NURS FPX 6412 Assessment 3 Manuscript for Publication

NURS FPX 6412 Assessment 3


Capella University


Professor Name

22 March 2024

Manuscript for Publication

Informing patients and healthcare providers’ shared decision-making regarding localized prostate cancer treatment is essential to predict patient-reported outcomes. A patient’s overall well-being is affected by various treatment methods, such as radical prostatectomy, intensity-modulated radiation therapy (RT), and active surveillance. A study conducted between 2011 and 2012 that involved 2563 men with localized prostate cancer examined the use of patient-reported outcomes (PROs) to predict quality of life (QOL) after treatment. After five years of treatment with intensity-modulated radiation therapy (RT), radical prostatectomy (RP), or active surveillance (AS), data on bowel, sexual, hormonal, and urinary function were gathered through the Expanded Prostate Index Composite (EPIC-26).

The goal of this research was to create an intuitive web-based tool that, using baseline EPIC-26 scores, body mass index (BMI), age, and race, can predict an individual’s quality of life (QOL) in these domains. The research demonstrated a reasonable level of effectiveness in forecasting some QOL dimensions, with sexual and hormonal function exhibiting the highest R-squared values. In prostate cancer studies, the Extended Prostate Cancer Index Composite (EPIC) is a commonly utilized instrument for measuring patient-reported outcomes (PROs) about different areas of their health, such as bowel function.

Traditionally, the average of individual ratings for various bowel difficulties (e.g., urgency, frequency) is used to create scores for EPIC bowel function subcategories. Assuming equal weight for every concern, this method might not accurately represent the priorities of the patients. Moreover, the aim of this study was to assess patient preferences for bowel dysfunction by applying best-worst scaling (B-W scaling) to the bowel subscale of the EPIC instrument (up to 0.386) (Laviana et al, 2020).

Evaluating the Use of Electronic Health Records (EHR) for Interprofessional Care Teams/Stakeholders

The goal is to configure the EPIC tool to fully support the diverse needs of various providers working collaboratively. Changing Requirements in a Patient-Centered Approach Using EPIC system. It will be required to adopt new strategies for EHR adoption and evaluation as healthcare moves toward a patient-centred model. This model highlights the utilization of resources such as patient portals in an EPIC system to include patients and their families in healthcare choices (e.g., EPIC patient portals). Teams of Interprofessionals, cooperation between different healthcare professionals, with information exchange and communication made easier by the EPIC system. Informaticists are crucial members of the healthcare team if this approach is to thrive.

Their knowledge guarantees that the EPIC system is set up to satisfy the requirements of all parties involved in the interprofessional setting, taking into account features like role-based access control and secure messaging (Gilliam et al, 2020). Difficulties with Using EPICs in Conventional Settings Even in well-established healthcare environments with clearly defined roles and responsibilities, implementing electronic health record (EHR) systems can be challenging. Conflicting needs may arise for physicians, nurses, administrators, and patients, among other stakeholders.

This may impede the adoption of novel patient-care models by resulting in hierarchical decision-making processes that mirror current power systems. Model of Formative Assessment for Interprofessional Environments is an interprofessional, interagency free-clinic setting EHR system configuration and implementation methodology is being developed. This model makes use of a formative assessment procedure that prioritizes usability ensuring that medical professionals, nurses, and social workers can easily utilize the EHR system. obtaining input from the interprofessional team during the implementation process to tailor the system to their unique requirements (Ledesma et al, 2024).

Evaluating the Impact of Enhanced Information System Workflows on Patient Safety and Quality Outcomes

One often used technique in prostate cancer clinical trials is the Extended Prostate Cancer Index Composite (EPIC) tool. It gauges patient-reported outcomes (PROs) associated with a range of health-related characteristics, such as hormone, sexual, bowel, and urine functions. CDSS is intended to support medical professionals in their decision-making, which may result in higher calibre and safer patient care. A thorough search of the literature from 1997 to 2010 was undertaken by the researchers, with a particular emphasis on reviews that examined CDSS applications and their effects on healthcare quality and safety.

Traditionally, individual ratings for each characteristic (e.g., side effects) are averaged to obtain summary values for EPIC subscales. Of 121 evaluations on eHealth technologies, 41 looked into CDSS in particular. Healthcare providers’ performance can be enhanced by CDSS in the following areas Promotion of preventive care following clinical recommendations. This is particularly true when CDSS smoothly interacts with current operations and offers precise, real-time information. Less clear research was done on how CDSS affected patient outcomes, with findings indicating not a big advantage, Variable gain a modest advantage, Among the possible dangers of CDSS, are technical difficulties (e.g., erroneous data) and Modifications to the routine clinical processes.

Using the best-worst scaling (B-W) approach on the bowel subscale of the EPIC instrument, this study examined patient preferences for bowel dysfunction. B-W scaling entails asking prostate cancer patients (in this case, 174 participants from two institutions) to rank various bowel disorders (frequency, urgency, incontinence, etc.) according to the amount of bother that they would feel at different severity levels (Neves et al, 2024).

Analyzing Alignment with Organizational Strategic Plan through Enhanced Information System Workflows

This study emphasizes the application of a PFCSP framework developed by a hospital as a guide for attaining noteworthy QI outcomes by using the EPIC tool system. Easy to comprehend Straightforwardly in line with existing quality domains (maybe with a citation to an accepted model such as the Institute of Medicine [IOM] domains of quality) According to the study, the PFCSP possesses motivated medical staff promoted active involvement in accomplishing the hospital’s objectives and vision After applying this paradigm for five years, the study concludes heightened engagement across all quality domains and significant progress made in a moderate amount of time.

According to the study’s findings, a major contributor to their success thus far has been the structure of this framework for strategic planning (Lee et al, 2021). It is possible to support multidisciplinary collaboration during hospital ward rounds with the help of EHRs. In order to accomplish this, it is necessary to address various design issues, such as the social ergonomics of the devices involved, the inclusion of paper records, and support for improving information systems.

A variety of care coordination opportunities are available through EPICs. As academic health science centres (AHSCs) deal with increasing complexity, leadership development is crucial. Leading institutions such as the Academy of American Medical Colleges emphasize the importance of cultivating targeted leadership and analyzing its effects. In the medical area, there is a dearth of comprehensive studies on leadership culture evaluation, despite its recognition as a crucial aspect of business. The Medical University of South Carolina implemented a structured strategy for developing strategic leaders inside the company in 2015.

After reviewing the institution’s current leadership programs, a multidisciplinary committee determined the main motivators for the new strategic plan. Leadership retreats were created by a strategic leadership advisory council to evaluate the needs for individual and group leadership development (Brownfield et al, 2020). Results and Upcoming ActionsThree crucial characteristics that are necessary for the institutional approach to be implemented successfully were found via study.

The ideal institutional leadership culture is to be fostered in four key areas. These categories comprised certain acts or mannerisms that leaders ought to emulate. The MUSC Leadership Institute was established by MUSC with this basis in mind. To create a thorough plan for leadership development, future initiatives will make use of insights into the important strategic drivers, desirable leadership traits, and the present leadership culture. Senior leadership involvement, possible organizational reorganization, the creation of new initiatives, and an openness to trying new things are all part of this ongoing process.

Delicate and thorough analysis will be essential to determine which model works best. MUSC will prioritize monitoring certain strategic goals in addition to focusing on the development of individual leaders (Brownfield et al, 2020).

Enhancing EHR Utilization: Recommendations to Support Stakeholder Needs, Improve Outcomes, and Enhance Patient Satisfaction

 The accuracy and completeness of documentation significantly improved as a result of the adoption of EHRs. Over two years, the proportion of charts with comprehensive problem lists rose significantly. Several clinical procedures were made more efficient by the EHR, including the considerably quicker turnaround time for prescription refills.

Furthermore, there were fewer charts following visits with insufficient documentation. Accurate coding of higher-level visits led to enhanced billing procedures with the EHR, which raised revenue collection. Medical records personnel were able to work more productively with a smaller team since the EHR cut down on the amount of time required to manage medical records because of user inexperience and training requirements, the EHR adoption initially resulted in a brief drop in appointment availability. Understanding the activities of healthcare providers in hospitals depends on measuring the amount of time spent using Electronic Health Records (EHRs).

There were no reliable techniques for evaluating EHR utilization time based on EHR audit records in earlier studies on inpatient care. As a result, there was a dearth of trustworthy measures that represented real clinical work patterns. By contrasting the EHR time data from audit logs with directly witnessed EHR use time recorded via screen captures, this study attempted to verify the correctness of the EHR time data. Over a month in 2020, the study was carried out in a pediatric intensive care unit (PICU). Physicians (attendings, fellows, and hospitalists) and advanced practice doctors who often utilised the EHR system were among the participating providers  (Merker et al, 2021).

Strategizing for Maximum Efficiency and Safety: Leveraging EHR for Patient Satisfaction and Care Support

The analysis revealed significant variations in how patients prioritize these bowel issues. Loss of bowel control emerged as the most concerning side effect, followed by bowel urgency. In contrast, increased bowel frequency was ranked as the least bothersome side effect, followed by bloody stools and pelvic/rectal pain. This research suggests that considering patient preferences for specific bowel dysfunctions is crucial when evaluating treatment outcomes using the EPIC instrument.

Understanding the activities of healthcare providers in hospitals depends on measuring the amount of time spent using Electronic Health Records (EHRs)There were no reliable techniques for evaluating EHR utilization time based on EHR audit records in earlier studies on inpatient care. There was a dearth of trustworthy measures that represented real clinical work patterns. The purpose of this study was to compare directly witnessed EHR usage time recorded by screen recordings with EHR time data taken from audit logs to verify the correctness of the former. In 2020, a month-long study was carried out at a pediatric intensive care unit (PICU).

Physicians (attendings, fellows, and hospitalists) and advanced practice doctors who often utilised the EHR system were among the participating providers. The real amount of time spent engaging with the EHR system was tracked via screen recordings. Feedback on the features and functionality of the prototype was given by fifteen FDA employees. With the prototype, we were able to extract text from EHRs and detect known, labelled adverse medication responses connected to opioids. About accuracy, recall, precision, and F1 scores, the AI-enabled model obtained values of 0.66, 0.69, 0.64, and 0.67 (Sorbello et al, 2023).

NURS FPX 6412 Assessment 3 Manuscript for Publication Conclusion :

An easy-to-use tool was created to forecast how patients will respond to prostate cancer therapies in terms of hormones, bowel movements, urine, and sexual activity. This instrument utilizes data from the EPIC-26 survey. The research looked at how to set up the EPIC system to better assist different healthcare professionals, such as doctors, nurses, and informaticists.

Interprofessional care teams benefit from improved information exchange and teamwork as a result. Studies have demonstrated that the inclusion of patient representatives in the EPIC system’s EHR data analysis enhances the choice of clinical trial outcome metrics, making them more applicable and useful to patients. Increasing QOL and Involvement of Patients for analysis of EHRs’ potential use in predicting prostate cancer patients’ Quality of Life (QOL) following various treatment modalities was conducted. Using the EPIC-26 bowel subscale, research has concentrated on comprehending patient preferences for treatment side effects. This enables better management of any adverse effects by customizing treatment regimens.

Increasing Security and Effectiveness To evaluate unstructured text data in EHRs, a novel software prototype using technology and Natural Language Processing (NLP) approaches was created. This may be applied to detect possible opioid drug safety issues. A useful tool for tracking patient-reported outcomes (PROs) in many health domains after prostate cancer therapy is the EPIC tool. Research has demonstrated that it is a useful tool for predicting patients’ quality of life (QOL) in areas such as hormone and sexual function. Studies also emphasize how critical it is to take patient preferences into account when assessing EPIC ratings.

For example, patients are more concerned about the loss of bowel control than about increasing the frequency of bowel movements. Implementing an EHR has advantages such as more accurate paperwork, quicker medication refills, and better billing practices. On the other hand, early difficulties include interrupted workflows and a loss in appointment availability as a result of inexperienced users. User-centred design and efficient training are essential for the effective adoption of EHRs (Chen et al., 2019).

NURS FPX 6412 Assessment 3 Manuscript for Publication Refrences :

Ledesma, J. R., Ma, J., Zhang, M., Basting, A. V. L., Huong Thi Chu, Avina Vongpradith, Novotney, A., LeGrand, K. E., Yvonne Yiru Xu, Dai, X., Sneha Ingle Nicholson, Stafford, L. K., Carter, A., Ross, J. M., Hedayat Abbastabar, Meriem Abdoun, Deldar Morad Abdulah, Richard Gyan Aboagye, Abolhassani, H., & Woldu Aberhe Abrha. (2024). Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Infectious Diseases. https://doi.org/10.1016/s1473-3099(24)00007-0

Neves, J., Hsieh, C., Isabel Blanco Nobre, Sandra Costa Sousa, Ouyang, C., Maciel, A., Duchowski, A., Jorge, J., & Moreira, C. (2024). Shedding light on AI in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning. European Journal of Radiology, 172, 111341–111341. https://doi.org/10.1016/j.ejrad.2024.111341

Sorbello, A., Syed Arefinul Haque, Hasan, R., Jermyn, R., Hussein, A., Vega, A., Zembrzuski, K., Ripple, A., & Mitra Ahadpour. (2023). Artificial Intelligence–Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study. JMIR AI, 2, e45000–e45000. https://doi.org/10.2196/45000

Sinha, A., Stevens, L. A., Su, F., Pageler, N. M., & Tawfik, D. S. (2021). Measuring Electronic Health Record Use in the Pediatric ICU Using Audit-Logs and Screen Recordings. Applied Clinical Informatics, 12(04), 737–744. https://doi.org/10.1055/s-0041-1733851

Merker, V. L., Lessing, A. J., Moss, I., Hussey, M., Oberlander, B., Rose, T., Thalheimer, R., Wirtanen, T., Wolters, P. L., Gross, A. M., & Plotkin, S. R. (2021). Enhancing Neurofibromatosis Clinical Trial Outcome Measures Through Patient Engagement. Neurology, 97(7 Supplement 1), S4–S14. https://doi.org/10.1212/wnl.0000000000012430

Laviana, A. A., Zhao, Z., Li Ching Huang, Koyama, T., Conwill, R., Hoffman, K., Goodman, M., Hamilton, A. S., Xiao Cheng Wu, Paddock, L. E., Stroup, A., Cooperberg, M. R., Hashibe, M., O’Neil, B. B., Kaplan, S. H., Greenfield, S., Penson, D. F., & Barocas, D. A. (2020). Development and Internal Validation of a Web-based Tool to Predict Sexual, Urinary, and Bowel Function Longitudinally After Radiation Therapy, Surgery, or Observation. European Urology, 78(2), 248–255. https://doi.org/10.1016/j.eururo.2020.02.007

Mishra, M. V., Thayer, W. M., Janssen, E., Hoppe, B., Eggleston, C., & Bridges, J. F. P. (2020). Patient preferences for reducing bowel adverse events following prostate radiotherapy. PLOS ONE, 15(7), e0235616. https://doi.org/10.1371/journal.pone.0235616

Brownfield, E., Cole, D. J., Segal, R. L., Pilcher, E., Shaw, D., Stuart, G., & Smith, G. (2020). Leadership Development in Academic Health Science Centers: Towards a Paradigm Shift. Journal of Healthcare Leadership, Volume 12, 135–142. https://doi.org/10.2147/jhl.s263533

Gilliam, E. H., Nuffer, W., Brunner, J. M., Kosirog, E., M. Suzanne Metcalf, Thompson, M. E., & Chavez, B. (2020). A multi-method evaluation of an interprofessional IPPE in an underserved clinic. Currents in Pharmacy Teaching and Learning, 12(6), 663–670. https://doi.org/10.1016/j.cptl.2020.01.036

Brownfield, E., Cole, D. J., Segal, R. L., Pilcher, E., Shaw, D., Stuart, G., & Smith, G. (2020). Leadership Development in Academic Health Science Centers: Towards a Paradigm Shift. Journal of Healthcare Leadership, Volume 12, 135–142. https://doi.org/10.2147/jhl.s263533

Chen, Y., Lehmann, C. U., Hatch, L. D., Schremp, E., Malin, B. A., & France, D. J. (2019). Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs. Methods of Information in Medicine, 58(04/05), 109–123. https://doi.org/10.1055/s-0040-1702237


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