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Ritania Ramirez-Ehiwarior

Sep 18 12:07am| Last reply Sep 18 7:58pm

Manage Discussion by Ritania Ramirez-Ehiwarior

Reply from Ritania Ramirez-Ehiwarior

Clinical Issue of Interest: Staffing Shortages and Hospital-Acquired Infections (HAIs)

Staffing shortages in acute care hospitals continue to be a pressing issue that directly affects the incidence of hospital-acquired infections (HAIs). HAIs pose grave patient safety risks, extend hospital stays, raise healthcare costs, and may lead to higher mortality charges. Nurses play a pivotal role in preventing infections that involve clinical practices of wound care, hand hygiene, catheter care, and patient monitoring. Nearly half of the nursing homes across the country continue to struggle to secure PPE and adequately staff their facilities (Gibson, 2020). This clinical issue will be the foundation for exploring how staffing strategies influence infection rates, aiming to detect current solutions to alleviate the risks of inadequate staffing.

PICOT Question

How can hospitals implement flexible staffing models compared to traditional fixed staffing schedules, and how does this affect the rate of hospital-acquired infections (HAIs) over one year?

 Search Results

The initial searches involved databases such as CINAHL Plus, the Nursing & Allied Health database, and MEDLINE (Walden University Library. (n.d.-a). Typing the terms “staffing shortages” and “HAIs” yielded about 2,500 to 3000 articles, and adding terms with Boolean operators such as “AND” flexible staffing models” reduced the results to about 800 articles. Refining the search type with “acute care hospitals” and “infection control” narrowed the results to 300 original research articles.

Strategies to Increase Rigor and Effectiveness

Different strategies enhanced the search process, such as applying specific medical subject headings (MeSH).  Multiple databases, like the Cochrane Library, have comprehensive sources and diverse perspectives. Terminologies such as “nurse staffing strategies” and “nosocomial infections” targeted relevant research. Applying filters to limit searches is beneficial, such as having peer-reviewed studies within the last five years to ensure current relevance. Another example is optimizing the use of “AND,” “OR,” and “NOT” to refine searches based on specific data and needs. Exploring a list of references is an effective strategy for further relevant studies to elaborate on more details and understanding of the topics. For example, I could examine the reference lists of the articles I have already identified to find additional sources of evidence (Schmidt & Brown, 2025).  By implementing these strategies, you can investigate how various staffing models impact HAI rates. It serves as a valuable insight for improving healthcare policy and practice.

 

References

Gibson, D.M. and Greene, J. (2020). State Actions and Shortages of Personal Protective Equipment and Staff in U.S. Nursing Homes. J Am Geriatr Soc, 68: 2721- 2726. 
to an external site.

Schmidt, N. A. & Brown, J. M. (2025). 
Evidence-based practice for nurses: Appraisal and application of research (6th ed.). Jones & Bartlett Learning

Walden University Library. (n.d.-a). 

Databases A-Z: Health & nursingLinks to an external site.Links to an external site.

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Walden University Library. (n.d.-c).
 Evidence-based practice research: CINAHL search helpLinks to an external site.Links to an external site.
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Colleen Sandra Mckenzie

Sep 17 10:52pm| Last reply Sep 19 2:41pm

Manage Discussion by Colleen Sandra Mckenzie

Reply from Colleen Sandra Mckenzie

Working in a nursing home has given me the privilege of caring for numerous patients with dementia, particularly Alzheimer’s disease (AD), the most prevalent form among older adults. This firsthand experience has deepened my awareness of the challenges surrounding timely diagnosis and intervention. A critical issue in primary care is the delayed detection of AD, often stemming from inconsistent cognitive screening practices. Although primary care providers serve as the initial point of contact for aging populations, they frequently encounter time constraints and workflow limitations that hinder routine cognitive assessments. Integrating electronic medical record (EMR) prompts for cognitive screening, which may offer a practical solution—enhancing the frequency of evaluations, facilitating earlier diagnosis, and enabling more effective care planning for patients at risk of AD.
PICOT is a robust framework that transforms vague clinical curiosities into precise, searchable questions, streamlining the evidence search process. By breaking down a clinical inquiry into five components—Population, Intervention, Comparison, Outcome, and Time—PICOT helps clinicians identify relevant keywords and filters for databases like PubMed or CINAHL. This structure not only sharpens the focus of a search but also improves the quality of retrieved studies by aligning them with the clinical scenario. For example, instead of broadly searching for “hypertension treatment,” a PICOT-guided query might target “older adults with hypertension (P) using low-sodium diets (I) compared to standard diets (C) to reduce systolic blood pressure (O) over six months (T).” This specificity level enhances the evidence’s relevance and applicability, supports critical appraisal, and strengthens the foundation for evidence-based practice decisions.
To explore the impact of EMR-based prompts on cognitive screening in primary care, I developed the following PICOT question: In patients aged 65 and older seen in primary care settings (P), does the use of electronic medical record (EMR) prompts for cognitive screening (I), compared to no EMR prompts (C), increase the frequency of cognitive assessments performed (O) over 12 months (T)? This question reflects a critical gap in practice, as early detection of Alzheimer’s disease (AD) is often delayed due to inconsistent screening and workflow barriers. I searched two Walden University Library databases, CINAHL Plus with Full Text and PubMed Central, to investigate this issue and ensure adequate nursing and interdisciplinary clinical research coverage. Using Boolean operators and keywords such as “Alzheimer’s disease,” “cognitive screening,” “primary care,” “EMR prompts,” and “early intervention,” I filtered results to include only peer-reviewed articles (no systematic reviews) published between 2019 and the present. The search yielded four original studies addressing EMR-based interventions to improve cognitive assessment rates in older adults. These articles were selected using prioritized keywords—beginning with ‘EMR,’ followed by ‘Alzheimer’s patient,’ and concluding with ‘interventions in the primary care setting.’ Although I ultimately implemented a different intervention, these sources offer a strong foundation for evaluating system-level strategies to enhance early diagnosis and care planning within primary care environments.”

 

References

Dehghani, F., Derafshi, R., Lin, J., Bayat, S., & Bento, M. (2024). Alzheimer disease detection studies: Perspective on multi-modal data. Yearbook of Medical Informatics, 33(1), 266–276.
one article from pubmed

 

Fick, D. M., Steis, M. R., Mion, L. C., & Walls, J. L. (2011). Computerized decision support for delirium superimposed on dementia in older adults. Journal of Gerontological Nursing, 37(4), 39–47.
Let me know if you’d like help summarizing or integrating this into a literature review or EBP matrix. I’ve got your back.
CNLT fll text/PRoquest

Wang, L., Sha, L., Lakin, J. R., Bynum, J., Bates, D. W., Hong, P., & Zhou, L. (2019). Development and validation of a deep learning algorithm for mortality prediction in selecting patients with dementia for earlier palliative care interventions. JAMA Network Open, 2(7), e196972.
CNLT full text

Weldingh, N. M., Mellingsæter, M. R., Hegna, B. W., Saltyte Benth, J., Einvik, G., Juliebø, V., Thommessen, B., & Kirkevold, M. (2022). Impact of a dementia-friendly program on detection and management of patients with cognitive impairment and delirium in acute-care hospital units: A controlled clinical trial design. BMC Geriatrics, 22, Article 266.
DNTL full text

 

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