Data is the cluster of facts and statistics obtained for a specific purpose. The information derived from data is raw and needs to be analyzed to be useful in the decision-making activities of a health organization. Quality collection, storage, and analysis of data is a mandatory requirement in a health organization that seeks to be competitive in providing quality and world-class patient care (Wu et al., 2017). The need for high-quality data analytics calls for the investment by health facilities in top-notch technology used for handling data.
Importance of Data Quality
The quality of data in a health care facility plays a fundamental role in decision-making and future success within the organization. High-quality data is characterized by completeness, comprehensiveness, reliability, accuracy, legitimacy, consistency, precision, validity, timeliness, granularity, availability, relevance, accessibility, and uniqueness (Abidi & Abidi, 2019). The data from Orange Park Hospital that was presented in the week four assignment lacks completeness and comprehensiveness. For example, the statistic for hospital measures of the use of medical imaging and patient satisfaction, specifically in regard to the communication from nurses, is missing. This represents poor quality data, and it has the effect of lowering efficiency, causing frustration for patients and resulting in the poor making of policies.
Effects of Sampling and Probability
Sampling and probability affect quality data on public reported sites such as Hospital Compare in various ways. Probability sampling techniques like simple random sampling and stratified random sampling are mind-numbing and time-consuming. These demerits can lead to bias during data collection and loss of advantages derived from performing probability sampling. Cluster sampling, if employed to collect data from Hospital Care due to the large amounts of data present on the website, is likely to be biased during cluster formation. This cluster bias prevents the making of correct inferences and creates a cascade of wrong impressions along the health care chain. Non-random sampling may lead to a systematic bias whereby the collection of data from Hospital Care is prone to manipulation. The data collected can be choreographed by the agency or study investigator performing the data collection to falsely give a good public impression or a bad public impression to a specific health organization. This eventually leads to poor quality data that results in vague conclusions that are of no benefit to a health facility.
Importance of Data Mapping and Scrubbing
Data mapping refers to the making of parallels between two systems of data storage so that data can be transferred from one system to another without the data being compromised. The scrubbing of data refers to the process of proofreading data either manually or using software before transferring it to another system to ensure that there is no duplication of data. The data for Orange Park Hospital from the week four assignment presented in tables had to be mapped with data from other hospitals in Jacksonville, FL Florida, and nationally for comparisons to be made.
The data mapping that happened to enable analysis happens smoothly facilitated the integration of data, migration of data, and other data management tasks. Data scrubbing had to be done before matching the fields from the different databases. This was done to ensure the data has high quality that translates into correct findings and appropriate policy formulation in the health facility (Wu et al., 2017). Both data mapping and data scrubbing lead to obtaining high-quality information that results in better patient care and prevents the feeling of frustration among patients and distrust of the technology by employees.
Errors in Data
The two possible errors in data that could cause issues for the health care facility in the week four assignment involve completeness and comprehensiveness of the data. Therefore, the first possible error is the absence of statistics for hospital measures of the use of medical imaging. Simultaneously, the second possible error is the absence of statistics for hospital measures of patient satisfaction, specifically in regard to the communication from nurses. The first error made the patient wait for another appointment for the magnetic resonance imaging test to be taken, which obviously led to the frustration of the patient. This creates a bad impression about the health facility, and other patients who hear of the experience of the frustrated patient would hesitate to visit the hospital to seek health services. All these culminate into a detrimental ripple effect that threatens the future prosperity of the health facility.
The Ways of Ensuring Data is Clean
The health care facility can ensure their data is clean by dealing with missing data, standardizing the process of data cleaning, validating the accuracy of data, removing duplicated data, repairing structural errors, and removing unwanted observations. The missing data in a database should not be ignored as this could lead to detrimental effects on the health organization. One way of handling missing data is by putting zero for the observation whose data is missing. This is better than completely removing the observation or extrapolating the values from other observations to find an approximate figure for the missing value. In conclusion, all health organizations have to embrace investment in advanced data analytics technology to be competitive and provide the best patient care.
Abidi, S. S. R., & Abidi, S. R. (2019). Intelligent health data analytics: A convergence of artificial intelligence and big data. Healthcare Management Forum, 32(4), 178-182. .
Wu, P.-Y., Cheng, C.-W., Kaddi, C., Venugopalan, J., Hoffman, R., & Wang, M. D. (2017). Advanced Big Data Analytics for -Omic Data and Electronic Health Records: Toward Precision Medicine. IEEE Transactions on Bio-Medical Engineering, 64(2), 263-273.