4 Key Benefits of AI in Healthcare Data Management
AI sifts through enormous volumes of healthcare data, speeding up research and development and improving care outcomes. It also improves administrative workflows and frees staff to spend time on patient-facing tasks.
For instance, IBM’s Watson combines data to create a radiation plan for cancer patients and predict side effects. Atomwise’s AI helps to speed up drug discovery by screening 10 to 20 million genetic compounds each day.
1. Improved Insights
AI has been helping businesses move faster by automating routine tasks and delivering insights and enhancing data management. It enables faster response times and provides new opportunities to deliver customer service with a personalized touch.
The healthcare industry is no exception. AI is increasingly being used to support business needs in many healthcare departments, including patient management, research and clinical trials. For example, AI-enabled software can help physicians manage complex patient care plans by tracking the status of each individual’s condition, as well as provide a single view of their data across multiple departments or sites.
In addition, AI technology is being developed to help clinicians save time and avoid unnecessary procedures by alerting them of a patient’s early signs of deterioration. By using predictive analytics and machine learning, it is also possible to identify high-risk patients and flag them for a hospital stay or home-based care options.
Furthermore, AI can assist with the clinical trial process by accelerating data processing and helping to eliminate manual, error-prone tasks. This will enable researchers to more quickly test potential drug treatments and clinical trials, and may even increase the number of participants enrolled in a study.
AI is also being used to improve the quality of medical images. For instance, a radiologist can use an image-analysis algorithm to automatically detect abnormalities and provide a more accurate diagnosis. Similarly, patients can be provided with a more accurate prognosis based on the analysis of historical medical records.
However, it is vital to keep in mind that AI-enabled tools can only be as good as the information they are fed. Without appropriate testing, risk mitigations and human oversight, AI can make errors that are costly at best and dangerous at worst.
In addition, AI technologies can be used to streamline the revenue cycle by identifying and correcting billing errors. This can result in significant savings for healthcare organizations. Additionally, AI can be used to monitor compliance with various healthcare regulations, such as HIPAA in the US and GDPR in the EU. This can prevent data breaches and penalties. As a result, incorporating AI into your healthcare organization can provide numerous benefits for your patients, employees and shareholders.
2. Streamlined Operations
Healthcare is a specialized field with unique requirements and regulations. AI tools can streamline many of the administrative tasks associated with it, freeing up human time for more important work. For example, Waystar’s Hubble is an AI-powered revenue cycle management system that optimizes billing processes in medical practices by automatically identifying and applying appropriate codes to bills and ensuring compliance with billing regulations (source).
AI algorithms can also automate the process of pulling data from various sources and transforming it into the form that healthcare teams need for analysis. This can improve operational efficiency and reduce the risk of error. Moreover, it allows clinicians to focus on providing better patient care and enhance the overall patient experience.
AI can also enable healthcare organizations to make more informed decisions and provide more individualized care. By analyzing large amounts of data and using predictive analytics, AI can identify potential health risks for individuals and groups, enabling providers to offer preventive services or intervene earlier when necessary. It can also enhance decision-making by incorporating insights from a variety of sources, including data from wearable devices and remote monitoring systems.
Clinicians are often pressed for time and stressed by the demands of their work. AI can help them perform repetitive, high-volume tasks more quickly and accurately than humans, which helps alleviate some of the burden on busy healthcare professionals. This can help avoid burnout and allow them to spend more time on patient-facing activities like consultations and surgeries.
In addition, AI can also help speed up the development of new drugs. It can identify potential drug targets and match them with existing molecules, which can significantly reduce the time to market for new medicines. It can also assist with the design of clinical trials by creating digital twins of patients and allowing doctors to test treatments in a virtual environment before implementing them in real-world settings. This can help ensure that treatments are safe and effective, as well as increase the likelihood of success. This is particularly important in complex areas of healthcare, such as genetic testing, where there are numerous potential outcomes.
3. Enhanced Data Accuracy
Unlike traditional data analysis tools, AI can process massive amounts of information in real time to identify errors and inconsistencies. It can also learn and adapt over time, catching patterns that may be missed by humans. This translates to higher accuracy for business processes, including data migration and management, ensuring the integrity of data for better decision-making.
Healthcare organisations are beginning to use AI to improve their data analytics capabilities, including enhancing the accuracy of patient records. Using natural language processing (NLP), AI can read unstructured documents, such as clinical notes or research articles, and automatically generate structured data for analysis. This eliminates the need for manual intervention, reduces risk of human error, and allows healthcare professionals to focus on more complex tasks.
In addition, AI can be used to automate the migration of data from one system to another. This can be a complex task, but AI can streamline it by identifying any inconsistencies or missing values, and automatically updating the target system. This saves time, increases efficiency, and helps ensure data integrity.
Another area where AI can help improve data quality is in identifying potential risks. For example, AI can be used to detect duplicate entries in a database, which can result in inaccurate results and costly mistakes. AI can also identify erroneous data or out-of-date records and flag them for action.
Moreover, AI can be used to monitor and alert healthcare workers of any issues with data, such as inconsistencies or anomalies. This can prevent delays in diagnosis and treatment, which could ultimately lead to improved outcomes for patients.
Finally, AI can be used to analyze large datasets and predict trends. This can help healthcare providers allocate resources more effectively, improve public health strategies, and enhance patient care. For example, by analyzing genomic, medical history, and lifestyle factors, AI can create personalized treatments for each patient. This can improve outcomes and reduce side effects, while also providing a more holistic approach to patient care.
Clearly, AI has the power to transform many aspects of healthcare. However, it’s important to note that these benefits are only possible if the right foundation is in place. This includes ensuring that AI systems are built on accurate, up-to-date, and unbiased data, training the system to avoid bias, and incorporating ethical guidelines for the design of AI tools.
4. Improved Patient Experience
Patient experience has become a key focus for healthcare organizations as patients demand streamlined and individualized care. This is especially true for unexpected health events that can’t be scheduled, such as an emergency room visit or a hospitalization. AI can provide a number of patient-facing improvements to improve the customer journey, including helping healthcare teams quickly and accurately communicate with customers, and automating repetitive tasks such as insurance claim scrubbing, payment processing and provider credentialing.
AI can also help support telehealth and virtual appointments, making it easier for patients to connect with their doctors remotely. This can be particularly helpful for elderly patients or those living in rural areas, and it can help to ease the burden on healthcare staff. For example, natural language processing in AI can be used to facilitate communication during virtual appointments, providing a more personalized and seamless experience for patients.
In addition, AI can make it faster and easier for healthcare professionals to access the data they need. For example, an AI system could automatically sift through large amounts of patient data to identify the most relevant information and quickly deliver it to physicians when needed. This helps reduce the need for manual sifting through patient records and can save significant time and effort when compared to current methods.
Finally, AI can improve patient safety by preventing fraud and ensuring compliance with regulatory standards. For example, AI can use advanced encryption techniques to secure and protect patient data in transit and at rest, reducing the risk of cybercriminals accessing sensitive health data. Additionally, AI can be used to spot fraudulent activities like unbundling (billing for individual steps of a procedure as though they were separate procedures) and performing unnecessary tests to take advantage of insurer payments.
While there are some concerns about the use of AI in healthcare, most experts agree that it offers enormous benefits and can be safely implemented to enhance patient experiences, improve clinical decision-making, accelerate drug discovery and much more. As such, incorporating AI into data management processes is vital to achieving the most valuable outcomes.