Data-driven Innovation in Australian Digital Health
“It is health that is real wealth and not pieces of gold and silver.” — Mahatma Gandhi
Introduction
Australian healthcare organizations are getting digitalized with tremendous data generated from a diversified digital health channel. Digital health is about using electronic devices to connect so that health information can be shared securely (Australian Digital Health Agency, 2020). Ultimately it can support and improve decision making and providing continuous care for patients. Australian Department of Health is currently establishing the electronic health record — My Health Record (MHR). Until Feb 2020, there are 22.71 million total MHR registered, and the views of these records soar dramatically by more than 300% since March 2019 (My Health Record, 2020). Meanwhile, Australians are among the highest users of digital technologies globally, with 91% own a smartphone, and 22% of Australians owns fitness bands to track their physical health in 2019 (Deloitte, 2019). The production of data in 2020 will be more than 44 times in 2009, and organizations should shift from a volume-based business strategy to value-based business (Rebecca & Williams, 2014). Through this discussion paper, some opportunities and innovations will be discussed how personal activity data can be utilized along with MHR and benefit thousands of Australian.
Innovations
There are 1.81 billion documents uploaded, including 57.1 million clinical documents, 112.4 million medicine documents, 1.65 billion Medicare documents (My Health Record, 2020). Although there are a lot of data, patients’ information can be outdated due to digital health systems/health providers are not linked, and data is not centralized (Schofield et al., 2019). For example, it does not make full use of patients’ daily performance regarding heart rate, physical activity times, sleep hours, calorie consumption to improve decision making and personalize medical solutions. On the one hand, trackers collect almost continuous data on oneself, and it makes daily life activities involved in medical decision making (Greenfield, 2015). On the other hand, because of frequent notification ability, therapeutic interventions, and reminders can help oneself with daily medicine intake, especially for the elders. For example, Apple Watch has an ECG app which can provide users with necessary information regarding their heart health. Apple Watch keeps analyzing and send a notification if it finds an irregular rhythm, which is suggestive atrial fibrillation- AFib (Healthcare — Apple Watch, 2020).
Digital Health Intervention
Digital Health intervention (DHI) can help patients to develop healthy behaviors and provide scalable interference such as cardiovascular disease, smoking cessation, weight loss, etc. It requires combining both medical history (MHR) and current physical status data to give predictive analysis. The outcome should involve healthcare professionals — GPs who get alert about their patients regarding medical behavior and potential health risk. Warning and reminders can be triggered when a patient should get a new lab test or take a new prescription from doctors. Study shows DHI has positively affect behavior patterns and physical activity leading to decrease the risk of cardiovascular disease (Widmer et al., 2015). For instance, patients suffered from chronic diseases like diabetes, high blood pressure, and cardiovascular disease, their data can be continuously collected daily basis and share the data analysis result along with MHR. Ultimately it will help doctors to make decisions more accurately. Both MHR and wearable devices can create an eco-system, and this system can be used by clinical and healthcare providers in delivering high-quality care and performance to their patients (Agnola, 2018).
Preventive Healthcare
The predictive analysis and prevention in advance can dramatically increase the possibility to avoid or prolong the disease. Study shows that preventive healthcare can improve medical risk, provide needed service more efficiently, and optimize existing operations (Wang, Kung, & Byrd, 2018). Moreover, using the patients’ daily health-related data such as lifestyles, disease management, and surveillance, organizations can provide a comprehensive understanding and predict future healthcare trends (Groves et al., 2013).
An example in Brazil that organizations are using machine learning classifiers of the electronic patient records to assist healthcare professionals in making decisions to identify children with developmental problems. This system uses Surveillance Levels (SLs) to indicate the type of healthcare procedure and potentially available services (Pollettini et al., 2012).
In Australia, the MHR includes a lot of information, such as more than 40 million pathology reports and 6 million discharge summaries (My Health Record, 2020). Organizations and healthcare providers can make use of this data and engage people with potential medical risks. The community healthcare data can also help the government to predict the local health trend and prevention epidemic outbreak.
Diagnostic Imaging
Medical imaging is vital for diagnosing, and data science can boost this process as well. From January 2019, there are 1.9 million views by General Practitioners, an increase of more than 11% (My Health Record, 2020). Analyzing and sorting these images can take lots of effort since radiologists and GPs need to examine each image. The lack of time and healthcare professionals make diagnose images in high demand, especially during special conditions like a global pandemic — COVID 19 recently. For example, as the COVID-19 spreads globally, CT imaging departments are facing the pressure that never had before. Alibaba Could have launched a technology to assist in identifying characteristics of coronavirus pneumonic in CT scans with about 96% accuracy (AlibabaCloud, 2020).
With accurate analyzing algorithms and a large volume of patients’ imaging data, the government and organizations can build up powerful artificial intelligence applications. Furthermore, better efficiency can lead to fewer preventable hospitalizations, reduce duplication, and operation costs (Hambleton & Aloizos AM, 2019).
Personalized Healthcare Product and Medical Solutions
Analyzing historical medical records, lifestyle, and behavior patterns collected from trackers can contribute to personalized healthcare products such as customized medicine, treatment, and even insurance. More importantly, a personalized, integrated healthcare system can enable patients to care for themselves with accurate information corresponding to their condition(Schofield et al., 2019).
For example, Fitbit focused on making wearable data more useful to physicians by integrating the health data into electronic health records and other IT systems used by healthcare professionals (Landi, 2019). Another example is using data analytics in DNA to find a potential cure for cancer and other diseases. The Australian Government will provide over $65 million in research to unlock the power of personalized medicine through genomics hiding in patients’ cells (Health, 2019).
Impacts
The impacts of implementing data science within the digital healthcare sector can be plentiful and important in Australia. A lot of studies show that data science has the potential to meet the growing medical demands and improve the quality and efficiency of healthcare delivery(Wang, Kung, Wang, et al., 2018). When healthcare providers and other stakeholders in the healthcare delivery system are cooperating under the analysis of big data, the synergy can be created, which leads to higher-quality care at a lower cost as well (Raghupathi & Raghupathi, 2014).
Improve Efficiency and Effectiveness
The waiting time of Australians who are undergoing elective surgery and receiving emergency department care in public hospitals is increasing in most states and territories (Australian Institute of Health and Welfare, 2019). By making use of predictive analysis and My Health Record, real-time reporting can provide timely insights and discoveries about prevention, diagnosis, and treatment. Decision-support tools will relieve clinicians of most administrative tasks, diagnoses, and additional safety checks. For example, the foundation of the National health information technology platform which allows patients in Australia to access their health information and healthcare providers share and exchange interposable data (Allen et al., 2019).
With the aid of big data analytics, practice-based clinical data such as patient geography, demographics, lifestyle, and treatment can be effectively used and personalized medical treatment (Wang, Kung, Wang, et al., 2018). Moreover, cohort treatment such as smoking cessation, obesity campaigns can be a better target in the community (Watson, 2019).
Reduce Expenditure
Chronic disease is becoming more and more common about 50%, and it is the leading cause of death, disability in Australia, according to AIHW (Australian Institute of Health and Welfare, 2018). By implementing predictive early-stage intervention and prevention, expenditures on both public health systems and individuals will decrease.
“Predictions on the likelihood of disease and chronic illness based on historical data could create early interventions that aim to reduce the financial and resource load on the public health system in the future.” (Watson, 2019)
Challenges
While implementing innovations in digital healthcare, there are some barriers and obstacles that need to be addressed. Here we are going to mention some of them, but it sure has more potential risks and difficulties.
Data Governance and Integration
“Strong data governance ensures that the right information, of the right quality, is available to the right person, for the right purpose, at the right time.” — Evan Rawstron, KPMG Global Healthcare D&A Lead
When organization analyses and research on multi-health data, the primary challenges are related to data integration and data governance that where their data lives and how to access it (Lydia Lee et al., 2018). Those data reside on a different platform with different technologies, not to mention new technologies are coming out. This results in heterogeneity problems in deploying data models, data schemes, labeling conventions (Sujansky, 2001). To achieve the innovations above, organizations should think about their management process over data shortly.
Privacy and Ethics Concern
The predictive data analysis model cannot avoid collecting and analyzing personal data. Especially for medical records which contain highly sensitive information such as pathology test results, mental health problem, etc. which patients do not want to share, but most of the patients are not aware of this. In an Australian national survey, the results are not very delightful. It shows that only 9% of all respondents with an awareness of the personally controlled electronic health record (RCHER) and the majority(82%) respondents believe that it is important to have access to their record (Lehnbom et al., 2014). Furthermore, except for the low awareness, the difficulties when patients experienced trying to access their health data can also impact the success for the healthcare providers to provide personalized needs (Heart et al., 2017). For the data collected by the wearable device, the situation is not very pleasing as well. The awareness of their data sharing is still deficient at just 7% of respondents citing awareness (Deloitte, 2019).
Conclusions
There are plenty of potential innovations and challenges in Australian digital healthcare organizations. The benefits are significant and profound in the next few decades if the organization deals with data appropriately under a standardized regulation environment. In other words, centralizing and intertwining data from both MHR and wearable devices can provide personalized medical care and early prevention, which can improve efficiency and effectiveness in Australian. However, the organization should not oversight the potential risks and challenges while utilizing the power of data science.
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