Results of the CSIRO multi site national trial of telehealth for the - - PowerPoint PPT Presentation

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Results of the CSIRO multi site national trial of telehealth for the - - PowerPoint PPT Presentation

Funded by the Australian Government under the National Telehealth Pilots Program Results of the CSIRO multi site national trial of telehealth for the management of chronic disease in the home Branko Celler, Leila Alem, Surya Nepal, Marlien


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Results of the CSIRO multi‐site national trial of telehealth for the management of chronic disease in the home

Branko 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: branko.celler@csiro.au

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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

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SLIDE 3

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

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SLIDE 4

CSIRO NBN Telehealth Trial – 6 Sites

  • Townsville
  • Penrith
  • Nepean Blue Mountains / ARV
  • Canberra and ACT
  • Ballarat and the Grampians
  • Launceston / Northern Tasmania

Number of patients at each site

  • 25 Test Patients
  • 50 Control Patients

Total

  • 150 Test patients
  • 300 Control Patients

Trial Design

  • Case Matched controls
  • Before-After-Control-Impact (BACI)
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SLIDE 5

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

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SLIDE 6

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

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SLIDE 7

Telemedcare Clinical Monitoring Unit

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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
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SLIDE 9

Patient Selection

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SLIDE 10

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
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SLIDE 11

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 SIEFA Socio‐ 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 $ Importance weights 0.2 1 1 0.16

# $

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SLIDE 12
  • 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

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Integration of multiple data sources

CSIRO Digital Productivity & Services Flagship | Aged Care into the Digital Era

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SLIDE 14

Evaluation Framework

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SLIDE 15

Internet Usage

20 40 60 80 100 120 50 100 150 200 250

Total number of connected patients 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, 1

Overall

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SLIDE 16

Clinician login to patient portal

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

Minutes spent per 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‐13 Aug‐13 Sep‐13 Oct‐13 Nov‐13 Dec‐13 Jan‐14 Feb‐14 Mar‐14 Apr‐14 May‐14 Jun‐14 Jul‐14 Aug‐14 Sep‐14 Oct‐14 Nov‐14 Dec‐14

Minutes spent online

Axis Title

Time spent by clinician per day (minutes)

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SLIDE 17

RESULTS

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Final Numbers

Total enrolled N=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

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SLIDE 19

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 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.

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SLIDE 20

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

Percentage of the respective group 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 Index

SEIFA Indexes

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SLIDE 21

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%

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SLIDE 22

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%

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SLIDE 23

Variable Control Before Test Before P Value Number of visits to GPs

4.2 (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 Specialists

1.3 (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 Patients

  • ver 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!

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SLIDE 24

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 Tasmania Alive at the end of the study 38 24 100 Died during the trial 4 1 50 NSW Alive at the end of the study 6 4 160 Died during the trial 71 QLD Alive at the end of the study 19 27 124 Died during the trial 1 2 55 ACT Alive at the end of the study 16 22 404 Died during the trial 16 TOTAL (excluding VIC) Alive at the end of the study 79 77 788 Died during the trial 5 3 192

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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 CONTROL

Number 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 hospitalisation Period of intervention varied from 30‐480 days

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SLIDE 26

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 CONTROL

Length 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 hospitalisation Period of intervention varied from 30‐480 days

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SLIDE 27

Time Series Analysis of PBS Dispensing Costs – for TEST patients

30 day intervals, synchronised to start of intervention Log(PBS dispensing costs +1)

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SLIDE 28

Time Series Analysis of PBS Dispensing Costs – for CONTROL Patients

Log(PBS dispensing costs +1) 30 day intervals, synchronised to start of intervention

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Time Series Analysis of Total MBS Item Costs – for TEST patients

30 day intervals, synchronised to start of intervention Log(MBS dispensing costs +1)

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Time Series Analysis of MBS Item Costs – for CONTROL patients

30 day intervals, synchronised to start of intervention Log(MBS dispensing costs +1)

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Model Projection of MBS Item Costs for TEST patients

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Model Projection of One Year Cost Savings from telehealth intervention in MBS Item Costs

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SLIDE 33

Model Projection of PBS Dispensing Costs for TEST patients

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Model Projection of One Year Cost Savings from telehealth intervention in PBS Dispensing Costs

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SLIDE 35
  • 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

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SLIDE 36

Health economics of Aged Care

The Numbers ‐ Aged Care Cost Per Year: Home health monitoring $1,600 /year ($2,550 in Aust) In Home Nursing Visitation $13,121 /year Nursing Home $77,745 /year Source – US Veterans Health Administration (VHA) The Numbers: Health Care Cost Per Day: Telecare $3.46 /day Telehealth $7.14 /day Acute Hospital Bed $967.00 /day Source ‐ Feros Care (Aust) – Telehealth Care Pilot Program

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SLIDE 37

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 pa

ESTIMATED ROI = 3.07

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SLIDE 38

ANY QUESTIONS?

  • Prof. Branko Celler

CSIRO eHealth Research Program Phone: 0418 228 297 E-mail: branko.celler@csiro.au