The healthcare industry is perhaps second only to finance when it comes to the sheer amount of historical data available for use with artificial intelligence. Data from EMRs, insurance claims, clinical trials, and drug research and development can all be pulled into a machine learning algorithm to generate insights on patient behavior, patient risk, and effective treatments for a variety of conditions, among a variety of others.
In this article, we’ll dive into software available for housing and storing the large volumes of data that healthcare companies collect over time, as well as AI software that uses that data to generate analytics dashboards and garner business value for hospitals, clinics, and other healthcare companies.
These companies are listed below in addition to the AI applications of their data platforms:
- IBM – Tracking how patients progress through treatments
- Health Catalyst – Predicting patient behavior, including the readmission rates of recently discharged patients
- Intersystems – Sharing patient medical data across multiple EMR systems to improve the quality of patient care
- Elsevier – Drug development, including the ability to understand the behavior of certain diseases and how they might respond to drugs
Before exploring the applications individually, we’ll explain some of the important findings from our secondary research on big data in healthcare, and explain some of the concepts and trends that will matter most to business readers:
Big Data in Healthcare – Insights Up Front
All of the software discussed in this report are data platforms of some sort. The machine learning built into them work from both client data and in some cases data from a corpus of data drawing from millions of patients. Some of these companies are unclear about where they acquired the stores of data on which their software are trained.
In general, software for housing and working with healthcare data come with built-in search functions that in some cases seem to run on natural language processing. That said, the insights that the software can generate for customers are for the most part garnered through standard machine learning methods and predictive analytics functionality.
This report focuses mainly on software for housing, managing, and working with big data in the healthcare industry, but readers interested in how AI can use that data to generate a variety of actionable insights for hospitals, clinics, and other healthcare companies may want to read our report on predictive analytics in healthcare.
The companies listed in this report are all likely to be genuine in their claims to leveraging artificial intelligence. IBM, Health Catalyst, and Elsevier all employ people with PhDs and Master’s degrees in either computer science, hard sciences (such as physics) or statistical fields. InterSystems is a large company with nearly 1,500 employees, but we were unable to find evidence of serious data science or machine learning talent on their team.
Older technology companies sometimes try to reposition themselves as AI-focused companies in order to win press and customers. Although it’s very likely that InterSystems offers a robust, useful platform for working with large volumes of healthcare, we simply weren’t able to verify that the platform makes use of artificial intelligence.
That all said, the healthcare space in general has a high density of case studies when it comes to AI applications. The data platforms discussed in this report are no exception. Every company we discuss presents significant evidence of success, which bodes well for their clients.
In addition, we should note that, of the companies covered in this report, Health Catalyst and InterSystems are the most targeted to the healthcare industry. IBM and Elsevier offer software to a large variety of industries.
We’ll start our analysis of four AI vendors offering data platforms and data management software to healthcare companies with IBM:
IBM offers IBM Explorys, which it claims can help healthcare companies better understand a disease’s history, progression, and economic impact on populations, as well as identify patient segments that could benefit from a treatment for the disease.
The company claims that the platform, called Explorys Therapeutic Dataset, which falls under the purview of IBM Watson Health, contains data on lab tests, biometrics, and patient-reported information of about 50 million unique and anonymized individuals and 344,000 unique health care providers.
The company explains that the data is collected from connected primary care, specialty, and community centers; hospital inpatient, emergency, and surgical settings; as well as post-acute care settings such as long-term care, rehabilitation, and home health. The system administrators can schedule periodic refreshes with new patients and data as needed.
To access the data, a user types a query into the browser-based search tool. IBM Watson’s algorithms then identify relationships and recognize patterns and trends in the data. The company did not specify the type of AI used, but we can infer that this could involve natural language processing and classic machine learning.
Below is a short five-minute video demonstrating how Explorys works: