Sources of Data for Text Mining.
Patient health records, order entries, and physician notes aren’t the only sources of data in healthcare. There are other sources as well such as:-
1. The Internet of Things (think FitBit data)
2. Electronic Medical Records/Electronic Health Records (classic)
3. Insurance Providers (claims from private and government payers)
4. Other Clinical Data (including computerized physician order entries, physician notes, medical imaging records, and more)
5. Opt-In Genome and Research Registries
6. Social Media (tweets, Facebook comments, etc.)
7. Web Knowledge (emergency care data, news feeds, and medical journals)
Just how much health data is there from these sources? More than 2,314 exabytes by 2020, says BIS Research. But adding to the ocean of healthcare data doesn’t do much if you’re not actually using it. And many may agree that utilization of this data is… underwhelming.
Improving Customer Care While Reducing Medical Information Department Costs.
Every physician knows how annoying it can be to get a drug-maker to give them a straight, clear answer. Many patients know it, too. For the rest of us, here’s how it works:
- You (a physician, patient or media person) call into a biotechnology or pharmaceutical company’s Medical Information Department (MID)
- Your call is routed to the MID contact center
- MID operators reference all available documentation to provide an answer, or punt your question to a full clinician
Hearing How People Really Talk About and Experience, ADHD.
The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely. Adevanced text analytics using NLP techniques are surely helping healthcare providers connect with their patients and develop personalized treatment plans.
Guiding Communications Between Pharmaceutical Companies and Patients.
Pharmaceutical marketing teams face countless challenges. These include growing market share, demonstrating product value, increasing patient adherence and improving buy-in from healthcare professionals. Previously, companies relied on basic customer surveys and some other quantitative data sources to create their recommendations. Now with the aid of NLP, companies are trying to categorize large quantities of qualitative, unstructured patient comments into “thematic maps.”