With healthcare continuing to be digitized and transformed, it is now one of the industries most equipped to leverage data science and machine learning. Since 2015, venture capital funding for AI-powered healthcare firms has expanded 22 times in Europe alone.
Data science and machine learning are transforming the pharmaceutical industry, patient care, and other healthcare domains. But to fully utilize data science in the healthcare sector, a number of considerations must be made, including compliance, data governance and supervision, data culture, and the availability of data talents.
DATA SCIENCE’S ROLE IN PATIENT CARE:
Organizing appointments: Healthcare professionals can employ automated appointment scheduling with machine learning and rule-based artificial intelligence to minimize resource mismanagement and improve patient outcomes.
Early detection and prevention: Machine learning and descriptive analytics are used by wearables and health monitoring apps to provide important insights into a variety of personal health-related topics. These tools can help people make data-driven decisions about their health and detect potential ailments early on.
Patient triage: By using machine learning-powered symptom checkers, medical practitioners can rank patients in order of importance and necessity. As a result, wait times for patients may be greatly reduced, and healthcare workers may experience notable increases in productivity.
Imaging and diagnosis in medicine may be one of the most important uses of data science and machine learning.
DATA SCIENCE’S ROLE IN PHARMACEUTICALS:
Drug research: According to the AI Index Report 2021, companies that use AI to support drug discovery have drawn the greatest amount of private AI financing of any industry. The advantages of employing machine learning (ML) in medication development are starting to show themselves and have the potential to significantly enhance public health.
Planning the supply chain: Using data science and machine learning to plan the supply chain can help pharmaceutical companies save manufacturing time, expedite vaccine distribution, and maximize supply chain costs.
Forecasting excellence: By using population health data and simple to complex forecasting techniques, pharmaceutical companies may predict supply and demand for particular drugs and optimize company operations in finance, marketing, sales, and other areas.
Improving clinical trial processes: Wearables and data science can assist reduce patient risks by automatically monitoring and identifying any unfavorable effects during clinical studies. Machine-learning based tools that assess patient eligibility, prescreen, and randomize can also speed up clinical trials.
RISKS AND CHALLENGES: One of the biggest challenges facing any company looking to operationalize and grow data science and machine learning is putting in place a modern, secure, centralized, and discoverable data infrastructure. Generating large databases is challenging due to a lack of data interoperability and a disparity in quality among healthcare providers. This issue is especially severe for healthcare organizations whose data is currently being digitized.
Data protection laws such as the California Consumer Protection Act (CCPA) and the General Data Protection Regulation (GDPR) in the European Union provide obstacles for healthcare organizations when it comes to connecting and integrating heterogeneous information for data science and machine learning applications. Strong data governance and compliance are therefore necessary for operationalizing data science and machine learning in the healthcare industry.
