By Jorge Torres
Artificial intelligence (AI), machine learning, and predictive analytics continue to transform multiple business sectors in the modern economy, and the healthcare industry is no exception. A variety of interesting use cases abound, including AI helping doctors diagnose patients, healthcare insurance providers using machine learning models to predict high-cost claims, and data analytics crafting an effective drug treatment plan.
The emergence of these technology innovations offers hope to optimize everything from patient treatment to healthcare insurance processing. It’s a major reason why the worldwide market for AI healthcare applications is nearing $7 billion, according to Grand View Research. With an eye toward a better understanding of the growing influence of AI on the world of healthcare, here are some of biggest trends affecting the industry in the post-COVID-19 era.
Machine Learning Helping to Identify At-Risk Patients
Healthcare typically generates massive amounts of data. AI and machine learning help medical companies and insurance providers glean actionable information out of medical records stored in databases and unstructured data sources. Machine learning is making a significant difference in identifying at-risk patients and subsequently improving their diagnoses.
As previously noted, machine learning models that are trained to analyze images help doctors with early identification of diseases and other ailments. This approach helps doctors craft preventative treatment plans leading to a better outcome. Furthermore, machine learning models—combined with mobile technology—allow caregivers to highlight anomalies in medication and at-home treatment compliance.
The models identify those patients at risk of not following their treatment regimen, enabling a nurse or clinician to remotely assist them in staying on track. These personalized interactions between patients and their health providers are paving the way for optimized telehealth. With the help of automated systems, medical professionals can now get deeper into preventive medicine and suggest different ways for a patient to improve their health through exercise, meditation, and a proper diet. Wearables and mobile apps can tie it all into a viable complete approach to patient-centric care.
Healthcare Insurers Leverage Data Analytics to Predict High-Risk Claims
Health insurers also benefit from machine learning models identifying high-risk claims, helping them calculate more accurately, which saves costs in the long run. The industry considers high-cost claimants to be those with over $250,000 in claims on an annual basis. Notably, they make up only 0.16 percent of those insured but ultimately account for 9 percent of all healthcare spending.
Considering the massive amount of medical claims records, healthcare insurance providers are increasingly turning to machine learning to better identify these claimants. These models tend to be highly sophisticated, with thousands of variables taking into account both clinical and demographic data. In addition to finding persons potentially at high risk, the model’s algorithm also returns a score denoting their overall risk level.
Caregivers use the output of these machine learning models to stage interventions with the potential high-risk claimants. A doctor uses the risk score along with the overall health of the patient to suggest additional diagnostics or a referral to a specialist if necessary—similar to the approach noted earlier. These interventions are aimed at improving the patient’s overall health and the prevention of conditions requiring expensive medical care.
In the long run, the significant cost savings and improved patient outcomes from the use of machine learning is likely to lead to more insurers and medical organizations following this trend. Of course, more sophisticated models that take advantage of improved medical records data are likely to improve the efficacy of this approach in the future.
The Democratization of AI in the Healthcare Industry
As the use of AI and machine learning in healthcare continues to mature, expect more medical caregivers to be able to run machine learning models without the assistance of an expert. New tools even provide a simplified user interface that returns an explainable AI result set, providing valuable insights into how the model generated its findings. This helps the healthcare professional truly understand the data and its impact on patient care.
As noted earlier, mobile technology also lets healthcare providers access model results remotely, an especially useful benefit for a number of medical use cases. Additionally, many patients now use smartwatches to both generate and view their health data. As such, expect wearables to become a new frontier for the use of machine learning in healthcare.
Ultimately, the healthcare industry is realizing that machine learning is not a replacement for clinicians and caregivers. The idea of AI replacing doctors will remain in science fiction for now. What these trends show us is that the vision for a better healthcare system is the active collaboration of healthcare professionals with AI/machine learning systems to augment the decision-making capabilities of healthcare providers and payers to benefit the populations they are responsible for.
Jorge Torres is the co-founder and CEO of MindsDB.
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