The Application of Data to Problem-Solving

by | May 29, 2025 | Data

In the modern era, there are few professions that do not to some extent rely on data. Stockbrokers rely on market data to advise clients on financial matters. Meteorologists rely on weather data to forecast weather conditions, while realtors rely on data to advise on the purchase and sale of property. In these and other cases, data not only helps solve problems, but adds to the practitioner’s and the discipline’s body of knowledge.
Of course, the nursing profession also relies heavily on data. The field of nursing informatics aims to make sure nurses have access to the appropriate date to solve healthcare problems, make decisions in the interest of patients, and add to knowledge.
In this Discussion, you will consider a scenario that would benefit from access to data and how such access could facilitate both problem-solving and knowledge formation.

 

Sample solution

Dante Alighieri played a critical role in the literature world through his poem Divine Comedy that was written in the 14th century. The poem contains Inferno, Purgatorio, and Paradiso. The Inferno is a description of the nine circles of torment that are found on the earth. It depicts the realms of the people that have gone against the spiritual values and who, instead, have chosen bestial appetite, violence, or fraud and malice. The nine circles of hell are limbo, lust, gluttony, greed and wrath. Others are heresy, violence, fraud, and treachery. The purpose of this paper is to examine the Dante’s Inferno in the perspective of its portrayal of God’s image and the justification of hell. 

In this epic poem, God is portrayed as a super being guilty of multiple weaknesses including being egotistic, unjust, and hypocritical. Dante, in this poem, depicts God as being more human than divine by challenging God’s omnipotence. Additionally, the manner in which Dante describes Hell is in full contradiction to the morals of God as written in the Bible. When god arranges Hell to flatter Himself, He commits egotism, a sin that is common among human beings (Cheney, 2016). The weakness is depicted in Limbo and on the Gate of Hell where, for instance, God sends those who do not worship Him to Hell. This implies that failure to worship Him is a sin.


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God is also depicted as lacking justice in His actions thus removing the godly image. The injustice is portrayed by the manner in which the sodomites and opportunists are treated. The opportunists are subjected to banner chasing in their lives after death followed by being stung by insects and maggots. They are known to having done neither good nor bad during their lifetimes and, therefore, justice could have demanded that they be granted a neutral punishment having lived a neutral life. The sodomites are also punished unfairly by God when Brunetto Lattini is condemned to hell despite being a good leader (Babor, T. F., McGovern, T., & Robaina, K. (2017). While he commited sodomy, God chooses to ignore all the other good deeds that Brunetto did.

Finally, God is also portrayed as being hypocritical in His actions, a sin that further diminishes His godliness and makes Him more human. A case in point is when God condemns the sin of egotism and goes ahead to commit it repeatedly. Proverbs 29:23 states that “arrogance will bring your downfall, but if you are humble, you will be respected.” When Slattery condemns Dante’s human state as being weak, doubtful, and limited, he is proving God’s hypocrisy because He is also human (Verdicchio, 2015). The actions of God in Hell as portrayed by Dante are inconsistent with the Biblical literature. Both Dante and God are prone to making mistakes, something common among human beings thus making God more human.

To wrap it up, Dante portrays God is more human since He commits the same sins that humans commit: egotism, hypocrisy, and injustice. Hell is justified as being a destination for victims of the mistakes committed by God. The Hell is presented as being a totally different place as compared to what is written about it in the Bible. As a result, reading through the text gives an image of God who is prone to the very mistakes common to humans thus ripping Him off His lofty status of divine and, instead, making Him a mere human. Whether or not Dante did it intentionally is subject to debate but one thing is clear in the poem: the misconstrued notion of God is revealed to future generations.

 

References

Babor, T. F., McGovern, T., & Robaina, K. (2017). Dante’s inferno: Seven deadly sins in scientific publishing and how to avoid them. Addiction Science: A Guide for the Perplexed, 267.

Cheney, L. D. G. (2016). Illustrations for Dante’s Inferno: A Comparative Study of Sandro Botticelli, Giovanni Stradano, and Federico Zuccaro. Cultural and Religious Studies, 4(8), 487.

Verdicchio, M. (2015). Irony and Desire in Dante’s” Inferno” 27. Italica, 285-297.

Discussion: Leveraging Data in Nursing Leadership for Problem-Solving and Knowledge Formation

The modern healthcare landscape is undeniably data-driven, and the nursing profession, particularly at the leadership level, stands to gain immensely from effectively leveraging data. As the prompt highlights, nursing informatics plays a crucial role in ensuring nurses have access to the right information at the right time to make informed decisions and contribute to the collective body of nursing knowledge.

Let’s consider a hypothetical scenario within a hospital setting that would significantly benefit from strategic access to data, and how that access could facilitate both immediate problem-solving and long-term knowledge formation.

Discussion: Leveraging Data in Nursing Leadership for Problem-Solving and Knowledge Formation

The modern healthcare landscape is undeniably data-driven, and the nursing profession, particularly at the leadership level, stands to gain immensely from effectively leveraging data. As the prompt highlights, nursing informatics plays a crucial role in ensuring nurses have access to the right information at the right time to make informed decisions and contribute to the collective body of nursing knowledge.

Let’s consider a hypothetical scenario within a hospital setting that would significantly benefit from strategic access to data, and how that access could facilitate both immediate problem-solving and long-term knowledge formation.

Hypothetical Scenario: Reducing Hospital Readmissions for Congestive Heart Failure (CHF) Patients

Scenario: Our urban teaching hospital has recently observed a concerning trend: an increase in 30-day readmission rates for patients discharged with a primary diagnosis of Congestive Heart Failure (CHF). This trend is impacting patient outcomes, increasing healthcare costs, and potentially affecting our hospital’s quality metrics and reimbursement rates. As a nurse leader overseeing the cardiology unit and discharge planning, I’ve been tasked with understanding the underlying causes of this increase and developing evidence-based interventions.

How Data Access Facilitates Problem-Solving

In this scenario, immediate and comprehensive access to various data points would be crucial for pinpointing the root causes of the rising readmission rates.

  1. Patient Demographics and Comorbidities:

    • Data Needed: Age, socioeconomic status, primary language, living situation (alone vs. with support), transportation access, and specific comorbidities (e.g., diabetes, renal failure, COPD) for readmitted CHF patients versus those who were not readmitted.
    • Access/Collection: This data would be accessible through the Electronic Health Record (EHR) system’s reporting functionalities. Specific queries could be run to extract and compare demographic and comorbidity data for the two groups.
    • Problem-Solving: This data could reveal that readmitted patients disproportionately come from certain zip codes with limited resources, have a specific language barrier, or have a higher burden of particular comorbidities. This immediately directs initial problem-solving efforts towards social determinants of health, language-appropriate discharge instructions, or enhanced coordination with community resources.

  2. Discharge Planning and Patient Education Compliance:

    • Data Needed: Documentation completeness of discharge instructions (medication reconciliation, diet, activity restrictions, follow-up appointments), patient comprehension scores (if assessed), and completion rates of post-discharge phone calls by nurses.
    • Access/Collection: EHR documentation audits, post-discharge call logs within the EHR or a separate care coordination software.
    • Problem-Solving: Analysis might show that patients who are readmitted often have incomplete discharge education documentation, or that they missed their post-discharge follow-up calls. This identifies a critical gap in the discharge process itself, allowing for immediate interventions like mandatory discharge education checklists, improved patient education tools (e.g., simplified handouts, video resources), or standardized protocols for post-discharge nurse follow-up calls.

  3. Medication Adherence and Access Post-Discharge:

    • Data Needed: Prescribed medications at discharge, documented medication reconciliation, and, if available through pharmacy partnerships or patient self-reporting, actual medication fill rates post-discharge.
    • Access/Collection: EHR medication lists, pharmacy claims data (if integrated or accessible via patient consent), and follow-up survey data from patients/families.
    • Problem-Solving: This data could highlight that a significant portion of readmissions is linked to patients not filling their diuretic prescriptions post-discharge due to cost, transportation issues to the pharmacy, or lack of understanding. This allows for immediate problem-solving interventions like implementing medication assistance programs, linking patients with mail-order pharmacies, or providing a “starter pack” of essential medications upon discharge.

  4. Staffing Levels and Nurse Workload during Discharge:

    • Data Needed: Nurse-to-patient ratios on the cardiology unit during discharge hours, documented time spent on discharge education per patient, and nurse self-reported workload assessments.
    • Access/Collection: Hospital staffing schedules, time-tracking software, and nurse surveys.
    • Problem-Solving: Analysis might reveal that readmission spikes correlate with periods of high nurse workload or lower staffing ratios during discharge, suggesting that nurses are rushed and unable to provide thorough education. This would lead to staffing adjustments, dedicated discharge nurses, or protected time for discharge education.

How Data Access Facilitates Knowledge Formation

Beyond solving the immediate problem, the systematic collection and analysis of this data contributes significantly to the broader body of nursing knowledge, enabling continuous improvement and evidence-based practice.

  1. Developing Predictive Models for Readmission Risk:

    • Knowledge Formation: By analyzing vast amounts of patient data (demographics, comorbidities, social determinants, previous admissions, lab values, etc.), we can develop sophisticated predictive analytics models. These models can identify patients at highest risk for readmission before discharge, allowing nurses to tailor intensive interventions for these individuals. This moves nursing practice from reactive to proactive, fundamentally changing how risk is assessed in CHF management.
    • Contribution to Knowledge: This data can inform the development of new risk stratification tools applicable beyond our institution, contributing to population health management strategies. It demonstrates the utility of big data in identifying complex, multi-factorial risk profiles.

  2. Establishing Evidence-Based Discharge Planning Protocols:

    • Knowledge Formation: The findings from analyzing discharge education completeness, medication adherence, and follow-up rates will allow us to create refined, evidence-based discharge protocols. We can formally establish which specific educational components, communication methods, and follow-up schedules are most effective for different CHF patient subgroups.
    • Contribution to Knowledge: These refined protocols can be published as best practices, shared with other institutions, and integrated into nursing education curricula. It adds to the body of knowledge on effective transitional care models for chronic conditions, especially regarding patient education and self-management support.

  3. Understanding the Impact of Social Determinants of Health on Clinical Outcomes:

    • Knowledge Formation: By correlating readmission rates with socioeconomic data, transportation access, and language barriers, we gain a deeper understanding of how social determinants directly impact clinical outcomes for CHF patients. This moves beyond a purely clinical view of disease management.
    • Contribution to Knowledge: This knowledge can inform broader healthcare policy discussions, advocating for integrated social services alongside clinical care. It validates the critical role of nurses in addressing these systemic issues and contributes to the growing body of literature on the intersection of public health, social justice, and clinical practice.

  4. Optimizing Nurse Workflows and Resource Allocation:

    • Knowledge Formation: Analyzing staffing data against patient outcomes and discharge efficiency allows us to determine optimal nurse staffing models, particularly for high-acuity units or critical processes like discharge planning. We can learn what nurse workload is sustainable while ensuring quality care.
    • Contribution to Knowledge: This contributes to the administrative and operational science of nursing, providing data-driven insights for nurse leaders across the country on how to staff units effectively, allocate resources efficiently, and support nurses in delivering high-quality care while preventing burnout.

In conclusion, access to and skilled interpretation of data, facilitated by nursing informatics, transforms the nurse leader from a manager reacting to problems into a strategic innovator driving systemic improvements. In the CHF readmission scenario, data not only provides immediate answers for problem-solving but also generates new, actionable knowledge that refines clinical practice, shapes policy, and ultimately enhances patient outcomes and the entire discipline of nursing.

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