Transforming Healthcare Through Technology

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Telehealth is indeed an excellent solution with the potential of addressing issues such as the shortages of work staff, low accessibility of certain places of residence, and uneven distribution of medical cadres in the United States. The present technology has also proven to be invaluable during an unprecedented time that is the COVID-19 pandemic. However, the administrative challenges described in the discussion posts are not the only issues with telehealth. Indeed, the questions of reimbursement and compensation of telehealth programs are relevant. However, what should not be left out of the discussion is the insufficient evidence base for telehealths effectiveness. For instance, Fraiche et al. (2015) show that studies on the subject matter use different strategies, and the findings are sometimes conflicting, which makes developing a common framework challenging. Besides, there is also a psychological aspect of using telehealth technologies. As pointed out by Bull et al. (2016), mistrust is common among users, and it is often a good rapport between the patient and the provider that helps with treatment compliance.

Electronic health records (EHR) indeed have the potential to revolutionize health care. The topic starter made excellent points, mentioning the speed and convenience with which data are gathered, stored, and exchanged at a medical facility that uses EHR. Safety stops are also a valuable contribution as they allow for safer medicine administration. Those are not the only benefits of using electronic health records, as recent research has focused on more medical data processing applications. For instance, Goldstein et al. (2016) an opportunity to use data for EHR-based risk prediction. Specific metrics could be selected to determine a patients risk for diseases, which would allow care providers to be proactive and address issues before they manifest themselves. Jin et al. (2016) mention a more specific application of health data analysis. They show the efficiency of neural networks for predicting heart failure. However, to translate it into practice, researchers and providers would have to address EHRs sparsity and heterogeneity.

References

Bull, T. P., Dewar, A. R., Malvey, D. M., & Szalma, J. L. (2016). Considerations for the telehealth systems of tomorrow: An analysis of student perceptions of telehealth technologies. JMIR medical education, 2(2), e11. Web.

Fraiche, A. M., Eapen, Z. J., & McClellan, M. B. (2017). JACC: Heart Failure, 5(4), 297-304. Web.

Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. (2017). Journal of the American Medical Informatics Association, 24(1), 198-208. Web.

Jin, B., Che, C., Liu, Z., Zhang, S., Yin, X., & Wei, X. (2018). Predicting the risk of heart failure with EHR sequential data modeling. Ieee Access, 6, 9256-9261. Web.

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