Management

Data is widely used by the health care industry, its stakeholders, and the community at large to understand and anticipate trends in diseases and other health issues, treatments, and preventions. Therefore, it is imperative that the data provided to all stakeholders is timely, accurate, and useful.

Write a 350- to 700-word paper that explains data advanced techniques in data analytics. You will reference your Week Four Benchmark Assignment—Using Data to Address Quality Measures to complete this presentation.

Include the following in your paper:

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

Cite 2 peer-reviewed, scholarly, or similar references to support your assignment.

Format your assignment according to APA guidelines.

ANSWER

Data Advanced Techniques in Data Analytics

Introduction

Data advanced techniques in healthcare have enabled organizations to capture health data for advanced medical care. Ideally, healthcare data is collected from various devices and systems such as patient portals, health tracking devices, electronic health records, and diagnostic systems. Data analytics has enabled medical professionals to make informed decisions on the delivery of care and treatment plans. However, for data to be effective in healthcare, it has to be of high quality to facilitate quality patient care.

Importance of Data Quality

Healthcare organizations source quality data and build stronger processes of managing the data in a structured manner. The impact of quality data is on efficiency and performance gains and the ability to gain better insights that could not be obtained through manual analysis (Reddy and Aggarwal, 2015). Ideally, poor data quality can lead to patient mistreatment, frustration, and ineffective and poor policy decisions. For instance, when data sources have been improperly entered into the electronic health records system, it leads to mistreatments and delays that lead to poor experiences for the patients. On the other hand, data quality improves decision-making strategies among healthcare professionals. In addition, data quality has boosted customer relationships since clients are assured of quality treatment, giving the healthcare organization a competitive advantage.

Sampling and Probability

Sampling and probability are highly utilized in clinical decision-making since they help collect public health data and analyze health data to make decisions and support interventions. The quality and reliability of Hospital compare depends on sampling and probability of the entire population that is used to track trends and reflect opinions and behaviors. Depending on how the information has been retrieved, this can influence results. For instance, hospital compare can gain data from various sources such as PubMed, Center for Disease Control, and Veterans Health Administration which are reputable sources that ensure the precise outcome. Hospital compare evaluates data from variables which they use to build database solutions and running reports. When the sources of data have been poorly obtained, they impact the accuracy of the websites and accurate reimbursement for website services.

Data Mapping and Scrubbing

Data mapping entails matching between source and target, such as between two databases that have similar data elements. Data mapping has been used in the electronic health records to ensure that the systems and software have enabled a meaningful exchange of outcomes reporting, exchange of patient information, and reimbursement claims (Davis and Shiland, 2018). Data mapping allows the moving of data from one system to another and informs patient care and national healthcare policy decisions. On the other hand, data scrubbing entails identifying incomplete and incorrect data and removing repeated data in the database. Data scrubbing improves data quality, consistency, reliability, and accuracy.

Possible Errors in Data

One of the possible data errors is the recording of the wrong patient information. This is related to diagnosis mistakes, medications, and medical history. For instance, overburdened healthcare practitioners tend to import inaccurate medical lists and omitting critical information, limiting the accuracy of the recorded data, therefore leading to medication errors (Hersh, 2014). Another possible error in data includes duplicate medical records, which happens when a single patient is linked to more than one medical record, which creates a combined inaccurate record. Duplicate medical records led to incomplete and inaccurate medical history, leading to wrong treatment and medication due to lab test results.

Clean Data

To ensure that data is clean, the healthcare facility should develop a system in place to limit wrongful data entry. The system will offer processes for removing duplicates, data cleaning, splitting, and merging records to ensure consistent, accurate, and complete data. For instance, the Master patient index clean-up process can deliver greater data accuracy. Additionally, the healthcare facility should focus on training the healthcare practitioners on maintaining accurate, consistent, and relevant patient data records, which will avoid duplicates, omissions, and incorrect treatment, thus maintaining data integrity.

Conclusion

Data analytics has improved the quality of care, performance, and cost-saving measures due to accurate, useful, and timely information. Data analytics has been used to offer accurate and customized solutions based on the information offered. Unfortunately, data errors in health records have influenced data analytics, thus leading to medication errors that cost lives. Healthcare practitioners should maintain clean data to prevent wasteful duplications, medication errors, delayed diagnosis, and incorrect treatment.

References

Davis, N., & Shiland, B. (2018). Statistics and data analytics for health data management.

Hersh, W. R. (2014). Healthcare data analytics. Health informatics: practical guide for healthcare.

Reddy, C. K., & Aggarwal, C. C. (Eds.). (2015). Healthcare data analytics (Vol. 36). CRC Press.