Healthcare Statistics and Research
Purpose of Healthcare Data
Healthcare data helps in the prevention of disease outbreaks. Epidemiologists and doctors may be able to detect potential risks and trends throughout different demographics and places using a common database. It also promotes the storage of all medical data in the exact location; hence, when healthcare professionals have a vivid view of a patient’s medical history, they may provide a comprehensive treatment plan. Besides, it helps implement artificial intelligence (Kumar et al., 2020). Applying artificial intelligence in health, such as natural language and computer learning processing, allows healthcare professionals and insurers to process the massive amounts of unprocessed patient data entered into EHR systems regularly. Furthermore, healthcare data helps healthcare professionals make effective decisions because it is feasible to collect and analyze patient data and findings quickly and effectively. It also promotes market competition as accurate patient data gathering can significantly improve patient service delivery, resulting in enhanced company positions and more revenues.
Relevance of Healthcare Data to Patient Outcomes and Reimbursement
When healthcare practitioners have accessibility to updated patient data, they can give more effective, better, safer, and personalized care and coordination. Besides, patients who examine their healthcare data acquire knowledge about how their health changes over time. They will be significantly empowered and informed about their health, allowing them to more readily alter their lifestyle, which will improve their quality of life and treatment outcomes. Furthermore, they will communicate more effectively with their healthcare providers (Kumar et al., 2020). Finally, healthcare data for scientific research will hasten the emergence of new pharmaceutical devices and therapies for those in need.
Internal vs. External Sources of Healthcare Data
Internal sources include accident or incident records, medical reports and records, maintenance logs and reports, internal audit records, committee records, and risk assessment reports. On the other hand, external sources include international legislation, protocols, codes of practice, operation instructions, safety guidelines, and manufacturer datasheets.
Qualitative vs. Quantitative Data in Healthcare
Qualitative data is usually written, such as interview summaries, while quantitative information is in numerical forms, such as IQ score and weight, and is analyzed with statistics. Qualitative data has many details and can establish novel insights, while quantitative data is analyzed easily, is reliable, objective, and presented in graphs (Green & Thorogood, 2018). On weaknesses, qualitative data is complex in analysis, time-consuming, and subjective, while quantitative data lacks details, is reductive, and makes it challenging to acquire insights. Qualitative data is gathered using questionnaires, interviews, observations, grounded theory, and content analysis, while quantitative information is collected using experiments, questionnaires, and observations.
Data Analytics and Its Use in Healthcare Management and Delivery
The healthcare business creates a massive quantity of data, but it struggles to turn it into knowledge that enhances patient outcomes and operational effectiveness. The goal of healthcare data analytics is to assist practitioners in overcoming barriers to the broader implementation of data-derived insight. It helps enhance healthcare data more easily shared among coworkers and external stakeholders and more easily visualized for general consumption. Besides, providing adequate data-driven projections in real-time enables healthcare providers to react to changes in healthcare markets and surroundings more swiftly. It also improves data innovation and collaboration among healthcare companies to transform analytics-established data into business-established information by automating limited-impact data managerial activities (Kamble et al., 2018). The three categories of data analytic tools include Program cleaning, validating, and analyzing data, software acquiring patient data, and software building analysis results.
Data analytics is used in healthcare management and patient care delivery. The studies aim to improve clinical care, promote illness prevention, and determine the efficacy of various treatment choices (Kamble et al., 2018). It also uses prediction, early detection and research of diseases, automation of healthcare administrative processes, and patient care personalization. Furthermore, it is effective in new medication discovery, efficient documentation of patients, and precise health insurance rate calculations.
Green, J., & Thorogood, N. (2018). Qualitative methods for health research. Sage.
Kamble, S. S., Gunasekaran, A., Goswami, M., & Manda, J. (2018). A systematic perspective on the applications of big data analytics in healthcare management. International Journal of Healthcare Management. https://doi.org/10.1080/20479700.2018.1531606
Kumar, Y., Sood, K., Kaul, S., & Vasuja, R. (2020). Big data analytics and its benefits in healthcare. In Big Data Analytics in Healthcare (pp. 3-21). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-31672-3_1
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entral), Peer Responses are due by Saturday (11:59:59pm Central).
Primary Task Response: Within the Discussion Board area, write 400–600 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions with your classmates. Be substantive and clear, and use examples to reinforce your ideas.
There are many purposes for and types of healthcare data. In this discussion, you will summarize the purpose for and types of healthcare data and discuss the emerging field of data analytics and Big Data as they are used in healthcare management. Include the following in your discussion post:
Explain the purpose of healthcare data and its relevance to patient outcomes and reimbursement.
Discuss the difference between:
Internal and external sources of data in healthcare
Qualitative and quantitative data in healthcare
Discuss data analytics and how it is being used in healthcare management and healthcare delivery.
Respond to Another Student: Respond to at least 2 of your fellow classmates with at least a 100-word reply about their Primary Task Response regarding items you found to be compelling and enlightening. To help you with your discussion, please consider the following questions:
What did you learn from your classmate’s posting?
What additional questions do you have after reading the posting?
What clarification do you need regarding the posting?
What differences or similarities do you see between your posting and other classmates’ postings?
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