Utilizing DMAIC six sigma and evidence-based medicine to streamline diagnosis in chest pain.
OBJECTIVE
The purpose of this study was to quantify the difference between the current process flow model for a typical patient workup for chest pain and development of a new process flow model that incorporates DMAIC (define, measure, analyze, improve, control) Six Sigma and evidence-based medicine in a best practices model for diagnosis and treatment.


METHODS
The first stage, DMAIC Six Sigma, is used to highlight areas of variability and unnecessary tests in the current process flow for a patient presenting to the emergency department or physician's clinic with chest pain (also known as angina). The next stage, patient process flow, utilizes DMAIC results in the development of a simulated model that represents real-world variability in the diagnosis and treatment of a patient presenting with angina. The third and final stage is used to analyze the evidence-based output and quantify the factors that drive physician diagnosis accuracy and treatment, as well as review the potential for a broad national evidence-based database.


RESULTS
Because of the collective expertise captured within the computer-oriented evidence-based model, the study has introduced an innovative approach to health care delivery by bringing expert-level care to any physician triaging a patient for chest pain anywhere in the world. Similar models can be created for other ailments as well, such as headache, gastrointestinal upset, and back pain.


CONCLUSIONS
This updated way of looking at diagnosing patients stemming from an evidence-based best practice decision support model may improve workflow processes and cost savings across the health care continuum.
Kumar
Sameer
S
Opus College of Business, University of St. Thomas, Minneapolis, Minnesota 55403, USA. skumar@stthomas.edu
Thomas
Kory M
KM
D002637:Chest Pain / Q000175:diagnosis*; D004526:Efficiency; D019317:Evidence-Based Medicine; D006801:Humans; D015397:Program Evaluation; D017749:Total Quality Management / Q000379:methods*