Almost all NDIS and Home Care providers know that their systems are siloed. A rostering solution here, a payroll solution there, a care management solution that isn’t integrated with the other two, and a set of spreadsheets silently stitching all of this together in the background.
The impact on operations is clear - in the emails requesting timesheets, the adjustments needed post-payroll, the frantic activity ahead of an audit, and the coordinators who are always busy, but somehow always behind.
But much less apparent is the financial impact. Not how much each system costs on a monthly basis, but what the silos themselves are actually costing the business as a whole - in wasted labor hours, in uncollected revenue, and in overservicing that could have been avoided.
A financial analysis has been conducted by Appoyo to provide this very insight. The results are projected for three types of provider organizations - small (~40 people), medium (~150 people), and large (~400 people) - and for conservative, base case, and optimistic scenarios. Every figure is backed by assumptions.
This is what the analysis revealed.
But before digging into the numbers, it is important to recognize where exactly these costs originate. According to the assessment, there are four buckets that define the structure of economic losses.
Admin friction is the biggest one. The separation of HR, rostering, payroll, and care management in different systems means that each and every operational question about a shift, expiry date of First Aid certificate of a worker, payment status of an invoice will require asking someone. This someone will stop whatever they do, look into a different system and send the email with an answer. Independent studies show that up to 20-30 percent of the workweek is spent on information searching by knowledge workers. Given the specifics of care operations and high frequency of changes in rosters and the fragmented structure of systems, it takes the upper boundary of this percentage range.
The second bucket is preventable error. Ineffective handovers between rostering, payroll, and billing systems create a continuous flow of little mistakes – wrong shift time, missing penalty rate, superseded service agreement. Every mistake takes 15 to 45 minutes of time of billing officer or payroll officer, while the most expensive mistakes end up in conflicts with clients.
Revenue leakage is silent yet impactful. There are two components contributing to this problem – misclassification of NDIS line items (staff select a low cost item as they are uncertain about the policy) and overservicing (services provided in excess of the available budget for the participant). According to the industry statistics, the erroneous classification of NDIS line items ranges between 0.5 and 1.5% of all billings. At the same time, overservicing accounts for 1-4% of service hours rendered. For a $13m provider, 2% overservicing amounts to $260,000 in terms of service hours delivered yet not reimbursed.
The final inefficiency is related to the workforce. Care management software traditionally used in the industry is notoriously difficult to operate. It takes a new coordinator about three to four weeks to become productive, during which a senior staff member invests from 15 to 25 hours into his/her training. Support workers spend 15-20 minutes writing up case notes after every shift; an AI-powered platform reduces the time investment required to 5-7 minutes.
The analysis models nine distinct savings factors. Here’s a layperson’s description of each of them.
1. Connected system. With all users able to find their own answers within one connected system to their operational questions, no time is wasted searching for information between systems. The analysis models saving 15 minutes per day for admin and coordinator roles, 20 minutes per day for management, and 3 minutes per shift for support staff.
2. NDIS line-item automation. Appoyo correctly identifies which line item from NDIS should be applied depending on shift schedule, location, and participant price book – during scheduling, not billing time. The base case analysis models 80 percent reduction in billing work and 0.8 percent revenue recovery from NDIS billings.
3. ROC-to-Roster automation. Manual conversion of a participant’s Roster of Care into a schedule rosters usually takes 3 to 6 hours for a coordinator. Appoyo’s ROC-to-Roster Builder uses the NDIS standard ROC Excel template and generates recurring shift schedules automatically, compressing hours into minutes.
4. AI rostering and auto-shift filling. The rostering process is assumed to be modelled with an improvement of 30 percent in terms of time efficiency. Auto-fill is used for shift cancellation (modelled as 7 percent per week), providing a 15-minute gain per shift as opposed to the current-state value of 20 to 45 minutes.
5. Funding controls. In-appoyo, one can track the real-time funding position during the process of rostering. There is a governance control which either warns about or prevents the creation of shifts exceeding the budget of participants. The model assumes a 65 percent reduction in overservicing as compared to the baseline rate of 2 percent, resulting in pure savings because all avoided money is unrecoverable spent cost.
6. Intuitive user experience. The analysis implies that new coordinator hires will result in 42 hours of lost productivity during ramp up process being recovered, and there will be 15 hours of saved training time for coordinators. New support worker hires avoid 6 hours of initial system training.
7. SCHADS Payroll Integration. Since the payroll system sits on the same platform as rostering and care management, there is no fortnightly export/ import/ validate/ reconcile process. The analysis model assumes that there will be a 50 percent reduction in pay run administration time and a 75 percent reduction in payroll corrections.
8. Compliance Audit Chain. Evidence is gathered while doing the job, including shift, attendance, notes, incidents, and billing. The credentials of workers are automatically checked before rosters can be entered. The analysis model assumes a 55 percent reduction in compliance monitoring and a 55 percent reduction in audit preparation.
9. Automated Document Writing. The creation of case notes goes from 15 minutes to 5 or 7 minutes. Progress reports, support plans, and coordinator documentation go from one hour down to 15 minutes. There is a conservative 25 percent monetisation rate assumed for time saved by workers.
Savings per year for the nine drivers in the base case are:
|
Metric |
Small (~40 staff) |
Medium (~150 staff) |
Large (~400 staff) |
|
Annual revenue |
$3.5m |
$13m |
$35m |
|
Annual savings - base |
$223,789 |
$831,426 |
$2,206,213 |
|
Annual savings - conservative |
$123,084 |
$457,284 |
$1,213,417 |
|
Annual savings - optimistic |
$302,115 |
$1,122,425 |
$2,978,387 |
|
Year 1 all-in investment |
$34,360 |
$122,350 |
$505,400 |
|
Payback - base |
1.8 months |
1.8 months |
2.7 months |
|
3-year net benefit |
$617,408 |
$2,297,247 |
$5,540,469 |
|
3-year ROI |
701% |
709% |
392% |
These amounts constitute around 6.4 percent of annual income from the NDIS for all three types of providers.
Under the base case, each type of provider achieves breakeven in less than three months since the date of launch. Under even the conservative case, where the amount of savings is reduced, all provider types will achieve breakeven in less than five months.
The sensitivity analysis examines the break-even point — the share of base-case savings that has to be realized in order to reach zero net benefits for the first year.
The break-even point for a small provider is 15.4% of base savings; for a medium provider – 14.7%; and for a large provider – 22.9%.
What this means in practice is that, even if the provider realizes only a small part of savings from the model (only the most conservative hard savings, and not any revenue recovery), the investment still justifies itself in Year 1.
About 52% of the savings realized from the model are hard savings – labor time saved through admin, coordinator, manager, and support worker positions. This is the most conservative savings, the one that is realized regardless of the billing process or the extent of overservicing before. The rest 48% of the savings are soft savings, which will be realized only if certain actions are taken.
A number of benefits have been excluded from the analysis precisely because they are real but difficult to quantify in an appropriately defensible manner. They include:
• Lower staff turnover resulting from improved tooling
• Lower regulatory risk due to improved compliance stance
• Higher satisfaction of participants and their families
• Value of management capacity that is liberated
• Lower costs associated with retiring current tool subscriptions
This means that the figures cited in the analysis have been consistently lowballed. Should a provider decide to account for these in addition to what was included, it would only improve the bottom line further.
The figures generated in this analysis are possible only if the system is indeed put into practice. Companies that adopt the Appoyo system without any sort of change management effort, allowing employees to make use of email threads and spreadsheets whenever possible, would benefit from a fraction of the potential outlined.
Companies that adopt the system in conjunction with clear communication of the new way of working, workflow definition, and consistent expectations regarding adoption would be able to realize the more optimistic end of the spectrum. This is not a comment on technology. This applies to all change efforts.
Modeling of the financial analysis within this report has been done based on three different representative profiles of providers. All assumptions have been clearly spelled out, so that you can use your own numbers and check if the findings are valid for your organization.
You can download the Appoyo Financial Analysis Report and view the model in full – the nine savings drivers, three years financial outlook, sensitivity analysis and CFO recommendation.
Or if you’d rather run the numbers according to your individual facility’s information, such as number of employees, revenues, shifts, and cost of your current system, talk to us at Appoyo. We can do a model tailored to your facility’s needs based on your information.
Is this analysis unique to Appoyo, or will this be true of any platform shift?
The savings are unique to Appoyo and the process efficiencies and levers described in each of the above drivers. Different platforms will deliver different outcomes based on the automation provided by that platform, their data models, and how the NDIS-related requirements (line item automation, funding controls, and SCHADS payroll) are addressed.
What is meant by "hard savings" and "soft savings"?
Hard savings refer to the labour time recaptured in the form of hours no longer spent by your coordinator, payroll officer, or manager on tasks that don’t add value. Soft savings relate to revenue recovered through proper application of line items and avoiding overservicing. This represents a greater dollar amount but is dependent on the billing discipline you have in place currently.
Our provider already has decent billing discipline. How does that impact the numbers?
Yes. The revenue recovery and overservicing prevention drivers rely upon the fact that the provider has been either under-billing or overservicing in the current state. However, your billing discipline has no impact on the hard savings driver, which constitutes about 52 percent of the base savings.
Is the cost of switching – which includes training staff – captured in the model?
Yes. In the Year 1 all-in investment amount, we have included implementation/setup costs, as well as internal change management costs. The payback calculations are using the entire year one investment as the denominator.
If we adopt more slowly than assumed in the base case?
The conservative case reduces base savings by 45 percent, and is intended to capture any slower adoption and process change assumptions. Payback, even in the conservative case, will be less than five months for each provider size tested.
How do you arrive at SCHADS labour rates?
We assume fully burdened labour costs (base SCHADS award rates + super, leave loading, WorkCover, payroll tax, etc.) at the 2026 Australian market midpoint. Support workers are modelled at $45/hr, admin & coordinators $55/hr, and team leaders & service managers at $80/hr.
Is there an applicability in terms of sizes of providers?
The calculation takes into account three different sizes of providers – small (around 40 employees, $3.5m of turnover), medium (around 150 employees, $13m of turnover) and large (around 400 employees, $35m of turnover). Providers outside of these ranges can calculate it on their own or get support from Appoyo team to build a custom model for them.
Does it include any costs of replacing our old solutions?
No. The calculation does not take into account any cost savings that will occur due to the retirement of old solutions. It makes the model conservative as far as providers use at least five to seven or even more individual tools.