How Artificial Intelligence Helps in Healthcare
Artificial intelligence (AI) can help healthcare providers and doctors tailor patient care to each patient. It also reduces administrative burden, improves clinical trials, and can help predict a patient’s risk of hospital admission. This article will describe some of the latest AI advancements in healthcare. It also explores the ways AI can improve patient care.
AI enables doctors to personalize care
AI is already helping doctors and other healthcare professionals to better understand patients and their needs. Its algorithms can read vast amounts of data and make sense of it all, allowing doctors to focus on what matters most to each patient. AI has been shown to reduce the risk of a patient suffering from a health crisis, which is why it’s an important tool in improving care.
The healthcare AI market is estimated to grow at a 40% compound annual growth rate, reaching $6.6 billion by 2022. According to Accenture, AI will save US healthcare systems over $150 billion per year by 2026. The technology can be applied to a variety of fields, including health insurance, drug discovery, and surgical aid robots.
AI also has the potential to improve healthcare administration. It can automate routine administrative tasks, such as following up on unpaid bills, pre-authorizing insurance, and keeping patient records. AI can also analyze large data sets, pulling together insights from patients and predicting outcomes.
AI reduces administrative burden
Artificial intelligence (AI) can help reduce administrative burdens in healthcare. Traditionally, human healthcare providers spend most of their time on administrative tasks. With the help of AI, these tasks can be redistributed, giving healthcare providers more time to focus on patient care. It can also improve workflows, reducing physician burnout.
Healthcare providers are increasingly seeking ways to improve patient care and minimize costs. AI can help bridge the reimbursement gap between providers and payers by automating administrative tasks. It can also prioritize high-value claims, manage documentation, and prevent claims denials. These solutions can help speed cash flow and decrease days in accounts receivable. Furthermore, they can improve the management of staff and claims data.
AI can also be used in healthcare organizations and hospital systems to reduce administrative burdens. Many administrative tasks place significant strain on healthcare organizations, including financial tasks. With intelligent automation, unnecessary tasks can be eliminated, cutting costs and improving patient care.
AI improves clinical trials
Artificial intelligence (AI) is redefining the way clinical trials are conducted. By predicting the outcomes of trials, AI can mitigate potential risks. AI can streamline protocol development and reduce or eliminate the need for patient visits. It can also improve patient monitoring and reduce dropouts. Moreover, AI can identify cohorts of patients that require more research. For instance, it can identify participants with specific medical conditions and conduct sub-trials of those patients.
Artificial intelligence can be used to improve clinical trial outcomes by using computer vision, reinforcement learning, and temporal data. The technology can also be used in virtual, decentralized, and wearable medical trials. These trials make use of wearable medical devices, patient-driven virtual healthcare interfaces, and sensory-based technologies.
AI can also help optimize the selection process of patients. The recruitment process typically involves searching for potential patients from clinics and hospitals, but AI can help identify the best cohorts based on their characteristics. For example, AI can help identify regions where certain medical conditions are more prevalent than others. Similarly, it can help speed up the cohort identification process, which in turn will help improve clinical trials.
AI predicts risk of hospital admission
A new AI tool has been developed that can predict the risk of hospital admission in patients with occupational injuries. It uses data from a patient’s medical history, latest clinical assessment and cumulative labor progress to provide an accurate risk score. In a study published in June, researchers found that the AI model performed well in real clinical workflows.
Researchers found significant associations between different features and readmission rates. The p-values of these features were shown in Table 1. The two most significant features were ICU admission and COVID status. These factors were associated with increased risk of readmission after hospital discharge. In addition, patients were more likely to be readmitted if they had previously undergone surgery.
The XGBoost model performed best on the tests, outperforming the six other ML models. This algorithm was able to predict the risk of hospital readmission with an accuracy of 91.7%. In addition, the model had a high sensitivity, specificity and F-measure. Furthermore, the model showed good performance in COVID-19 readmission prediction. It is possible that the developed algorithm can be applied in other domains of hospital resource use.