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Behavioral variables and nanobots in healthcare

Big data and machine learning platforms are in a unique position to analyze one of the most challenging aspects of medical research: behavioral variables that are not reported accurately by common assessment methods. While EMRs (electronic medical records) prompt healthcare providers to collect a great deal of subjective information that impacts healthcare, such as compliance with medication regimes or alcohol intake, the validity of the information collected is questionable.

The subjective nature of the reasons people conceal or alter information given to a health care provider are as complicated at the whole of the human population. People feel social pressures to conform and please a questioner. They don’t want to admit to money problems that impact health care. They do not accurately see their own behavior. Cultural norms regarding personal information vary widely, as does disclosure by age and gender and social class. But new methods of gathering and quantifying data across populations has the potential to give greater insights into human behavior that can change the results of medical research.

Relying solely on patient reports of behavior is a method of gathering data that is extremely limited and may significantly impact the results of healthcare research. But gathering self reports, along with subjective research reports, pharmacy records, laboratory test results, social media, buying behavior, financial records, employment records, and other sources of data, and then analyzing across populations, can give a more accurate picture of what people are actually doing. By having a more accurate picture of human behavioral variables, healthcare research can more accurately assess the impact of human behavior on health care outcomes, and propose treatment modalities that are fine-tuned to the people we actually are. Continue reading