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Ai In Healthcare: We Must Focus On Improving Outcomes For Patients

According to Accenture analysis, when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026. But savings are not the main reason AI should be applied in healthcare.

Explosive growth

With immense power to unleash improvements in cost, quality and access, AI is exploding in popularity. Accenture estimates that growth in the AI health market is expected to reach $6.6 billion by 2021 – that’s a compound annual growth rate of 40 percent.

The top three applications that represent the greatest near-term value are robot-assisted surgery ($40 billion), virtual nursing assistants ($20 billion) and administrative workflow assistance ($18 billion). As these, and other AI applications gain more experience in the field, their ability to learn and act will continually lead to improvements in precision, efficiency and outcomes.

Robot-assisted surgery leads the AI pack in terms of value potential. Cognitive robotics can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician’s instrument precision. The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights. Such improvements enhance overall outcomes and consumer trust for AI applicability across surgical areas of practice. Robotics outcomes include a 21 percent reduction in length of stay, according to Accenture analysis. The value will only increase with the development of robotic solutions for a greater diversity of surgeries.

Any savings from AI should be reinvested in healthcare

Virtual nursing assistants are another frontrunner of AI value. When AI solutions remotely assess a patient’s symptoms and deliver alerts to clinicians only when patient care is needed, it reduces unnecessary hospital visits. It can also lessen the burden on medical professionals. In the case of nurses, AI can save 20 percent of RN time through avoided unnecessary visits. As virtual nursing assistants become accustomed to patient diagnoses and conditions, their abilities will grow beyond effective triage into expertise and recommendations around patient treatment.

Timesaving administrative workflow assistant capabilities – such as voice-to-text transcription-eliminate non-patient care activities including writing chart notes, prescriptions and ordering tests. This equates to a work time savings of 17 percent for doctors, and 51 percent for registered nurses based on Accenture analysis.

What chances does AI offer to patients?

During this year’s European Health Forum Gastein EHFG, we asked Lydia Makaroff, Director of the European Cancer Patient Coalition, what are the chances and threats related to AI.

“AI has a lot of potential, including for better clinical trials. Machine learning can improve efficiency in clinical trials, especially if linked to intelligent sensors and remote monitoring of symptoms, which reduces the burden on the patient in having to travel to the clinic.

AI can help empower patients, by giving them access to their data and also helping them to interpret and understand what the data mean. Patients are the custodians of their own healthcare data.

Patients will always need human interaction with healthcare professionals. Accenture suggests that $20 billion could be saved by using Virtual Nursing Assistants. However, we need more trained nurses to support patients, not fewer nurses. Nurses are often the only humans who have time to support cancer patients through all stages of their difficult journey. Society should make AI augmentation of human capabilities, and not substitution of human capabilities.

AI and machine learning are connected to the “right to be forgotten”. If an algorithm in the near future will be in charge of deciding whether to grant or not mortgages, loan, it should be made clear that they have no access to all personal data, such as a previous cancer diagnosis, though. Protection of this data is therefore essential.

Any savings from AI should be reinvested in healthcare. Patients must be at the heart of sustainable cancer care, and we must focus on improving outcomes for patients.”


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