Clinical Information Structure and Logic in Electronic Health Records.

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    Clinical Information Structure and Logic in Electronic Health Records.
    Clinical Information Structure and Logic in Electronic Health Records.

    The basic science and clinical foundations of modern medicine are evolving so rapidly and broadly that the advantageous and efficient capture, access, and use of this vast amount of data have generated a worldwide challenge to effectively use modern health information technology (HIT) to improve the health of the world’s citizens.

    The US government recently embarked on an unprecedented program to convert the current US medical health records system into a universal, interoperable, clinically meaningful electronic system by 2015. This has been mandated in the context of the extremely rapid and virtually universal adoption of computerized practice management systems in the United States in the past 2 decades.

    A recent estimate for the adoption rate of electronic medical records (EMRs) for single-doctor offices is between 30% and 37%, while the estimate of the adoption rate for offices with 6 to 10 doctors is between 63% and 65%.1

    It has commonly been assumed that such systems (1) would be developed as elaborations of currently existing clinical computer technology; (2) could be modeled on current specialized systems, such as radiology and emergency medicine HIT, for other specialties, such as ophthalmology and dermatology; and, (3) that there would be significant improvements in safety, quality, and efficiency that would rapidly lead to lower healthcare costs.

    These assumptions have not been scientifically demonstrated and may not be valid, and the failure to take these possibilities into consideration may lead to qualitative and fiscal degradation of health care.

    Despite the obvious incentives and the example of successful implementation in several specialties, there has been widespread reluctance and uncertainty about the general adoption of such systems by most medical practitioners, if not by administrators, politicians, public advocates, and others. Visit here; CMap charts.

    Physicians have widely adopted costly and complex new diagnostic and therapeutic instruments. They have been willing to undertake extensive continuing medical education to learn newer and better techniques of patient care. Most physicians will use tools that they find are consistent with or can be adapted to their personal approach to patient care and practice management.

    It is our premise that the structure of current HIT is inconsistent with the core of clinical medicine and that this inconsistency has motivated the inertia that has resulted in slow and limited adoption of HIT for clinical documentation.

    One of the commonly accepted approaches to conceptualizing the information access and use in a clinical encounter has the acronym SOAP, which describes a process of subjective evidence collection, objective evidence collection, evidence assessment and diagnostic and interventional treatment plan.

    For example, an encounter may consider such evidence at a single point in time or over a period of time and may look at trends, clusters, and other data relationships. Generally, a clinician will use evidence and the assessment process to hypothesize a diagnosis to explain the evidence and to provide an organizing principle on which to base planning. This information use process has been described as medical reasoning.

    The evidence collection process is iterative and continues as new information is discovered or acquired. The collection continues until sufficient information has been acquired to confirm a diagnosis and/or disconfirm alternative diagnoses and complete the planning process appropriate to the current encounter. Evidence collection may continuously change the prognosis and planning process.

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