The Healthcare Industry in UsaThe healthcare industry in USA has been trying to fight the dual challenge of cost control and quality improvement. There is an emerging consensus that healthcare is an information and knowledge-intensive enterprise and the future of health industry depends on effectively implementing information technology to collect, manage, analyze and disburse the pertinent information and knowledge. Partners Healthcare Systems (PHS) was one of the pioneer companies to maintain a centralized digital records library of more than 4.6 million patients augmented in real time made available to the physicians and researchers. PHS was established by partnering Brigham and Women’s Hospital and Massachusetts General Hospital to produce an integrated health care delivery system that offered patients a continuum of coordinated high-quality care.
The National Academy of Sciences has observed that the persistent problems in healthcare is not because of the incompetence of the professionals but rather a consequence of the complexity of the system. With the assumption that organized systems lead to solutions and keeping the goal of quality improvement and cost control, we propose a few modifications to the emerging transformational health care delivery through Electronic Medical Record (EMR) and Computerized Patient Order Entry (CPOE) and analyze the potential risks. The transformation is classified into five scenarios based on their function.
The I2B2/National Center for Biomedical Computing utilizes the 4.6 million patient record as a resource for tracking and studying specific medical therapies facilitating the study of the response of a genome to a medication or therapy. This lowers the research cost substantially by making valid inferences through the cross functional use of the same data, as opposed to spending millions of dollars on new experiments. This system helps determine the favorable drug use outcome, avoid drug ineffectiveness, direct new drug development, lower cost and risk of drug development and administration.
This system has enormous unused potential. First, integrating this system with other bioinformatics and medical informatics databases would provide valuable data for biological and medical research. For example, if this system can be integrated with medical research website like NCBI, then the specific genes that are involved in certain kinds of diseases can be easily characterized. If the information can be sorted by features like sex, age, ethnicity, etc. it would provide further insight. Several research institutions in Europe provide similar services but we could not find a counterpart in USA. We feel that such data with research interest would attract federal funding or other endowments thus enabling them to further reduce the cost. A major risk is that untrained researchers working on such metadata could make invalid inferences. For this reason several scientists still believe in getting primary data as opposed to
a
many others say that some other way to get such data is to use a human genome. One such site[/b] may need to include a genome-sorting tool (e.g. https://www.nature.com/content/10.1038/ncomms23) such as BLAST. With that being said, this could become very useful later in life.
A key goal for our team is to bring the data to the public through medical research, as much as possible.
We understand that many medical researchers (or biophysicists) do not use such a tool. It is not an ideal idea to develop and use such an application, because in our experience it would lead to very costly data collection. We believe that the cost of that data collection would be prohibitive, because this data could be used as a way to gather much more information.[/b] It is not that this should be done, as long as it is not tied to any particular person or science. All of us work in biotech.[/b] Since our data would only be collected over a long period of time (or under the same time period), it would likely not be possible to collect much meaningful information. The following examples illustrate such a situation where this data could be used to gain some insight and insight about the underlying medical diseases. Our first example used data on the lung transplanting patients while they had two successful lung transplants.[/b]. A large portion of this data gathered was obtained from one patients (13%) through another patient (7%). During the period of the transplant the data collected came from the two patients and the data from the other patients was then combined with our analysis of their results.[/b] To our knowledge, this is not the first time that this type of data has been used in this method.[/b] In the case above, each of the data from the two patients was compared with data for each other. We then compared these data to our data collected for each patient to see what would cause the difference.[/b] The data obtained from the patients was then used to generate our 3D model (which is more or less a biomorphic model of the lungs in the 3D data generated from this study), based on which of the lungs were identified of which group. This method greatly reduces the time it takes for each patient to complete their transplant process. This allowed us to use it at our disposal to improve patient outcomes. On that subject we propose to improve the accuracy of data generation in future work. The data obtained from the two patients were also analyzed with a different set of methods to derive their survival and outcomes, and then for each patient, the data was analyzed in more precise terms.[/b] This method would allow for the extraction of a highly selective sample of this type of data via a higher level of statistical inference. It would improve the scientific approach and inform the use of non-standard methods in various fields (e.g., biophysical data, gene therapy etc.) that are currently restricted to more standard statistical methods such as regression.[/b] In the recent development, such a technique was discovered by Peter Charnley, and we believe this technique can be employed in various health fields on a large scale. The data generated from the two patients are used in our work to learn more about their illnesses, and we believe we can help other health care system with this application. (One recent research study at the Yale School of Medicine (YUM) used the same approach for each patient when they received the three years of this study, and these data were analyzed with non-standard methods in order to extract more relevant information about the disease history and outcomes of the patient as well