Hospitals must get more hands on deck after the worldwide health crisis overburdened already exhausted medical ecosystems. In July 2021, while 91% of nurses were actively looking to leave their position, 42% of healthcare and social assistance workers who quit did so without having a new job.
On top of the great resignation, two in five healthcare specialists will be 65 years old in the next decade, bringing about a predicted shortage of up to 139,000 physicians by 2033—machine learning (ML) tools provide hope.
Rising lifespan, retiring medical specialists, and goals to increase healthcare access are the top drivers for introducing ML techniques. While artificial intelligence (AI) has significantly impacted automating processes and enhancing data quality, ML is an advanced extension. Today, scientists are training machines to learn from experience and new data without being explicitly programmed.
With the vast amount of data in healthcare, including electronic health records (EHRs), datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins, ML supports speed and accuracy in analysis. As the tools collect new information and cross-examine past datasets, they help to predict known diseases and identify patterns that have yet to be discovered.
Let's take a closer look at how ML's role in identifying and predicting diseases can support healthcare systems.
Reduces accidental hospital injuries Medical errors are avoidable, yet they considerably harm patients annually. According to Center for Disease Control (CDC), in 2020, the fourth leading cause of death after heart disease, malignant neoplasms (or cancerous tumors), and COVID was accidental hospital injuries. But where are Operation Warp Speed and investments for minimizing medical errors?
The Agency for Healthcare Research and Quality reports eight root causes of medical errors:
1. Communication problems: verbal or written between physicians, nurses, and patients.
2. Inadequate information flow: for example, between facilities and hospitals.
3. Human problems: such as knowledge-based errors or poor documentation.
4. Patient-related issues: including inappropriate patient identification.
5. Organizational transfer of knowledge: or insufficiencies in training to those providing care.
6. Staffing patterns and workflow: issues relating to communication and information flow.
7. Technical or equipment failures: such as life support machines or medical devices.
