Electronic Medical Record (EMR)

Quick Pitch

We build electronic medical records (EMR) s in many ways, but until we post patient data including genomic material into databases as discrete data points, it will not be possible to analyze the data in a meaningful way.

Patient records repeat the same words and phrases many times. The chart could be three inches thick, but if one were to reduce it to only the repeated words and phrases and index them in a database, the record might cover only a couple of pages. In a sense, this is compression, but the compressed elements are now accessible and correlated with other information in a relational database.

The same strategy applies to current medical information and terminology. As data points on a modern relational database, specific terms defining diagnosis, criteria or treatment become available for programmed analysis, statistical use, machine logic, artificial intelligence (AI), research and data mining. New biomedical information floods the system beyond the pace of human processing. New medical information is highly perishable difficult to access, expensive and time consuming.  A credible EMR must include a continuously updating database of current medical knowledge.

EMRs strive for many things. One of them involves computer decision support systems (CDSS). A successful decision support offers the clinician diagnostic possibilities, suggestions for further testing, statistical probabilities and treatment options derived from patient data and current medical information not otherwise accessible to the clinician – specifically differential diagnosis. In design, we place far too much emphasis on reimbursement, and treatment and pay not enough attention to patient care and diagnosis.

One clinician in a year will likely produce over a thousand records. The total grows year to year, so after thirty or so years the total will exceed say thirty thousand records. Such a database affords opportunity to correlate data both in real time and retrospectively.  Combine one clinician’s records with others in the region and you have a database exceeding the size of most major studies. The bigger the database, the greater grows the value. Uploading the data anonymously to a related institution, for instance the medical school makes it available for educational focus, CME and ongoing research, even an opportunity to correlate genetic data with real world pathology. Critically, medical information, current diagnostic terms and criteria must flow back down into the clinical computers.

With the government grants for deploying and substantially using EMRs, we have the opportunity to build not just an EMR but also a relational database of medical information (MIDB) that corrects itself based on actual outcome and statistical analysis.

We must keep all of these programs out from between the patient and the clinician, maintaining a sense of humanity and the art of medicine.