AI Articles and Analysis in Healthcare

The Patient Protection and Affordable Care Act (ACA) has significantly changed the landscape for how healthcare providers can be reimbursed by insurers. They are now reimbursed based on the guidelines set by Value-based Purchasing (VBP) models. This type of payment model reimburses the healthcare provider based on the quality of their work with the insured patient.

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.

Business leaders in pharma and life sciences have more specific business intelligence needs than peers in other industries. In addition to common business drivers like marketing, profit and loss, and customer churn, pharmaceutical companies need information regarding patients and clinical trial results.

McKinsey estimated that embarking on digital transformation to restructure value chains and drive R&D innovation across the pharmaceutical industry could be worth $50–150 billion of earnings before interest, taxes, depreciation, and amortization. In particular, machine learning is likely to continue finding a place in the pharmaceutical industry. Pharmaceutical companies have found applications for machine learning ranging from drug discovery to clinical trial retention.
The State of AI in the Asian Pharmaceutical Industry
AI seems to be making its way into the pharmaceutical space in Asia over the last two or three years, particularly in China and Japan. For the most part, the companies offering or using AI for drug discovery are just starting to acquire funding and talent. XtalPi seems to have the highest density of talent with a decent likelihood of being able to work with machine learning.

Episode Summary: Recently, we were called upon by the World Bank to do a good deal of research on the potential of applying artificial intelligence to health data in the developing world. Diagnostics was a very big focus of the information that we presented. It appears as though diagnostics is an area of great promise with regards to AI, and that's what we're focusing on in this episode the podcast.

There are few companies claiming to offer artificial intelligence solutions to orthopedics companies. We found that these solutions are intended to help orthopedics companies with at least one of the following business problems:

Sensors and mobile devices are in many ways working with AI software for business intelligence purposes in a few industries, including insurance and oil and gas. In the healthcare space, mobile devices and wearables allow patients to receive information on possible diagnoses for their symptoms and to monitor metrics such as their heart rate.

There are several companies claiming to offer AI-based medical transcription software, specifically speech recognition software, to hospitals and healthcare companies. We found that these solutions are intended to help hospitals and healthcare companies with medical transcription in different forms, transcribing speech into text in order to fill out and update patient medical records in electronic health record (EHR) and electronic medical record (EMR) databases.
Machine Learning for Medical Transcription - Insights Up Front
We can’t seem to find evidence that the prominent companies offering speech recognition software for medical transcription have what we would expect in terms of talent at their company, except for Nuance Communications. It isn’t clear how exactly their solutions could work without natural language processing, a kind of artificial intelligence. Nuance employs many data scientists with PhDs and Master’s degrees in computer science and hard sciences like physics. This is generally what we look for when it comes to vetting a company on their claims to leveraging artificial intelligence and cutting through the marketing hype we so often see on AI vendor websites.