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Harnessing health data for sustainable care | 2030

· Tech

Big data combined with advanced analytics can help deliver sustainable healthcare when the human body is being analysed and described at levels of detail never previously possible.

A learning healthcare system leveraging integrating data streams and assisted decision making may help the world’s complex and diverse healthcare systems to find cost-effective solutions to meet the evolving needs of the world’s patients, where the growth in health spending is out-pacing the rest of the global economy. In order for the world to meet the health-related SDGs, a fundamental shift in policies and strategies is required.

Impact on society Personalized treatments and services could vastly improve the health of patients, who, ideally, should also have control over their data. In addition, humanmachine hybrid workforces enable more effective resource allocation and treatment options to improve organizational efficiencies and patient treatment. However, privacy violations and the misuse of personalized medical information for unethical reasons pose significant risks, as does unequal access to the benefits of big health data, which could deepen existing inequalities and create new ones.

AI in clinical systems and decision making

Advancements in artificial intelligence, or AI, are laying the foundation for this developing technology to be used in clinical decision making. AI is already prevalent in the interpretation of medical images, with 14 algorithms FDA-approved as Class II medical devices to date. However, patients are being represented in increasingly complex and clinically relevant ways. Some molecular profiling methods, in particular, could inform clinicians’ risk stratification and patient management and/or treatment tailoring strategies. Health systems also stand to gain greatly from the application of AI, including the development of risk-based outcome predictions. Natural language processing could be used both as information readers and writers (or scribes) for the effective digitization of clinical observations and notes. Large gains in efficiency and productivity alongside improved quality of care could also be realized through AI-improved workflows. This, in turn, could reduce the occurrence of medical errors, which also has positive implications for patient safety. Finally, AI could reverse the decline in face-to-face time between clinician and patient, where doctors’ and nurses’ time may be freed for increased patient interaction. This is an important point because, despite the potential of AI in healthcare, the ultimate aim is synergy rather than replacement of human care givers.

Source : DNVGL

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