In June 2004, the National Board of Medical Examiners added a patient-actor based assessment to the medical licensing examination. This event had a profound impact on medical training and assessment. No longer is competency in medicine solely based on a pencil and paper test. While this was a much needed addition to the credentialing process, the medical field has barely scratched the surface in creating competency assessments that test the full range of clinical skills needed for the safe delivery of patient care. Simulation technology has great potential to address some of the more difficult assessment areas. One critical area that has yet to be addressed is the objective evaluation of hands-on clinical skills. Our approach to evaluating hands-on clinical skills involves the innovative use of sensor and data acquisition technology embedded in manikin based trainers. In addition to having a unique approach to fabricating the sensor enabled manikins, our laboratory has the capability to simulate over 100 different clinical presentations. Our long term goal is to build valid and reliable technologies that can be used to ensure that minimum performance standards are met by all healthcare professionals who perform hands-on clinical examinations and procedures at the point of care. Our immediate goal is to build and validate a set of sensor-enabled breast examination simulators that can be used to define performance standards for the clinical breast examination. We hypothesize that palpation techniques used in the clinical breast examination can be quantified and that improper techniques that result in missed diagnosis can be identified using sensor enabled breast models. To test our hypothesis, we will execute the following specific aims:
SPECIFIC AIM 1 : To develop and evaluate a set of sensor enabled breast models that can be used to quantify clinical breast examination skills.
SPECIFIC AIM 2 : To validate a set of sensor enabled breast models as reliable measurement tools that can be used to ensure minimum performance standards for the clinical breast examination.
We will use innovative sensor technology to build valid and reliable assessment tools that can be used to ensure minimum performance standards for all healthcare professionals who perform hands-on clinical examinations and procedures at the point of care. Our results will help set clinical performance standards.
|Nathwani, Jay N; Garren, Anna; Laufer, Shlomi et al. (2017) Improving diagnosis in healthcare: Local versus national adoption of recommended guidelines for the clinical breast examination. Am J Surg :|
|Laufer, Shlomi; D'Angelo, Anne-Lise D; Kwan, Calvin et al. (2017) Rescuing the Clinical Breast Examination: Advances in Classifying Technique and Assessing Physician Competency. Ann Surg 266:1069-1074|
|Azari, David P; Pugh, Carla M; Laufer, Shlomi et al. (2016) Evaluation of Simulated Clinical Breast Exam Motion Patterns Using Marker-Less Video Tracking. Hum Factors 58:427-40|
|Laufer, Shlomi; Rasske, Kristen; Stopfer, Lauren et al. (2016) Fabric Force Sensors for the Clinical Breast Examination Simulator. Stud Health Technol Inform 220:193-8|
|Laufer, Shlomi; Ray, Rebecca D; D'Angelo, Anne-Lise D et al. (2015) Use of simulators to explore specialty recommendation for a palpable breast mass. Am J Surg 210:618-23|
|Laufer, Shlomi; Cohen, Elaine R; Kwan, Calvin et al. (2015) Sensor technology in assessments of clinical skill. N Engl J Med 372:784-6|
|Azari, David; Pugh, Carla; Laufer, Shlomi et al. (2014) Evaluation of Hands-On Clinical Exam Performance Using Marker-less Video Tracking. Proc Hum Factors Ergon Soc Annu Meet 58:793-797|
|Laufer, Shlomi; Pugh, Carla M; Van Veen, Barry D (2014) Characterizing touch using pressure data and auto regressive models. Conf Proc IEEE Eng Med Biol Soc 2014:1839-42|
|Laufer, Shlomi; Cohen, Elaine R; Maag, Anne-Lise D et al. (2014) Multimodality approach to classifying hand utilization for the clinical breast examination. Stud Health Technol Inform 196:238-44|
|Salud, Lawrence H; Kwan, Calvin; Pugh, Carla M (2013) Simplifying touch data from tri-axial sensors using a new data visualization tool. Stud Health Technol Inform 184:370-6|
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