Like many industries, the healthcare industry has learned valuable lessons from the pandemic. Perhaps the most important lesson from 2020 has been the role of digital as a channel.
Technology has always been the foundation of the healthcare industry. However, what has changed in recent times against the backdrop of the pandemic outbreak, is the need to respond to unpredictable market changes quickly. The pandemic has acted as a catalyst for digital transformation. Digitalization in the healthcare industry enabled hospitals previously burdened with disparate legacy systems to simplify and standardize healthcare delivery systems. In doing so, healthcare businesses and millennial patients can reduce costs and improve the quality of care, all while leveraging leading-edge technologies.
AI and ML offer significant advantages over traditional analytics and clinical decision-making techniques. It helps gain deep insights into symptoms and treatment of covid 19. They are giving healthcare organizations the ability to learn new ways of fighting and managing global health threats. In this post, we discuss how AI and ML technologies are playing an essential role in supporting the phases of testing, tracing, and treatment after a global pandemic outbreak; and how healthcare businesses are using AI and ML-driven solutions to create safer, more resilient environments.
Contact Tracing
Contract tracing was all over the headlines last year when scientists found out that it can help slow the spread of COVID-19. One of the biggest roadblocks to contain the pandemic outbreak is identifying people who have come in physical contact with people diagnosed with the infection. AI and ML give health officials and data scientist teams accurate ML models to accomplish contract tracing effectively. Leveraging AI and ML is a faster way to accelerate contract tracing at scale.
AI-powered apps in early diagnosis and drug discovery
Perhaps one of the most significant benefits of AI and Ml in healthcare during the global health crises is accelerating drug discovery. This is leading to many advantages over conventional clinical decision-making methodologies. A notable example of this was the usage of AI models from Mount Sinai that were trained to predict critical COVID-19 cases. Researchers at Mount Sinai’s Icahn School of Medicine used EHR data from the pandemic’s first wave. They then developed AI tools to assess the short-and medium-term complications of COVID patients. Organizations use AI to predict early diagnosis accurately and make changes in the early intervention for prompt and effective treatment of a disease.
AI-based algorithm to identify chest X rays
Physicians can quickly glean insights from AI-based algorithm to identify chest X-rays of Covid-19 patients at high risk of chronic conditions and perform the right interventions. With a focus on delivering proactive critical care interventions specific to each patient, healthcare-technology and services vendors developed a unique artificial intelligence-based red dot algorithm. The red dot® algorithm is an intelligent algorithm that is designed to identify suspected findings(pneumonia). When done right, this intuitive system could take the complexity out of the healthcare systems burdened with the need to diagnose and treat COVID rapidly. Whether the need is to improve patient experience or cost-savings to health services or improve healthcare delivery systems, the red dot® AI algorithm is essential to accomplish these goals.
Prioritize patients for COVID-19 shots and vaccine supply chain management
Worldwide, healthcare organizations and governments are vaccinating citizens against Covid-19. However, they are not sure how to prioritize. The decision of who should be vaccinated first can’t be a shot in the dark. Healthcare organizations can address the growing questions around high-risk conditions or other factors by leveraging AI to prioritize patients for COVID-19 shots. From identifying High-risk patients to vaccine supply chain management, AI and ML algorithms accelerate complex processes. AI also helps in streamlining patient communications and prioritizing access.
The care team’s goal is to reach out to high-risk individuals and offer help. To do this, they must have patient data that is relevant, timely, and trusted. ClosedLoop.ai’s AI-based predictive model offers care teams the capabilities needed to prioritize care management outreach to patients. As Pharmacy companies navigate the complex vaccine supply chain management and clinical research journey. Managing risks across all contracts, including unwanted expiries/renewals, and non-compliant clauses, prioritizing resources is a common hurdle. Pharma industries turn to AI for digitizing vaccine supply chain distribution. It also helps quickly identify any risks and obligations surrounding vaccine rollout.
Fighting misinformation with AI
People impacted by the pandemic are disconnected from each other and live in a virtual world already overloaded with information from various sources. As a result, it is difficult to identify the source of information and spot fake ones. With intelligent, enterprise-class, AI-driven new computer vision classifiers, and local feature-based instance matching, Facebook could weed out fake news from its platform.
The main challenge of identifying misinformation surrounding articles is figuring out how to detect images that a human eye can’t spot but AI algorithms can. Facebook’s systems make it easier to successfully spot the difference by a “similarity detector” driven by artificial intelligence. Facebook’s AI tool is designed to filter out images that look graphically similar but have malicious information.
Adopting AI and ML in Healthcare: What are the challenges?
Inaccurate data can cause inconsistencies
Businesses seeking to capitalize on the potential of AI and Ml initiatives to improve the speed and accuracy of decision-making shouldn’t overlook the accuracy of clinical data. However, AI algorithms are only as good as the quality of the data being fed into them. In the world of AI, data accuracy is all it matters. Organizations need data labeled correctly, be free from errors, and have no missing values. This enables them to set up AI solutions quickly and respond to their dynamic environments.
AI algorithms are not free from bias
Despite the strong focus on AI and ML efforts, many organizations do not believe their AI algorithms are free from bias. Healthcare organizations are unsure of the benefits AI and Ml will bring to their business. AI offers healthcare businesses promising opportunities, however, it is good to be wary of data input. A move to AI should make life easier by reducing human efforts. However, the lack of the right dataset can often add complexity to build robust models. Organizations must make more efforts to create bias-free algorithms for a powerful AI.
Maintaining data confidentiality
Healthcare organizations need to comply with various health data privacy regulations, including HIPAA, to maintain data confidentiality. It comes as no surprise that organizations are getting the most value from patient data. However, they are equally struggling to keep up with new data privacy requirements. Continuously delivering AI systems with the right quality of data should be a business priority. To achieve it, organizations must integrate strong ethical and moral guidelines and build proper processes in their way of working.
Conclusion
Despite the array of disruptive AI and ML technologies in healthcare, many organizations are still struggling with their foray into implementation. AI and ML technologies are intuitive systems. However, it often takes new ways of working and effective strategies to ensure implementation is done efficiently and correctly. Inefficient AI and ML solutions harm adoption efforts and lead to an excessive increase in project cost.
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