Artificial intelligence and big data are gaining momentum and have begun assisting medical professionals to complete processes in ways that humans never could.
Medical organizations have accumulated huge amounts of data: medical records and images, demographics, insurance claims, and clinical trial results. AI technologies are ideal for analyzing this data, identifying patterns, and making connections that are simply invisible to people.
In this article, we will explore specific use cases on how the healthcare industry is leveraging AI and big data.
Big Data in Healthcare
This McKinsey report shows how big data can create an additional source of cost recovery and improve the quality of care. At the heart of big data is the ability to combine information stored in four primary data sources that are currently unrelated.
- data obtained during research and testing
- data from clinics on case histories and diagnostics
- data on patient behavior, purchases, and reviews
- data on the provision of services, the dispensing of drugs, and pricing throughout the healthcare market
Based on an analysis of all these points, big data can be leveraged in the following ways:
- Operational activities of medical institutions. It’s now possible to study the effectiveness of treatment by processing all available information pertaining to treatment practice. Based on an analysis of all known case histories and diagnostics, the widespread use of decision support systems will enter physicians’ practices, allowing clinicians to gain unprecedented access to the experiences of thousands of their colleagues across the country.
- Pricing and payment system. An analysis of invoices and receipts using automatic procedures based on machine learning and neural networks will reduce the number of errors and thefts in payments. Forming price plans that consider the real possibilities of the population and the need for services also increases the overall income from patients.
- Research and development. The most significant effect here will most likely come from the new possibilities introduced by predictive modeling in drug development. Statistical algorithms and big data tools have an equal impact on clinical trial planning and patient recruitment. Processing the results of these tests is another important application of big data.
- New business models. Based on digital health data, these models can complement existing ones or even compete with others. These are data aggregators that deliver analyzed and assembled data blocks that meet the specific conditions of third parties. For example, all medical histories of patients who used a particular pharmacological drug are essential for pharmaceutical companies who are ready to buy such data.
- Mass screening, prevention, and detection of pandemics. This direction is based on Big Data. The development of technologies now allows for building both geographical and social models of public health and predictive models for developing epidemic outbreaks.
The use of big data analysis allows healthcare providers to reduce the length of stay of patients in the hospital, save money, and reduce the number of readmissions.
Artificial Intelligence Use Cases in Healthcare
We’ve explored how big data can benefit the healthcare industry. But to process it correctly, quickly, and successfully, artificial intelligence should step in. Here are the most common use cases of leveraging AI for healthcare.
Patient information can be stored in dozens of clinics and medical records. This complicates the collection of anamnesis and diagnosis. The interpretation of analyses, tests, and images may also not be accurate due to the amount of data. Even if a doctor has all the necessary information on hand, they cannot always interpret it correctly and notice every detail. The lives of patients may depend on it.
Artificial intelligence systems make it possible to recognize diseases even at the earliest stages. For example, Zebra Medical Vision and Arterys services help diagnosticians communicate with patients and eliminate the need to peer into the smallest details of lung scans and ultrasounds of the heart.
These types of AI programs are not only used by doctors but also by patients. For example, the service 23andMe analyzes genetic information and tells the user about the health conditions of their ancestors.
Vaccine development and subsequent clinical trials are lengthy and expensive processes. AI can reduce the development time for new drugs by several times by analyzing the molecular structures of existing drugs and suggesting new ones according to given requirements.
For example, in 2019, Insilico Medicine created several drug options to treat muscle fibrosis in this way. For this task, the algorithms took 21 days, after which the scientists selected the most suitable drug options and conducted a test on laboratory animals for 25 days. Thus, it took 46 days to identify the most suitable remedy.
According to the World Health Organization, low-income countries need an additional 18 million health workers to ensure people around the world have access to health care by 2030. In the future, the situation, most likely, will not stabilize due to population growth, the aging of society, and changes in the clinical picture of diseases. These factors will only increase the demand for highly qualified health workers and make access to medical care more difficult.
Therefore, innovative technologies should contain artificial intelligence and a knowledge base in the subject area. If done correctly, they will free doctors from routine daily tasks and allow them to focus on diagnosing serious issues and treatment choices.
Remote consultations expand access to quality medical care, especially in sparsely populated areas where it is needed the most. In addition, online consultations reduce healthcare costs and allow patients to get a second opinion on research results to clarify a diagnosis and treatment plan much sooner. AI is used for remote diagnostics, collecting medical indicators, and working with patient information.
For example, Google has already developed an algorithm that detects diabetic retinopathy from retinal photographs.
In a Nutshell
With the help of AI, medical institutions can analyze large amounts of data faster and more accurately, greatly simplifying the lives of both staff and patients.
Healthcare organizations can leverage their data, resources, and assets to the best of their ability with AI-assisted pattern analysis technologies. Due to this, all medical, financial, and administrative processes are carried out much faster and more efficiently.
Medical data is often fragmented and stored in different formats. AI makes it possible to connect all this disparate data and form a more coherent view of each patient.
In order to successfully leverage big data and implement AI, start with professional tech consulting. Unicsoft’s end-to-end approach to implementing AI solutions for businesses helps you focus on your primary business needs by preventing the most common AI pitfalls.