Find out how data analytics with integrated AI helps healthcare organizations improve their services.
Healthcare data makes up a significant part of the world’s data volume. By 2025, the CAGR of data for the healthcare industry is expected to reach 36%. Only big data analytics copes with such a volume of data, and predictive analytics plays an important role.
There is a traditional descriptive approach to analytics. It uses historical data. There is a more advanced approach: predictive analytics with integrated artificial intelligence (AI). It uses both info from the past and real-time data to identify trends and forecast future events.
AI analyzes large amounts of data that a healthcare organization accumulates. This data includes clinical results, images, medical claims, etc. AI identifies trends and patterns in this data that are difficult for people to detect manually.
The Precedence Research report notes that the global market size for AI in healthcare is expected to surpass around USD 187.95 billion by 2030. The CAGR from 2022 to 2030 will be 37%.
How Is AI Used for Healthcare Analytics?
Healthcare businesses have long been using AI for the prediction of adverse events and optimization of operating-room scheduling. And now there is a new tool – generative AI.
With this technology, you engage with analytics by asking questions in plain language and getting responses back also in plain language through AI Health Bots.
To achieve this, specialized large language models (LLMs) are used, which are trained on data sets from particular healthcare organizations, using special LLM training approaches like fine-tuning or retrieval-augmented generation (RAG).
Diagnosis and Prediction of Diseases
It was found that AI models could be as accurate in diagnosis as experienced radiologists.
Analytics solutions with integrated AI identify patients from an “at-risk group”, for example, those at risk of cardiovascular diseases. Analytics checks clinical info (cholesterol levels, blood pressure, etc.) about patients and social determinants (environment, income level, food, etc. ) that influence diet and exercise. It predicts that specific patients are at risk of cardiovascular outcomes such as heart failure, readmission to a hospital, and so on. Such “predictions” allow the provider to plan the care of at-risk patients, minimize the risk of health decline, and reduce the overall burden of hospital care.
Treatment Outcomes Prediction
Whether kidney function will be restored in patients with acute kidney injury? Whether children will recover after traumatic brain injury? Whether a hip replacement is needed? How will the immune system respond to the success of tumor immunotherapy (whether patients are candidates for immunotherapy)? Research shoWhether kidney function will be restored in patients with acute kidney injury? Whether children will recover after traumatic brain injury? Whether a hip replacement is needed? How will the immune system respond to the success of tumor immunotherapy (whether patients are candidates for immunotherapy)? Research shows that machine learning models predict how patients will respond to therapy, and the likelihood of serious complications.ws that machine learning models predict how patients will respond to therapy, and the likelihood of serious complications.
AI in Pharma
There are complex patterns and factors that influence demand. Pharmaceutical companies want to anticipate demand and forecast customer preferences to optimize production. AI allows them to do this. It is enough to provide it with the following information for analysis: customer reviews, market reports, sales figures, and weather patterns. Novo Nordisk, AstraZeneca, and Pfizer are already reporting how they have optimized their supply chains by tens of percent using the latest technologies.
How AI transforms Business Intelligence
Traditional analytics works with structured data. It summarizes source data and provides access through dashboards and reports. However, 50% to 90% of healthcare data is not ready for analysis and is usually scattered in different places. That means fewer chances to predict emergencies, improve diagnoses, and optimize treatments. Analysts have to spend most of their time just organizing the data to make it usable for analysis. There is little time for the actual analysis.
AI usually needs a lot of resources, and it’s not always possible to provide them locally, but with large cloud providers, this is not a problem.
The question is, how does this combination work? First of all, the data for analytics needs to be uploaded to the cloud. Usually, cloud providers offer so-called data lakes. These data lakes store clinical, visual, and medical device data.
A few words should be said about Text Analytics. What does this module do? Since it is based on an ML model, it reads text like clinical notes, extracts diagnoses, medication names, symptoms/signs, or age, and places them into the appropriate tables. Isn’t it a great replacement for manual work by analysts? Naturally, such data is easier to analyze, and we solve the main problem we mentioned earlier. Now analysts have time for deep analysis, rather than reading and copying data from clinical notes into tables.
Without AI, data engineers manually create analytics models. They work not as well as when ML models are connected. Take, for example, a model like Trial Matcher – it’s simple enough to give it lists of patients and requirements from clinical trials to get a list of suitable candidates. Or the Onco-Phenotype model. Life for oncologists has been significantly simplified, now it’s enough to give a list of clinical records of oncology patients to get information on the cancer stage, tumor location, and histology. Yes, this is not a replacement for the doctor, but it is a significant help.
The bottom line
The amount of data in healthcare is growing. The hype around data analytics and generative AI has already passed. Now organizations allocate budgets to implement it. News like “the British healthcare system is saving a billion dollars annually thanks to AI” no longer surprises anyone. Integrating AI into data analytics may be complex. However, in the long run, such transformation is unavoidable.
