ANSWER
Predictive Analytics in Healthcare
Predictive analytics studies trends and forecasts future results using data science, data mining, and machine learning. By means of proactive interventions, risk reduction, and result enhancement, it has the power to change patient care in nursing practice. Combining massive datasets (“Big Data”) with algorithms, predictive analytics seeks trends that direct therapeutic decisions. By means of patient-specific data from electronic health records (EHRs), for instance, one can forecast readmissions, sepsis, or bad drug responses, therefore enabling nurses and healthcare teams to respond early.
Applied Practicing in Nursing Education
Predictive analytics can be applied in a medical-surgical facility to track patients who run the danger of pressure injury development. Predictive algorithms can flag high-risk patients for nurses by examining data like mobility, nutritional state, skin evaluations, and comorbidities (e.g., diabetes). Early identification helps nurses to apply focused treatments, such regular repositioning or specialised pressure-relieving beds, thereby lowering the pressure injury incidence.
In behavioural health, too, by examining elements like medication adherence, treatment attendance, and mood or behaviour noted in patient notes or wearable devices, predictive analytics can assist identify individuals at risk for suicide or relapse.
Opportunities and Difficulties
Difficulties include:
Predictive models mostly depend on accurate and comprehensive data, so EHRs might show missing or inconsistent information.
Using predictive algorithms without interfering with nursing processes might be difficult.
Two major ethical questions are data privacy and algorithm bias. Predictive tools have to avoid reflecting prejudices in historical data so aggravating healthcare inequalities.
Prospective:
Early diagnosis and intervention made possible by predictive analytics improves patient outcomes and helps to lower healthcare expenditures by means of intervention.
Tailored therapies grounded in predictive insights fit ideas of patient-centered care.
Predictive analytics can also help to maximise resource allocation, lower emergency department crowding, and adjust staffing.
Last Thought
Early interventions and improved patient care in predictive analytics help to transform nursing practice. Effective application of it depends on addressing issues such data quality, integration, and ethical questions, though. As front-line healthcare practitioners, nurses are very important in making sure predictive tools are applied sensibly and successfully to support patient-centered, holistic treatment.
Notes of reference
Bates, D. W., with Sheikh, A. (2022). The part big data plays in raising patient safety. BMJ Quality & Safety, 31 (2), 85–90. https://doi.org/10.1136/bmjqs-20211-013993
McGonigle, D.; Mastrian, K. G. (2022). Nursing informatics and the basis of knowledge ( Fifth edition ). Jones and Bartlett Learning.
2020 Sendak, M. P.; D’Arcy, J.; Kashyap, S.; Gao, M.; Balu, S. a route for bringing machine learning solutions into use in healthcare systems. BMJ Health & Care Informatics, e100104, 27(1). 10.1136/bmjhci-2019-10104 https://doi.org/10.1136/bmjhci-2019-10104
QUESTION
Transforming week 5 discussion
- Review this week related topics: Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning.
- Consider the process and application of each topic.
- Reflect on how each topic relates to nursing practice.
The assignment:
Post a summary on how predictive analytics might be used to support healthcare. Note: These topics may overlap as you will find in the readings (e.g., some processes require both Data Mining and Analytics).
In your post include the following:
- Describe a practical application for predictive analytics in your nursing practice (you can do behavioral health or med surg). What challenges and opportunities do you envision for the future of predictive analytics in healthcare? INCLUDE 3 REFERENCES