IMD2S

There is a need in the modern military to optimize the time physicians interact with patients. As described in the solicitation, "Few military clinicians have supporting staff members working for physicians whose job focus is to gather the appropriate patient data (most recent labs graphed with previous values, pharmacy compliance data, recent radiology reports, recent ECG, recent consult report) into a packet prior to the appointment". Additionally, doctors spend no more than 15 to 30 minutes with the patient during routine outpatient appointments and a great deal must be accomplished during this time, ranging from a review of the vital signs and assessment of the patient's conditions; through a diagnosis and ordering of prescriptions, laboratory tests, etc.; to a possible referral or follow up; to discharge. During this process, information must be received, reviewed and recorded by the physician.

The proposed program will assist physicians to better maintain the health of their patients. The overall goal is to give the physician a "gestalt" overview of the patient's past history, current conditions and anticipated problems. A "gestalt" vision of the patient means that the entire constellation of medical conditions is viewed as a whole, not in isolation or out of context; a physician should be able to obtain all the necessary information about the patient in a single glance. The proposed program will accomplish this in two distinct, yet coupled ways. The first is to make the extremely large amounts of information in a patient's records available to the physician in a way that is easily interpreted and utilized. The second is to provide visually-oriented aids to assist the physician in dealing with both the current assessment and with potential future problems. This proposed program is not trying to replace the EMR, rather, it is a program running on top of EMR that provides more specific views of clinical data and helps the physician to quickly understand key aspects of the patient data based on the patient's problems.

To assist in meeting these goals, and in response to OSD's SBIR solicitation, Ontar has designed and implemented IMD2S -- the Cognitive Integrated Medical Data Display System. The Phase I solicitation gave the program objectives as: "SBIR proposals should suggest novel ways to display complex integrated clinical data. Few military clinicians have working for them, a supporting staff member whose job focus is to gather the appropriate patient data (most recent labs graphed with previous values, pharmacy compliance data, recent radiology reports, recent ECG, recent consult report) into a packet prior to the appointment. Programs exist in the area of clinical trials that look for trends, graph labs with graphical representation of high and low limits and arrows/colors coded with intervention; but they have not been applied to support normal, non research oriented, clinical practice."

The Ontar Phase I program met and exceeded these objectives. IMD2S uses several unique approaches to assist in the management of different views of clinical data. It applies a unique approaches to data retrieval that manages the interrelations of clinical data by a problem-driven mechanism, and data representation. It also builds in guideline-centric medical knowledge base technologies to maintain the correlation with clinical data, providing quality help for the physician. We also provide a dynamic approach for enhancing the traditional snapshot view.

We developed several patient data overviews, each of which represents clinical data in ways to assist clinicians. All the views can be integrated by a single clinical data model which is compliant with the HL7 (Healthcare Level seven) Reference Information Model (RIM). Since all the data are obtained from the same data set, each display view can be easily recreated, traced back to the source and combined with other data and views.

In recent years, we have seen an explosion of interest in clinical practice guidelines and protocols. The use of guidelines promises to reduce inter-practice variation and provide evidence-based medicine [1]. By using medical guidelines, we can generate context relevant recommendations. We have used the medical guidelines to help manage the clinical domain specific data in an Electronic Medical Record Systems (EMRs). However, there are many situations which are not well defined in medical guidelines, we have investigated other approaches, e.g. a semantic network, that combine medical guideline implementation to solve the problems.

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