When it comes to the complexities patient care, there will always be more questions than answers. Clinicians and researchers are discovering new diseases on a regular basis, reclassifying conditions and treatments, and expanding their understanding of the human condition exponentially each year.
New physicians are often told that half of what they learned in medical school will be wrong or outdated within five years of graduation – but it’s impossible to know which half that will be.
And with the constant pressure of caring for patients, meeting regulatory requirements, and wrestling with technology, it’s just as impossible for most clinicians to keep up with the avalanche of journal articles and studies that detail the latest updates to clinical guidelines, the best new pharmaceuticals to try, and the most cutting edge advances in precision medicine.
That’s where big data analytics is supposed to come in. By synthesizing huge batches of electronic health records, insurance claims, patient-generated health data, and even genomic testing results, analytics engines aim to deliver actionable clinical decision support that closes the gap between what a physician knows about patient care and what has happened during those five, ten, or fifty years since med school.
Unfortunately, not even the latest and greatest of traditional analytics has been able to keep pace with the accelerating demands of modern healthcare. Even small-scale EHR data mining can be difficult and expensive, and may run into any number of data integrity obstacles that render the results less than completely trustworthy.
Big data analytics, which can be defined as the practice of synthesizing multiple data streams to unlock previously unavailable insights, is similarly fraught with challenges. Accessing clean, complete, and normalized data sources is hard enough, but understanding how to translate results into better outcomes for patients can be a lifelong journey.
Even if an organization successfully overcomes these initial roadblocks and starts to participate in population health management, they will still be constrained by the very nature of the technology they are using.
In the first of a new HealthITAnalytics.com series on advanced big data analytics techniques, we explore the limitations of current analytics tools and the potential for semantic databases to change the way healthcare organizations engage in population health management.
Semantic analytics is rooted in same concepts as most current iterations of big data analytics: the relational database. This schema was developed in the 1970s and has served the world of data science very well since then.
At its most basic, a relational database is a spreadsheet. Rows and columns representing different data elements are laid out in a logical order, and users can create formulas, algorithms, and queries to compare and contrast the information to extract a relatively narrow set of possible results.
For example, a healthcare organization might create a database with a patient’s name, address, and insurance carrier.
Using this dataset, a user can ask questions such as “What insurance does Mary Fletcher have?” or “Where does James Wong live?” or “How many of my patients live on Main Street?”
However, if the user wants to add data to generate additional insights, he needs to carefully think about what new, specific questions he wants to answer, such as “Does Many Fletcher have diabetes?”
Adding diagnosis data to the database will help to answer the query. Diagnosis data must be normalized and standardized and then deliberately added to the system for a predefined set of patients. It could be all patients, or it could just be a few.
Technically, this means the organization is using big data analytics, since the diagnosis data previously lived in a separate location, and now it’s part of a larger dataset.
This adds a new level of complexity to the information.;
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