 behospitalized 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 lastyear
–Number of medications
Methods
•Included: Patients 65 and older
•Excluded: Moderate to severe dementia or onhospice
•2 tools (Pra and VES) administered by researchassistant when patient roomed
•Provider filled out brief survey – blinded to resultsof 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 EDvisits at 6 months and 1 year
•Do the PRA, VES-13, and physiciansidentify the same patients as high risk?
•Are these tools useful & feasible in aprimary care setting with patients ages 65and older ?
Patient Demographics
•Sample included 60 patients
•Age
–44 females (73.3%), 16 males (26.6%)
–Minimum age: 65
–Maximum age: 94
–Average age: 78.16 (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 forhospitalization (OR 6.00; p< .01)
•Hospitalization in prior year
–Associated with increased risk forhospitalization (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 19.17-63.93]
•Specificity 90% [95% CI 76.32-97.15]
•Positive predictive value: 66.7%
•Negative predictive value: 75.0%
Prior Hospitalization in past year
•Sensitivity: 70.0% [95% CI 45.73-88.03]
•Specificity: 65.0% [95% CI 45.80-77.26]
•Positive predictive value: 50.0%
•Negative predictive value: 81.25%
Physician Survey
•Physician estimate of likelihood ofhospitalization
–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 26.38-62.33]
•Specificity: 78.57% [95% CI 59.04-91.65]
•Positive predictive value: 70.00%
•Negative predictive value: 55.00%
Pra + Physician Rating
•Sensitivity: 58.33% [95% CI 27.75-84.68]
•Specificity: 84.62% [95% CI 65.11-95.55]
•Positive predictive value: 63.6%
•Negative predictive value: 81.5%
Discussion
•Feasible and easy to administer in primarycare setting
•Strongest predictors: Pra, hospitalization inprevious year, and PCP rating
•Combining tools may enhance predictivevalue
•Negative predictive value is high—identification of low-risk patients is moreaccurate
Limits
•Small sample size
•Relatively sicker, urban population
•Pra—copy-righted
•Tools don’t include measures ofwillingness to participate in caremanagement interventions
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