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Healthcare providers face even more pressure to rein in costs while delivering high-quality services and improving treatment outcomes. Meanwhile rising rates of chronic diseases, aging populations, and an increasingly informed and demanding public are placing additional burdens on already overworked healthcare professionals.
With the rapid advancements of AI technologies such as text, speech, and image processing, algorithmic modeling, and machine learning (neural networks and deep learning), medical institutions are now racing to address these challenges by extracting valuable insights from their vast stores of medical data.
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By leveraging AI’s superior analytical, predictive, and learning capabilities, medical institutions, researchers, and insurers, in partnership with tech companies and startups, are producing solutions to enhance a variety of operational, clinical, financial, and workflow outcomes.
The following are just some of the many ways AI is being used in healthcare:
Already the industry standard for call centers, chatbots, and personal assistants offer a means of providing round-the-clock service delivery cost-effectively, whether by phone, online, or via apps. Routine calls and repetitive tasks such as scheduling appointments and visit and medication reminders are regularly automated by AI-driven systems.
Beyond this, AI is allowing service providers to better understand patients and provide more targeted, individualized care. Cognitive AI will, for example, be able to recommend specialists based on a patient’s profile, preferences and previous behavior, as well as support online consultations.
Widespread digitization and the introduction of electronic medical records (EMR) have made available a vast amount of patient data. Searching for patterns and correlations in this data can unlock insights that can then be applied in practice to improve and accelerate everything from preventative care, diagnoses, treatments, and drug development.
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While data mining is common practice, new initiatives from startups and tech giants like IBM and Google, are putting AI tools in the hands of patients, nurses, doctors and researchers, and enabling them to achieve greater accessibility, relevancy and actionability of their data. By aligning decisions with data-backed insights, AI can accelerate clinical treatments, enable repeatable improved outcomes and support administrative prioritization and action.
One key challenge faced by hospitals is the delayed detection of illnesses especially high-risk cases resulting in the avoidable death of patients. AI, the Internet of Things (IoT), and wearables such as the Apple Watch and Android-based devices are enabling people to manage their own health and maintain better habits.
AI, the Internet of Things (IoT), and wearables such as the Apple Watch and Android based devices are enabling people to manage their own health and maintain better habits.
By analyzing and keeping track of test results, vitals and applying statistical probabilities, AI can actively lookout for possible causes for concern, and help alert doctors to early signs of anything from sleep apnea, to cardiac disease, to cancer — faster, and potentially save lives.
The accuracy of a doctor’s diagnosis and success of treatments can vary depending on their level of training, knowledge, and experience, as well as their work processes and the systems that support them. While in the past clinicians and researchers may have turned to colleagues or journals to help with diagnosing illnesses, AI speeds up the search process, tapping in on a vast knowledge base and making proactive suggestions or prompts to treat each individual based on their history and data.
https://youtu.be/HrKzXLgGohA
Dr. Shinjini Kundu discusses how machine learning dramatically improved the early detection of osteoarthritis.
In addition, by tracking the work processes of doctors with high treatment success rates, administrators and educators may be able to pinpoint and establish best practices to improve systems and workflows.
Through the use of AI-backed virtual reality, medical students also benefit from learning and practicing with more accurate and realistic simulations of operations and procedures based on actual cases.
Finally, by utilizing image recognition software to analyze images with a specific diseases label such as tumors and melanoma, deep learning algorithms can be trained to differentiate between, for example, a malignant carcinoma from a benign seborrheic keratosis. This ability to more accurately screen for diseases at accelerated speeds will boost early detection and thus survival rates.
As personal medical data becomes increasingly proliferated, cybersecurity will become increasingly important. To protect sensitive EMR data, AI security tools can uncover hidden threats and automate insights to respond to threats at greater speeds and scale. An integrated security framework may offer greater insight, intelligence, and control across an environment, so IT staff can gain greater visibility into who was accessing what resources and the ability to more quickly correlate and analyze log data, and more effectively govern data access, manage mobile devices, and uncover anomalies that may signal a security threat.
In healthcare especially, time is critical. If more effective drugs can be developed faster, critical conditions such as sepsis can be detected prior to organ dysfunction, or if cancers can be identified earlier to give doctors a greater lead time, mortality rates can be reduced — and ultimately, AI will quite literally be a lifesaver
In short, the three key advantages which AI offers are:
As the industry restructures and realigns itself to the new paradigm shift, it will be those medical organizations that are integrated, capitated, and technology-enabled that will be best suited to succeed and set the standards in the new health and wellness landscape.
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