Date post: | 27-Mar-2015 |
Category: |
Documents |
Upload: | noah-malone |
View: | 213 times |
Download: | 1 times |
Identifying and Intervening with Patients at High Risk of Hospital
AdmissionAcademy Health Annual Research Meeting,
June 5th 2007
Maria C. Raven, MD, MPH, MScJohn C. Billings, JD
Mark N. Gourevitch, MD, MPHEric Manheimer, MD
NYUMedicalCenter
Bellevue Hospital Center
High Cost Care Initiative (HCCI): Research Initiative at Bellevue Hospital Center, NYC
Supported by United Hospital Fund Goals:
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
Why focus on high cost Medicaid patients?
How can we target high cost patients to identify them for interventions?
What we have learned from identifying patients?
What are the next steps?
High Cost Medicaid Patients: the 80-20 rule
NYC MEDICAID SSI DISABLED ADULTSMedicaid Managed Care “MMC” Mandatory [Non-Dual, Non-HIV/AIDS, Non-SPMI] 2003- 2004
3.0%
30.0%
7.0%
25.9%
10.0%
17.0%80.0%
27.1%
0%
20%
40%
60%
80%
100%
Pe
rce
nt
of
To
tal
Patients Expenditures
Source: NYU Center for Health and Public Service Research, UHF, NYSDOH, 2006.
72.9%
Not only is it where the money is… These are some of the patients with the greatest
need Many moving into managed care
What used to be “revenue” is now “expense” Improved care offers potential for cost savings
Why Focus on High Cost Cases?
Predictive algorithm can identify high-risk patients
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 Based on prior utilization Higher risk scores (>50) predictive of higher risk of
admission in next 12 months
(Reference)Admission
Year 4 Year 5Year 3Year 2Year 1
General Approach for Development of Risk Prediction Algorithm
Examine utilization for prior 3+ years
(Reference)Admission
Year 4 Year 5Year 3Year 2Year 1
General Approach for Development of Risk Prediction Algorithm
(Reference)Admission
Examine utilization for prior 3+ years
Predict admission next 12 months
Year 4 Year 5Year 3Year 2Year 1
General Approach for Development of Risk Prediction Algorithm
Bellevue-specific predictive algorithm
Pulled last five years of Bellevue’s Medicaid billing data 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
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 Qualitative/quantitative measures
Inclusion/Exclusion criteria
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 algorithm-predicted admission occurred
Interview instruments
Quantitative data from 50 patients 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
36,457 adult fee-for service Medicaid patients with visit to
Bellevue, 2001-2006
2,618 with algorithm-based risk score>50
139 admitted during 2-month study period
50 patients consented and interviewed
•89 ineligible or discharged prior to approach
•11 refusals
Recruitment scheme for Bellevue pilot project
Billings’ algorithm
Daily computer query checked past 24 hours’ admissions against 2,618 high-risk patients
Strength of algorithm
PPV=0.67 Of all admitted high risk patients, over 20 bounce-
backs among 16 patients 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
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
History of over 30 detox admissions One rehab Homeless on street Depression No other medical problems
Ms. C
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
Age in years 18-34 35-49 50-64Mean age=44.3
Ethnicity African American Hispanic White Other
72%
20%42%38%
24%54%14%8%
Education and work history
Characteristic % of total
Education Less than high school High school/GED or greater Unknown
Income source None Public Assistance Social security Work Friends/family
60%36%4%
8%34%38%4%12%
Diagnoses
Characteristic% ofTotal
Any chronic disease 68%Multiple chronic disease 44%
Stroke 6%Cancer 36%
Any mental illness 62%Schizoprhenia 10%Psychoses 20%Bi-polar/major depression 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
Health Limits Moderate Physical Activity A Lot A Little Not at all
6%24%70%
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
Disproportionate admissions for substance use, mental illness, and substance use-related medical problems among homeless subjects
Similar differential in claims data
% of Total
CharacteristicPermanent
Housing
Staying WithFriends or
Family
Homelessor
In Shelter
Any chronic disease 85% 83% 39%Multiple chronic disease 65% 50% 17%
Stroke 10% 8% 0%Cancer 70% 17% 11%
Any mental illness 55% 75% 61%Schizoprhenia 5% 0% 22%Psychoses 15% 25% 22%Bi-polar/major depression 15% 50% 28%
Alcohol/substance abuse 45% 58% 94%
Mental illness or Alc/substance abuse 65% 83% 100%
Substance use: ASSIST data
74% had mid-high substance use risk scores (37/50) Risk for harmful use/dependence with related social,
legal, health problems 14% tobacco only (7/50) 60% multiple substances (30/50)
Majority tobacco and alcohol, followed by cocaine and opioids
7 pts had used IV drugs
Mental Health
SF-12 Mental Composite Score Lower scores = higher levels of anxiety and
depression Compared to the general US population:
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 68% (34/50) cases
Characteristic % of total
Usual Source of Care
None 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
Usual Source of care
Access to care
Source: High Cost Medicaid Project – Bellevue Hospital Center, NYU Center for Health and Public Service Research, 2006.
% of Total
CharacteristicPermanent
Housing
Staying WithFriends or
Family
Homelessor
In Shelter
Usual source of care
None 15% 17% 17%
Emergency department 15% 50% 67%
OPD/Clinic 25% 25% 11%
Community based clinic 20% 0% 0%
Private/Group MD/other 25% 8% 6%
Social isolation
Characteristic % of total
Marital Status Married/living with partner Separated/divorced Widowed Never married
Lives aloneNo close friends/relativesTwo or fewer friends/relativesLow perceived availability of support
14%26%4%56%
52%16%48%38%
Medicaid expenditures prior year
CharacteristicMeanCosts
Bellevue costs prior yearInpatient $37,418Emergency department $174Primary care $168Specialty care $150Outpatient substance abuse treatment $343Outpatient mental health treatment $299Other costs $636
Total costs prior year $39,188
How much can we pay for an intervention, and still expect to save? (or break even) Depends on:
Risk score level Projected reduction in inpatient admissions in the
following year Based on annual Medicaid expenditures in our
cohort: 25% reduction in future admissions over 1 year allows
intervention spending of $9350 per patient
Limitations
Observational study-no control group Limited to English and Spanish speaking, non-
HIV, Medicaid fee-for service Bellevue Hospital population
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
Intervention being informed by: 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
Begin at patient’s bedside in hospital, continue after his/her discharge into the community Housing component
Flexible, intensive care management model, multi-disciplinary team approach, tailored to needs of each patient Bellevue-based team will partner with CBOs
Thank You
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
Hospitalized high-risk patients identified using predictive algorithm
Small comprehensive multi-disciplinary team 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
12-month follow-up measures collected
Intervention team intensive care managementfor 12 months
In addition, health services use/costs,and intervention costs tracked
Baseline measuresIntervention team assigned, needs assessment
If homeless, Common Ground to bedside: Housing application begins: patient d/c to stabilizaition housing
150 subjects: Intervention
Usual care for 12 monthsIntervention team to track health services use
and related costs
Baseline measuresFollow-up information
150 subjects: Usual Care
Consent obtained25 subjects enrolled/month for 12 months
Randomization
Medicaid/UninsuredAlgorithm-based risk score>50Admitted to Bellevue Hospital
Bellevue intervention project baseline measures (RCT)
Baseline assessments: Self-report generated Charlson Comorbidity Index:
patient-reported disease severity measure predictive of 1-year mortality
Socio-demographic measures (e.g. age, gender, income, education)
Diagnoses obtained from subject’s electronic medical record
Bellevue intervention project baseline measures (RCT)
Baseline assessments (validated tools): 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
Primary outcome Hospital admissions and associated expenditures
Secondary outcomes 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 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
23/30 (82%) : Substance use, psychiatric, medical condition related to substance use 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 All with past or current substance use
Admission diagnoses, 22/50 (44%) housed 1 Diabetes/coagulopathy 3 Lupus 5 Cancer 1 Dialysis/pain medication related 3 non-compliance resulting in disease exacerbation
anemia, adrenal crisis, gastroparesis 2 Alcohol complications
Hepatitis and ESLD 3 infections (2 PNA, 1 cellulitis) 2 COPD/asthma 1 ortho 1 psych
Admission Diagnosis # of total
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
5 3 8 2 4 1 1 2 1 4 1 1 1 1 1 1 1 5 9 3 2 1
Medication
43% on medication at admission had missed at least one dose in prior week
Most common reasons 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)