An Overview

The healthcare industry is patient-intensive and its success relies majorly on on fast, accurate diagnosis and treatment decisions. However, there are several challenges to overcome the healthcare sector including growing patient traffic, mismanagement of medical prescriptions, poor connectivity for collaborative treatment, and the growing load of patient data.

Industries are using artificial intelligence services to develop a range of AI solutions that can learn and think like human using Text Analysis, Speech Recognition, Natural Language Processing (NLP) and Machine learning feature.

Effective diabetes management, improved drug discovery, advanced oncology care, and improved organizational performance are prominent trends that are transforming the healthcare sector. IBM’s Watson can rescue the healthcare sector from severe failures by systematically addressing the aforementioned challenges.

How Watson can Address Healthcare Challenges?

To support medical professionals in decision-making, Watson uses NLP capabilities, hypotheses generation, and evidence-based learning. For instance, a physician can use Watson to diagnose and treat patients. Initially, physicians might pose a query to the system, describing symptoms and other related factors. Afterward, Watson parses the input to identify the key pieces of information. It then finds relevant facts about current medications, family history, and other existing conditions by mining the patient data. It combines this information with current findings from tests and instruments, examining all available data sources to form hypotheses and test them. Watson incorporates research data, treatment guidelines, doctors’ and nurses’ notes, clinical studies, and patient information into analytics data.

In addition, Watson provides a list of potential diagnoses along with a score that indicates the confidence level for each hypothesis.

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Effective Diabetes Management:

In partnership with Medtronic, IBM Watson Health has recently launched an application, named as Sugar IQ. It addresses data-driven obstacles of diabetes and enables patients to diagnose their illnesses. Also, it can predict alterations in glucose levels up to three hours in advance, making it disruptive for diabetes management. It was presented at the American Diabetes Association (ADA) with study results of its real-world application where people with diabetes could spend 36 minutes more per day in a healthy glucose range using the app.

With the increasing cases of diabetes, effective care planning through AI platforms such as Watson can significantly relieve the disease burden.

Improved Drug Discovery:

A comprehensive drug discovery assistant for pharmaceutical companies, medical device companies, and academic institutions, Watson for Drug Discovery is a cloud-based platform. It enables researchers to identify new drug targets and repurpose their drug discoveries. 

Researchers can generate new hypotheses with the help of NLP trained in the life sciences domain, evidence-backed predictions, and dynamic visualizations. Unlike human operations, the platform can synthesize a comprehensive dataset in tabular as well as unstructured format to derive insights. By using machine learning, predictive analytics evaluate and learn through reasoning algorithms and ultimately novel hypotheses for future analysis.

Researchers can visualize connections, identify properties of interest, and quickly screen to supporting devices through dynamic visualization which enhances the data visibility. As such, Watson can transform traditional ways of drug discovery by delivering novel therapies in lesser time with such accelerated novel drug identification.

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Advanced Oncology Care:

Cancer is among one the leading causes of death worldwide. According to WHO, it has taken lives of 8.8 million globally in 2015. American Cancer Society says that advances in healthcare have significantly reduced the mortality rate by 25% in the past two decades. However, amidst the shortage of oncologists worldwide, new cancer cases are expected to rise by 70% over the next two decades.

Watson, with the implementation of cognitive computing, can analyze a huge amount of data in a matter of a few seconds. Also, it can identify 300 alternative therapies, which can seem challenging for a team of physicians. Offerings of Watson oncology can support at least 12 cancer types, representing 80% of the global cancer incidents. Besides, Watson for Genomics is empowering precision cancer care on the backdrop of collaborations with Quest Diagnostics and Illumina among others.

Improved Organizational Performance:

An increasing number of patients in the healthcare sector has intensified chaos and failure of appropriate care delivery. Data-driven AI, cloud-technology, and machine learning can mitigate the effects of mismanagement, as the sector faces a shortage of workforce.

Organizations, with the implementation of Watson-based solutions, can integrate a large pool of information from multiple systems and care providers. Further, they can devise an automated management workflow for effective healthcare delivery.

The platform works smarter in terms of:

  • Summarization of health

  • Individualized creation of care plans

  • Using cognitive computing for holistic care management

  • Intuitive user-interface for role-based access

A smooth workflow saves time as well as expenditure. It can effectively address dose error detection, preliminary diagnosis, faster clinical trial participant identifier, fraud detection, and cyber-security, and much more.

Conclusion

Watson addresses complex healthcare problems by taking context into account during the hypothesis generation and scoring phases of the processing pipeline. It helps doctors and patients to make more informed and accurate decisions. Watson Analytics can significantly transform the future efficiency of the healthcare sector. It can address the management of diabetes and cancer, leading causes of mortality, improving administrative operations, and better drug discovery.

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