Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals

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


• 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


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+


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


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


Patient Flow @ AEHRC Enabling hospitals to better manage their resources & hence reduce waiting times

www.csiro.au/patientflow


Predicting the Risk of Unplanned Readmission

Partners: Logan Beaudesert Health Coalition , Centre for Healthcare Improvement, Queensland Health



Current Process


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)


Current State of the Art HARP


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


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.”



PARR (UK)

C-statistic – 0.68



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).



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.



Identifying High-Impact Users (UK) C-statistic – 0.70~0.75



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.”



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.



LACE (Canada)

C-statistic – 0.684



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.”



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.



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



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.”



Proposed Process


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


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


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


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


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


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


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


• 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


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


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


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


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


Model 1

Incomplete Data – tracing the 19719 UIDs through the Client Directory


Model 2

HBCIS EDIS

ASIM

OSIM

APP

CHIMS IPharmacy

eLMS

Logan Beaudesert

Princess Alexandra

QEII

Logan

Our Data Sources


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


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


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


Understanding Discharge Timing

Partners: Queensland Health, South Australia Health



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:


Does discharge peak timing affect ED LOS and Access Block ?

Does discharge peak timing affect ED LOS and Access Block ?

0

50

100

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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

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9.5

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0.5

1.0

1.5

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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


Can we quantify the impact of Early Discharge ? What happens if overcrowding delays Discharge ?

0

50

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300

350

400

450

55

60

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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%.


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 ?


What does this mean for My Hospital ?


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

Health & Medicine

Predicting Potentially Preventable Readmissions and Improving Discharge Planning In Australian Hospitals