Physicians around the world use Isabel to help construct or broaden a differential diagnosis. By entering the information normally captured in a patient workup, Isabel provides physicians with a list of possible diagnoses to help them construct their thinking and make an accurate final diagnosis quickly and easily at the point of care.
The Premier Diagnosis Decision Support Tool
<em>Isabel</em> helps clinicians improve diagnosis quality, leading to more cost-effective care through:
- More appropriate tests and referrals
- More appropriate admissions
- Reduced length of stay
- Increased patient satisfaction
- Reduced clinical risk
- Ensuring the appropriateness of care
- Reducing litigation risk
- Supporting medical education and training
- Encouraging review of the latest evidence via DynaMed
A Valuable Resource at the Point of Care
<em>Isabel</em> can be deployed either as an integrated part of the electronic health record (EHR) system or as a standalone application. <em>Isabel</em> uses the information routinely captured during the patient workup—whether free text or structured data—and instantly provides a differential diagnosis checklist for review.
<em>Isabel</em> flags critical “don’t miss diagnoses” and diagnoses that could cause significant harm if not considered. When integrated into an EHR system, Isabel can provide one-click seamless diagnosis support with no additional data entry.
Seamless Integration with DynaMed
Experience powerful clinical decision support by combining the premier diagnosis technology of Isabel with the latest evidence-based clinical information from DynaMed. <em>Isabel</em> and DynaMed work together to give physicians the evidence-based information they need to provide the best care to their patients.
Clinicians can easily start their diagnosis journey from either platform by accessing <em>Isabel</em> differential diagnosis from the DynaMed homepage or linking directly to DynaMed topics from <em>Isabel's</em> list of potential diagnoses.
<em>Isabel</em> has set standards for the clinical testing of decision support systems in health care, having undergone a robust, peer-reviewed validation process to demonstrate its accuracy, effectiveness and value.
Dozens of articles, including independent clinical studies, multi-center collaborative studies and internal studies have appeared in prestigious peer-reviewed journals.