DISCUSSION RESPONCE  DATA SCIENCE APPLICATIONS AND PROCESSES  DATA SCIENCE APPLICATIONS AND PROCESSES Read a selection of your colleagues’ responses

DISCUSSION RESPONCE

 DATA SCIENCE APPLICATIONS AND PROCESSES 

DATA SCIENCE APPLICATIONS AND PROCESSES

Read a selection of your colleagues’ responses and respond to two of your colleagues. Expand upon your colleague’s posting or offer an alternative perspective.

PEER #1



Shinu Mary Rajan

Data Science, Application and Process
Leveraging data is essential for supporting initiatives in nursing practices and healthcare organizations to improve patient quality and safety. Utilizing data to support patient care changes requires applying big data, data science, data mining, analytics, and machine learning. Large volumes of organized and unstructured data gathered from wearables, medical devices, electronic health records (EHRs), and patient surveys, among other sources inside healthcare systems, are called “big data.” Healthcare organizations may discover trends, anticipate possible threats to patient safety, and get insights into patient populations by evaluating big data. Analyzing EHR data may assist in identifying trends in medication errors or adverse events, allowing for preemptive measures to reduce risks and enhance patient outcomes. Researchers assert that using big data in nursing science can improve decision-making processes and personalized care delivery methods, Caruso R. et al. (2020).
Applying statistical, computational, and algorithmic tools to healthcare data allows data scientists to glean valuable insights and information. Data scientists use sophisticated analytics tools and approaches to find hidden patterns, correlations, and trends in healthcare data. Healthcare organizations can detect elements contributing to patient safety concerns, such as surgical complications or hospital-acquired infections, using data science approaches to patient data. According to researchers, telemedicine, mobile health technologies, data science, and artificial intelligence (AI) integration will propel further advancements in the field of CIS, resulting in more patient-centered, intelligent, and effective healthcare systems, as well as an overall improvement in global healthcare outcomes, Hackl, W. O., Neururer, S. B., & Pfeifer, B. (2023).

Finding patterns and links in massive datasets using automated algorithms is known as data mining. Data mining techniques may be used in healthcare to forecast patient outcomes, find risk factors for adverse occurrences, and improve treatment regimens.
Healthcare practitioners can proactively intervene and avoid adverse events by using data mining algorithms to identify patients at a high risk of medication non-adherence or readmission. Data analytics entails systematically studying healthcare data to provide valuable insights and support decision-making. Healthcare organizations use data analytics to assess quality indicators, evaluate performance, and compare results to industry norms. Through the utilization of data analytics, nursing practices can pinpoint opportunities for improvement, including but not limited to decreasing patient wait times, augmenting care coordination, or optimizing resource distribution to enhance patient safety and care quality. Computers can learn from data and make predictions or choices without explicit programming due to a subset of artificial intelligence called machine learning. Machine learning algorithms can evaluate vast amounts of patient data in the healthcare industry to spot trends and forecast treatment outcomes or patient outcomes. For instance, machine learning algorithms may predict the likelihood of hospital-acquired infections depending on patient demographics, clinical characteristics, and environmental factors. According to researchers, the most popular data analytics techniques in healthcare applications were machine learning and data mining, which were becoming increasingly popular. Research guidelines like PRISMA are increasingly being used, and healthcare data analytics studies typically search through four well-known databases to find 25–100 primary studies, Taipalus T. et al. (2023). Healthcare organizations may enhance patient safety by implementing machine learning into clinical decision support systems to provide individualized suggestions and treatments based on each patient’s needs. According to researchers, how clinical doctors recall pertinent medical knowledge is influenced by how big data interacts with technology, services, and other significant data resources at different levels. Institutions with and without platforms exhibit distinct patterns of interaction. To improve clinical capabilities, governments and institutions can use this study as a reference when designing big data environments, Yuan, J., Mi, L., Wang, S., Cheng, Y., & Hou, X. (2023).

                                                       
  How Predictive Analytics might be used to support Healthcare.

By using both historical and current data to predict future occurrences and trends, predictive analytics has great potential to transform the healthcare industry. Predictive analytics may be used in various healthcare sectors to improve patient outcomes, optimize resource use, and assist clinical decision-making. Predictive analytics may help identify people at risk of developing specific medical illnesses, forecast patient outcomes, and maximize treatment options by evaluating massive datasets using sophisticated statistical models and machine learning algorithms. Early intervention and disease prevention are two important uses of predictive analytics in healthcare. Predictive analytics examines patient data, including demographics, medical history, and lifestyle variables, to determine who is more likely to acquire chronic conditions like diabetes, heart disease, or cancer. From there, healthcare professionals can take proactive measures to reduce the risk and enhance patient outcomes by implementing targeted therapies, lifestyle changes, or preventative screenings.
Predictive analytics may also improve care coordination, optimize resource allocation, and estimate patient demand, all of which can improve hospital operations. Predictive analytics, for instance, can forecast patient admission rates, allowing medical facilities to effectively manage personnel levels and resource allocation to serve patients better. Predictive analytics may also be used to identify patients at a high risk of readmission to the hospital. This enables medical professionals to create individualized discharge plans and follow-up treatment to reduce needless readmissions. According to researchers, there has been an increasing interest in predictive analytics in the healthcare industry due to high patient data from electronic health records and the growing number of published predictive models, including those that predict clinical deterioration and hospital readmission [ 1]. Using data mining, statistics, modeling, and artificial intelligence methods, predictive analytics uses data to forecast risk, Rojas, J.C. et al. (2022).

                                              
Predictive Analytics in Nursing Practice: Challenges and Opportunities for the Future
Predictive analytics may find valuable applications in medication administration and adherence in nursing practice. Nurses frequently work with patients who have complicated drug schedules, and failure to take prescription medicine as directed can result in adverse health effects and readmissions to the hospital. Nurses can detect patients who may not adhere to their prescription regimen by using predictive analytics to evaluate patient data, including medication history, health state, and sociodemographic characteristics. To increase adherence and avoid issues, nurses can proactively intervene with targeted education, reminders, or support services by using predictive models to identify which patients are most likely to stray from their medication regimen. Predicted analytics in healthcare may face future difficulties due to data quality and interoperability issues, privacy issues, and the requirement for trained staff to comprehend and act on predicted findings. Predictive model accuracy and dependability largely depend on the availability and quality of data, which can vary widely throughout healthcare systems and formats. Additionally, there are moral and legal ramifications to sharing and analyzing sensitive health data while maintaining patient privacy and confidentiality. Healthcare organizations must train personnel in data literacy and analytics to use predictive insights in clinical practice successfully. Despite these obstacles, predictive analytics offers a wealth of chances to improve patient outcomes and change how healthcare is delivered. With technological improvements and data analytics skills, predictive models can become more precise and valuable, allowing healthcare professionals to give more proactive and individualized treatment. Nurses and other medical professionals can detect at-risk patients sooner and customize therapies using predictive analytics. Ultimately, predictive analytics in healthcare can transform clinical practice and enhance patient care globally.

There are many opportunities and challenges in store for predictive analytics in the healthcare industry in the future. One of the challenges is ensuring data quality, privacy, and interoperability across different sources, requiring solid data governance frameworks. Furthermore, the need for more proficient professionals with expertise in data science and analytics within the healthcare workforce must be addressed to leverage predictive insights effectively. Still, there are a ton of outstanding possibilities among these difficulties. The development of more precise and valuable predictive models that can direct personalized treatments, population health management, and clinical decision-making is possible due to advancements in artificial intelligence and machine learning algorithms.
Harnessing the full potential of predictive analytics to transform healthcare in the upcoming years will depend critically on seizing these opportunities and resolving related issues.

 

Caruso, R. (2020) ‘The Byzantine Role of Big Data Application in Nursing Science:
 A Systematic Review,’ Computers, informatics, nursing: CIN, 39(4), pp. 178–186. doi:10.1097/CIN.0000000000000673.

Hackl, W. O., Neururer, S. B., & Pfeifer, B. (2023). Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications.
 Yearbook of Medical Informatics, 32(1), 127–137.

Rojas, J. C., Rohweder, G., Guptill, J., Arora, V. M., & Umscheid, C. A. (2022). Predictive Analytics Programs at Large Healthcare Systems in the USA: a National Survey. 
Journal of General Internal Medicine, 37(15), 4015–4017.

Yuan, J., Mi, L., Wang, S., Cheng, Y., & Hou, X. (2023). Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform. BMC Medical Education, 23(1), 956.
Taipalus, T., Isomöttönen, V., Erkkilä, H., & Äyrämö, S. (2023). 
Data Analytics in Healthcare: A Tertiary Study. SN Computer Science, 4(1), 87.


 ReplyReply to Comment

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PEER # 2



Fallon Shelton

Initial Post

Fallon Shelton

 

Data Science Applications and Process

The contemporary healthcare sector has seen increased adoption of technological implementations to increase the efficiency of healthcare services delivered to patients. For example, compared to the past, contemporary healthcare has seen wide usage of big data, data science, data mining, data analytics, and machine learning to inform the delivery of care (Van Calster et al., 2019). For example, currently, most nurses have adopted the use of predictive analytics to offer patient-centered care. Thus, the following paper will summarize the applications of productive analysis in health care.

A Summary of How Predictive Analytics Might be Used in Health

Predictive analytics uses data analysts and machine learning as part of data science and processes to make meaning out of patient’s data and information. This is because, currently, there are increased collections of patients’ data concerning their medical history, way of life, and other factors. This element has made it difficult for care providers to use such information on their own to make informed decisions. However, predictive analytics is an aspect of data sciences that uses technology to sift through a large amount of patient information to identify early issues and signs that indicate future health problems to patients (Khanra et al., 2020). This will then inform care providers to develop adequate measures that can be used to address such issues based on the available evidence. Therefore, predictive analytics in healthcare is an essential element because it can be used to develop preventive and treatment measures that can be used on patients before their health conditions become worse, helping to save lives. This has resulted in increased accuracy.

Practical Applications of Predictive Analytics in Nursing Practices

In nursing practice, there are currently many applications of predictive analytics. For example, the technological aspect can be used to predict the worsening of one’s health conditions, especially for patients with cardiac problems. For instance, currently, there has been an increased adoption of cardiac implantable electronic devices (CIED) that are used to monitor and collect patients’ information about their heart health. These devices include pacemakers and defibrillators. They collect data about how one is fairing and transmit such data to the CIED center. This element makes it easier to monitor the patient’s progress (Van Calster et al., 2019). Therefore, the data collected can be fed to the predictive analytic technology, which can inform nurses about how the patient is faring. For example, nurses use the CIED to monitor and collect patients’ data regarding vital signs. These elements can be analyzed using data mining and data science to identify if a patient’s cardiac condition is deteriorating, leading to early measures being taken. Therefore, using predictive analytics and such patient information, the nurse can understand if the patient’s cardiac state is declining, resulting in increasing management of sepsis, cardiac arrest, and respiratory failure (Lee et al., 2020). This may lead such care providers to develop preventive strategies that can help manage the mentioned conditions.

Challenges Associated with Predictive Analytics in Nursing Practices

Regardless of the enormous positives associated with predictive analytics, such as their potential to reduce human suffering by proposing preventive and curative approaches, it still has some drawbacks. That mentioned, low capabilities to use technology, especially among age group over 40 years is a leading challenge. More than half of the nurses and some other care providers over 40 years struggle with applying predictive analytics in their everyday practices. The unproductive use of technologies has led to the inability of these assets to generate an effective contribution (Ramachandran et al., 2020). Among other concerns linked to predictive analytics are ethical and privacy issues. This is because predictive analytics is related to acquiring and examining the personal information of the patients. Therefore, such data can be misused if it is found in the wrong hands. This element can result in issues of data privacy and security. Patient confidentiality may also be harmed. Besides, there is a significant problem with the predictive analytic of data in terms of available quality sources. For predictive analytics to work effectively, it needs to have sources that provide accurate and complete data (Benda et al., 2020). However, the majority of healthcare facilities still struggle to have accurate data that may inform preventive actions.

Opportunities Associated with Predictive Analytics in Nursing Practices

On the other side, preventive analytics presents its own fair share of opportunities. To illustrate, there has been increased automation of physical documents, patient records and similar activities due to the increased use of digital devices. This has created new opportunities and roles for predictive analytics to be used in healthcare. It makes it possible to use such data to develop and deliver treatment and prevention measures. Besides, after all, the majority of nurses actually have developed a higher degree of technological competency, as stated by the researchers Khanra et al. in 2020. Therefore, this will enable the method to be adopted easily by the nurses and contribute to increased effectiveness in the delivery of these much-needed services.

It is thus clear that there has been a marked rise in the applications of data science in the modern world. Take, for example, predictive analytics, which have been rooted in a nursing practice where patients’ data are employed to analyze and find out the problems associated with a patient’s health. Though there are many advantages, unfortunately, there are also challenges to predictive analytics in the delivery of healthcare that, if taken into account, can lead to a positive shift.

 

 

References

Benda, N. C., Das, L. T., Abramson, E. L., Blackburn, K., Thoman, A., Kaushal, R., … & Ancker, J. S. (2020). “How did you get to this number?” Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study. 
Journal of the American Medical Informatics Association
27(5), 709-716.

Khanra, S., Dhir, A., Islam, A. N., & Mäntymäki, M. (2020). Big data analytics in healthcare: a systematic literature review. 
Enterprise Information Systems
14(7), 878-912.

Lee, T. C., Shah, N. U., Haack, A., & Baxter, S. L. (2020, July). Clinical implementation of predictive models embedded within electronic health record systems: a systematic review. In 
Informatics (Vol. 7, No. 3, p. 25). MDPI.

Ramachandran, A., Kumar, A., Koenig, H., De Unanue, A., Sung, C., Walsh, J., … & Ridgway, J. P. (2020). Predictive analytics for retention in care in an urban HIV clinic. 
Scientific reports
10(1), 6421.

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019). Predictive analytics in health care: how can we know it works? 
Journal of the American Medical Informatics Association
26(12), 1651-1654.

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