Use of Clinical Systems to Improve Outcomes and Efficiencies Essay

Use of Clinical Systems to Improve Outcomes and Efficiencies Essay

The use of technology in healthcare delivery cannot be underestimated. Healthcare system is keen to use a new or implement an existing technology. Before technology is embraced, adequate research must have been conducted to prove its feasibility. There is associated improved patient safety, outcomes and work process with the use of technology in healthcare delivery (McGonigle & Mastrian, 2017). However, technology is not always an answer to improve patient safety and outcomes. Therefore, nurses are required to gain knowledge in basic technology and health informatics as an entry level nursing competency (McGonigle & Mastrian, 2017). The purpose of this annotated bibliography is to review and synthesize credible resources that examine the application of technology-based clinical systems in the improvement of patient outcomes and care efficiencies.

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Su, D., Michaud, T., Estabrooks, P., Schwab, R., Eiland, L., Hansen, G., DeVany, M., Zhang, D., Li, Y., Pagan, J., & Siahpush, M. (2019). Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology. Telemedicine And E-Health, 25(10), 952-959. https://doi.org/10.1089/tmj.2018.0205

In this article, Su et al. (20190 conducted a quantitative research to determine how effective remote patient monitoring (RPM) is on diabetic patients. The objective was to determine how patients’ levels of engagement with RPM devices impact post program hemoglobin A1C (HbA1C). In their study, the dependent variables include the patients’ level of participation and engagement, and the post program hemoglobin A1C. The primary outcome variable which is HbA1C was measured at two points, baseline and post program. Further patients were divided into two groups depending on Hb levels, >9% and <9%. The findings from the research show lower HbA1C levels with increased use or engagement with RPM (Su et al., 2019). The findings imply a negative correlation between participation and post program HbA1C. Patients who engaged maximally with the RPM devices had lower HbA1C levels and the vice versa is true. According to Eyth and Naik (2019), HbA1C serves as an indicator of glycemic control. Therefore, the higher the HbA1C, the poorer the glycemic control and therefore higher risks for diabetes and its complications. This is a case where technology is used to improve patient outcomes. Su et al. (2019) conclude that both the patient and the care givers’ efforts are crucial for the success of the RPM, and recommend an increased use of such technologies in the future care delivery.

Tubaishat, A. (2017). The effect of electronic health records on patient safety: A qualitative exploratory study. Informatics For Health And Social Care, 44(1), 79-91. https://doi.org/10.1080/17538157.2017.1398753

The last demi decade has experienced massive research in the role of electronic health records (EHR) in healthcare. Tubaishat (2017) conducted a quantitative exploratory study to determine the effect of EHR on patient safety. The author, Tubaishat is a specialist and a professor in health information systems. The general purpose of the study was to explore nurses’ perception of EHR on patient safety. Semi-structured interviews were administered to the nurses from ten hospitals which had used EHR in the past between 1 and 5 years. The findings from the research reveal that EHR is either directly or indirectly associated with decreased medical errors, improved data documentation and enhanced sustainability of data. According to Tubaishat (2017), e-prescriptions provide clear and precise medication doses as compared to paper-based prescription. Further, documentation is easier and less time consuming as compared to paper documentation (Tubaishat, 2017). Moreover, HER are sustainable and protected using networks and passwords. The study is a typical example where technology is leveraged in improvement of patient care efficiencies, outcomes and safety. Even though the study affirms positive impacts of EHR on patient safety, it concludes that safety concerns such as technical problems and data entry errors should be addressed.

Rieckert, A., Reeves, D., Altiner, A., Drewelow, E., Esmail, A., Flamm, M., Hann, M., Johanson, T., Klaassen-Mielke, R., Kunnamo, I., Loffler, C., Piccoliori, G., Sommerauer, C., Trampisch, U., Vogele, A., Woodham, A., & Sonnichsen, A. (2020). Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomized controlled trial. BMJ, m1822. https://doi.org/10.1136/bmj.m1822

Polypharmacy is a common problem especially among the geriatric population due to the multiple health conditions they have. Rieckert et al. (2020) conducted a research to determine the effects of a computerized decision support tool in reduction of polypharmacy among the elderly population living with chronic diseases. A clustered randomized control study design, which is pragmatic and multi centered was used. 3904 patients aged 75 years and above, and on eight or more medications per day participated in the study. The participants were divided into two groups, the intervention group (1953) which was assigned to a computerized decision support tool and the control group (1951) which received treatment as usual. The findings of the study do not reveal a significant difference in the primary outcome (unplanned admissions or deaths) between the two groups; however, there is a subtle difference. Among the intervention group, 44.6% of the participants experienced the primary outcomes while 48.4% (slightly higher) experienced the primary outcomes (Rieckert et al., 2020). Further, the secondary outcomes (number of drugs at final follow-up) were lower in the intervention group than the control group. The results indicate that the computerized decision support tool prompted the reduction in prescription and decreased the adverse consequences such as unplanned admissions and deaths. This publication is relevant to the current research as it espouses the fact that the use of computerized decision support tools should be extensive in healthcare as its efficacy in improving patients’ outcomes is proven.

Guo, Y., Chen, Y., Lane, D., Liu, L., Wang, Y., & Lip, G. (2017). Mobile Health Technology for Atrial Fibrillation Management Integrating Decision Support, Education, and Patient Involvement: mAF App Trial. The American Journal Of Medicine, 130(12), 1388-1396.e6. https://doi.org/10.1016/j.amjmed.2017.07.003

Mobile technologies are increasingly used in the management of cardiovascular diseases. Of all the cardiac arrhythmias, atrial fibrillations are the most common (Guo et al., 2017). Guo and his colleagues conducted a research on the use of mobile health technologies in management of atrial fibrillation. A randomized control trial study was used on 113 patients with atrial fibrillations. The patients were divided into two groups, the intervention group which used the mobile application (mAF App) and the control group that did not use the app. The mobile application incorporates the following details: patients’ personal health records, stroke and bleeding risk assessments, patient involvement self-score items and patient educational programs. The findings reveal that patients in the intervention group had better drug adherence, knowledge improvements and anticoagulant satisfaction as compared to the control group. In summary, quality of life significantly improved in patients who used the mAF App compared to the team that received usual care (Guo et al., 2017 Use of Clinical Systems to Improve Outcomes and Efficiencies Essay). The study had an upper hand being the first research in the use of mobile of mobile technologies in management of atrial fibrillation.

Synthesis of Findings and Conclusion

The use of technology in healthcare is expanding. Su et al. (2019) study found that use of a RPM in management of diabetes is effective in glycemic control. The findings of the research are as follows: patients’ participation and engagement with RPM devices causes lower post program HbA1C. Further, the study found that the frequency of use of the RPM also determine post program HbA1C levels; for instance, patients who used the RPM twice or more times a day had decreased HbA1C. In a different study by Tubashat et al. (2017), use of EHR is associated with decreased medical errors, improved documentation of data and increased sustainability of data. According to the study, EHRs are a better alternative to traditional paper based documentation and prescription systems. Further, Rieckert et al. (2017) conducted a research on the use of electronic decision support tool to mitigate polypharmacy among elderly patients. The findings indicated reduced adverse events such as unplanned admissions and deaths among the patients who use the tools. Further, there was a reduction in the prescribing among the patients in the intervention group. The final study about use of mobile applications in management of atrial fibrillation show positive correlation. The patients who used the mAF App showed improved educational levels and quality of life. Therefore, technology in healthcare in deed leads to improved patient outcomes, safety and care efficiencies.

References for Use of Clinical Systems to Improve Outcomes and Efficiencies Essay

Eyth, E., & Naik, R. (2019). Hemoglobin A1C. Ncbi.nlm.nih.gov. Retrieved 16 January 2021, from https://www.ncbi.nlm.nih.gov/books/NBK549816/#_NBK549816_pubdet.

Guo, Y., Chen, Y., Lane, D., Liu, L., Wang, Y., & Lip, G. (2017). Mobile Health Technology for Atrial Fibrillation Management Integrating Decision Support, Education, and Patient Involvement: mAF App Trial. The American Journal Of Medicine, 130(12), 1388-1396.e6. https://doi.org/10.1016/j.amjmed.2017.07.003

McGonigle, D., & Mastrian, K. (2017). Nursing Informatics and the Foundation of Knowledge (4th ed.). Jones and Batrlett Learning.

Rieckert, A., Reeves, D., Altiner, A., Drewelow, E., Esmail, A., Flamm, M., Hann, M., Johanson, T., Klaassen-Mielke, R., Kunnamo, I., Loffler, C., Piccoliori, G., Sommerauer, C., Trampisch, U., Vogele, A., Woodham, A., & Sonnichsen, A. (2020). Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomized controlled trial. BMJ, m1822. https://doi.org/10.1136/bmj.m1822

Su, D., Michaud, T., Estabrooks, P., Schwab, R., Eiland, L., Hansen, G., DeVany, M., Zhang, D., Li, Y., Pagan, J., & Siahpush, M. (2019). Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology. Telemedicine And E-Health, 25(10), 952-959. https://doi.org/10.1089/tmj.2018.0205

Tubaishat, A. (2017). The effect of electronic health records on patient safety: A qualitative exploratory study. Informatics For Health And Social Care, 44(1), 79-91. https://doi.org/10.1080/17538157.2017.1398753