Introduction
Data entails the collection of raw facts and figures used for scrutiny and analysis to derive useful information from them. It is the fundamental pillar from which treatments and decisions are made in healthcare. Without it, no operations can be performed as a physician cannot operate on a patient or make a viable prescription on the type of treatment that best fits the patient’s condition. In this regard, the purpose of this paper is to describe the various types of data found in healthcare, as well as the processes involved in presenting these data.
Discussion
The extraction of various patient data is the foundation upon which services are provided in every healthcare facility. According to Dash et al. (2019), no hospital can function without the major types of data such as medical records, patient information, clinical information, and health insurance information. Furthermore, he posits that medical records including laboratory test results, diagnoses, and medical history are the primary source of patient information from which doctors derive their decisions to administer the appropriate treatment.
Consecutively, patient information is an important type of data to describe in this paper. It includes any information gathered about a patient during their medical care. According to Dash et al. (2019), this information may range from medical records and clinical data to health insurance information and billing records. Patient data is critical in healthcare as it can be used to track patients’ health over time, research new treatments and procedures, and improve the healthcare system’s efficiency (Provost & Murray, 2022). Clinical data, on the other hand, refers to any information obtained about a patient’s health during their medical treatment. It includes medical records and laboratory test results, as well as information collected during doctor’s visits and hospital stays. It is valuable in tracking a patient’s health over time. Another important type of data in healthcare is health insurance. It includes any information gathered about a patient’s health insurance coverage. It details everything from the insurer’s name and policy number to the patient’s co-pay and deductibles.
However, for these data to be understood, various processes must be followed to harness their analysis. First, physicians are required to understand the different types of data that are available and how they can be used. This involves reviewing patient data, clinical data, medical records, and health insurance data. Second, they must understand the purpose of the analytics, which involves determining what questions they want to answer with the data. The purpose of analytics is to dictate the type of data to collect. For example, if a physician wants to use the data to improve the quality of care a patient receives, they will need to collect clinical data.
Third, doctors are required to select the appropriate data sets. This involves selecting the data sets that are most relevant to the questions they want to answer. These data sets are supposed to be appropriate for the type of analysis that the doctors desire to perform. For example, if a nurse wants to use data to improve the quality of care provided to a patient, they must choose data sets relating to the patient’s diagnoses, medications, and laboratory test results (Dash et al., 2019). The fourth stage involves cleaning and preparation of data which embroils ensuring that the data is in the correct format without invalid or missing data. Finally, the last step is the analysis and interpretation of the collected data. Here the physician will use statistical techniques to answer the questions they have identified. The type of analysis a doctor performs will depend on the type of data they have collected.
Conclusion
In conclusion, it is evident that various types of data in healthcare are critical to the operation of any healthcare setting including everything from medical reports to health insurance information. However, if these data are not properly processed for analysis, the facilities may suffer from a variety of operational issues. As a result, every hospital must clearly state the procedures and steps for preparing these data for analytics.
References
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.
Provost, L. P., & Murray, S. K. (2022). The health care data guide: learning from data for improvement. John Wiley & Sons.