Title: CASE FINDING ALGORITHM FOR PATIENTS AT RISK OF REHOSPITALISATION
1CASE FINDING ALGORITHM FORPATIENTS AT RISK OF
RE-HOSPITALISATION
27 October, 2005
Health Dialog Data Service
NYU Center for Health and Public Service Research
2WHAT IM GOING TO TALK ABOUT
- The need for more effective health and social
care management of high cost patients - The importance of an effective case finding tool
- How the Patients At Risk of Re-hospitalisation
(PARR) case finding algorithm was developed - What the PARR algorithm can do
- What the PARR algorithm cant do
- Next steps you might consider
3THE NEED FOR MORE EFFECTIVEHEALTH AND SOCIAL
CARE MANAGEMENTOF HIGH COST PATIENTS
4THE NEED TO IMPROVEHEALTH AND SOCIAL CARE
MANAGEMENTCASE 1
5THE NEED TO IMPROVEHEALTH AND SOCIAL CARE
MANAGEMENTCASE 2
6THE NEED TO IMPROVEHEALTH AND SOCIAL CARE
MANAGEMENTCASE 3
7PREDICTIVE CASE FINDING PROJECT
- Phase 1 Literature review
- Phase 2 PARR case finding algorithm
- Inpatient data
- Phase 3 Combined data case finding algorithm
- Inpatient data
- AE data
- Hospital outpatient data
- GP electronic medical record data
- Community data
- Social services data
- Etc.
8ABOUT THE IMPORTANCE OFCASE FINDING
- An effective case finding tool is likely to be
critical - The key to any management improvement initiative
is matching patient needs with available
resources - One size does not fit all
- May invest more in interventions for very high
risk patients - Match intervention design to patient need
- Any new interventions are likely to have to meet
a business case test - The cost of the intervention offset by savings
somewhere else - The somewhere else is likely to be the hospital
9ABOUT THE IMPORTANCE OFCASE FINDING
Patient Smith
Patient Jones
Patient Shah
10OUR APPROACHPATIENT AT RISK OF
RE-HOSPITALISATION (PARR)CASE FINDING ALGORITHM
11PARR ALGORITHMSGENERAL APPROACH
- Developed using 5 years of HES data
- Designed to be used by PCTs, SHAs, GP Groups with
ClearNet Admitted Patient Care (APC) data - Can be used either
- In real time (while patients are hospitalised)
- With archived data only (monthly or annually)
12PARR ALGORITHMSGENERAL APPROACH
- Look for a recent hospitalisation
- In real time (while the patient is hospitalised)
- Or on a monthly/annual basis (reviewing recent
discharges) - Use information in hospital discharge records to
predict patients at high risk for
re-hospitalisation - Logistic regression techniques
- An algorithm produces a Risk Score of 0-100 for
each patient - Two methods
- Narrow (PARR1) Focus on emergency admissions
for reference conditions - Potentially preventable/avoidable
- Often lead to re-hospitalisation
- Broad (PARR2) Look at all emergency admissions
13PARR ALGORITHMSGENERAL APPROACH FOR
DEVELOPMENTOF PARR ALGORITHM REAL TIME
Admission
Year 4
Year 5
Year 3
Year 2
Year 1
14PARR ALGORITHMSGENERAL APPROACH FOR
DEVELOPMENTOF PARR ALGORITHM REAL TIME
Examine utilisation for prior 3 years
Admission
Year 4
Year 5
Year 3
Year 2
Year 1
15PARR ALGORITHMSGENERAL APPROACH FOR
DEVELOPMENTOF PARR ALGORITHM REAL TIME
Examine utilisation for prior 3 years
Predict adm next 12 months
Admission
Year 4
Year 5
Year 3
Year 2
Year 1
16PARR ALGORITHMSTYPES OF VARIABLES IN MODEL
- Prior utilisation
- Admissions (emergency and elective examined
separately) - Number
- Time intervals (recentness)
- Day cases/attendances
- Type and number of different day case specialty
types - Diagnostic information from current/prior
utilisation - Chronic disease
- Other conditions with high rates of subsequent
admission - Multiple conditions/hierarchical DCG grouping
- Patient characteristics Age, gender, ethnicity
- Reference condition re-hospitalisation rates at
hospital of current admission - Contextual information about area of residence
- Age/sex adjusted admission rates for reference
conditions - Race/ethnicity
- Deprivation index
17PARR ALGORITHMREFERENCE CONDITIONS
- Conditions potentially responsive to more
effective care management with high rates of
re-hospitalisation - CHF
- COPD
- Diabetes
- Coronary artery disease
- Sickle cell
- Asthma
- Chronic liver disorders
- Chronic pancreatic disease
- Cystic fibrosis
- URI
- Etc, etc, etc
18PARR ALGORITHMSRESULTS AND CHARACTERISTICSOF
FLAGGED PATIENTS
19PARR ALGORITHMSRESULTS FROM PARR ALGORITHM
PARR1 REFERENCE CONDITIONS
PARR2 ALL EMERGENCY PATIENTS
Note Data are from use of real time" methods
20PARR ALGORITHMSNUMBER OF RE-ADMITTED PATIENTS
IDENTIFIED ANNUALLY FOR A TYPICAL PCT REAL
TIME METHOD
PARR2
PARR1
Number of Re-Admitted Patients Identified
Risk Score Threshold
21PARR2 ALGORITHMNUMBER OF RE-ADMITTED PATIENTS
IDENTIFIED ANNUALLY FOR A TYPICAL PCT
(PARR2 Algorithm)
REAL TIME
MONTHLY
ANNUAL
Number of Re-Admitted Patients Identified
Risk Score Threshold
22PARR2 ALGORITHMCHARACTERISTICS OF PATIENTSAT
HIGH RISK FOR RE-ADMISSION
Note Data are from PARR2 Algorithm "Real
time" method
23PARR2 ALGORITHMCHARACTERISTICS OF PATIENTSAT
HIGH RISK FOR RE-ADMISSION
Note Data are from PARR2 Algorithm "Real
time" method
24PARR2 ALGORITHMCHARACTERISTICS OF PATIENTSAT
HIGH RISK FOR RE-ADMISSION
Note Data are from PARR2 Algorithm "Real
time" method
25PARR2 ALGORITHMCHARACTERISTICS OF PATIENTSAT
HIGH RISK FOR RE-ADMISSION
Note Data are from PARR2 Algorithm "Real
time" method
26PARR2 ALGORITHMTOP 25 EMERGENCY
RE-HOSPITALISATIONSFOR FLAGGED PATIENTS Risk
Score 50
Note Data are from PARR2 Algorithm "Real
time" method
27PARR ALGORITHMSBREAK EVEN FOR TYPICAL PCT
COSTS/SAVINGS(INTERVENTION COST 500/PATIENT)
Note Data are from use of real time" methods
28PARR ALGORITHMSBREAK EVEN FOR TYPICAL PCT
COSTS/SAVINGS(15 REDUCTION IN RE-ADMISSIONS --
COST 500/PATIENT)
Net Costs/Savings (000s)
PARR1
PARR2
Risk Score Threshold
Note Data are from use of real time" methods
29PARR2 ALGORITHMBREAK EVEN FOR TYPICAL PCT
COSTS/SAVINGS(INTERVENTION COST 500/PATIENT)
30PARR2 ALGORITHMBREAK EVEN FOR TYPICAL PCT
COSTS/SAVINGS(REAL TIME METHOD - COST
500/PATIENT)
Net Costs/Savings (000s)
10 Reduction
15 Reduction
20 Reduction
Risk Score Threshold
Note Data are from PARR2 Algorithm "Real
time" method
31PARR2 ALGORITHMBREAK EVEN FOR TYPICAL PCT
COSTS/SAVINGS(REAL TIME METHOD 15 REDUCTION
IN RE-ADMISSIONS)
Net Costs/Savings (000s)
500 Per Patient
750 Per Patient
1,000 Per Patient
Risk Score Threshold
Note Data are from PARR2 Algorithm "Real
time" method
32PARR ALGORITHMSEXPECTED REDUCTION IN ANNUAL
EMERGENCY DAYS FORTYPICAL PCT (REAL TIME
METHOD 15 REDUCTION IN ADMS)
PARR2
PARR1
Reduction in Total Emergency Days for PCT
Risk Score Threshold
33PARR2 ALGORITHMEXPECTED REDUCTION IN ANNUAL
EMERGENCY DAYS FORTYPICAL PCT (REAL TIME
METHOD)
10 Reduction
15 Reduction
20 Reduction
Reduction in Total Emergency Days for PCT
Risk Score Threshold
Note Data are from PARR2 Algorithm "Real
time" method
34PARR ALGORITHMSPARR1 or PARR2?
PARR1 ALGORITHM
PARR2 ALGORITHM
- Advantages
- Breaks even at a lower risk score
- Patients identified may be more amenable to
intervention(?) - Disadvantages
- Finds fewer patients
- For real time method, requires admission DX
- Advantages
- Finds more patients
- Does not require admission DX for real time
method - Disadvantages
- Breaks even at a higher risk score
- Patients identified may be less amenable to
intervention(?)
35PARR ALGORITHMSPARR1 or PARR2?
PARR1 ALGORITHM
PARR2 ALGORITHM
- Advantages
- Breaks even at a lower risk score
- Patients identified may be more amenable to
intervention(?) - Disadvantages
- Finds fewer patients
- For real time method, requires admission DX
- Advantages
- Finds more patients
- Does not require admission DX for real time
method - Disadvantages
- Breaks even at a higher risk score
- Patients identified may be less amenable to
intervention(?)
36PARR ALGORITHMSREAL TIME or ARCHIVAL?
REAL TIME METHOD
ARCHIVAL METHODS
- Advantages
- Finds more patients
- Permits discharge planning as component of
intervention - Disadvantages
- Must be updated daily
- For PARR1 algorithm, requires admission DX
- Advantages
- Easier (no daily updates)
- Comparable business case (for monthly update
model) - Disadvantages
- Finds fewer patients
- Does not permit discharge planning as component
of intervention
37PARR ALGORITHMSREAL TIME or ARCHIVAL?
REAL TIME METHOD
ARCHIVAL METHODS
- Advantages
- Finds more patients
- Permits discharge planning as component of
intervention - Disadvantages
- Must be updated daily
- For PARR1 algorithm, requires admission DX
- Advantages
- Easier (no daily updates)
- Comparable business case (for monthly update
model) - Disadvantages
- Finds fewer patients
- Does not permit discharge planning as component
of intervention
38PARR ALGORITHMWHAT IS REQUIRED FOR PCTs, SHAs,
orGP PRACTICES TO RUN THE ALGORITHM
- Three years of archived ClearNet Admitted Patient
Care (APC) data with patient identifiers for area
residents - For Real Time method - Daily updated list of
admitted patients with identifiers - Access software and IT staff with moderate level
of Access knowledge
39WHAT THE PARR ALGORITHM CANT DOAND POSSIBLE
NEXT STEPS
40AMONG THE THINGSTHE PARR ALGORITHM/HES
DATACANT DO
- By itself reduce PCT emergency patient days by 5
- Identify emerging risks (patients who have not
been admitted previously) lt Perhaps Phase 3 of
the project - Provide information on the factors that
contributed to prior admissions - Provide information on social context/needs of
patients flagged by the algorithm - Provide assurance that it is really possible to
reduce future admissions - Tell you everything you need to know to design an
effective intervention - Tell you what an intervention would cost
41PARR ALGORITHMSOME RECOMMENDED NEXT STEPSFOR
PARR USERS
- Run the algorithm in real time to identify
30-40 patients with - high risk scores
- Interview these patients and their providers to
learn - Circumstances that led to admission
- Factors that would help reduce future admissions
- Design/tweak an intervention based on this
information (letting a few flowers bloom) - Implement intervention(s) in a manner to learn as
much as possible - Randomise patients/practices/hospitals into
intervention and non-intervention and track
outcomes - or
- Track subsequent utilisation and compare to
historical controls/expected use rates