Healthcare AI has already started gaining traction across applications ranging from medical imaging to pharmaceuticals. An industry observer notes that by 2026, key clinical health AI applications can create $150 billion in annual savings for the US healthcare economy. The CAGR for the AI health market is pegged at an astounding 40%!
It’s not just cost savings. AI is helping doctors identify and classify out-of-place lesions in mammograms and predicting whether a patient is susceptible to develop cognitive diseases at a later stage in life. But most importantly, AI is giving doctors more time to connect with their patients by taking over mundane, but essential tasks like taking notes and reading scans.
The healing touch of new-age technology is pushing the boundaries of medical science thus opening several new opportunities for service providers in what is inarguably an evergreen field.
Data Science in Healthcare
Wearable technology powered by IoT is generating volumes of data that is key to a AI-driven healthcare ecosystem.
This data is being used in predictive analysis by firms like Iquity. Analyzing over 4 million data points from 20 million residents, their predictive analysis algorithm was able to correctly predict the onset of multiple sclerosis 8 months in advance with ~90% accuracy.
With the advent of deep learning, microscopic deformities in scanned images that would otherwise go undetected are diagnosed more accurately. Artery is one such medical imaging platform that uses cloud innovation to deliver insights at extraordinary speed.
Data science and machine learning are revolutionizing the drug discovery process too. Pharma companies use insights from patients such as mutation profiles and other metadata to drastically reduce the time-to-market for a new drug.
Berg LLC’s supercomputers are merging biology and technology to map the future of disease and reduce the time taken for advancing drugs into the market.
Their patient-first approach to healthcare is at the core of all analytics-driven healthcare solutions.
Personalized Patient Care with NLP
Cognizant’s NLP models analyzed 900,000 caregiver records for approximately 200,000 patients to extract actionable insights for delivering better patient care.
Caregivers extensively document a patient’s health history. But since these are written by different caregivers at different points in time, it is impossible for a human to derive a holistic picture from them. Using NLP-driven text-mining algorithms, it was possible to carefully analyze these records and identify and contextualize the social determinants of health (SDH).
Amazon Comprehend Medical is one such service that automatically extracts medications and medical conditions from a variety of doctor’s notes, clinical trial reports and patient health records.
Key benefits of such solutions include:
- Easier medical cohort analysis
- Clinical decisions supported by data
- Improved medical coding in revenue cycle management
This has enabled healthcare stakeholders to provide 360-degree care to patients by also taking into account their SDH. These anonymized records also contain data from the patient’s previous visits for the same condition, thus helping earlier identification of people in need of specialized care using ML algorithms.
Early Detection Using Machine Learning
Proactive patient engagement is a key emerging area in healthcare. Digitally empowered consumers are in a position to demand access to preventative care. This means that healthcare service providers should rebuild their inefficient customer service functions.
This fact becomes even more relevant when the whole world is going through a pandemic.
By leveraging ML, pattern recognition and NLP, service providers can build solutions to proactively engage with patients. Data-driven insights generated on individual patient conditions facilitates effective preventive care for holistic wellness management.
Combined with a patient’s medical history, habits and other data curated from wearable devices, a complete picture of health emerges in the form of data.
Making Sense of Pharma Data with AI/ML
Pharmacovigilance (PV), the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem has proven to be a key battleground for AI.
The PV industry generates volumes of data from varied sources such as journals, patents, articles and even social media. When the time came to upgrade their legacy IT infrastructure to handle this exponentially growing volume of data, key players in the industry turned to AI.
IT major TCS developed an AI solution for PV that employs ML & AI to solve industry problems with cognitive automation. The solution built an encyclopedia of biological concepts from multiple sources. It also facilitates supervised learning with adequate datasets to develop hypotheses that enable intelligent decision making.
Promising AI Healthcare Applications
In addition to the above broad areas, the market for AI in
- Robot-assisted surgery
- Virtual nursing assistants
- Dosage error reduction
- Preliminary diagnosis and
- Automated image diagnosis
is expected to add up to over $6 billion by 2021.
Use of AI in healthcare business operations is rising too. The adoption rate in Service operations (46%), Supply chain management (21%) and product and/or service development (28%) is encouraging.
This growing adoption rate has opened the doors for service providers to partner with the healthcare sector to deliver life-saving solutions.
Adjoining areas in healthcare automation is expected to add even more opportunities for service providers.
Draup’s account intelligence and real-time sales signals tracking platform empowers service providers with the right toolset to seize sales opportunities. Service providers can leverage the industry-specific to curate their offerings and finetune their bidding proposals.
