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Published in Dag K. Brune, Christer Edling, Occupational Hazards in the Health Professions, 2020
The more serious ergonomic problems stem from inadequacies of the management system. When a patient is admitted as an “emergency”, no preliminary planning is possible and sometimes the hospital staff is exposed to great difficulties. For example, a patient was brought to a London teaching hospital after having suffered a cerebrovascular accident (CVA). She was confused and helpless and she weighed 220 kg. The only hoist available in the hospital had a safe working load limit of 160 kg. No lifting aids were apparently available. Bed sores being a major problem, the decision was made that ten people should be assembled each time the patient was moved, but as the patient was short in height and wider than the bed, there was no way in which her weight could be evenly shared between ten people. More than one nurse was injured. It has to be accepted that, while no preliminary planning was possible, the want of foresight for such an eventuality raises a number of questions concerning administrative competence.
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
This game involved a fair amount of precision even in a relatively low-resource setting. It appears to have achieved a fair amount of accuracy, although the game is going to be long, as thyroid-suppressive treatment takes a long time to maintain remission in toxic adenomas. There is always a chance of a malignancy showing up sometime later. This patient was actually referred to our CBBLE from a very distant community, nearly 2000 km away from our tertiary care teaching hospital. VP and RB were already evaluating the patient’s inputs posted by one of our CBBLE community health workers, who also resides in the same community 2000 km away, in the patient’s village/town. A tentative plan for evaluation was made online, following which the patient was further evaluated in the hospital after she made the long journey. The revised inputs on the patient after evaluation by the hospital residents and faculty were again posted to the online network on WhatsApp, and a final plan of available intervention was drawn. There was a question raised by the team evaluating the patient in the hospital about the uncertainty of future malignancy prediction in this patient when an online CBBLE member suggested that this could become feasible in the near future using BRAF (V600E) ctDNA. At the current point in time, this is out of reach from a rural Indian tertiary teaching hospital perspective, but it is possible that resources and research could make this available even in less than a decade, in the coming years. Until then, we may have to live with the uncertainty posed.
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
This game involved a fair amount of precision even in a relatively low-resource setting. It appears to have achieved a fair amount of accuracy, although the game is going to be long, as thyroid-suppressive treatment takes a long time to maintain remission in toxic adenomas. There is always a chance of a malignancy showing up sometime later. This patient was actually referred to our CBBLE from a very distant community, nearly 2000 km away from our tertiary care teaching hospital. VP and RB were already evaluating the patient’s inputs posted by one of our CBBLE community health workers, who also resides in the same community 2000 km away, in the patient’s village/town. A tentative plan for evaluation was made online, following which the patient was further evaluated in the hospital after she made the long journey. The revised inputs on the patient after evaluation by the hospital residents and faculty were again posted to the online network on WhatsApp, and a final plan of available intervention was drawn. There was a question raised by the team evaluating the patient in the hospital about the uncertainty of future malignancy prediction in this patient when an online CBBLE member suggested that this could become feasible in the near future using BRAF (V600E) ctDNA. At the current point in time, this is out of reach from a rural Indian tertiary teaching hospital perspective, but it is possible that resources and research could make this available even in less than a decade, in the coming years. Until then, we may have to live with the uncertainty posed.
Patient-centric surgeons’ case mix problem
Published in IISE Transactions on Healthcare Systems Engineering, 2022
Teaching hospitals play a key role in the majority of healthcare systems as they provide medical care to the community and train future healthcare professionals. For example, at surgical units of a teaching hospital in the United Kingdom (UK), a Medical Consultant (MC), who is a senior doctor who manages the care of patients admitted to the surgical unit, oversees the qualified doctors in specialty training to become surgeons to those patients (British Medical Association, 2017). The MC annually plans the mix and volume of patients of surgeons, or the surgeons case mix, which could be challenging, especially when surgeons have different experience levels. While the observance of hospital treatment guidelines and protocols is critical for good medical outcomes that patients value directly, including providing the patient with enough information about the surgical procedure and surgeon qualifications and experience in order for the patient to make an informed decision whether to have the procedure done, the surgeons’ case mix could also be a critical factor impacting patient outcomes.