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Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
Vivek Podder, Binod Dhakal, Gousia Ummae Salma Shaik, Kaushik Sundar, Madhava Sai Sivapuram, Vijay Kumar Chattu, Rakesh Biswas
Now to illustrate the concepts further, the above case may have the following mentioned outcomes: The elderly patient’s sputum comes out to be positive and once he is begun on antitubercular therapy, he recovers. His cough subsides and weight improves and his sputum culture report that comes after 6 weeks also turns out to be positive for tuberculosis. This is an example of precise and accurate diagnosis and treatment.The elderly patient’s sputum turns out to be negative and yet once he is begun on antitubercular therapy, he recovers. His cough subsides and weight improves and his sputum culture report that comes after 6 weeks turns out to be positive for tuberculosis although his initial sputum smear was negative. This would be an example of initially imprecise, but finally accurate outcomes.The elderly patient’s sputum turns out to be negative and a cartridge-based nucleic acid amplification assay test (CBNAAT) sent at the same time also turns out to be negative for tuberculosis and once he is begun on antitubercular therapy, he does not appear to recover at all. His cough worsens along with his appetite and his weight loss increases. A bronchoscopy with bronchoalveolar lavage is performed and sent again for malignant cytology, AFB, CBNAAT, and culture. His sputum culture report that comes after 6 weeks turns out to be positive for drug-resistant tuberculosis. Although, he receives second-line therapy for his drug-resistant tuberculosis, his condition worsens, and he dies.The above is an example of a precise approach that still leads to inaccurate patient outcomes. We can be inaccurate in spite of being precise because of the current limitation of information and knowledge that does not always allow us to be accurate. The role of research and learning is to address this limitation and push the boundaries of current knowledge. Precision medicine develops and positively evolves with better research and learning.The elderly patient’s sputum turns out to be negative for tuberculosis and no further tests are done due to lack of resources. He is put on empirical antitubercular therapy, but he does not appear to recover at all. His cough worsens along with his appetite and his weight loss increases. One day he has a sudden shortness of breath and dies. An autopsy reveals bronchogenic carcinoma and pulmonary embolism.
Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2019
Vivek Podder, Binod Dhakal, Gousia Ummae Salma Shaik, Kaushik Sundar, Madhava Sai Sivapuram, Vijay Kumar Chattu, Rakesh Biswas
Now to illustrate the concepts further, the above case may have the following mentioned outcomes: The elderly patient’s sputum comes out to be positive and once he is begun on antitubercular therapy, he recovers. His cough subsides and weight improves and his sputum culture report that comes after 6 weeks also turns out to be positive for tuberculosis. This is an example of precise and accurate diagnosis and treatment.The elderly patient’s sputum turns out to be negative and yet once he is begun on antitubercular therapy, he recovers. His cough subsides and weight improves and his sputum culture report that comes after 6 weeks turns out to be positive for tuberculosis although his initial sputum smear was negative. This would be an example of initially imprecise, but finally accurate outcomes.The elderly patient’s sputum turns out to be negative and a cartridge-based nucleic acid amplification assay test (CBNAAT) sent at the same time also turns out to be negative for tuberculosis and once he is begun on antitubercular therapy, he does not appear to recover at all. His cough worsens along with his appetite and his weight loss increases. A bronchoscopy with bronchoalveolar lavage is performed and sent again for malignant cytology, AFB, CBNAAT, and culture. His sputum culture report that comes after 6 weeks turns out to be positive for drug-resistant tuberculosis. Although, he receives second-line therapy for his drug-resistant tuberculosis, his condition worsens, and he dies.The above is an example of a precise approach that still leads to inaccurate patient outcomes. We can be inaccurate in spite of being precise because of the current limitation of information and knowledge that does not always allow us to be accurate. The role of research and learning is to address this limitation and push the boundaries of current knowledge. Precision medicine develops and positively evolves with better research and learning.The elderly patient’s sputum turns out to be negative for tuberculosis and no further tests are done due to lack of resources. He is put on empirical antitubercular therapy, but he does not appear to recover at all. His cough worsens along with his appetite and his weight loss increases. One day he has a sudden shortness of breath and dies. An autopsy reveals bronchogenic carcinoma and pulmonary embolism.
Relating Task Demand, Mental Effort and Task Difficulty with Physicians’ Performance during Interactions with Electronic Health Records (EHRs)
Published in International Journal of Human–Computer Interaction, 2018
Prithima Reddy Mosaly, Lukasz M. Mazur, Fei Yu, Hua Guo, Merck Derek, David H. Laidlaw, Carlton Moore, Lawrence B. Marks, Javed Mostafa
Interestingly, these findings could be related back to the concepts of limited resource theory (Broadbent, 1958; Kahneman, 1973; Meister, 1976; Sperandio, 1971). That is, we believe that participants who used templated procedure experienced more mental effort (i.e., increased TEPR) that potentially contributed to omission errors as they “stopped” paying attention to our instructed list of tasks (attention tunneling or narrowing), and instead focused their attention on the listed orders presented in the Epic EHR system after they admitted the patient using “Admissions” module. For example, in PN case, 3 of the 17 participants using templated procedure checked “high risk for venous thromboembolism (VTE) prophylaxis” (high risk of blood clots) instead of “low risk for venous thromboembolism (VTE) prophylaxis” as instructed. These orders were presented in the EHR as a list format as “VTE Risk Category – High” being listed first followed by “Low Risk of VTE”, with a check box in front of them. Participants chose the first box on the list by seeing the term VTE and not reading the complete phrase (term “High” was presented at the end of the phrase and term “Low” was presented at the beginning of the phrase for VTE risk), thus leading to an unconscious human error. Similar issues were also seen for orders like “sputum culture” (to find bacteria or fungi that are cause infection of the lungs or the airways leading to the lungs) for PN. Such challenges might be especially relevant during handoffs and cross-cover situations (Mazur et al., 2016; Mosaly et al., 2013). Our findings emphasize that some task-related characteristics that must be exercised by physicians when using templates in EHRs (e.g., decision rate per EHR display space, complexity of decisions, relationship between decisions) could enhance or constrain creative clinical thinking and promote automaticity (Hartzband & Groopman, 2008). In addition to task characteristics, our findings also highlight some technological characteristics (e.g., usability, functionality, search engines) to minimize mental effort as well as implementation of proper quality assurance (QA) check to assure self-regulation (or slow-down) of physicians’ behavior to facilitate careful check of their work before approval.