Why Depend on Only One Source for Modeling AI in Healthcare?We may be missing many of the ways AI can help us
Domestically, the United States appears to have a single source for modeling known as the Institute for Health Metrics and Evaluation, founded in 2007. IHME publishes data for policymakers and funders based on what are assumed to be impartial evidence-based assessments. However, a single source of public health information risks capture by financial contributors, funders, and selected collaborators.
As an article in Health Care Weekly (September 2019) noted, medicine is a complex business and AI, wisely used, can certainly help healthcare. Long term and short term illness drives all other lanes to the healthcare industry. Variations, complications and multiple illness conditions of individuals challenge scientists, doctors, clinical trials and laboratory professionals. Illness can be hereditary, can be caused accidentally or by personal habits. One solution is for cases to be digitized without violating HIPAA Security Rules. Standards already exist for safeguarding electronic health information for confidentiality, integrity, and security. Trends, probabilities, and tracking using AI provides user functionality at the industry environment level all the way to the direct treatment level of the patient. Proper symptom evaluations, diagnosis and treatment enabled by access to AI would deliver a defined menu of medical solutions at all levels of the healthcare industry.
AI can help combat illness in medicine, surgery, and lifestyle management:
- Medication research and development is a constant pursuit of chemistry where scientists review and test existing medications and their effects, along with evaluations of dosage changes or combinations of chemical algorithms.
- AI has a large role in wading through the chemical inventory for balancing and assessment of clinical and laboratory trials. AI applications to summarize calculations can streamline bureaucracy not only at the level of day-to-day research but aid in evaluations of animals and humans and help the Food and Drug Administration in testing and final approval, speeding the process.
- AI has a role in surgery too. It enables us to develop artificial devices to restore normal body functions. AI guides the role of applications and inventions of raw materials for many types of replacement surgeries where the advanced use of acrylics, titanium, or carbon variations are not only more commonplace today but are opening pathways for the continued use of other newer materials.
- Robotic learning via AI has enabled more accuracy and precision in surgeries by reducing the incidence of missing steps, surgical instruments left behind, and misread scans or imaging. The wait time for surgery is reduced, medical personnel can be assigned more efficiently, and recovery time and long term patient outcome is improved. If a facility does not have surgical robotic technology, a patient can be transferred to one that does or, alternatively, medical personnel can be provided with the machine-learned intelligence to assist in their own location.
Lastly, there is the use of AI to help patients with a needed lifestyle change, as new gadgets and apps launch every day. The impact and reshaping of individual life is hard to measure, however the human connection is growing vastly. AI can also improve the workplace and workflow for safety and health by offering an inventory of work environment recommendations. Security camera footage can improve learning, interactions, and safety. These machine-learned suggestions go beyond eating a healthier diet and recommended daily exercise; AI-learned information can help alter behaviors and empower health literacy. For one thing, machine learning offers individuals access to comprehensive information beyond a brochure or public service message.
Beyond that, improved nutrition and supplements or substitutions learned through AI would dramatically improve immunity and body function regulation, and minimize risk and the need for other preventive measures. Self-care is not generic; it is based on individual biological makeup, yet with consultation with a medical professional, AI output and summaries can offer analytics perhaps otherwise not considered for health management. AI can offer choices for maintaining hygiene, treating allergies, heart disease, diabetes, cancer(s), types of exercise or daily physical therapies, over the counter supplements, pain management, and even obesity. Self-care is well-care and AI output has a significant role to mitigate health risk.
AI also is positioned to address the bias and bureaucracy when machine learned collaboration is shared and applied. Synthesizing data, chemical formulations, modifications, and clinical trial results via AI offers digitized weapons in the battlefield of healthcare burdens across the globe. AI can provide quantum learned information to address mechanisms, capabilities, plans, policies, and procedures for drills and exercises to ensure that governments and the healthcare industry are poised to deal with illness outbreaks and contagions.
Considering all the uses to which AI may be put in health care, getting our guidance exclusively from the Institute for Health and Metric Evaluation for modeling is reckless. Across the world, there are health organizations, specialist health scientists, healthcare providers with many research studies and trials in both the public and private domain which may not be included in a wider scope of metrics and evaluations. Can we afford the risk of ignorance of the opportunities?
Also by Denise Simon:
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