Simulation reflection

Answer

Data Mining in Healthcare

Obtaining Data in Clinical Practice:

Data mining is a collection of diverse techniques for providing necessary strategies to retrieve and gather adequate knowledge from an exhaustive collection of factual information. Among the most used strategies include machine learning (Mastrian & McGonigle, 2019). Data visualization, multi-dimensional databases, machine learning, soft computing, and statistics. The data mining project for my practice area –a psychiatrist unit- would be using data mining procedures to detect and prevent fraud and related forms of abuse. In this practice, data mining techniques are employed to establish normal patterns and identify unusual medical claims patterns by healthcare providers such as clinics, labs, and doctors (Mastrian & McGonigle, 2019).

Further, I will use the data details to predict the volume of patients admitted in each category and develop effective processes to ensure that patients receive effective care at the right time and place. Thus, it will help gather critical information concerning patients' totality and the number of care providers allocated to them to ensure all their health demands are well taken care of. Data mining as a procedure holds great potential to streamline health systems. Thus, I would use data analytics for identifying best practices, improving care costs to patients while reducing unnecessary medical incidents (Mastrian & McGonigle, 2019). The goal is to predict data trends and industrial procedures and analyze how to go about them.

Introducing technology in healthcare fraud detection remains significant. It helps both clinicians and managers locate valuable data patterns and improve resource utilization and overall approaches to patient health in psychiatry. I can make sure that I select and develop data mining strategies that best fit my data by first selecting information to be collected from diverse sources, preprocessing the gathered data, then transforming the information into a practical and suitable format to facilitate further processing (Mastrian & McGonigle, 2019). Afterward, I would introduce the most suitable data mining technique to extract valuable details before its interpretation and evaluation.

The assessment phase helps in analysis and further decision making. I would use the association data mining technique to contrast components such as patients' registration, causes of health events, symptoms, and cost to detect any imbalances or uncertainties in operation. For instance, a care provider can assess the consequences of medications of diverse patients with similar medical issues yet managed using various medications. Thus, it is easy to discover the best effective treatment regarding a patient's well-being and costs.

As a clinician reading these studies, my next step would be to research within our healthcare practice area to understand the various opinions that nurses have concerning rotations and how it can be fixed to avoid further complications such as burnout and exhaustion (Mastrian & McGonigle, 2019). I would also introduce meetings to discuss the routines that work best for every person to ensure the delivery of quality care standards to patients at all times. It is important to weigh whether working for 12 hours is more draining for care providers than 8 hours, and this is a practical exercise that can involve even the rest of other departments in place.

Additional data should be retrieved from both the staff, families, and patients concerning their opinions on the 8 or 12- hour shifts and how they feel it impacts the delivery of sustainable care in practice. Researchers in place can use observations, questionnaires, or examine documents detailing efficiency in practice during both shifts. More details can be gathered from patient-reported information, reviewing hospital medical records, and collecting raw data, especially during the 12 hours round, to assess these allegations' truth.

Translating Data/Research into Practice:

As a part of the management team of a hospital that operates 12-hour shifts, the best recommendations to give to the management team would be to focus on managing nurse burnout in practice (Mastrian & McGonigle, 2019). The team should hold discussions with care providers to understand their opinion of the 12-hour shifts and whether they feel the need for adjustments. The program should be designed in a way that allows care providers to take breaks during their shifts and embrace self-care strategies during this time, such as reading a novel, relaxing, and eating right (Braunschneider, 2013).

Embracing a culture that allows nurses time to rest and unwind from duty is imperative to control the aftereffects associated with burnout, fatigue, and overall exhaustion (Braunschneider, 2013). Thus, leaders should be trained to recognize and manage burnout by setting flexible work routines, familiarizing with fatigue symptoms, and improving nurse-to-patient ratios. The management team should also include nurses' representatives in policy discussions, decision-making, and voice concerns by participating in strategy execution (Braunschneider, 2013). The management should further involve nurses in scheduling to give nurses more control of their schedule, reduce fatigue levels, and create timelines that favor each person in the institutionpurchasing powers and are ready to buy the commodity irrespective of the prices.

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