Data Innovation in the Healthcare Sector

Introduction

In the current age of information technology and smart devices, integration of data-driven organisational processes provides many possibilities for innovation. Data analytics facilitates greater understandings of organisational environments as well as environments in which organisations operate, creating possibilities for organisations to implement change, and in turn, drive improvement and innovation processes (Galetsi et al., 2019). In light of the COVID-19 pandemic, data as a tool for change is especially relevant in the public health and healthcare industry in assisting precision public health. For example, Australian state government implementation of public health contact tracing procedures via QR codes have led to a dramatic surge in usage of the formerly scarce barcodes (Patrick et al., 2020). It demonstrates the tremendous data analytics potential that can be achieved by healthcare organisations, utilising stores of individual data to produce highly meaningful public health insights and subsequent innovation. Despite these successes, the implementation of data-driven innovations can reveal complex issues, particularly in core data management and analytic processes.

Context

Innovation is a creative process where there is formation and realisation of novel and improved ideas to increase practicality and value (Seaden and Manseau, 2001). It differs from improvement in that it tends to focus on larger, more significant changes, rather than the emphasis on enhancement within existing systems as is seen in improvement processes (Queensland Government, 2018). In terms of data-driven innovation, the contextual influences in an organisational environment need to be recognised as fundamental to the initiation of innovation processes. Rosemann (2012) categorises these influences into 3 main drivers of innovation: problems, constraints and opportunities. These are defined as instances driven by a desire to solve an issue, boundaries in the course of usual organisational processes, and realisation of new possibilities. Prominently, problem and constraint-driven innovation has been seen with the challenge of managing non-communicable diseases and emerging infectious diseases, such as obesity and the COVID-19 pandemics. Most healthcare systems are hindered by the reactive nature of their approach to health management: provision of care is often highly disjointed, non-cooperative and potentially ineffective (Sagner et al., 2017). This compounds with the challenge of inadequate resources in funding, recruitment, development and retention of a capable healthcare workforce (Harrington and Jolly, n.d.) while maintaining quality of care, adherence to regulation and cost reduction (Natarajan, 2006). Further, even with provision of adequate healthcare, the health sector faces issues of maintaining patient engagement and compliance with treatment (Ding et al., 2020).

Externally, the trends of population growth and urbanisation raises health management concerns due to social and economic variation across land areas. Increased COVID-19 morbidity in Wuhan, China, was seen to be correlated with increased population density, construction land proportion and retail sales in particular regions (You et al, 2020). There is also the challenge of providing healthcare to those areas of lower population density, with rural and remote populations often having much lower access and usage of health service (Australian Institute of Health and Welfare, 2019). This is further exacerbated by the tendency for rural and remote populations to be of low socio-economic status and therefore experience worse health outcomes and higher mortality rates due to poorer social determinants of health (i.e., employment opportunities, level of education, access to healthcare services, social capital and networks, lifestyle and behaviours) (Dixon and Welch, 2001).

Opportunity drivers of innovation, unlike problem and constraint-driven, is proactive. It enables enhancement of the organisation’s technological or other possibilities that revolutionise organisational models, products, services and processes (Rosemann, 2012).In relation to increasing technological capabilities in the healthcare sector, this could entail greater incorporation and use of health big data stored in a cloud database (Dolley, 2018); unconventional data collection methods cloud access to health records; as well as messaging between health practitioners, patients and other health-related agencies (Helms et al., 2008). Together, problem, constraints and opportunity drivers shape the context in which data innovation can develop.

Opportunities for and Impact of Data Innovation

The breadth of possibilities for innovation highlights the scope at which data could be used to transform the healthcare sector. One such mode by which data could innovate is in precision public health, a field characterised by use of specific description and analysis of population groups and individual people to increase population health overall. Precision public health is led by advances in big data and its technologies to enable rapid targeting and speed to uncover, verify and enhance healthcare strategies (Dolley, 2018). This approach would be relevant in numerous applications addressing the numerous opportunities, problems and constraints in the healthcare industry, especially in relation to disease control with respect to urbanisation, high population growth or distribution. For example, the areas of genomics, molecular research, medical imaging mining and population health has leveraged big data for the development of understanding, characterisation, control, diagnosis, prevention, management and care strategies for many infectious diseases (Dolley, 2018). This was seen currently in the COVID-19 pandemic where big data was applicable in enabling identification of infected persons and stages of the illness, viral molecular structure, contact tracing and travel history, greater susceptibility to the disease, early warning systems for possible contacts of COVID cases, as well as more rapid development of treatments (Haleem et al., 2020).

Similarly, there is much potential for use of big data in precision public health for the obesity pandemic, particularly in obesity aetiology. Transport and geospatial data sources could allow for developing greater insight into the interaction between environmental determinants of obesity in individuals with more direct health determinants. Determinants such as individuals’ interaction with built environment, pollution, or access to education and health services could be studied alongside with more obvious socio-economic, lifestyle and inherent biological contributory factors (Timmins et al., 2018). Likewise, wearable technology and smart devices could provide big data needed to establish lifestyle and behaviour metrics for obesity prevention with possible data attributes concerning acceptable levels of sedentary activity (Patrick et al., 2004) or sleep patterns (Chatterjee et al., 2020). The analysis could be easily applied to rural and remote populations that not as easily studied by conventional research methods (Timmins et al., 2018), revealing the specific health needs of particular groups to better assist the healthcare sector’s strategic realisation of universal health care. This use of big data in precision public health can in turn improve patient satisfaction and experience, reducing poor patient engagement and compliance rates through highly personalised support adjusted to a holistic view of an individuals’ health determinants (Pramanik et al., 2018) supported by an online delivery mode.

Internal focuses on the healthcare sector data innovation opportunities could include the optimisation of the workforce and resources for better quality of care and more cost efficiency in research and development. Big data can help mitigate risk of underutilisation of resources and more effective allocation of personnel, focus and funding (Pramanik et al., 2018). Use of “found” or unconventionally collected commercial weight management big data have offered a resource-contained mode of data collection and additional insights in obesity research through evaluation of programme effectiveness in real world contexts (Timmins et al., 2018). Analyses of hospital admission records, for example, could aid in predicting future requirements for recruitment, development and scheduling of staff (Pramanik et al., 2018). The abundance of opportunities for data innovation highlights the profound ways that it can impact healthcare sector, helping to create solutions for problems and constraints present in the context of the sector.

Data Innovation Challenges

Despite the significant potential opportunities and value that can be offered with greater integration of data-driven innovations into the health sector, there will be substantial costs and issues that will be faced in the process of transforming it to become more data intensive. This includes barriers from the external environment in which the health sector operates, the internal environment of the health sector, as well as the processes of data innovation itself; data collection, cleaning, validation, storage, security and privacy maintenance, transformation and analysis (Dolley, 2018). Data transformation starts with data collection, which in healthcare is cost and time intensive process due the potential need for large scale, longitudinal and high-quality health data (like that for determination of specific environmental determinants for obesity) for pragmatic, generalisable insights (Caruana et al., 2015). Resistance to data collection may be experienced due to the sensitive nature of the topic (e.g. bodyweight measurements) or health literacy, especially in conventional research settings: separation of the individual from children and sexual partners, neutral data collection location, anonymised names, icebreakers prior to interviews and phrasing questions matching the target population’s literacy may be required (Rimando et al., 2015). This could be reduced by use of unconventionally collected data which can present additional issues of quality and accuracy of data, and prolong other data innovation processes (Dolley, 2018).

Data cleaning and validation, following collection, evaluates the data accuracy and quality for transformation via data removal or altering and cross-referencing of sources, respectively (Tableau, 2021). Cleaning and validation use robustness indicators for availability, usability, reliability, relevance and presentation quality; these need to be assessed to prime stored data for converting of data formats in transformation and further exploration in analysis (Cai and Zhu, 2015). With healthcare data comprising 30 percent (the largest share) of the world’s electronic data stores, cleaning and validation can also be extremely cost and time consuming: healthcare organisations need skilled staff with data management capabilities to enable success in standardisation of various data structures and integration of multiple data sources (Pramanik et al., 2018). This could be experienced in attempts to merge widely differing data sets, such as geospatial and credit card transactional data for COVID-19 contact tracing (Korea Centers for Disease Control and Prevention, 2020).

To gain actionable insights and greater understanding of the remaining data, analysis must be completed. However, like much of the data innovation process, it is only completed by a small proportion of healthcare sector (data analysts). This could lead to bias in interpretation and presentation due to the specific knowledge and skill sets of analysts (Galetsi et al., 2019). Alongside these processes, maintenance of security and privacy of acquired health data should be upheld, fulfilling ethical and legal obligations of the healthcare sector. Data must be de-identified or encrypted, secured with adequate security architecture, to prevent breaches and traceability to the individuals to which the data belong (Rimando et al., 2015). Other barriers to data innovation encountered could also include organisational, political, regulatory and financial barriers (Galetsi et al., 2019); further limiting the scope at which data-driven processes can be completed, and in turn, application of data innovation.

Conclusion

Examination of the healthcare sector has revealed much insight into the various problem, constraint and opportunity drivers for data innovations in its context. Problem and constraint drivers relating to internal health sector challenges and determinants of health respectively, informed prospective opportunities for data innovation. This was primarily studied in terms of potential big data applications in precision public health and the significant improvements it could provide the sector. Opportunities for data innovations were found to be plentiful in the context of healthcare but were coupled with serious challenges. The sector would be able to derive substantial benefit from applications of data-driven processes despite the various internal factors, external influences or issues inherent to data innovation processes’ of management and analysis. It demonstrates the need for healthcare to match the external rapidly innovating and data conducive world in which it exists, to realise its own novel innovations and insights for protecting population well-being; and, an urgency for major sectoral development stressed by the scope of dynamic contextual influences driving opportunity for innovation.

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