results of the csiro multi-site national trial of telehealth for the
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Results of the CSIRO multi‐site national trial of telehealth for the management of chronic disease in the homeBranko Celler, Leila Alem, Surya Nepal, Marlien Varnfield, Ross Sparks, Jane Li, Simon McBride and Rajiv Jayasena
DIGITAL PRODUCTIVITY FLAGSHIP
Funded by the Australian Government under the National Telehealth Pilots Program
Contact: [email protected]
Summary CSIRO Is lead organisation Six clinical partners and three industry partners Total project size $5.4m ($3.02m from DOHA/DBCDE Pilot Program)
Six (6) Trial sites in Five (5) states and territories Focus on Chronic Disease Management (CDM) in the Community
Six different models of care represented Trial duration 18 months – ends 30th Dec 2014
NBN Telehealth Pilot Program CSIRO Telehealth Project
Additional presentations on the CSIRO NBN Telehealth trial
• Dr. Rajiv Jayasena (today) Organisational change management and models for sustainability
• Dr. Surya Nepal (Wednesday) Data architecture, data models, security and confidentiality
• Dr. Marlien Varnfield (Wednesday) Human factors. Acceptability and useability of telehealth by patients and
clinicians
CSIRO NBN Telehealth Trial – 6 Sites• Townsville• Penrith• Nepean Blue Mountains / ARV• Canberra and ACT • Ballarat and the Grampians• Launceston / Northern TasmaniaNumber of patients at each site• 25 Test Patients• 50 Control PatientsTotal• 150 Test patients• 300 Control PatientsTrial Design• Case Matched controls• Before-After-Control-Impact (BACI)
Key objectives of the CSIRO trial
• Identify and model the impact of introducing telehealth services into existing models for the management of chronic disease in the community.‐ Health and wellbeing outcomes‐ Socio economic outcomes‐ Acceptability and usability of telehealth services ‐ Impact on patients, carers and clinicians‐ Effect of workplace culture and capacity for organizational change
management
• Develop robust statistical models to automatically risk stratify patients using questionnaires and vital signs data
Ethics Approvals Received
ETHICS COMMITTEE APPROVAL #, DATE.
Commonwealth Science & Industrial Research Organisation
13/04, 25 March 2013.
Department of Health & Ageing 25/2013, 7 August 2013.
Department of Veterans Affairs Accepted DOHA Ethics Approval
Nepean Blue Mountains LHD LNR/13/NEPEAN/79, 1 July 2013.
Townsville MacKay LHD HREC/13/QTHS/56, 7 June 2013.
Ballarat LHD HREC/13/BHSSJOG/29, 27 May 2013.
Canberra Hospital and ACT Health ETHLR.13.122, 29 May 2013.
Tasmania North Health Service (Launceston Hospital)
Accepted CSIRO Ethics approval HREC 13/04
Telemedcare Clinical Monitoring Unit
Telehealth Services Provided
• Vital Signs (provided as appropriate to patient’s clinical condition)‐ Non Invasive BP (Auscultatory and Oscillometric)‐ Pulse Oximetry‐ Single lead ECG‐ Blood Glucometer‐ Spirometry (FEV1, VC, PEF)‐ Body Temperature‐ Body Weight
• Communications• Messaging• Video Conferencing
• Questionnaires• Large range of Clinical and Wellness questionnaires to choose from
Patient Selection
ICD‐10 Diagnostic Codes for subject selection
At least two unplanned admissions to hospital in the preceding year for one or more of the following chronic conditions;Chronic Obstructive Pulmonary Disease
• J41 – J44• J41 – J44 J47 and J20
(only with secondary diagnosis of J41, J42, J43, J44, J47)Coronary Artery Disease
• I20 – I25• I20 – I25
Hypertensive Diseases• I10, I11.9• I10 – I15 (I11.0: Hypertensive heart failure will be included in Congestive Heart
Failure)Congestive Heart Failure
• I11.0, I50, J81• I50, J81, I11.0
Diabetes• E10 – E14
Asthma• J45
Matching control subjects to test subjects• Perfect matching not possible given the limited number of cases
• The matching variables and associated importance weight, e.g.,
Test/Control
Age Gender Major Diagnosis
SIEFASocio‐Economic Indexes for Areas
Strength of the match (score of 0 equal perfect match)
Test 54 M COPD 1023
Control 1 56 M COPD 1015 1.68 #
Control 2 54 F HD 1022 2.16 $
Importanceweights
0.2 1 1 0.16
#
$
• PBS Data from DHS• MBS Data from DHS• Telemedcare Vital signs data and adherence logs• Health RoundTable Hospital Data• Recorded events in Trial portal• HIE and Business Analytics data
– Questionnaires and structured interviews
Data Resources
Integration of multiple data sources
CSIRO Digital Productivity & Services Flagship | Aged Care into the Digital Era
Evaluation Framework
Internet Usage
0
20
40
60
80
100
120
0
50
100
150
200
250
Total num
ber o
f con
nected
patie
nts
Data usage
Total monthly data usage (Gb)
Connected patients
Data usage
‐ 0.5 1.0 1.5 2.0 2.5
Average trafic (monthly) (GB)
0.00
0.02
0.04
0.06
0.08
Average trafic (daily) (GB)
ADSL, 19
NBN Fibre, 44
NBN ‐Wireless,
3
VDSL, 1Overall
Clinician login to patient portal
0.02.04.06.08.010.012.014.016.018.020.0
Minutes sp
ent p
er login
Axis Title
Time spent per login (minutes)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Jun‐13
Jul‐1
3Au
g‐13
Sep‐13
Oct‐13
Nov
‐13
Dec‐13
Jan‐14
Feb‐14
Mar‐14
Apr‐14
May‐14
Jun‐14
Jul‐1
4Au
g‐14
Sep‐14
Oct‐14
Nov
‐14
Dec‐14Minutes sp
ent o
nline
Axis Title
Time spent by clinician per day (minutes)
RESULTS
Final Numbers
Total enrolledN=287
ACT NSW QLD TAS VIC TOTAL
Test 16 16 26 29 26 113
Control 23 13 29 60 49 174
Demographics TEST CONTROL
Age (mean ± SD) 71 ±9.2 72±9.5
% Male 65 56
BMI (mean± SD) 30.6±8 28.0±7
Data Analysed
Test Control
101 139
Great Variability in PBS and MBS data!As an example: Number of entries in PBS Data
PARAMETER TOTAL 2010 2011 2012 2013 2014
MIN 1 0 0 0 0 0
MAX 1671 456 297 343 401 325
MEAN 412 85 72 81 88 87
SD 257 73 51 52 56 55
MEDIAN 375 72 68 76 82 80
Q75 535 112 99 109 115 112
Q25 241 32 37 43 55 53
IQR=Q75‐Q25 294 80 63 66 61 59
MEDIAN ‐1.5*IQR 81 ‐8 6 10 22 21
Because of this unexpected and inexplicable variability and missing data,data from 12 Test patients and 35 potential Control patients were rejected.
Patient Characteristics…. by disease condition and socio‐economic index for areas
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Cardiac Diabetes Respiratory Other
Percen
tage of the
respective grou
p
Broad Disease Categories
Test
Control
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
ACT NSW QLD VIC TAS
SEIFA Inde
x
SEIFA Indexes
Patient Compliance with questionnaires
Number of Scheduled
Tasks
Number of Tasks
Completed
Compliance Percentage
Anxiety and Depression 1069 536 50.1%
Quality of Life 4379 2254 51.5%
Medication Adherence 328 98 29.9%
Living With and Managing Medical Conditions 1092 630 57.7%
COPD Questionnaire 12269 4337 35.3%
User Acceptance and Satisfaction 94 32 34.0%
Dietry Habits and Active Australia 93 34 36.6%
Total 19324 7921 41.0%
Patient Compliance with Daily Measurement Schedule
Number of Scheduled Tasks
Number of Tasks Completed
Compliance Percentage
BloodGlucose 12,464 8,739 70.1%
BloodOximetry 30,834 20,216 65.6%
BloodPressure 30,679 20,551 67.0%
BodyTemperature 27,297 17,143 62.8%
ECG 30,327 19,817 65.3%
Forced Spirometry 20,692 10,876 52.6%
Weight 25,122 14,124 56.2%
Total 177,415 111,466 62.8%
VariableControlBefore
TestBefore
P Value
Number of visits to GPs4.2
(3.4 ‐ 5.1)5.7
(4.4 ‐ 7.1)0.04*
Cost of visits to GPs ($)183.7
(146 ‐ 223)245
(189 ‐ 306)0.35
Number of Allied Health visits 0.5
(0.3 ‐ 0.8)0.6
(0.3 ‐ 0.9)0.42
Cost of Allied Health Services ($)25.1
(14.6 ‐ 41.7)30.2
(17 ‐ 51.8)0.4
Number of visits to Specialists1.3
(0.9 ‐ 1.9)1.6
(1.1 ‐ 2.2)0.15
Cost of visits to Specialists ($)130.6
(85.6 ‐ 192)159.1
(105 ‐ 232)0.22
Number of medications prescribed
25.5(22 ‐ 28.4)
28.1(23.8 ‐ 31.9)
0.21
Cost of medications dispensed ($)
1076.7(867 ‐ 1288)
959.0(814 ‐ 1088)
0.3
Total Cost of Procedures/Tests ($)
525.1(320 ‐ 830)
625.1(385 ‐ 976)
0.35
Cost of Laboratory Tests ($)134.8
(91 ‐ 192)133
(89.3 ‐ 191)0.43
Cost of all MBS and PBS items ($)
2019.7(1633 ‐ 2406)
2029.9(1697 ‐ 2338)
0.17
Patient travel cost in visiting GPs ($)
39.2(27.3 ‐ 54.3)
44.4(30.0 ‐ 63.3)
0.48
Comparing Test Patients and Control Patientsover 100 days prior to monitoring commencing
No significant differences between all parameter except the Number of Visits to GPs
The non statistically significant differences observed suggest that Test patients may generally be a little more ill than Control Patients!
MORTALITY
• Calculated as % probability of death during the period of intervention For TEST patients
2/(3+77) = 3.75% For Control patients
5/(5+79) = 5.95%
• Hence telehealth monitoring reduces mortality by 37%
LOCATION Control patients
Test Patients
Neither
TasmaniaAlive at the end of the study 38 24 100Died during the trial 4 1 50NSWAlive at the end of the study 6 4 160Died during the trial 0 0 71QLDAlive at the end of the study 19 27 124Died during the trial 1 2 55ACTAlive at the end of the study 16 22 404Died during the trial 0 0 16TOTAL (excluding VIC)Alive at the end of the study 79 77 788Died during the trial 5 3 192
Note a significant greater reduction in Hospital length of stay for test patients than for the control patients. This was significant at the 5% level of significance using a generalised linear mixed effect model assuming that Hospital length of stay in days is a Poisson distribution. Test patients exhibited a 43.2% fall in the number of hospitalisations, whilst Control patients a 30.6% reduction
Rate of hospitalisation
NOTE: These plots were updated from those presented at the HIC2015 conference, which were plotted on the LOG scale
TEST CONTROLNumber of hospitalisation events
Before After Before After
6.0(4.6‐13)
3.4(2.6‐4.2)
5.7(4‐8.4)
4.0(2.6‐5.3)
31 Test Patients of 101 had a period of hospitalisationPeriod of intervention varied from 30‐480 days
Note a significant reduction in Hospital length of stay for test patients but not for control patients. This was significant at the 1% level of significance using a generalised linear mixed effect model assuming that Hospital length of stay in days is a Poisson distribution.
Test Patients had a 32.1% greater reduction in Length of Stay (LOS) relative to Control patients
Length of Stay
NOTE: These plots were updated from those presented at the HIC 2015 conference, which were plotted on the LOG scale
TEST CONTROLLength of stay (days) in hospital
Before After Before After
37.14(18‐58)
18.7(8.4‐29)
22.4(10‐42)
18.9(8‐42)
31 Test Patients of 101 had a period of hospitalisationPeriod of intervention varied from 30‐480 days
Time Series Analysis of PBS Dispensing Costs – for TEST patients
30 day intervals, synchronised to start of intervention
Log(
PB
S d
ispe
nsin
g co
sts
+1)
Time Series Analysis of PBS Dispensing Costs – for CONTROL Patients
Log(
PB
S d
ispe
nsin
g co
sts
+1)
30 day intervals, synchronised to start of intervention
Time Series Analysis of Total MBS Item Costs – for TEST patients
30 day intervals, synchronised to start of intervention
Log(
MB
S d
ispe
nsin
g co
sts
+1)
Time Series Analysis of MBS Item Costs – for CONTROL patients
30 day intervals, synchronised to start of intervention
Log(
MB
S d
ispe
nsin
g co
sts
+1)
Model Projection of MBS Item Costs for TEST patients
Model Projection of One Year Cost Savings from telehealth intervention in MBS Item Costs
Model Projection of PBS Dispensing Costs for TEST patients
Model Projection of One Year Cost Savings from telehealth intervention in PBS Dispensing Costs
• The results of the CSIRO NBN trial will make a significant contribution to the development of government policy and funding models.
• The CSIRO NBN Telehealth project has broken new ground in clinical trial methodology and analysis of time series health data
• The ongoing costs of managing chronically ill patients have been identified and the progression over time of these costs have been modelled.
• We have demonstrated that at home monitoring of vital signs can lead to significant savings over time of PBS dispensing costs and costs associated with MBS items
• Preliminary data suggests that telehealth monitoring will lead to a significant reduction in unscheduled hospitalisation and Length of Stay.
• At home telehealth monitoring is well accepted by clinicians and patients alike who can readily appreciate the benefits
• Return on investment on investing in telehealth for the management of chronic disease is likely to be between 2 and 3.
Preliminary Conclusions..it’s early days yet, but
Health economics of Aged CareThe Numbers ‐ Aged Care Cost Per Year:
Home health monitoring $1,600 /year ($2,550 in Aust)In Home Nursing Visitation $13,121 /yearNursing Home $77,745 /year
Source – US Veterans Health Administration (VHA)
The Numbers: Health Care Cost Per Day: Telecare $3.46 /dayTelehealth $7.14 /dayAcute Hospital Bed $967.00 /day
Source ‐ Feros Care (Aust) – Telehealth Care Pilot Program
Estimated Potential Return on Investment (…hypothetical, but plausible! Based on CSIRO Project experience)
• Minimum estimated Costs / month for telehealth management of chronically ill patient Capital costs averaging $850 amortised over 4 years at 6% pa $20 /month Internet costs (3/4G data costs, 10MB monthly plan) $5 /month Monitoring, hosting and maintenance @ $5/day $150 /month Nurse coordination (0.5 hours/week/patient + overheads) $50 / month
• ANNUAL COST ESTIMATE $2,700 pa ($7.40/day)
• ANNUAL SAVINGS ESTIMATES Savings in PBS dispensing $200 pa Savings in utilisation of clinical services $800 pa Reduced rate of Hospitalisation (1/annum) and reduced LOS >$6000 pa Reduced demand on community nurses
(Increased case load per nurse, equivalent to saving of 5% EFT) $4000 pa
TOTAL SAVINGS $11,000 paESTIMATED ROI = 3.07
ANY QUESTIONS?
Prof. Branko CellerCSIRO eHealth Research ProgramPhone: 0418 228 297E-mail: [email protected]