Response to Capstone Project Questions

Response to Capstone Project Questions

What, Where, When, Why, and How?
In the clinical medicine field, recording of assessment results across healthcare centers
has been insufficient despite the critical role data can play in improving healthcare services. In
the current world, healthcare PR actioners are shifting to data science in making diagnoses and
planning for the course of treatment by comparing the past trends from the recorded data (Maass
et al., 2018).
What is known about this topic, or what is the evidence on this topic (Scoping Search)?
It is a familiar concept today among healthcare personnel of the significant role that data
science plays in improving health outcomes. There has been a shift in the current world to data
utilization in decision-making regarding assessment protocols and treatment (Azar, 2021). For
instance, in mental health, records may give insight into the cause of the treatment mechanisms
utilized, helping healthcare personnel in decision making and ultimately bolstering the healthcare
outcomes.
What is the outcome of interest?
Data recording has been associated with a couple of positive outcomes in the healthcare
systems since clinicians can understand patients' health trends and make evidence-based
decisions on similar past healthcare issues (Watson, 2019). Poor data recording has been
associated with poor health outcomes. The main reason is that every clinician will have to start a
patient's diagnosis and treatment from scratch every time they pay a visit to a clinic and,
therefore, the chances of repeating an error in treatment. As a result, looking at a patient's
treatment records, may give an insight into the progress of the patient, an understanding of what

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they got right in the treatment, and where an adjustment needs to be made to improve the overall
healthcare outcomes.
What are the gaps in our understanding or knowledge related to this topic?
The reliability of the data presented for recording after assessment remains a gap in our
understanding regarding data science in improving healthcare outcomes (Goulooze et al., 2020).
Data accuracy is crucial in ensuring the accuracy of decisions, as inaccurate data may lead to
poor decisions and ultimately poor health outcomes. As a result, there is the need for proper
analysis of the data reliability and the implementation of proper guidelines in collecting accurate
data alongside the proper storage of the same data.

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References

Azar, K. M. (2021). The evolving role of nurse leadership in the fight for health equity. Nurse
Leader, 19(6), 571-575.
Goulooze, S. C., Zwep, L. B., Vogt, J. E., Krekels, E. H., Hankemeier, T., van den Anker, J. N.,
& Knibbe, C. A. (2020). Beyond the randomized clinical trial: innovative data science to
close the pediatric evidence gap. Clinical Pharmacology & Therapeutics, 107(4), 786-
795.
Maass, W., Parsons, J., Purao, S., Storey, V. C., & Woo, C. (2018). Data-driven meets theory-
driven research in the era of big data: Opportunities and challenges for information
systems research. Journal of the Association for Information Systems, 19(12), 1.
Watson, H. J. (2019). Update tutorial: Big Data analytics: Concepts, technology, and
applications. Communications of the Association for Information Systems, 44(1), 21.