Everyone knows that healthcare costs are out of control. The only question is: what can we do about it? It’s clear that current models of healthcare aren’t sustainable for much longer. However, with the rapid advancement of technology, there are new solutions emerging as well. One of those solutions is artificial intelligence (AI). AI has already proven itself in many industries and is now beginning to have a positive impact on the cost and sustainability of healthcare.

The AI-enabled future of healthcare is one in which the digital monitoring of patients and analysis of collected data have become more streamlined. However, this leaves healthcare leaders with the question: how can we use AI to reduce costs and improve patient care?

The case for considering ESG factors and potential real-world outcomes, when investing in AI, is particularly strong when it comes to responsible venture capital investments.

There are both positive and negative ESG implications associated with emerging and rapidly evolving technologies such as AI, not just for the companies developing, marketing, or using them, but for individuals and societies in general.

AI In Electronic Health Records

Digital transformation has become a key focus for healthcare organizations as they seek to reduce costs and improve efficiency. The use of AI in electronic health records (EHRs) is one example of how AI can be used in preventive care to reduce costs. Using AI, an EHR can deliver comprehensive care management across all providers in a single system. This can help patients receive care that is properly aligned with their health goals, which could lower the number of tests needed for diagnosis and the number of readmission rates.

An AI-enabled EHR can help providers improve the quality of care and reduce costs in several ways. It can help providers deliver better care by improving the ability to track and follow up with patients and increasing provider productivity. It can also help providers prevent medical errors by automatically identifying potential issues and alerting providers. This could result in fewer medical malpractice claims and curtail misdiagnosis potential, both of which can drive up costs.

Radiology and AI

Computer-aided detection or computer-aided diagnosis (CAD) uses AI to analyze images. It’s deployed in radiology and pathology to help diagnose diseases. CAD has been available since the 1980s, but it currently has limited capabilities. However, AI is expected to improve CAD significantly. For example, AI can be used to analyze images more accurately and eliminate false positives. It can also be used to automate the process of detecting diseases, which is currently done by humans. Cad systems that use AI can improve outcomes by reducing the number of biopsies and boosting the number of correct diagnoses.

AI-Enabled Diagnostics And Prognostics

Another potential use of AI in diagnostics is in prognostics—predicting the course and outcome of a patient’s condition. This could help providers determine when a patient is likely to be discharged and when they should be referred to another specialist for further testing. This could reduce the number of patients who are kept in the hospital longer than necessary, which could amount to cost savings for providers.

AI And Drug Discovery

Drug discovery is the process of designing new drugs and finding new uses for existing drugs. It’s an extremely expensive process that has seen limited success. Most drugs that come to market are based on just one mechanism of action. That’s because the drug discovery process is very lengthy and requires enormous sums of money. Healthcare AI can reduce the cost and time of drug discovery.

AI In Identifying And Preventing Fraud

Healthcare fraud is a huge problem in the industry, costing billions of dollars each year. AI can help pinpoint and prevent fraud by identifying anomalies or suspicious activity in claims, billing data, payments, or other organizational data. It can analyze large volumes of data in a short amount of time, allowing fraud analysts to quickly identify and address issues before they become problems.

One example of how fraud detection AI works is through the use of natural language processing (NLP), which allows computers to understand human language in the same way that humans do. This allows AI to identify signs of fraud that employees of healthcare organizations might not notice, such as unusual verbiage, claims being submitted from unusual locations, or incoherent data.

AI And Robotic Assistants In Hospital Rooms

While the concept of AI-enabled robots in hospital rooms may seem odd, they could be a significant way to reduce costs. Robots could help providers cut back on unreasonable costs of human labor and influence the sustainability of healthcare systems in a positive manner. They can also help with the accurate delivery of supplies and medication and circumvent infections owing to human error.

A computer-assisted delivery system could also help providers improve the accuracy of prescriptions by using data analytics to identify potential interactions with other medications. This could cut down on the number of wrong prescriptions and side effects that could be harmful.

AI Streamlines Supply Chain Management

Healthcare organizations need to stock everything from basic medications and medical equipment to specialized items such as MRI scanners and surgical instruments. Unfortunately, the supply chain for healthcare is notoriously inefficient and rife with problems. This is especially true given the current political climate and the threat of trade wars.

AI is ideally suited to help streamline your supply chain management. It can use machine learning algorithms to predict demand and prevent expensive shortages. What’s more, AI can also use analytics to identify inefficiencies in your supply chain and suggest ways to avoid them.

AI In Improving Resource Management

Healthcare providers can also reduce costs by improving resource management. This can include optimizing staff schedules and the use of medical equipment and supplies. For example, virtual assistants can be programmed to monitor and manage the usage of medical equipment. This helps to prevent equipment breakdowns and minimize maintenance costs.

Virtual assistants can also be programmed to monitor staff schedules to help them reduce overtime and shift swapping. This can be especially useful in areas where access to physician specialists is limited.

Is Healthcare AI Worth Your Investment?

Artificial intelligence is indeed a very exciting field, and we think that there’s a lot of potential for growth in the healthcare sector. When you’re deciding whether to invest in a particular subcategory of AI, you should always make sure that the technology is a good fit for you. When investing in healthcare AI, be sure to keep an eye on the following trends: the transition to data-driven decision-making, the rise of digital health, and the AI advantages covered above in the current state of affairs.


As the healthcare industry continues to “marry” AI technologies, it’s clear that the benefits go far beyond the hype. Although there are potential pitfalls and challenges along the way, there’s no doubt that AI boasts the capacity to help providers better the quality of care while ditching excessive costs. What remains to be seen is how quickly these benefits will be realized and to what extent providers will be willing to commit to new technology as time progresses.

  • Artificial intelligence has been hailed as a technological revolution. Finance and healthcare are among the first industries AI will disrupt. The world of artificial intelligence (AI) has been growing rapidly. We are now in an era where machines have the ability to process data, analyze it, and draw their own conclusions based on those analyses.

    read more
  • Artificial intelligence has been hailed as a technological revolution. Finance and healthcare are among the first industries AI will disrupt. The world of artificial intelligence (AI) has been growing rapidly. We are now in an era where machines have the ability to process data, analyze it, and draw their own conclusions based on those analyses.

    read more