Informatics week 5 replies
Ldominguez03
2 years ago
10
files (2)PatriciaMartinGiraldinoInformaticsreply.docx
GiovannaPalaciosOrregoInformaticsreply.docx
PatriciaMartinGiraldinoInformaticsreply.docx
Patricia Martin Giraldino
Health Care Informatics
Florida National University
10/2/2014
The Initiatives That Are Most Effective
One of the effective interventions was in case study one. In case study one, implementing an obstetrical order set was helpful and acceptable in decreasing the number of clicks for the nurses and maintaining the scope of practice (Chapter 19: The Case Studies, n.d.). This intervention led to quantitative gains, including increased revenue collection from patients and decreased 60-day reimbursement denial. Similarly, redesigning the sepsis screening process in Case Study Two resulted in the early recognition of many patients at risk (Chapter 19: The Case Studies, n.d.). The change in EHR design improved its alert system, and the reduction in system advisories improved satisfaction levels among nurses and physicians. This intervention was complemented by qualitative findings, which revealed that the staff trained in using this intervention found that it integrated well with their workflow and made the decision-making process less complicated.
Moreover, case study four was also practical since the organization successfully incorporated COVID-19 screening tools, compliance with which means following new CDC and state rules. The fact that the tool could be updated quickly as information changed was important practice (Chapter 19: The Case Studies, n.d.). Based on the findings related to this intervention, the outcome showed improvement in the specific measure of quality, which included affirmative returns from the staff concerning the simplicity of the tool and their ability to conduct screening practice (Chapter 19: The Case Studies, n.d.). These interventions proved most beneficial because they targeted both numbers, which showed that MTM helped gain more points for reimbursements and reduce denials, as well as feedback from staff, where there was an improvement in MTM’s operation and clinician satisfaction. Such an approach helped to provide satisfactory improvements to the quality of patient treatment and organizational activity.
How the Team Could Have Improved the Evaluation Strategies
The team could have also enhanced its evaluation approaches by including more pointed long-term quantity measures to examine the cumulative outcomes of the intervention. Some improvements included a near real-time uplift in revenue and a decline in denials, while longer-term monitoring of these factors gives a more solid idea about continued viability (Beninger, 2023). Furthermore, survey and interview studies with staff, which provide more qualitative insights than simple surveys, could have given further insights for the analysis of users' satisfaction and areas where further improvements could be found (Kidder, 2024). The evaluation would also be improved by including patient outcome measures to confirm that the interventions enhanced workflow and benefitted the quality of patient care and the patient's safety.
References
Beninger, P. (2023). Drug-Drug Interactions: How to Manage the Risk–A Stakeholder
Approach. Clinical Therapeutics, 45(2), 106-116. https://doi.org/10.1016/j.clinthera.2023.01.00
Chapter 19: The Case Studies (n.d.).
Kidder, D. P. (2024). CDC Program Evaluation Framework, 2024. MMWR.
Recommendations and Reports, 73. https://www.cdc.gov/mmwr/volumes/73/rr/rr7306a1.htm
GiovannaPalaciosOrregoInformaticsreply.docx
Giovanna Palacios Orrego
Florida National University
Health Care Informatics-DAX-DL01
Dr. Deborah Crevecoeur
10/02/24
Clinical decision support systems have demonstrated effectiveness across various healthcare scenarios, including COVID-19, normal newborn screening, sepsis detection, and obstetrical screening. In chapter 19, there are effective interventions and their evaluation measures, along with insights on their qualitative or quantitative nature and potential improvements in evaluation strategies.
Case Study: COVID-19
Clinical decision support systems were employed to triage patients based on COVID-19 symptoms and risk factors. Quantitative measures included reduced wait times for testing and increased testing capacity. For example, Ameri et al. (2024) reported a 30% increase in appropriate triage decisions. These interventions are primarily quantitative, focusing on metrics like patient throughput and testing accuracy (Chen et al., 2022). Additionally, including qualitative feedback from healthcare providers about usability could enhance the effectiveness of these tools and identify areas needing improvement.
Case Study: Normal Newborn Order Sets
Clinical decision support systems that incorporate standardized screening guidelines for newborns will be the intervention. Quantitative data showed improved screening rates for conditions like congenital hypothyroidism and phenylketonuria, with some studies noting adherence rates as high as 90% (Rao & Palma, 2022). These interventions are mainly quantitative, with some qualitative assessments through parent and clinician satisfaction surveys (Chen et al., 2022). Finally, adding qualitative evaluations to assess parental understanding of the screening process could provide deeper insights into the system's impact.
Case Study: Sepsis Detection (Think Sepsis)
Clinical decision support systems that utilize algorithms to flag potential sepsis cases based on vital signs and lab results. Quantitative metrics included reduced time to treatment, with studies reporting a 25% decrease in mortality rates due to timely interventions (Wulff et al., 2019). These interventions are primarily quantitative, but qualitative feedback from clinicians regarding the system’s alerts and workflow integration could enhance understanding of its real-world application (Chen et al., 2022). Gathering qualitative data on clinician experiences could help refine the alert thresholds and reduce alarm fatigue.
Case Study: Obstetrical Screening
Clinical decision support systems that provide reminders for screenings and help stratify risk in pregnant patients. Quantitative outcomes included increased screening rates for gestational diabetes, with compliance rates rising to over 80% in some settings (Cockburn et al., 2024). This intervention is largely quantitative, focusing on screening compliance and patient outcomes, supplemented by qualitative feedback from staff (Chen et al., 2022). A mixed-methods approach could be beneficial, combining quantitative data with qualitative insights from staff on workflow impacts.
In conclusion, while clinical decision support systems (CDSS) interventions have shown significant effectiveness across these areas, predominantly through quantitative measures, integrating qualitative evaluation strategies could enhance understanding of user experiences and system impacts. Improving evaluation strategies could involve a mixed-methods approach, ensuring that both numerical data and personal insights are considered to refine CDSS functionality and usability.
References
Ameri, A., Salmanizadeh, F., & Bahaadinbeigy, K. (2024). Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods. Health Science Reports, 7(2), e1919. https://doi.org/10.1002/hsr2.1919.
Chen, W., O'Bryan, C.M., Gorham, G., Howard, K., Balasubramanya, B., Coffey, P., Abeyaratne, A., & Cass, A. (2022). Barriers and enablers to implement and use clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. Implementation Science Communications, 3(1), 81. https://doi.org/ 10.1186/s43058-022-00326-x.
Cockburn, N., Osborne, C., Withana, S., Elsmore, A., Nanjappa, R., South, M., Parry-Smith, W., Taylor, B., Chandan, J.S., & Nirantharakumar, K. (2024). Clinical decision support systems for maternity care: a systematic review and meta-analysis. EClinicalMedicine, 76, 102822. https://doi.org/10.1016/j.eclinm.2024.102822.
Rao, A., & Palma, J. (2022). Clinical decision support in the neonatal ICU . Seminars in Fetal and Neonatal Medicine, 27(5), 101332. https://doi.org/10.1016/j.siny.2022.101332.
Wulff, A., Montag, S., Marschollek, M., & Jack, T. (2019). Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review. Methods of Information in Medicine, 58(S 02), e43-e57. https://doi.org/10.1055/s-0039-1695717
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