Importance of Advanced Techniques in Data Analytics

Importance of Advanced Techniques in Data Analytics

Introduction

Data quality is essential in the healthcare delivery industry. Healthcare organizations must utilize quality data in a structured way to enhance healthcare quality. Data analytics will help in decision-making, reduce healthcare costs, optimize healthcare processes, and improve patient outcomes (Borges do Nascimento et al., 2021). There are many stakeholders affected by healthcare data. They include patients, healthcare professionals, healthcare providers, and policy formulators. Healthcare data is important, and thus, more focus should be put on how to effectively use this data to optimize healthcare processes.

The Importance of Data Quality

Data quality is the extent to which data meets the end user’s requirements (Ehsani-Moghaddam et al., 2019).  Poor quality results in poor healthcare outcomes. It also negatively impacts the validity of research used in the healthcare delivery industry (Ehsani-Moghaddam et al., 2019). Healthcare organizations can use quality data to determine their areas of strength and weaknesses that require improvement. Paradise Valley Hospital can use data from research, survey results, and other sources of information to show the progress of its daily operations. The data can show this facility the areas that require more effort to provide quality care.  All healthcare must ensure that the healthcare data they use is of the highest quality. Utilizing incorrect and low-quality data can provide misleading information, negatively impacting the quality of services offered to their clients. One such source of data in the healthcare industry is Hospital Compare. This reputable website offers reliable and accurate data to the general public. This website helps consumers compare hospitals and determine which hospitals will promptly meet their healthcare needs (Sen, 2020).

How Sampling and Probability Affect Quality Data on Public Reported Sites

Sampling is important to reduce bias, while probability distribution helps perform statistical tests. The sampling method selected is important in quality data. Many sampling methods help reduce bias and obtain quality results that inform consumer choices. If the healthcare researcher fails to utilize proper sampling techniques, the data generated will be highly biased. This can lead to misinformation and inaccurate interpretation of the results. When most healthcare researchers collect data, they ensure that it follows a normal probability distribution. Normal probability distribution helps healthcare researchers to perform statistical tests. Different data types have different types of distribution, but sampling and distribution probability ensure that healthcare researchers use different techniques on the healthcare data. Sampling and probability thus affect data quality. Proper sampling and probability techniques will result in quality data with reduced biases.

The Importance of Data Mapping and Scrubbing

Paradise Valley Hospital can use data mapping to meet the interoperability of an information system. Data mapping allows the matching of a data source and target (Wenham et al., 2021). The target has the data in a different form.  Data mapping helps in healthcare information exchange. Paradise Valley Hospital can obtain and combine healthcare data from different data and use this data to perform healthcare analytics, forecasting, and case studies.

Data scrubbing involves removing or repairing inaccurate, incorrectly structured or formatted, incomplete, duplicative, or stale data (Ehsani-Moghaddam et al., 2019). Clean data is important in a hospital’s ability to execute digital migration. Data scrubbing is important since it will improve the data quality of Paradise Valley Hospital and thus enhance the overall productivity. Data cleaning leaves the highest quality of information.

Sources of Error

The two types of errors that can affect Paradise Valley Hospital are random bias and substitution errors. Random bias involves the addition of additional values uniformly distributed to an exact timestamp (Ward et al., 2015).  Manual data collection can cause random bias. The second type of error is substitution errors. Substitution errors occur due to the substitution of one data value with another (Ward et al., 2015).

How to Ensure Data is Clean

Paradise Valley Hospital should implement certain strategies to ensure their data is clean. The first strategy is to have an automated approach to errors that appear more often than they should (Ward et al., 2015). This surveillance system could result in the investigation of data quality and sources of errors. The second strategy is consistent data cleaning. Data cleaning should be consistent and thorough before data use to ensure quality data (Ward et al., 2015). The third strategy is establishing a data integrity champion who will monitor and report data quality (Ward et al., 2015).

Conclusion

Data quality is important in the healthcare delivery industry. It improves care quality, reduces healthcare costs, and enhances patient safety. Healthcare organizations should implement good strategies for data scrubbing and mapping to ensure they have high-quality data to help them in decision making.

References

Borges do Nascimento, I. J., Marcolino, M. S., Abdulazeem, H. M., Weerasekara, I., Azzopardi-Muscat, N., Gonçalves, M. A., & Novillo-Ortiz, D. (2021). Impact of big data analytics on people’s health: Overview of systematic reviews and recommendations for future studies. Journal of Medical Internet Research, 23(4), e27275. https://doi.org/10.2196/27275

Ehsani-Moghaddam, B., Martin, K., & Queenan, J. A. (2019). Data quality in healthcare: A report of practical experience with the Canadian primary care Sentinel surveillance network data. Health Information Management Journal, 50(1-2), 88-92. https://doi.org/10.1177/1833358319887743

Sen, S. (2020). An evaluation of Medicare’s hospital comparison as a decision-making tool for patients and hospitals. e-Service Journal, 12(1), 42. https://doi.org/10.2979/eservicej.12.1.02

Ward, M. J., Self, W. H., & Froehle, C. M. (2015). Effects of common data errors in electronic health records on emergency department operational performance metrics: A Monte Carlo simulation. Academic Emergency Medicine, 22(9), 1085-1092. https://doi.org/10.1111/acem.12743

Wenham, C., Wouters, O., Jones, C., Juma, P. A., Mijumbi-Deve, R. M., Sobngwi-Tambekou, J. L., & Parkhurst, J. (2021). Measuring health science research and development in Africa: Mapping the available data. Health Research Policy and Systems, 19(1). https://doi.org/10.1186/s12961-021-00778-y

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Data is widely used by the healthcare industry and the community to understand and anticipate disease trends and other health issues, treatments, and prevention. Therefore, the data provided to all parties must be timely, accurate, and useful.

Importance of Advanced Techniques in Data Analytics

Write a 350- to 700-word paper explaining advanced techniques in data analytics. To complete this presentation, you will reference your Week 4—Using Data to Address Quality Measures assignment.

Include the following in your paper:

Explain the importance of data quality to the healthcare facility referenced in your Week 4 assignment.
Explain how sampling and probability affect quality data on publicly reported sites, such as Hospital Compare.
Explain the importance of data mapping and scrubbing to the healthcare facility reference in your Week 4 assignment.
Identify 2 possible errors in data that could cause issues for the healthcare facility in your Week 4 assignment.
Describe how the health care facility can ensure their data is clean.