Time for Artificial Intelligence to Meet Healthcare Costs

Nov. 24, 2018
AI promises to process large amounts of data and make meaningful conclusions out of them, but one of the main barriers is cost.

Technology has improved the entire face of the healthcare industry. It has lessened the time taken to examine and diagnose a patient as well as eased the processing and retrieval of records. Artificial intelligence (AI) promises to do much more for the constantly changing industry. The changes envisioned include, lowered cost of healthcare, improved access to information, shorter service times and fewer errors due to misinformation or data mishandling.

AI promises to process large amounts of data and make meaningful conclusions out of them. The AI systems can process data just like informaticians do and use this feedback to ease operations within the organizations but much faster and with accuracy. For example, both patients and doctors can save a lot of time scheduling appointments and entering patient data into the electronic system. This can be made much faster by the application of artificial intelligence. The scheduling can be made much faster and with fewer conflicts as the data is checked by the system instead of manual work. The institutions can also manage process patient data and use it prepare the institutions facilities for changing needs. This is a better system than the one currently in place as it makes work easier.

AI can also help the government prevent and manage outbreaks that are caused by infectious diseases. This can be done through the use of a process called “modeling.” A mathematical model collects the surveillance data from healthcare workers and creates an outbreaks trajectory within the community. This trajectory is then used to classify community members according to their level of risk. Measures are then put in place to take care of the infected while isolating the recovered and the immune. The information will guide the government to coordinate health care efforts and direct all other related agencies. This process is accelerated by AI as the progress and effectiveness of the interventions can be determined using a similar AI model. Real-time data can also help healthcare workers to prepare themselves regardless of policy and protocol. The overall effect on the human immune system can also be examined.

AI can help reduce hospital readmissions by providing sound management for patients suffering from chronic diseases. The patients can work with the medical staff after they are discharged in order to design a self-reliant program. AI solutions can be programmed into devices such as smartphones and watches. These devices can monitor a patient's vital signs and they can be relayed real time to the hospital a standby team that uses this data to monitor patient's status. The insights from AI can help ensure the execution of the treatment protocol and to change treatment plans if deemed necessary. This will lower the rate of emergency hospitalizations and readmissions and will improve health outcomes and lower the cost of care dramatically. The AI systems may also plan and schedule post-discharge visits and lab and imaging follow up tests. AI can keep an up to date database of clinical and diagnostic information. AI can use this info to generate the alerts and reminders for the treating physicians and the patients to predict the ailments ahead of time.

Evidence-based medicine initiatives can be expanded and improved on in several ways. Tools can be developed to make the information more specific and focused. The starting point is developing a focused question that can provide guided answers. The most promising evidence can be gathered then be appraised by qualified personnel who will later front recommendations. The initiatives can then participate in well-controlled trials that will determine the commercial potential of the initiative. The results can further be published and presented to the relevant authorities as soon as possible. The initiatives can also be protected by enforcing policies that protect the freedom of expression and experience.

There are algorithms that can be used to fast-track the possible outcomes of evidence-based medicine initiatives. They often assist the research team to develop systems that operate like formula. The formula can help a researcher compare the possible outcomes of different patients with different combinations of treatment. The algorithms also utilize other data such as the age of the patients and other existing conditions and drug interactions. Algorithms may include deep learning in their setup that can quickly establish patterns that can be used to develop treatment plans swiftly.

AI can be used to reduce the exorbitant cost of drug trials and to shorten the length of time a drug trial is executed. Algorithms can be developed to eliminate the initial tests. They include things like toxicity tests and early screening for genetic markers and drug combinations. AI can also create a database with a list of the compounds are that didn't have any value. The AI systems may also classify and process background information into relevant information. Software to read through data also provides the best approach for researchers. They are able to generate a summarized report detailing the required communication. This way, time is spent doing other parts of the research. Finally, AI may contribute to the development of sound systems that make human life easier.

One of the main barriers to the implementation of AI systems that can improve the healthcare industry is cost. The systems have a high initial cost that put off many potential investors and stakeholders. Staffing of the trained system users for the institutions are the other major problems. The readily available informaticians and analysts may not give required support as they did not have the necessary skills to use the AI systems effectively. They may leave a lot of information unaccounted and hence won't give optimal results. The different regulatory and compliance bodies also pose problems in registration and approval. Human resistance from the stakeholders is sometimes risky and may even delay a good product from distribution.

Anil Patil is a physician and health IT professional experienced in inventing clinical IT tools for healthcare industries in the U.S., Canada, and India. He has been involved in bridging the gap between diverse healthcare professionals and development and design teams to define, build, and maintain intuitive clinical IT solutions that are critical to patient care, growth, engagement, and customer retention. For more information, please contact him at [email protected].

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