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Sankalp Khanna, The CSIRO Australian e-Health Research Centre, Brisbane, Australia delivered this presentation as part of the 4th Annual Reducing Hospital Readmissions & Discharge Planning Conference – A conference to identify, predict and prevent unplanned readmissions and improve discharge processes. IIR Healthcare's inaugural Canadian Reducing Hospital Readmissions & Discharge Planning Conference will take place in Vancouver in late October 2013. Find out more at http://www.healthcareconferences.ca/readmissions/agenda
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Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals (Selected Slides for Distribution) Sankalp KHANNA a The CSIRO Australian e-Health Research Centre, Brisbane, Australia
THE AUSTRALIAN E-HEALTH RESEARCH CENTRE
25th July 2013
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Presentation Overview
• Motivation - What the fuss is all about ?
• Introduction to CSIRO Patient Flow
• Predicting the Risk of Unplanned Readmission
• Understanding Discharge Timing
2 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Motivation Overcrowding in Hospitals: an International Crisis
Increased wait times. Increased walkouts. Increased medical errors. Ambulance diversion. Increased length of stay. Patient safety at risk. Increased medical negligence claims. Unnecessary deaths.
ED admissions
Elective surgery
3 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
• Health overtook retail in 2011 as largest employer
• Australian health expenditure rising: – $116B/a = 9% GDP (2009-10) – $130B/a (2011-12) – McKeon forecast $450B/a (2050) – Funded from tax revenues
• Public hospitals (acute care) largest cost $36B/a – Fed Govt oversight and fund $14B/a – State Govts manage and fund $19B/a
• GPs funded by Commonwealth
Source: Health Expenditure, AIHW, 2009-10
Motivation Australian Health: the growth industry we can’t afford
Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna 4 |
http://www.health.gov.au/internet/yourhealth/publishing.nsf/Content/report-redbook/$File/HRT_report3.pdf
Motivation Health Services Mission at a Glance
5 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
62% of our people hold university degrees 2000 doctorates 500 masters
CSIRO undertakes $~500M of externally funded R&D each year Work with partners in over 80 countries
Top 1% of global research institutions in 14 of 22 research fields Top 0.1% in 4 research fields Highest number of citations per scientist in Australia
People 6550
Locations 57
Budget $1B+
CSIRO Snapshot
Infra $3.5bn
Patents 3000+
Partners 1300+
6 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
CSIRO Health Services Research
Sustainable Health • What: Improving health
productivity through operational management
• How: Using operational & clinical data to improve processes & performance
Broadband Health • What: Collecting and
using data to connect clinicians and patients
• How: Telehealth, mobile health, personalised health, remote monitoring
eHealth Architecture • What: Accelerating takeup
and adding value to electronic health records
• How: Tools to transition to SNOMED CT, mining records for diagnosis & reporting
Clinically partnered, data focussed
7 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
AEHRC is the leading national eHealth research group in Australia currently 60-70 staff, students, visiting researchers
Funding from
• CSIRO • Qld Govt - DEEDI, Queensland Health • engagement partners • revenue
Investment into research programs National reach - Brisbane HQ, smaller teams nationally - NSW, Victoria, SA, and now
WA, and through CSIRO in Tasmania and ACT Success built on partnering - Government, clinicians, industry
• Local engagement to drive national benefit
The Australian e-Health Research Centre
9 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Patient Flow @ AEHRC Enabling hospitals to better manage their resources & hence reduce waiting times
www.csiro.au/patientflow
9 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Predicting the Risk of Unplanned Readmission
Partners: Logan Beaudesert Health Coalition , Centre for Healthcare Improvement, Queensland Health
Patient Flow @ AEHRC Enabling hospitals to better manage their resources & hence reduce waiting times
10 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Current Process
11 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Current State of the Art HARP [2] Key characteristics that were Identified as having the potential to influence HARP and non-HARP
patients
[2] Improving care - HARP Public Report – HARP: Hospital Admission Risk Program – Department of Health, Victoria, Australia. (http://www.health.vic.gov.au/harp/pubrep.htm)
12 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Current State of the Art HARP
13 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
PARR (UK) [3] Predicting : Readmissions Readmission Prediction range : 12 months. Data Used : • Admissions in England (NHS trusts) - 5
years (99-00~03-04) • 2001 Census data Initial variables set – 69 Technique : Stepwise Logistic Regression.
[3] J. Billings et al., Case Finding Algorithms for Patients at Risk of Re-Hospitalisation, PARR1 and PARR2, 22 February 2006, http://www.kingsfund.org.uk/document.rm?id=6209
Current State of the Art
14 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
PARR (UK) “For correctly flagged patients …. the average was 2.3 to 3.3 emergency
admissions in the next 12 months.” “This use of a broad range of variables is critical in improving the power
of the case finding algorithm.” “a not insignificant share are under age 65 (10-17%)” “Racial/Ethnic mix should be explored further” “Using only prior hospital data … it is not possible to predict future
admissions of patients with no prior admissions. Accordingly, the PARR algorithms … are less useful in identifying patients with emerging risks of high cost and high utilisation.”
Current State of the Art
15 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
PARR (UK)
C-statistic – 0.68
Current State of the Art
16 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Improving PARR with the Combined Prediction Tool (CPT)[4]
CPT = PARR + Outpatient Data + Accident & Emergency Data + GP Data “With the additional predictive accuracy achieved by introducing the OP, A&E, and GP data sets, the ‘break even’ analysis of the potential cost savings that can be achieved is enhanced when compared with PARR, particularly when identifying very high risk patients” “The PARR and Combined Models identify different patients,
even at the highest risk levels.” [4] Wennberg D, Siegel M, Darin B, Filipova N, Russell R, Kenney L, et al. Combined predictive model: final report and technical documentation. London: Health Dialog/King’s Fund/New York University; 2006. (http://www.kingsfund.org.uk/research/projects/predicting_and_reducing_readmission_to_hospital/#resources).
Current State of the Art
17 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Identifying High-Impact Users (UK) [5] Predicting : High Impact Users for Emergency Readmissions Readmission Prediction range : 12 months. Data Used : • Hospital Episode Statistics (HES) data - 5 years (Apr 99~Mar 04) • Linked Mortality File 2000/01-2003/04 Technique : Logistic Regression. • “No access to primary care records or out of hospital care”
[5] Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406-414.
Current State of the Art
18 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Identifying High-Impact Users (UK) C-statistic – 0.70~0.75
Current State of the Art
19 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Identifying High-Impact Users (UK) “High-impact users’ were defined as patients who had at least one
emergency inpatient admission and who then went on to have at least two further emergency hospital admissions in the 12 months following the start date of that index admission.”
“Nearly half of all patients who had three or more emergency admissions in
the previous year went on to become high-impact users and more than a third (36%) had died within 3 years of the index admission.”
“Any potential savings need to be compared with the costs of case
management, which we have not considered.”
Current State of the Art
20 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
LACE (Canada) [6]
Predicting : Readmissions or Death
Readmission Prediction range : 30 days.
Data Used : • Discharge records for Ontario – April 2004 – January 2008 • Linked records from National Ambulatory Care Reporting • Registered Patients Database • Discharge Abstract Database
Initial variables set – 48 patient level variables
Technique : Multivariable Logistic Regression.
[6] van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557.
Current State of the Art
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LACE (Canada)
C-statistic – 0.684
Current State of the Art
22 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
LACE (Canada) • “We chose a 30-day time frame for our primary outcome to increase the
likelihood that poor outcomes would be related to the index admission or discharge process and would be more likely to be remediable.”
• “All medications given at discharge were compared with those documented on the admission note to determine which discharge medications had been started in hospital.”
• “the index cannot be used reliably in patient populations that were not involved in its derivation.”
• “further work is required to identify additional factors that may increase the discrimination or accuracy of the index.”
Current State of the Art
23 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Identifying High Risk Medicaid Patients (USA) [7] Predicting : Readmissions Readmission Prediction range : 12 months. Data Used : • 5.5 years of Medicaid fee-for-service data (2000-2004), census data for
sociodemographic information. • Admissions, ED and outpatient clinic visits, and diagnoses for each
patient
Technique : Logistic Regression [7] Raven MC, Billings JC, Goldfrank LR, Manheimer ED, Gourevitch MN. Medicaid Patients at High Risk for Frequent Hospital Admission: Real-Time Identification and Remediable Risks. J Urban Health. 2009;86(2):230-241.
Current State of the Art
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Identifying High Risk Medicaid Patients (USA) Medical record ID for linkage Any alcohol clinic visits 1-365 days Any obstetrics clinic visits 731-1095 daysPatient Name - inpatient Any mental health clinic visits 1-365 days Any occupational therapy clinic visits 731-1095 daysZip code - inpatient Any methadone clinic visits 1-365 days Any visits for proc/test 731-1095 daysDOB - inpatient Any obstetrics clinic visits 1-365 days Any primary care clinic visits 731-1095 daysRace - inpatient Any occupational therapy clinic visits 1-365 days Any physical therapy clinic visits 731-1095 daysSex -inpatient Any visits for procedure/test 1-365 days Any rehabilitation clinic visits 731-1095 daysAny Admission last 3 years Any primary care clinic visits 1-365 days Any specialty clinic visits 731-1095 daysPatient Name-outpatient Any physical therapy clinic visits 1-365 days Any ambulatory surgery visits 731-1095 daysZIP code-outpatient Any rehabilitation clinic visits 1-365 days Any other clinic visits 731-1095 daysDOB-outpatient Any specialty clinic visits 1-365 days Number Emergency Admissions prior 365 daysRace-outpatient Any ambulatory surgery visits 1-365 days Any Transfer Admissions prior 366-730 daysSex-outpatient Any other clinic visits 1-365 days Any Against Medical Advice disposition 366-730 daysPrior diagnosis of diabetes Any dialysis clinic visits 1-365 days Any transfer disposition prior 366-730 daysPrior diagnosis of asthma Number primary care visits 1-365 days Any admission with alcohol service prior 366-730 daysPrior diagnosis of Coronary Heart Disease Number specialty care visits 1-365 days Any admission with psych service prior 366-730 daysPrior diagnosis of hypertension Number emergency department visits 1-365 days Any admission with Obstetrics service prior 366-730 daysPrior diagnosis of Congestive Heart Failure Any emergency department visits 1-365 days Any admission with Mental Health Diagnosis prior 366-730 daysPrior diagnosis of other lung disease Any outpatient visit prior 1-365 days Any admission with Alcohol/Substance use diagnosis prior 366-730 daysPrior diagnosis of stroke Any alcohol clinic visits 366-730 days Number Emergency Admissions prior 366-730 daysPrior diagnosis of chronic liver disease Any Mental Health clinic visits 366-730 days Any Transfer Admission prior 366-730 daysPrior diagnosis of chronic renal disease Any methadone clinic visits 366-730 days Any Against Medical Advice disposition 366-730 daysPrior diagnosis of cancer Any obstetric clinic visits 366-730 days Any transfer disposition prior 366-730 daysPrior diagnosis of sickle cell disease Preventable/Avoidable Admissions prior 1-90 days Any admission with alcohol service prior 366-730 daysPrior diagnosis of blindness/deafness Number of Admissions prior 1-90 days Any admission with psych service prior 366-730 daysPrior diagnosis of retardation Preventable/Avoidable Admissions prior 91-180 days Any admission with obstetric service prior 366-730 daysPrior diagnosis of schizophrenia Number of Admissions prior 91-180 days Any admission with mental health diagnosis prior 366-730 daysPrior diagnosis of psychosis Preventable/Avoidable Admission prior 181-365 days Any admission with Alcohol/Substance Diagnosis prior 366-730 daysPrior diagnosis of any mental illness Number of Admissions prior 181-365 days Number Emergency Admissions prior 731-1095 daysPrior diagnosis of alcohol/substance abuse Preventable/Avoidable Admissions prior 366-730 days Any Transfer Admissions prior 731-1095 daysPrior diagnosis of HIV/AIDS Number of Admissions prior 366-730 days Any Against Medical Advice disposition 366-730 daysDOB from inpatient data base Preventable/Avoidable Admissions prior 731-1095 days Any transfer disposition prior 731-1095 daysFinal name Number of Admissions 731-1095 days Any admission with alcohol service prior 731-1095 daysFinal zip code Any occupational therapy clinic visits 366-730 days Any admission with psychiatry service prior 731-1095 daysFinal race Any visits for procedures/tests 366-730 days Any admission with obstetrics service prior 731-1095 daysFinal sex Any primary care clinic visits 366-730 days Any admission with mental health diagnoses prior 731-1095 daysFinal DOB Any physical therapy clinic visits 366-730 days Any admission with Alcohol/Substance use diagnosis prior 731-1095 daysAge in years on 7/1/2006 Any rehabilitation clinic visits 366-730 days Any dialysis clinic visits 731-1095 daysAge 0-17 Any specialty clinic visits 366-730 days Number primary care visits 731-1095 daysAge 18-39 Any ambulatory surgery visits 366-730 days Number specialty care visits 731-1095 daysAge 40-64 Any other clinic visits 366-730 days Number Emergency Department visits 731-1095 daysAge 65+ Any dialysis clinic visits 366-730 days Any Emergency Department visits 731-1095 daysAge 18-64 Number primary care visits 366-730 days Any outpatient visit prior 731-1095 daysFemale Number specialty care visits 366-730 days Number of different specialty types consulted prior 3 yearsEthnicity = Black Number Emergency Department visits 366-730 days Number Emergency Department visits prior 1-90 daysEthnicity = Hispanic/Latino Any Emergency Department visits 366-730 days Number Emergency Department visits prior 91-180 daysEthnicity = Asian Any outpatient visit prior 731-1095 days Number Emergency Department visits prior 181-365 daysEthnicity = White Any alcohol clinic visits 731-1095 days Prior diagnosis of Chronic Obstructive Pulmonary DiseaseEthnicity = Other Any Mental Health clinic visits 731-1095 days Any methadone clinic visits 731-1095 daysAny outpatient visit prior 3 years
Current State of the Art
25 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Identifying High Risk Medicaid Patients (USA)
• “when it (case finding) is applied in different areas for different populations, different variables will likely prove important in predicting future admissions, and the costs / savings trade-offs will likely differ as well”
• “30 percent of subsequent admissions occur within ninety days of discharge, which confirms that improved discharge planning —preferably with an intervention that begins while the patient is still hospitalized — is likely to be critical to achieving future reductions in hospital admissions.”
Current State of the Art
26 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Proposed Process
27 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Model 1
HBCIS EDIS
ASIM
OSIM
APP
CHIMS eLMS
Logan Beaudesert
QEII Princess Alexandra
Logan QEII
Princess Alexandra
Logan Beaudesert
Princess Alexandra
QEII
Logan Logan Beaudesert
QEII Princess Alexandra
Our Data Sources
28 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
HBCIS
LAST DRG LAST ICD 1 LAST ICD 2
LAST Num ICD LAST Charlson Comorbidity Index
LAST Num Interventions LAST E11 Coded ? LAST I10 Coded ? LAST I25 Coded ? LAST Z86 Coded ? LAST Y92 Coded ? LAST E78 Coded ? LAST N18 Coded ? LAST J44 Coded ? LAST I50 Coded ? LAST Z72 Coded ?
Patient ID AAC Episode ID Medicare ? Admit Date DRG
Discharge Date ICD 1 Type of Record (ICD-ALL) ICD 2
Visit No (as per our record) Num ICD Length of Stay Charlson Comorbidity Index
Date of Last Discharge Num Interventions Previous Length of Stay E11 Coded ?
Return Time I10 Coded ? Age I25 Coded ? Sex Z86 Coded ?
Marital Status Y92 Coded ? Admit Unit E78 Coded ? Admit Type N18 Coded ? Australian ? J44 Coded ?
Ethnic Status I50 Coded ? Planned Same Day ? Z72 Coded ?
Insurance ?
Model 1
29 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
EDIS
ED Visits - Last 30 Days ED Visits - Last 60 Days ED Visits - Last 90 Days ED Visits - Last 180 Days ED Visits - Last 365 Days
Model 1
30 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
ASIM
OSIM
APP
Outpatient Visits - Last 30 Days Outpatient Visits - Last 60 Days Outpatient Visits - Last 90 Days Outpatient Visits - Last 120 Days Outpatient Visits - Last 180 Days Outpatient Visits - Last 365 Days
Model 1
31 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
CHIMS
CHIMS encounters - Last 30 Days CHIMS encounters - Last 60 Days CHIMS encounters - Last 90 Days CHIMS encounters - Last 120 Days CHIMS encounters - Last 180 Days CHIMS encounters - Last 365 Days
Model 1
32 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
ELMS Score 1 ELMS Score 2 ELMS Score 3 ELMS Score 4 ELMS Score 5 ELMS Score 6
Last ELMS Score 1 Last ELMS Score 2 Last ELMS Score 3 Last ELMS Score 4 Last ELMS Score 5 Last ELMS Score 6
eLMS
Model 1
33 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
CD Encounters from each hospital
Add all encounters for these patients from each hospital
Join across hospitals using Client Directory
Compute CCI and other local parameters
Compute Outpatient Encounters
Compute ELMS Scores
Compute ED Visits
Compute CHIMS encounters MODEL
FILTER
Model 1
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• Data: • 30505 observations (2005-2010) • 91 variables • large number of missing values in irregular patterns.
• Initial exploratory analysis: • regression and partition trees • identify potential interactions and strong predictor variables.
• Logistic regression to select optimal model and calculate AUC.
Model 1
35 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Regression and partition tree
Transformed variables
100 training and test sets
Stepwise regression, all two-way interactions. 19 variables
.....................
...........................
Variables in >60% of models chosen for final model
Estimate model coefficients and AUC on 500 training and tests
sets
Model 1 Analysis Methodology
36 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
1-specificity
sens
itivi
ty
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
AUC=0.64
Model 1
ROC Curve – Single Run
37 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
0.62 0.64 0.66 0.68 0.70
05
1020
30
AUC
NO
bs
Area under the curves values from 500 bootstrap samples. Vertical line is the mean AUC (0.645).
Average ROC – 100 runs
Model 1
38 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Model 1
Problems with Model 1 • Incomplete Data
• Need to explore logical/clinical variable grouping
• Need to explore complex interactions
• Need better improved prediction models
39 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Model 1
Incomplete Data – tracing the 19719 UIDs through the Client Directory
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Model 2
HBCIS EDIS
ASIM
OSIM
APP
CHIMS IPharmacy
eLMS
Logan Beaudesert
Princess Alexandra
QEII
Logan
Our Data Sources
41 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Model 2
• Data: • 60627 observations (2005-2010) • 184 variables
• Initial exploratory analysis: • regression and partition trees • identify potential interactions and strong predictor variables.
• Generalised estimating equations (GEE) to build model
• Logistic regression to select optimal model and calculate AUC.
• Advanced Machine Learning Techniques (Artificial Neural Networks, Support Vector Machines and Deep Learning) for more complex model
42 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Model 2
VISIT DETAILS
PATIENT DETAILS
KEY DIAGNOSTICS
PREVIOUS VISIT DETAILS
OTHER INDICATORS
OTHER DIAGNOSTICS VALIDATE
EncounterID sex qPD1 PREV-FCLTY_ID EDIS30 qOD1~qOD68 WillReturnIn30 PatientID age drg50 PREV-LOS EDIS60 qPR1~qPR57
VisitID marit_status NUMICD PREV-elect_status EDIS90 FCLTY_ID indig_status CCI PREV-same_day EDIS180
AdmissionDatenum aust_sth_sea_isl NUMPRO PREV-adm_unit EDIS365 DischargeDatenum medcr_elig Dialysis30 PREV-adm_stnd_unit OPD30
LOS cmpns_status PREV-adm_ward OPD60 ReturnedIn hosp_insur PREV-adm_stnd_ward OPD90 elect_status ACCT_CLASS PREV-sepn_mode OPD180 same_day employment_status PREV-qPD1 OPD365 adm_unit PREV-drg50 CHIMS30
adm_stnd_unit PREV-NUMICD CHIMS60 adm_ward PREV-CCI CHIMS90
adm_stnd_ward PREV-NUMPRO CHIMS180 sepn_mode PREV-Dialysis30 CHIMS365
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Where we are at • Final model for Trial
• Data interface
• Evaluation
• Ethics/Protocols of ongoing use
• Enhancing the model • Medications Data • Support Vector Machines • Deep Learning
44 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Understanding Discharge Timing
Partners: Queensland Health, South Australia Health
Patient Flow @ AEHRC Enabling hospitals to better manage their resources & hence reduce waiting times
45 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
5 hours
Admissions
Discharges‘d1’
5 hours
Discharges‘d2’
Category 1 Category 2 Category 4 Category 5
Category 3
Hour of Day
Num
ber o
f Pat
ient
sDefine discharge peak timing:
46 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Does discharge peak timing affect ED LOS and Access Block ?
Does discharge peak timing affect ED LOS and Access Block ?
0
50
100
150
200
250
75%
80%
85%
90%
95%
100%
105%
110%
115%
1 2 3 4 5
Acce
ss B
lock
Cas
es p
er d
ay
Occ
upan
cy (%
)
Category
23 HospitalsMean Occupancy (Y1 Axis)Mean PeakOccupancy (Y1 Axis)Mean AB Cases (Y2 Axis)
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1 2 3 4 5
Leng
th o
f Sta
y (h
ours
)
Leng
th o
f Sta
y (d
ays)
Category
23 HospitalsMean LOS (days)Mean EDLOS (hours)(Y2)
All Hospitals : Cat 5 Vs Cat 1 • 13% Higher Peak Occupancy • 60 cases/day higher Access Block • 0.7 hours higher Mean ED LOS
Khanna S, Boyle J, Good N, Lind J, Impact of Admission and Discharge Peak Times on Hospital Overcrowding, Proc. 19th Australian National Health Informatics Conference (HIC 2011), 2011, 82-88
47 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
Can we quantify the impact of Early Discharge ? What happens if overcrowding delays Discharge ?
0
50
100
150
200
250
300
350
400
450
55
60
65
70
75
80
85
90
95
100
105
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Disc
harg
es/h
our
Occ
upan
cy (
%)
Time of Day (hour)
2 Hours Early
1 Hour Early
Actual
1 Hour Late
2 Hours Late
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2 Hours Early 1 Hour Early Actual 1 Hour Late 2 Hours Late
Tim
e (%
)
Discharge Timing
Occupancy > 80%
Occupancy > 85%
Occupancy > 90%
Occupancy > 95%
Occupancy > 100%
Occupancy > 105%
2 Hour Early Discharge (all 23 Hospitals) : • Average Occupancy reduced from 93.7% to 91.6%. • Maximum Occupancy reduced from 110.8% to 106.1%. • Time spent above 95% occupancy reduced from 34.7% to 21.5%.
2 Hour Late Discharge (all 23 Hospitals) : • Average Occupancy increased from 93.7% to 95.8%. • Maximum Occupancy increased from 110.8% to 115.6%. • Time spent above 95% occupancy increased from 34.7% to 45%.
48 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
S. Khanna, J. Boyle, N. Good, and J. Lind, “Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block,” Emergency Medicine Australasia, vol. 24, no. 5, pp. 510–517, 2012.
What does this mean for My Hospital ?
49 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
What does this mean for My Hospital ?
50 | Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals | Sankalp Khanna
CSIRO Australian e-Health Research Centre Sankalp Khanna Postdoctoral Research Fellow t +61 7 3253 3629 e [email protected] w www.aehrc.com
CSIRO Australian e-Health Research Centre Justin Boyle Research Scientist t +61 7 3253 3606 e [email protected] w www.aehrc.com
THE AUSTRALIAN E-HEALTH RESEARCH CENTRE
Thank you