Revolutionizing Healthcare: Why Every Doctor Should Learn Data Science

Medicine has made significant advances over the past century, but rising healthcare cost and worsening outcomes present a perplexing paradox. Healthcare has been affected by three significant trends:

• Increasing complexity
• Growing data volumes
• Burnout among healthcare professionals

Here we explain how and why Artificial Intelligence (AI) may help address these issues

Healthcare is deteriorating while its cost is increasing

Life expectancy has stagnated and even decreased in recent years in the United States, with an increase in mortality among those aged 25–64 due to conditions such as hypertension and obesity. Limited treatment options and higher complexity are two reasons for this trend. Developing new therapies is costly, time-consuming, and often unsuccessful. Each new medicinal drug was estimated to cost almost a billion dollars in research and development.

The increasing realization that patients with similar symptoms may have different diseases with unique mechanisms underscores the importance of precision medicine. While traditional medicine has focused on developing therapies that can treat the largest possible number of patients with similar symptoms, individual variability in response to treatment cannot be ignored anymore. However, a side effect of evaluating patient heterogeneity is that complexity increases exponentially. Beyond what a doctor can handle.

Overwhelming Data Volume: Beyond Human Capacity

Keeping up with the ever-growing amount of healthcare data has become impractical for physicians. The healthcare industry generates approximately 30% of the world’s data volume, and it is expanding at a compound annual growth rate of 36% by 2025, outpacing all other industries. This increase is due to the digitization of healthcare data, high-resolution medical imaging, continuous physiologic metrics output, and OMICS science (genomics, proteomics, metabolomics, and transcriptomics). This data explosion includes a growing variety of health data sources, such as sensor data, new imaging techniques, gene arrays, laboratory tests, free text, and demographics, making it beyond human capacity to analyze.

Burnout

Burnout is increasingly common among healthcare providers, with up to half of physicians affected, leading to lower patient safety and higher risk of medical errors, substance addiction, stress, depression, and suicide. Administrative tasks are a leading cause of burnout, with physicians spending an extra 1–2 hours on these tasks for every hour spent on patient care, and nurses spending 25% of their time on administrative work. The need for high-quality care is a significant component of burnout, but with growing complexity and overload, preventable lethal incidents are on the rise despite efforts to avoid human error.

How Can Artificial Intelligence Help?

Artificial intelligence and machine learning have the potential to address the three main challenges facing healthcare today: patient heterogeneity, increasing healthcare data, and physician burnout. AI can generate insights, increase computational capacity, and reduce physician workload.

AI applications can handle massive amounts of data and uncover hidden patterns, which would otherwise be lost in the avalanche of wide medical data. By reducing mistakes and boosting precision, AI could reduce the workload for healthcare personnel while also improving the quality of care. AI applications are expected to save US$150 billion by 2026 in the US healthcare industry.

If you want to know more, read my scientific research paper published at: https://www.scielo.br/j/ramb/a/ytS3Ps6BRcWrrBKZ3MRgKcc/?lang=en