Identifying and Intervening with
Patients at High Risk of Hospital
Admission
Academy Health Annual Research Meeting,
June 5th 2007
NYU
Medical
Center
Bellevue Hospital Center
Maria C. Raven, MD, MPH, MSc
John C. Billings, JD
Mark N. Gourevitch, MD, MPH
Eric Manheimer, MD
High Cost Care Initiative (HCCI): Research
Initiative at Bellevue Hospital Center, NYC
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Supported by United Hospital Fund
Goals:
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Characterize high-cost patients with frequent hospital
admissions
Use data to inform intervention to reduce
admissions/costs and improve care
What we’re going to cover
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Why focus on high cost Medicaid patients?
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How can we target high cost patients to identify
them for interventions?
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What we have learned from identifying patients?
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What are the next steps?
High Cost Medicaid Patients: the 80-20 rule
NYC MEDICAID SSI DISABLED ADULTS
Medicaid Managed Care “MMC” Mandatory [Non-Dual, Non-HIV/AIDS, Non-SPMI]
2003- 2004
100%
27.1%
Percent of Total
80%
17.0%
60%
80.0%
25.9%
40%
20%
10.0%
0%
30.0%
7.0%
3.0%
Patients
Source: NYU Center for Health and Public Service Research, UHF, NYSDOH, 2006.
Expenditures
72.9%
Why Focus on High Cost Cases?
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Not only is it where the money is…
These are some of the patients with the greatest
need
Many moving into managed care
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What used to be “revenue” is now “expense”
Improved care offers potential for cost savings
Predictive algorithm can identify
high-risk patients
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Predictive algorithm created by John C. Billings
identifies Medicaid patients at high-risk for hospital
admission in next 12 months
Algorithm generates risk score from 0-100 for every
patient in a dataset
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Based on prior utilization
Higher risk scores (>50) predictive of higher risk of
admission in next 12 months
General Approach for Development of
Risk Prediction Algorithm
(Reference)
Admission
Year 1
Year 2
Year 3
Year 4
Year 5
General Approach for Development of
Risk Prediction Algorithm
Examine utilization
for prior 3+ years
Year 1
Year 2
(Reference)
Admission
Year 3
Year 4
Year 5
General Approach for Development of
Risk Prediction Algorithm
(Reference)Predict admission
Admission next 12 months
Examine utilization
for prior 3+ years
Year 1
Year 2
Year 3
Year 4
Year 5
Bellevue-specific predictive algorithm
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Pulled last five years of Bellevue’s Medicaid
billing data
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Inpatient, ED, outpatient department
Logistic regression created Bellevue-specific
case-finding algorithm
Created risk scores (0-100) applicable for any
patient with a visit in the past 5 years
Cohort with risk scores>50 = high risk for
admission in next 12 months
Subject Enrollment
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Cross-checked all admitted patients against our
high-risk cohort every 24 hrs
Identified and interviewed 50 such patients and
their providers during hospital admission
Determined medical/social contributors to
frequent admissions
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Qualitative/quantitative measures
Inclusion/Exclusion criteria
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Ages 18-64
Medicaid fee-for-service visit to Bellevue from
2001-2005
Excluded:HIV, dual eligible, institutionalized
when not hospitalized, unable to communicate
Patients enrolled when algorithmpredicted admission occurred
Interview instruments
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Quantitative data from 50 patients
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Demographics
SF-12 (health and well-being)
Usual Source of Care
BSI-18 (anxiety/depression/somatization)
Perceived Availability of Support Scale (social support)
Patient Activation Measure
WHO-ASSIST (substance use)
Medications (adapted from Brief Medication Questionnaire)
Qualitative data from 47 patients, 40 physicians and
16 social workers
Recruitment scheme for Bellevue
pilot project
36,457 adult fee-for service Medicaid
patients with visit to Bellevue, 20012006
2,618 with algorithm-based risk
score>50
139 admitted during 2-month study
period
Billings’ algorithm
Daily computer
query checked
past 24 hours’
admissions
against 2,618
high-risk patients
•89 ineligible or discharged prior to
approach
•11 refusals
50 patients consented and
interviewed
Strength of algorithm
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PPV=0.67
Of all admitted high risk patients, over 20 bouncebacks among 16 patients
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Of these 16 patients, 9 eligible, 8 interviewed
5 patients had >1 bounce-back during study period
Source: NYU Center for Health and Public Service Research, UHF, NYSDOH, 2006.
Some representative
patients…
Mr. O
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58 y/o man with COPD and CHF
Lives with daughter
Feels hospital admission is unavoidable when
he has difficulty breathing
Does not seek intervention at symptom onset
from primary doctor
Multiple admissions for COPD and CHF
Mr. R
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History of over 30 detox admissions
One rehab
Homeless on street
Depression
No other medical problems
Ms. C
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Severe lupus
Severe pain
Outpatient doctors won’t prescribe her the
narcotics she wants/needs
Repeated admissions for lupus flare and pain
control
Often with 24-48 hour stays and no changes to
outpatient regimen
Demographic characteristics
Characteristic
% of
total
Male
72%
Age in years
18-34
35-49
50-64
Mean age=44.3
20%
42%
38%
Ethnicity
African American
Hispanic
White
Other
24%
54%
14%
8%
Education and work history
Characteristic
% of
total
Education
Less than high school
High school/GED or greater
Unknown
60%
36%
4%
Income source
None
Public Assistance
Social security
Work
Friends/family
8%
34%
38%
4%
12%
Diagnoses
Characteristic
% of
Total
Any chronic disease
Multiple chronic disease
68%
44%
Stroke
Cancer
6%
36%
Any mental illness
Schizoprhenia
Psychoses
Bi-polar/major depression
62%
10%
20%
28%
Alcohol/substance abuse
66%
Mental illness or Alc/substance abuse
82%
Self-rated health
Characteristic
% of total
General Health Status
Excellent/Very good
Good
Fair/Poor
6%
24%
70%
Health Limits Moderate Physical Activity
A Lot
A Little
Not at all
45%
35%
20%
Housing
Characteristic
% of total
Current Housing Status
Apartment/home rental
Public Housing
Residential Facility
Staying with family/friends
Shelter
Homeless
34%
4%
2%
24%
8%
28%
60%
Housing
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Disproportionate admissions for substance
use, mental illness, and substance userelated medical problems among homeless
subjects
Similar differential in claims
data
% of Total
Characteristic
Permanent
Housing
Staying With
Friends or
Family
Homeless
or
In Shelter
Any chronic disease
Multiple chronic disease
85%
65%
83%
50%
39%
17%
Stroke
Cancer
10%
70%
8%
17%
0%
11%
Any mental illness
Schizoprhenia
Psychoses
Bi-polar/major depression
55%
5%
15%
15%
75%
0%
25%
50%
61%
22%
22%
28%
Alcohol/substance abuse
45%
58%
94%
Mental illness or Alc/substance abuse
65%
83%
100%
Substance use: ASSIST data
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74% had mid-high substance use risk scores (37/50)
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Risk for harmful use/dependence with related social,
legal, health problems
14% tobacco only (7/50)
60% multiple substances (30/50)
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Majority tobacco and alcohol, followed by cocaine and
opioids
7 pts had used IV drugs
Mental Health
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SF-12 Mental Composite Score
Lower scores = higher levels of anxiety and
depression
Compared to the general US population:
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38% (19/50) scored below the 25%ile
38% scored below the median
BSI-18 “cases” at high risk for psychopathology
based on anxiety, depression, and somatization
summary score
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68% (34/50) cases
Usual Source of care
Characteristic
Usual Source of Care
None
% of total
22%
ED
40%
Hospital outpatient
30%
Other
8%
Source: High Cost Medicaid Project – Bellevue Hospital Center, NYU Center for Health and Public Service Research, 2006
Access to care
% of Total
Characteristic
Usual source of care
None
Emergency department
OPD/Clinic
Community based clinic
Private/Group MD/other
Permanent
Housing
Staying With
Friends or
Family
15%
15%
25%
20%
25%
Source: High Cost Medicaid Project – Bellevue Hospital Center, NYU Center for Health and Public Service Research, 2006.
17%
50%
25%
0%
8%
Homeless
or
In Shelter
17%
67%
11%
0%
6%
Social isolation
Characteristic
% of total
Marital Status
Married/living with partner
Separated/divorced
Widowed
Never married
14%
26%
4%
56%
Lives alone
No close friends/relatives
Two or fewer friends/relatives
Low perceived availability of support
52%
16%
48%
38%
Medicaid expenditures prior year
Characteristic
Bellevue costs prior year
Inpatient
Emergency department
Primary care
Specialty care
Outpatient substance abuse treatment
Outpatient mental health treatment
Other costs
Total costs prior year
Mean
Costs
$37,418
$174
$168
$150
$343
$299
$636
$39,188
How much can we pay for an
intervention, and still expect to save?
(or break even)
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Depends on:
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Risk score level
Projected reduction in inpatient admissions in the
following year
Based on annual Medicaid expenditures in our
cohort:
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25% reduction in future admissions over 1 year allows
intervention spending of $9350 per patient
Limitations
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Observational study-no control group
Limited to English and Spanish speaking, nonHIV, Medicaid fee-for service
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Bellevue Hospital population
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Urban, underserved
Conclusions and Implications
• Patients with frequent hospital admissions comprise
small percentage of all patients, but account for
disproportionate share of visits and costs.
• Social isolation, substance use, mental health, and
housing issues were prevalent in our study population
•
Cited by patients/providers as contributing substantially to their
hospital admissions.
• Interventions focused on more effective management of
their complex issues could result in cost-savings via
decreased utilization and improved health.
Next Steps
Intervention project planning
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Intervention being informed by:
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Pilot data
Partnership with providers of homeless services
Successful components of similar programs in
other safety net settings around country*
Meetings with community providers (CBOs) of
other services (e.g. substance use, mental health,
HIV)
*Chicago Housing for Health Partnership, California Frequent Users of Health
Services Initiative www.chcf.org
Bellevue intervention project
model
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Begin at patient’s bedside in hospital,
continue after his/her discharge into the
community
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Housing component
Flexible, intensive care management model,
multi-disciplinary team approach, tailored to
needs of each patient
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Bellevue-based team will partner with CBOs
Thank You
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John C. Billings, JD
Marc N. Gourevitch, MD, MPH
Lewis R. Goldfrank, MD
Mark D. Schwartz, MD
Eric Manheimer, MD
United Hospital Fund
Supported in part by CDC T01 CD000146
Bellevue Hospital Intervention
Project
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Hospitalized high-risk patients identified using
predictive algorithm
Small comprehensive multi-disciplinary team
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Intensive assessment, arrange and follow to ensure
and assist with provision of post-discharge support
Housing, residential substance abuse treatment,
community based mental health treatment,
specialized medical outpatient care
Provision of temporary housing while awaiting
supportive housing placement/prompt placement
into permanent housing
Bellevue Intervention Randomized
Controlled Trial
Medicaid/Uninsured
Algorithm-bas ed ri sk sc ore>50
Admitted to Bell evue Hos pital
Consent obtained
25 s ubjects enrolled/month for 12 months
Randomi zation
150 subj ec ts: Interventi on
150 subj ec ts: Usual Care
Bas eli ne measures
Interventi on team ass igned, needs as sess ment
If homeless , Common Ground to beds ide:
Housi ng applic ati on begi ns : pati ent d/c to stabilizai tion housi ng
Interventi on team i ntens ive c are management
for 12 months
In addition, heal th servi ces use/c os ts ,
and interventi on costs trac ked
Bas eli ne measures
Foll ow-up information
Usual care for 12 months
Interventi on team to trac k health s ervic es use
and related costs
12-month follow-up measures c ol lected
Bellevue intervention project
baseline measures (RCT)
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Baseline assessments:
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Self-report generated Charlson Comorbidity Index:
patient-reported disease severity measure predictive of 1year mortality
Socio-demographic measures (e.g. age, gender, income,
education)
Diagnoses obtained from subject’s electronic medical
record
Bellevue intervention project
baseline measures (RCT)
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Baseline assessments (validated tools):
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Health and daily functioning
Substance use
Mental Health
Support Scale
Usual Source of Care
Housing status/living situation
Common Ground in-depth assessment
Bellevue intervention outcome
measures
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Primary outcome
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Hospital admissions and associated expenditures
Secondary outcomes
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Other health services (ED, outpatient clinics) utilization
Other health services expenditures
Intervention costs
Housing status
Change in psychosocial variables
Appt adherence
Benefits enrollment
Entry into substance use services
The intervention must pay for
itself
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Central goal: intervention that generates more savings to the
delivery system that it costs to implement and sustain.
Eliminate even small % admissions and substantial cost savings
can be had.
Comprehensive economic analysis planned that considers
 Changes in the numbers of inpatient admissions, ED visits, and
outpatient visits during the intervention period in addition to their
related expenditures
 All costs related to the intervention.
Ability of intervention to succeed in this goal will help determine
whether it is
 Sustainable
 Exportable to other sites.
Admission diagnoses: 30/50 (60%)
homeless/precariously housed
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23/30 (82%) : Substance use, psychiatric,
medical condition related to substance use
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9 detoxification services
3 alcohol/drug withdrawal or intoxication
4 psychiatric
7 drug/alcohol-related medical diagnoses
 CHF, trauma, chronic septic joint, cellulitis
5/30: infected ulcer, chest pain, catheter
infection, GI bleed, COPD
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All with past or current substance use
Admission diagnoses, 22/50
(44%) housed
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1 Diabetes/coagulopathy
3 Lupus
5 Cancer
1 Dialysis/pain medication related
3 non-compliance resulting in disease exacerbation
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2 Alcohol complications
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anemia, adrenal crisis, gastroparesis
Hepatitis and ESLD
3 infections (2 PNA, 1 cellulitis)
2 COPD/asthma
1 ortho
1 psych
Admission Diagnosis
Diagnoses
Cancer
Lupus erythematosos
Infection
Pneumonia
Cellulitis/foot ulcer
Dialysis catheter
Septic joint (IVDU)
Diabetes mellitus
Ulcer
COPD/asthma
CHF
Epilepsy
Fracture non-union
Adrenal Insufficiency
Anemia
Chest pain (ACS)
End-stage liver disease
Psychiatric
Detoxification services
Alcohol withdrawal/intoxication
Trauma
Alcoholic hepatitis
# of
total
5
3
8
2
4
1
1
2
1
4
1
1
1
1
1
1
1
5
9
3
2
1
Medication
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43% on medication at admission had missed at least
one dose in prior week
Most common reasons
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inability to pay for prescriptions (4)
forgetting to take a dose (3)
being unable to get to clinic or hospital for refills or
medication administration (3)
side effects (3)
substance abuse (3)