How to Embrace Sustainability across the Disability industry with Data Science
An overview of the industry and technological trends discussion
According to World Health Organisation, over 1 billion people live with some form of disability, which estimate to be about 15% of the world’s population. The Australian Bureau of Statistics (2019) has estimated that around an 18% of the population has some form of disability, from which around 6% had a profound or severe form of disability within Australia. At this point, recognising that a growing need for understanding challenges and solutions in the industry is crucial for the sustainability of the National Disability Insurance Scheme.
The Office of The United Nations High Commissioner for Human Rights (2014) discusses the definition of disability and explains that disability is a concept that is currently evolving, nevertheless is defined as individuals with impairments who are confronted by social barriers which impact their ability to participate in society. Furthermore, explains the diversity of individuals across such a broad definition and explains that therefore, government policies must work along these individuals to better improve their life outcomes.
Data science nevertheless has been an evolving concept in recent times for the National Disability Insurance Agency, and an evolving choice. Industry trends across providers have shown that major efforts in technology have come across the investment of electronic big data management systems to track client outcomes. (Harrison, 2020)
It might be for that same reason that the Council of Australian Governments agreed to establish a National Disability Data Asset (NDDA) whose purpose is to deidentified Commonwealth and services data, NDIS data, and service system data from states and territories. For the first time since the National Disability Insurance Agency creation 8 years ago, the government has agreed to decentralise the data collection and interpretation model of the system to a new model, participant centred and focussed on not just codesigning a multimillion-dollar ARC Industrial Transformation Centre co-designed by the University of Technology in Sydney but also redesigning the entire decision-making process of the organisation with the collaboration of data scientists, along with other internal and external partners of the university. (Berry, 2021)
Understanding the challenges; customer satisfaction, efficiency in deployment of resources and coverage
The Centre of Research Excellence in Disability and Health (2021) and the International Journal on Equity and Health (2013) have identified challenges across people with disabilities in Australia. Both organisations reassure that monitoring challenges across people with disabilities on a routine basis is crucial to an impactful and insightful reform of the scheme. Also, understanding those daily living challenges which participants. As an example, with education attainment, community involvement and mental health, from which they are strongly disadvantaged. (Centre of Research Excellence in Disability and Health, 2020)
Aside from that perspective of providers across the scheme, participants perspectives across the satisfaction of the service, as well as their outcomes from the scheme, do not differ. The Disability, Ageing and Carers Survey (Australian Bureau of Statistics, 2019) assured that less than half of the participants requiring household assistance were satisfied with the range of services offered under the scheme. Likewise, The NDIS Market report (2020) informed that almost 30% of participants rated their plan review experience as either neutral or poor/very poor.
Horner, Dobbertin, Lee and Andresen (2014) explained the financial disparity among disability participants from the American Association of People with Disabilities, explaining there was a historical trend of benefiting blind impaired participants compared to other conditions, but simultaneously providing unequal support to many participants with similar conditions and needs as well. Thus, promoting disparity in the healthcare and disability sector. Similarly, the National Disability Insurance Agency (2020) reiterates under the NDIS Market Report that the level of support per participant relies strongly on their age and their geospatial location, fostering more support to those living across metropolitan areas and in older age ranges rather than their disability and tailored goals.
Applications and their impact in the industry; areas for improvement
Refining customer satisfaction
According to Archenaa and Anita (2015), some of the potential applications of big data analytics include providing patient centred care by providing tailored solutions to each individual such as creating medicine based on clients genotype, service quality assessed in real-time, treatment improvements based on data insights about the participants, and even addressing basic needs quickly. This application of data science reduces the unemployment rate by predicting labour market needs in advance and provide special training to fill those market gaps. This application in data science is reflected upon solving problems such as coverage, tailoring geospatial analysis as an example, defining which local government areas require more human resources, and defining a strategy ahead. Examples of these applications include entity recognition, text categorization, factspotter and advanced text analysis. (Bessin & Das, 2013)
An application that could vastly improve participant outcomes and therefore customer satisfaction are more insightful applications on the usability of personal devices/equipment if not other accessories. As an example, in a study lead by Schuller and Schuller (2020) participants were asked to use multisensorial and multimodal devices such as dynamic microphones, or to provide consent for video-sensing devices (non-video) to recognise movements, food eaten and even time spent eating. Deep learning applications were utilised in this study, which purpose was to analyse and better understand patient food intake. (Schuller & Schuller et al., 2020, 187–201) Similarly, other applications such as heart rate monitoring, exercise and even internal organ activity can be applied for participants, depending on their condition and how to improve their outcomes. Applicable to the disability industry, participants could be supported by receiving tailored feedback on their goals in each stage of their plan.
Another example of a pilot project intended for the application of young participants in the disability sector, particularly for children under autism spectrum disorder (ASD) was conducted in Massachusetts, United States. Outcomes of the research from behavioural analytic intervention data analysis suggested insights and the possibility for improvement under specific approaches to this participant, despite requiring further research to better understand what those specific behaviours were. (Ho, Perry., & Koudys et al., 2021, p 25–27)
A big component of disability research outcome could come from applying artificial intelligence models such as machine learning and deep learning, to study patients and find new insights about human development and behaviour. It is suggested that there could be an opportunity in the industry to better understand disability and therefore, model-specific solutions tailored to those individual needs. (Green & Lavesson, 2019, p. 1120)
Building on efficiency on deployment of resources; both through allocation and scale
Another application aligned with improving customer outcomes is the usability of cloud computing, which could even store petabytes for a national system level. It has been suggested under healthcare systems not to utilize SQL solutions due to the complexity of some datasets, which could include images, text types and even sounds. (Celesti, Lay-Ekuakille, Wan, Fazio, Celesti, Romano, Bramanti, Villari, 2020) This is also applicable to the disability industry, which is a subsegment of the healthcare industry. Information could be updated in real-time through devices and other wearable devices, and similarly utilised to provide real-time feedback and individual insights from the participants. These insights could be anything from updating health status, personal records and goals, receiving treatments and relevant information on the surrounding environment.
Bettering coverage
Other applications suggested by Costin, Schuller and Florea (2020) are “the merging of big data repositories, heterogeneous models and algorithms and user interfaces for web/desktop/mobile applications” which could be a great way of increasing collaboration among industry partners and increase big data management efforts. This big data management system would enable more accurate and real-time predictability, which would enable the National Disability Insurance Agency to increase efforts if required, to better understand and decide where to allocate those efforts, to foster coverage among states or local government areas and to increase customer satisfaction overall because of those improvement efforts.
Mitigating challenges
One of the biggest challenges across the industry has been since the creation of the National Disability Insurance Scheme, the political consensus among the transformation and redesign of the system while ensuring inclusion for external stakeholders including participants to make their input across this new redesign proposal. (Australian Government Department of Social Services, 2020)
While understanding the challenges across financial sustainability, The National Disability Insurance Agency has decided to allow this new independent organisation, the National Disability Data Asset to play a vital road in the decision-making process of the industry. (National Disability Insurance Agency, 2020)
Another risk, vital to play in the application of data science practices in the industry is customer trust. Hence, utilising applications such as multi-geographical environments could mitigate challenges concerning personal data privacy and user trust. (Costin, Schuller., & Florea et al., 2020, p 13)
The future of the industry
While the industry is shifting to a new model where participants play a more vital role in that decision making process, new opportunities arise.
Is crucial for the National Disability Insurance Agency to work in collaboration with partners, to base decisions on that new customer centred and rich data analysis coming ahead and to elaborate new questions and data insights. This would enhance the sustainability, not just financially but overall, through all components of the triple bottom line. (Vergunst, Berry, Rudkasa, Burns, Molodynski, Maughan, 2020)
References
Archenaa, J., & Mary Anita, E.A. (2015). A Survey of Big Data Analytics in Healthcare and Government. Elsevier, 50, 408–413. https://doi.org/10.1016/j.procs.2015.04.021
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Bessin, J., & Das, A. (2013). Big Data Analytics Federal Business Analytics. Xeros Corporation. https://www.xerox.com/downloads/services/white-paper/big-data-analytics.pdf
Celesti, A., Lay-Ekuakille, A., Wan, J., Fazio, M., Celesti, F., Romano, A., Bramanti, P., & Villari, M. (2020). Information management in IoT cloud-based tele-rehabilitation as a service for smart cities: Comparison of NoSQL approaches. Measurement : Journal of the International Measurement Confederation, 151. https://doi.org/10.1016/j.measurement.2019.107218
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Green, D., & Lavesson, N. (2019). Chaos theory and artificial intelligence may provide insights on disability outcomes. Mac Keith Press, 61(10), 1120–1120. https://doi-org.ezproxy.lib.uts.edu.au/10.1111/dmcn.14328
Harrison, L. (2020, September). National Disability Insurance Scheme Providers in Australia. IBISWorld. https://my-ibisworld-com.ezproxy.lib.uts.edu.au/au/en/industry/x0029/operating-conditions
Ho, J., Perry, A., & Koudys, J. A systematic review of behaviour analytic interventions for young children with intellectual disabilities. Journal of intellectual disability research, 65(1), 11–31. https://doi.org/10.1111/jir.12780
Horner, W., Dobbertin, K., Lee, J. C., & Andresen, E. (2014). Disparities in Health Care Access and Receipt of Preventive Services by Disability Type: Analysis of the Medical Expenditure Panel Survey. Blackwell Publishing Ltd., 49(6), 1980–1999. https://doi.org/10.1111/1475-6773.12195
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