An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Today’s healthcare decisions are not completely foolproof in that they can lead to fatal errors. Statistics show that an estimated 850,000 medical errors occur each year, costing over £2 billion. Each year in the U.S., approximately 12 million adults who seek outpatient medical care are misdiagnosed, according to a new study published. The third most deadly killers of Americans are medical errors, accounting for more than 250,000 deaths each year, according to the analysis. These medical errors arising from incomplete or inaccurate analysis could have easily been prevented. Therefore, it is critical to understand why misdiagnoses occur; and the problem requires careful evaluation of diagnostic systems and processes. Uncovering and re-mediating flaws in existing techniques can greatly reduce the risks associated with misdiagnoses.
Erroneous healthcare decisions often result from the lack of data in relevant areas, due to compartmentalization stemming from patient data lying in silos and historical data not being available for analysis by qualified diagnosticians. In other words, the problem involves data compartmentalization and/or for the failure of data to be proactively shared with those in the best position to make the best use of it. This fosters a pattern of highly assumptive decisions and a high potential for erroneous heuristic analysis.