Background
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Health care outcomes
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Health economic benefits
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Impact on clinical work force availability and deployment
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Human factors (acceptability, usability by patients, carers, nurses, GPs and administrators)
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Workplace culture
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Capacity for organisational change management and business processes
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15% reduction in A&E Visits
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20% reduction in emergency admissions
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14% reduction in elective admissions
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14% reduction in bed days
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8% reduction in tariff costs and
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45% reduction in mortality rates
Aims and objectives
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Effect of telemonitoring on health service utilisation
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Unscheduled visits to hospital, visits to GPs and Nurse visits
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Cost and frequency of consultations, laboratory tests and other clinical procedures
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Effect of telemonitoring on patients outcomes
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Quality of life, progression of chronic condition, wellbeing, medication adherence
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Service implementation and deployment
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Existing model of care, service design, adoption and appropriation
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User experience and service implementation
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Satisfaction, useability, acceptance, workload, anxiety and strain among study participants including health professionals, administrators, patients and carers
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Service implementation issues
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How the new home monitoring service is implemented at each site. What impact has this had on the process and outcomes of normal care delivery?
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How are existing service practices evolving as a result of the new service?
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What can be learnt from different implementation approaches?
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Cost effectiveness analysis
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Analysis of reductions/increases in costs borne by patients as a result of telehealth
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Analysis of reductions/increases in costs borne by the commonwealth and on-the-ground service providers for patients as a result of the deployment of telehealth services
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Demonstrate and document how telehealth services can be successfully deployed across Australia, by piloting services in five different settings across five states with a range of health service provider’s, including Local Health Districts, Medicare Locals and not for profit community organisations. This will be demonstrated by deploying and demonstrating the operation of Telehealth monitoring in a multi-site multi-state case matched control trial (Before-After-Control-Impact (BACI) design) of chronically ill patients living in their own homes in the community. This has never previously been attempted in Australia.
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Provide the clinical and health economic evidence on how Telehealth services can be scaled up nationally to provide an alternative cost effective health service for the management of chronic disease in the community.
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Provide evidence that at home telemonitoring has the potential to reduce unscheduled admissions to Accident and Emergency (A&E) compared to the control group.
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Provide evidence for an impact on hospital admissions, mortality, clinical events and symptoms and improvements in functional measures and patients' and carers’ experiences with care.
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Evaluate health economic benefits
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Evaluate impact on clinical work force availability and deployment
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Evaluate impact of human factors (acceptability, usability by patients, carers, nurses, GPs and administrators, impact on workplace culture)
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Evaluate impact of workplace culture
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Evaluate impact of organisational change management and business processes
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Develop a new evidence based data analytical technique for the risk stratification of patients’ health status daily and demonstrate that this facilitates the management of large numbers of patients by orchestrating an optimal and timely allocation of resources to avoid unnecessary hospitalisation
Methods/Design
Organisation and management
Selection of trial participants
Criteria | Type | Description |
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Age | Inclusion | 50 years old and over at consent. |
Cognitive capacity | Inclusion | Abbreviated Mental Test (AMT) [23] score >7. |
Unplanned acute admissions | Inclusion | A rate of unplanned acute admission with the required principal diagnosis code(s) indicated below: |
a) ≥2 in the last 12 months, or | ||
b) ≥4 in the previous 5 years. | ||
ICD-10-AM principal diagnosis code(s) for each unplanned acute admission | Inclusion | Code(s) for each unplanned acute admission indicate a diagnosis for one or more of the following chronic conditions: |
a) Chronic Obstructive Pulmonary Disease (J41 – J44, J47 and J20, with secondary diagnosis of J41-J44, J47), | ||
b) Coronary Artery Disease (I20 – I25), | ||
c) Hypertensive Diseases (I10 – I15, I11.9. Note: Hypertensive Heart Failure (I11.0) is included in Congestive Heart Failure), | ||
d) Congestive Heart Failure (I11.0, I50, J81), | ||
e) Diabetes (E10 - E14), | ||
f) Asthma (J45). | ||
Unsuitable conditions | Exclusion | The study team considers the presence of the following conditions to be unsuitable for participation in the study: |
a) Any form of cancer, | ||
b) Any neuromuscular disease | ||
c) Any psychiatric conditions. | ||
Care team | Inclusion | The eligible patients must be under the care of any of the following: |
a) General Practitioner | ||
b) Community Nurse | ||
Care programs | Inclusion | Participation in one of the following government care programs: |
a) Commonwealth Chronic Disease Management | ||
b) Commonwealth Coordinated Veterans’ Care Program | ||
c) NSW Connected Care Program | ||
Unsuitable care programs | Exclusion | Participation in one of the following government care programs: |
a) Commonwealth Extended Aged Care in the Home |
Test/control | Age | Gender | Major diagnosis | Seifa1index for postcode | Strength of match (perfect match = 0) |
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Test | 54 | M | COPD | 1023 | |
Control | 56 | M | COPD | 1025 | 1.682
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Control | 54 | F | HD | 1022 | 2.163
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Weights | 0.2 | 1 | 1 | 0.16 |
Subject enrollment and consent
Section | Source/questionnaire |
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1-3 | CSIRO Standard Screening Medical Questionnaire [29] + additional trial specific questions |
Selected questions from Living with Diabetes Study [30] | |
Selected questions from Fat and Fibre Barometer [31] | |
4 | Active Australia [32] |
5 | Kessler 10 [33] |
6 | Dimensions from HeiQ (Living with and managing medical conditions) [34] |
7 | EuroQol EQ-5D [35] |
8 | Dimensions from HeiQ (Social Isolation) [34] |
9 | Morisky Medication Adherence [36] |
Selection of Tele-monitoring service provider
Data architecture
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Entry and Exit Questionnaires are administered on line by PO’s when Test and Control patients are consented and are stored in OpenClinica
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Periodic Questionnaires (daily, weekly or monthly) are scheduled on the TMC clinician website and are presented and administered directly on the patient telemonitoring system. The results are stored in the TMC servers.
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Patient vital signs are recorded as longitudinal records and original waveforms are recorded and stored in the TMC server for quality control and diagnostic purposes. All records are accessible to the clinicians via the TMC clinician portal.
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Hospital Data is sourced from the Patient Administration Systems of hospitals servicing the trial sites and is supplied in the format of the Hospital Roundtable [28]. This comprehensive data set is requested for four years prior to enrolment and for the duration of the trial.
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MBS Data available from the Department of Human Services following Ethics Approval. This provides a comprehensive record of all primary care services provided under the national health insurance scheme
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PBS Data available from the Department of Human Services following Ethics Approval. This provides a comprehensive record of all medications dispensed under the national health insurance scheme. MBS and PBS Data is available for a total of 4.5 years prior to and including the duration of the trial.
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HIE Data from focus groups and structured interviews are transcribed and annotated before storage in OpenClinica.
Questionnaire instruments
Questionnaire | Administering schedule |
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COPD (Developed by the Austin Hospital) | Daily |
CHF (Developed by the Austin Hospital) | Daily |
EQ-5D (Quality of life)] | Weekly |
Kessler 10 (Mental health) | Monthly |
heiQ – selected domains (Self monitoring, Health services navigation and Social isolation) | Entry, 6 months, Exit |
Morisky medicine adherence scale | Entry, 6 months, Exit |
Data models
Objective/outcome | Data variable | Data source |
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Define the study cohort/confirmation of selection critera and exclusions | Admitted to hospital for their condition at least twice in the previous year, or ≥ 4 times in previous five years | Hospital health roundtable records - obtained from local hospital for previous five years. |
• Date admitted | ||
Exclusions are mental health and cancer patients | • Date discharged | |
• Reason for admission (ICD 9/10 Codes) | ||
• Procedures carried out | ||
Establish if telehealth | Number of unscheduled admissions to hospital for their condition | MBS Flag (In hospital) Health roundtable record |
Improves patient outcomes/reduced hospitalisation | • Date admitted | |
• Date discharged | ||
• Reason for admission (ICD 9/10 Classification) | ||
• Medication administered | ||
• Procedures carried out | ||
Establish If telehealth improves patient outcomes/reduced use of clinical services (Impact on clinical workforce availability and deployment) | Number of visits to/by GP | MBS records |
Number of visits to/by specialists | MBS records | |
Number of visits by community nurse | MBS records | |
Number of visits to/by allied health (ie occupational therapist) | MBS records (If reimbursable from Medicare) | |
Changes in prescription history | PBS | |
Communication with CCC | CCC Logs from CSIRO Portal | |
Organisational change management and impact on workplace culture | Administrative/operational changes implemented/required in order to implement the Telehealth service. | Questionnaires and structured interviews. |
• Within first three months | ||
• Every six months thereafter | ||
Useability of monitoring equipment | Compliance with monitoring schedule, recorded daily. | TMC Logs |
Extra measurements taken by patient (When? Which?) | TMC Logs | |
Compliance with questionnaire administration (When? Which?) | TMC Logs | |
Use of video conferencing | TMC Logs | |
Overall data usage | iiNET provided logs | |
Useability/acceptability | Ease of use | Questionnaires delivered via TMC |
For patients of monitoring Equipment | Quality of training received | • One month after first deployment |
Patient embarrassment if visitors know they are being monitored | • Midpoint of trial | |
Acceptability as an item of furniture | • At end of trial | |
Easy or hard to take measurement | ||
Important/not important in patients' self management | ||
Responsiveness of clinical care coordinator in responding to changes | ||
Quality of training received | ||
Patient embarrassment if visitors know they are being monitored? | ||
Easy or hard to take measurement | ||
Carers experience with telehealth (Community nurse/carer) | Ease of use of (i) equipment and (ii) Clinician website | Questionnaires and structured interviews of community nurses |
Changes to previous clinical models of care | • One month after first deployment | |
Effectiveness in improving ability to deliver care | • Midpoint of trial | |
Impact on workload | • At end of trial | |
Carer's experience with telehealth (Relative or other carer) | Effect on carer stress | Questionnaires and structured interviews |
Effect on carer workload | • At first deployment | |
Effectiveness in improving ability to deliver care | • Midpoint of trial | |
Access to clinician web site | • At end of trial | |
Gp experience with telehealth | Ease of use | Questionnaires and structured interviews of Patients' GP |
Changes to clinical models of care | • Within 3 months of first deployment | |
Effectiveness in improving ability to deliver care | • Midpoint of trial | |
Impact on workload | • At end of trial | |
Useability, acceptability of clinician web interface | Ease of use? | Questionnaires and structured interviews |
Quality of training received | • One month after first deployment | |
How many hours required | • Midpoint of trial | |
Value and ease of use of Video conferencing | • At end of trial | |
Health economic outcomes | Daily cost of hospitalisation | Health roundtable data |
Cost of procedures carried out whilst in hospital | Health roundtable data | |
Cost of visits to/by GP | MBS Data | |
Cost of visits to/by Allied Health (ie Chiropodist or OT) | MBS Data | |
Cost of visits by community Nurse/carer | MBS Data | |
Cost of travel to GP | MBS Data | |
Loss of earnings if patient is still employed, from days taken off for illness or visits to health professionals | Use Google Maps to determine distance travelled from home address to address of service location, then apply standard costing model. Ie flag fall + km charge. | |
Estimate from patient salary and time spent on each visit | ||
Cost of delivering telehealth services | Cost of clinical care coordinator(s) | Health service provider and logs recorded |
Cost of clinical nurses/carers | Health service provider and logs recorded | |
Cost of providing network services | iiNET billing at commercial rates | |
Cost of providing Telehealth monitoring services | TMC commercial daily subscription costs | |
Depreciated costs of capital equipment | Our own project records | |
Estimate of cost of space for monitoring centre at each site | Estimates from Health service Provider |
Data analysis
Classical BACI designs and extensions
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μ is the overall mean
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α i is the effect of period (before and after)
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τk(i) is the repeated measures within periods (assumed to be a random effect)
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βj is the effect on jth matched patients (intervention or control)
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(αβ)ij is the interaction between period and matched patient groups
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θ is the overall slope relating to time in months if no differentiation is made between the groups (B-A, C-I)
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θij is the slope relating to time (k) in months for the various combinations of the groups (B-A, C-I).
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eijk is the random error term of the model that is assumed to be normally distributed with homogeneous variance.
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Log of cost plus one will be treated as normally distributed with log of the number of days in the month as the offset. Sometimes the square root transformation may be used to stabilise the variance. We are hoping there are not too many zero cost periods or zero counts. If this fails we will use the zero adjusted inverse Gaussian distribution for the model – fitting them using the gamlss (package in R) using random() for including random effects [40].
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τk(i) is a random effect in the above model that is assumed to be normally distributed with mean zero and constant variance.
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The assumption in the previous dot point and the assumption for eijk in the model thus assumes that measurements made at the same time segments (e.g., on the same quarter) have the same correlation and homogeneous variances for all repeated measures.
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The above model treats the study as a fully-designed experiment with the appropriate randomisation. However, this is seldom the case because most impact studies are observational in nature.
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The assumption is that each measurement for the intervention patients is matched with a measurement for one or more control patients. This blocking is expected to control for the non-randomisation in the design. The complexity of this analysis can be greatly reduced by taking the differences between the Test and Control measurements
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The model above tests whether a significant change has occurred by testing the significance of the interaction term of the model for the before after indicator variables and the control-intervention indictor variable. For example if the coefficient for intervention patients and after intervention duration has lower insured costs that before the intervention after adjusting for controls, then the intervention has had a significant impact on costs.
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The random effects terms and random error term are assumed to be uncorrelated in time.
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The control patient is generally selected to control for all covariates. In this study this means that control patients should be identical to the intervention patient in terms of age, gender, SEIFA index and major comorbidities.
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The samples are selected over time (therefore they are time series rather than repeated measures made at the same time). So it may seem unlikely that the model errors will be independently distributed but hospital costs are measured a month apart and this should be enough to for the assumption of independence to be valid.
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The assumption that all repeated measures have the same variance is unlikely to be true. If the gamlss package is used then this change in variance can be accounted for. Although theoretically longitudinal data structures can be modelled by random effects in gamlss [40] but at present no computationally feasible implementation for large sample sizes and complex models exists.
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Assume that the trend in the response variable over time is approximately linear. This assumption is likely to be reasonable over the 5 year study period, but is unlikely to be true for longer time periods.
Power calculations
Outcome measure all on the monthly scale | Effective sample size? | Assumed normal distribution | Shift amount (K) | SPower |
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PBS Total cost | 30 | Log(PBS Total cost+1) | 1 | 1.00 |
MBS out of hospital costs | 30 | Log(MBS out of hospital costs+1) | 1 | 1.00 |
MBS in hospital costs | 30 | Log(MBS in hospital costs+1) | 1 | 0.84 |
Number of hospital admissions | 30 | Square root the number of hospital admissions | 0.5 | 0.99 |
Number of GP visits during working hours | 30 | Square root of number of GP visits during working hours | 0.5 | 0.89 |
Number of GP visits outside of working hours | 30 | Square root of number of GP visits outside of working hours | 0.1 | 0.50 |
Total number of GP visits | 30 | Square root of total number of GP visits | 1 | 0.97 |
Total number of either Specialist, Psychiatric, Allied Health visits and Procedures | 30 | Square root of total number of either Specialist, Psychiatric, Allied Health visits and Procedures | 1 | 0.77 |
Total number of Laboratory tests | 30 | Square root of total number of Laboratory tests | 1 | 0.97 |
Number of Laboratory | 30 | Square root of number of Laboratory | 1 | 0.96 |
Tests | Tests |
Discussion
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Concern over funding models. The National Health Insurance system has historically funded provider – patient clinical consultations. There are concerns that telehealth services may lead to cost blowouts in essentially uncapped federal and state healthcare budgets.
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State and Federal Government cost shifting. In Australia the Federal Government funds primary care and aged care and the State Governments fund hospital services. If the Federal Government funds telehealth to reduce unnecessary hospitalisation of those with chronic conditions, the primary beneficiaries will be the state governments. Hence there is a mis-alignment of those that pay and those that benefit!
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Limited awareness and support for telehealth services among clinicians, service providers and patients.
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Varying levels of organisational readiness within State Governments, local health districts and not for profit health service providers for the deployment of telehealth services.
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A lack of data on how to identify those patients that would benefit most from at home telemonitoring for their chronic conditions, and a robust process for allocating tele monitoring resources throughout the disease life cycle from early intervention for early stage disease conditions such as Type II diabetes, through to complex chronic conditions with multiple co-morbidities such as CHF patients with COPD and CHD.
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A robust process for selecting competitive at home telemonitoring services that provide the best quality patient data and opportunity for clinical diagnosis. Ensuring that systems are inter operable and standards based and can automatically transfer data securely to either provider controlled or national electronic health records.