4 Potential Aspects of AI in Healthcare
healthcaretechoutlook

4 Potential Aspects of AI in Healthcare

By Healthcare Tech Outlook | Tuesday, October 01, 2019

AI has immense potential in the healthcare industry owing to its ability to leverage raw data sets and extracting insights out of it.

FREMONT, CA: Healthcare impacts people from all walks of life. Earlier, healthcare companies strictly followed an ‘innovate-manufacture-sell’ model. While several companies are still leveraging the same model, the top performers are aware of a new revolution in healthcare, which is going to make a difference in the next few years. The next revolution would focus on the overall effectiveness in terms of innovations, cost-cutting, patient outcomes, number of professionals trained, as well as the incorporated software programs. Especially after the proliferation of artificial intelligence (AI) in the sector, technologies such as robotics and big data are all set to collectively enhance the patient experience as well as to offset the healthcare costs.

 AI Leveraging Other Technologies

 AI represents a technology that can analyze massive data sets and generate relevant insights, thereby catering to the cause at hand. Technologies such as robotics and big data have immense potential to assist AI and to amplify its usefulness. With the exponential rise in data, new methods need to be deployed to encompass the huge reservoirs of data. The impact of changing environment can be determined from the fact that earlier medical research consisting of a few thousand patients was considered a large study. Even the hospitals used to conduct their experiments independently, thus limiting their perspective. Using AI, millions of test cases can be analyzed before narrowing down to a particular conclusion.

 Despite AI’s dependence on other valuable incorporations like big data, AI is still the most exciting addition among all the different technologies. Its ability to build algorithms and software to approximate human cognition is aiding the decision making, which was confined to human involvement in the past.

 Clinical Uses of AI in Healthcare

Due to its ability to leverage large data sets, AI has a great potential in diagnostic applications such as imaging scans or pathology report analysis. Moreover, AI-driven solutions can offer quick answers to the diagnostic questions based on the insights gained after the analysis of similar cases. Such solutions can also lead to the discovery of the sources of casualty and counterintuitive patterns for future clinical breakthroughs. The breakthroughs would mean less intervention of a doctor.

 AI and its applications are not an alternative to what a physician does. It’s merely a tool that is growing in its capabilities and can enable nurses and doctors to excel at their jobs by providing them with data-driven and timely recommendations. The doctors can then accept or discard those recommendations based on personal judgment of the situation

Operational Uses of AI

Everyday operations have always been a challenge for healthcare. Contradictory requirements often complicate the situation and make it extremely difficult for the hospitals to offer smooth services in terms of low waiting times for the patients, better utilization of assets, managing appointment slots in case of a heavy rush. Usually, it is upon the staff members to manage many of the above-mentioned tedious tasks such as deciding on how to allot a particular slot to a patient or when to reschedule a patient's appointment. Often, the tasks don't work as per the plan, and the relevant personnel is forced to depend on half-formed tables and predictions to make such decisions.

 A wrong decision may not be catastrophic, but it can significantly impact the patient flow in the longer run. It also affects the reputation of the medical institution, especially when there are numerous alternatives for the patients. AI and ML can play a transformative roles in improving the effectiveness of the decisions on a daily basis and streamline the operational process while delivering the appropriate care. It's a major concern and can't be left unattended. Incorporation of the AI-based solution is possible but not a simple task either. It will require a detailed simulation with comprehensive data sets projecting past records, patient arrivals, time distribution, and management for each task. Such factors can be included in AI's performance algorithm that recommends appointment schedules for the patients and can change dynamically as the relevant factors change. 

 Again, the intent is not to provide an alternative to the front line’s decision-making capabilities. Rather, the insights from AI-driven simulation models will help the front line to make more informed decisions, thereby leveraging the wisdom of the optimization algorithms.

 Cost of Transformation

While a change is often carried out with good intent, the cost of the change may not always be feasible. It’s one of the primary reasons why the medical institutions get skeptical when it comes to the incorporation of AI-driven models. However, the investment is worth the returns, especially in the longer run. AI-powered models learn from historical and new data sets on a daily basis.

 The ability of AI-models to leverage the increasing data sets means that the effectiveness of these models will certainly increase with time. Moreover, AI-based solutions also assist the patients by providing them with early appointment slot, which will release the undue stress on the individuals who are eager for checkups.

 Rescheduling and cancellations are also common affairs in healthcare. The sheer magnitude and complexity of the tasks make it difficult for the front line staffs to avoid an unintended error. AI-model comes to aid yet again. Using good predictive software, mistakes can be almost nullified as it would provide better transparency into the operations.

 Thus AI offers a promising future to the healthcare sector. The primary consideration must be to implement it in a way such that is relevant to the given situation, and the data sets that are fed to the models reflect the real scenario.

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