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 to the point wherein clinicians struggle to keep up. New medical information is highly perishable difficult to access and expensive demanding both time and money. 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, testing suggestions, statistical probabilities and treatment options derived from patient data not otherwise accessible by the clinician. Specifically, what we are after here is differential diagnosis. In design, we place far too much emphasis on reimbursement, and treatment and 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 a great opportunity for correlating data both real time and retrospectively. Combine that one clinician’s records with others in the region and you have a database exceeding the size of the Framingham study. Uploading the data anonymously to the related institution, say the medical school makes it available for educational focus and ongoing research. The other side of the coin will be a continuous download and updating of the clinician’s computer with current diagnostic terms and criteria.
With the government grants for deploying and substantially using EMRs, we have the opportunity to get this right in a way that will improve medical care. If we leave the data in the hands of clinicians and academics, we may even reduce costs.
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 to the point wherein clinicians struggle to keep up. New medical information is highly perishable difficult to access and expensive demanding both time and money. 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, testing suggestions, statistical probabilities and treatment options derived from patient data not otherwise accessible by the clinician. Specifically, what we are after here is differential diagnosis. In design, we place far too much emphasis on reimbursement, and treatment and 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 a great opportunity for correlating data both real time and retrospectively. Combine that one clinician’s records with others in the region and you have a database exceeding the size of the Framingham study. Uploading the data anonymously to the related institution, say the medical school makes it available for educational focus and ongoing research. The other side of the coin will be a continuous download and updating of the clinician’s computer with current diagnostic terms and criteria.
With the government grants for deploying and substantially using EMRs, we have the opportunity to get this right in a way that will improve medical care. If we leave the data in the hands of clinicians and academics, we may even reduce costs.
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