Essay Sample on The Importance of Advanced Techniques in Data Analytics

Modern healthcare relies on data to optimize patient care, reduce costs and create efficiency. Healthcare organizations require close attention to accuracy and validity of data and advanced data analytics to deliver on those requirements. Establishing a database with factual, relevant information can help healthcare organizations improve patient care and succeed in a growing world of patient quality standard requirements. Using advanced techniques, healthcare organizations can deliver quality patient care, successfully improve budgets, and optimize long term growth. 

Importance of Data Quality

The healthcare facility in the week 4 assignment failed in several aspects pertaining to a patient seeking an MRI in the Radiology department. With proper data quality, the facility can improve and redress the problem to help improve patient quality standards. Data on communication, wait times, and pre-existing patient history regarding MRI scan requirements, can all be researched and evaluated to improve on patient MRI scan wait times. Healthcare facilities focus on quality outcomes and using quality data, healthcare facilities can improve patient wait times, rescheduling needs, and communication to help improve patient quality outcomes.   

Sampling and probability can have an affect on data quality. Using websites that collect data and post findings can use data sampling and probability to make inferences about larger quantities of data. Samples resemble larger populations so sampling large quantities of data should give characteristics that are relevant to larger populations. This gives healthcare facilities the ability to have informed decisions on things such as health of populations and specific illnesses amongst certain communities. 

Flawed data and failed analysis are detrimental to any type of data involving healthcare. Being able to scrub data to ensure it is useable and beneficial is important to any healthcare organization furthermore, the healthcare facility in the week 4 assignment can benefit from having useful data that has been evaluated and filtered properly. “Data Mapping is an important step in Data Integration, Data Migration and Data Transformation processes. Data Mapping logic will determine the data quality of these data management processes and any inaccuracy in the mappings will have a huge impact on all these processes.” ("Data Mapping and its importance in Data Management," 2020). Data should be consistent and frequently evaluated for accuracy so that flawed data is not being used, otherwise it will skew the quality of the data. When dealing with large amounts of data, mistakes are inevitable when inputting the data so there must be a focus on mapping data in databases and scrubbing the data. There are possible mistakes in the scenario that can cause issues such as a mistake in the delay time of the MRI scan. Another possible mistake can be the age or the sex of the patient, these can all cause issues when looking at data on a larger scale because there will be inaccuracies in the data and inconsistent data is misleading. 

The healthcare facility needs to ensure it us using clean data. Leo Gimenez from Geotab states, “Data cleaning is the process of ensuring that your data is correct, consistent and usable.” (Gimenez, 2020). Data should be monitored for inaccuracies and mistakes. The healthcare facility should have a system in place to help input data consistently, furthermore, using software and manually reviewing the data is a great way to ensure that the data is clean. 

Conclusion

The use of data in the world has grown tremendously and ensuring quality data in healthcare should be a major focus of any healthcare organization. Quality data has the potential to reduce costs, improve patient outcomes, and identify potential trends and patterns in communities and the world. Consequences of poor quality data is a real concern. Collecting data and ensuring the quality of the data go hand in hand so ensuring the data that has been gathered is evaluated for mistakes and having a system in place to consistently input correct data and evaluate for inaccuracies is a great way to manage a database. Quality data cannot be measured and should be a major focus of any healthcare organization.