With the digitisation of healthcare in the last decade, health data is growing exponentially at a rapid pace and humans have created more data in the last two years than the entire history. Improvements in AI & machine learning along with the growth in computing and cloud infrastructure that allows for large quantities of data processing in real-time, have helped clinical decision-makers take advantage of greater intelligence being built into diagnostic equipment and patient devices that can collect patient data, upload it to the cloud or a centralized data centre for analysis or diagnosis, and then receive instructions or recommendations based on the patient’s specific needs. Analysing patient & clinical data is central to the transformation of the healthcare industry. It has significantly improved time to market for new drugs, diagnose chronic diseases at early stages with the use of data tracked from wearable and other tracking devices, improve accuracy in diagnosis reducing error rates and provide personalised care & recommendations to patients in real-time.
Interestingly, we are shifting into a trend where consumers can claim a property interest in their medical record for its permitted use and privacy. Tech companies like Apple are changing how information flows in healthcare and is opening up new possibilities for AI, specifically around how clinical study researchers recruit and monitor patients.
Images and free text cannot be easily categorized in the same way that a structured, numerical data point can and hence It takes AI to deliver insights out of unstructured data. Application of AI on unstructured data has proven to be valuable in many areas in healthcare such as medical imaging where deep learning is applied by trained using millions of images to identify abnormalities on X-rays, MRI and CT scans. One of the startups I worked in Ophthalmology used deep learning to detect severity in Diabetic retinopathy by labelling & training millions of fundus images.
Some of the challenges that I can comprehend with unstructured data for using it in business include:
- Lack of tools to easily manage unstructured data in parsing text, taxonomy and metadata management. The available tools are usually expensive.
- Difficulty integrating unstructured data with existing information systems for analytics and decision making.
- Shortage of skills in existing staff which require text mining and computer vision experts.
- Missing a sense of urgency for managing unstructured data due to the preconceived notion that it would be less useful.
Healthcare has realised the advent of AI revolution through disruptions that are evident with the application of AI & ML today. Today, companies like Google, Apple have invested heavily in healthcare research projects as they see the value that AI could bring to this industry. AI will increasingly be able to outperform tasks that are the prerogative of human doctors, including diagnosis, treatment, and prognosis. AI will not replace the physician completely as health and disease are strongly influenced by emotional, subjective and social factors. But it is almost certain that doctors who use AI will replace doctors who do not use AI.
Pharma and med-tech organisation make use of virtual & mixed reality solutions to inform doctors on the efficacy, indications and safety of their products through a virtual and gamified experience. In my previous role, I had the opportunity to build VR solutions to train asthma patients in learning about the correct inhaler techniques and educate doctors on the efficacy of the drugs across Ophthalmology, respiratory & oncology therapy areas. VR solutions are well received by physicians compared to a traditional marketing collateral which is left by the rep on their table as the gamified & realistic experience help in communicating the key messages clearly and effectively.
The impact of VR goes beyond education & training, to help children alleviate their fear of taking injections, help burn victims cope up with extreme pain and tailor the virtual environments to improve the autism therapy in children. I was fortunate to work with a health-tech startup in Singapore which utilised VR with eye trackers to detect glaucoma whose applications can be used for eye screenings at Ophthalmology clinics. The advent of mixed reality has taken this one step further with applications enabling live surgeries to be conducted by sharing the same patient data across multiple HoloLens within the same holographic space (operating room) although the surgeons are located remotely.
Corporate organisations possess resources and legitimacy that startups aspire for, while startups have nimble, agility and novel ideas that corporations value. A strategic partnership with the right startup is a win-win for both. In my previous stint at Pharmaceutical, I worked closely with many digital health startups and I think this is one of the ways to spur innovation in the organisation. This is because testing out innovative ideas in corporations have always been challenging due to highly driven processes and the nature of healthcare regulations surrounding them. Collaborating & Incubating startups helps corporates to trial and test novel ideas and if successful, it can be easily scaled faster & brought to market.
Healthcare Companies looking to be relevant between now and 2025 will need to understand the role data plays in their organization across various functions be it research, marketing or commercial excellence. I joined BD a few months ago and I am working within the Diabetes Care business across the region in helping to scale the business with commercial & consumer insights.
BD understands the importance of data-driven decision making and has taken various efforts in transforming themselves into a data-driven organisation. Some of the key priorities of my role would be to define a roadmap to scale data-driven decision making, assess the competency & quality of the source of data to help map BD’s performance in the market, identify new market opportunities, deliver customer insights and validate growth drivers for the business with data.
The challenge with warehouses and Analytics is that they alone aren’t enough because the Analytics / Reporting / Dashboard they provide are not actionable: they just tell you what’s happening, but they cannot explain why it’s happening and what one can do about making the right outcomes happen. One of my key priorities for FY 20 is to improve the analytics capability from descriptive to predictive to enable competency of utilizing data for business growth.
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