Posted: December 15th, 2022

Health Care Data Management

Health Care Data Management
Compare traditional paper health records to electronic records. What can one do that the other cannot? Some of the formats of electronic data include spreadsheets and databases. What are some of the trade-offs between using a spreadsheet versus a database to record, analyze, and retrieve data in a health care setting?

In a hospital setting that uses an enterprise HER (electronic health records) system with multiple interfaces to other health information platforms. What types of data would be kept in a system with multiple interfaces and what steps would be needed to allow for analytics to be performed across multiple disparate platforms?

What are the benefits or disadvantages of ER (Entity Relationship) diagrams? Why bother with logical design and not just create tables and columns (physical design) directly? How does understanding the logic of a database Help clinical resources? How does being involved with workflow development Help IT resources?

Who would be the ideal candidate(s) to elicit information from during the requirements gathering and analysis phase? Explain why.

Compare bottom-up to top-down modeling. What is the best approach to arrive at an effective data model?

Health Care Data Management
Compare traditional paper health records to electronic records. What can one do that the other cannot? Some of the formats of electronic data include spreadsheets and databases. What are some of the trade-offs between using a spreadsheet versus a database to record, analyze, and retrieve data in a health care setting?
The traditional paper health records entail the physical storage and retrieving of health records while electronic records entail the virtual storage, management and retrieval of health information in different health facilities (Zandie et al., 2008). In this regard, the paper records need additional personnel, support paper files to handle and organize countless health documents. On the other hand, the electronic medical records need less staff, time and less or no physical storage space. Consequently, the cost of handling physical medical records increase worth time as the documents increase due to increased staff and physical storage equipment. On the other hand, the cost of handling medical records decreases with time when using electronic medical records. The initial cost of acquiring electronic medical record equipment is high but the management and cost of handling data reduce significantly.
Some functions can that EHR can do and paper health records (PHR) cannot do. In this case, the EHR can give accurate, complete and up-to-date on the patients’ issues and information while the PHR remains dormant unless there is human intervention (Zandie et al., 2008). Furthermore, EHT enhances the instant and secure sharing of health records. On the other hand, on PHR health records are only shared physically bypassing of records from one point to another.
The EHR uses different formats of electronic data such as databases and spreadsheets in the course of storing and managing patient records (Zandie et al., 2008). The database is used over the spreadsheet to ensure that data is easily searched and retrieved when needed. Moreover, the database is effective in the long-term storage and management of data as compared to spreadsheets since they are powerful and manageable when handling large and complex data.
In a hospital setting that uses an enterprise EHR (electronic health records) system with multiple interfaces to other health information platforms. What types of data would be kept in a system with multiple interfaces and what steps would be needed to allow for analytics to be performed across multiple disparate platforms?
The EHR system with multiple interfaces stores different health records in different types of data. In this case, the system can store quantitative, qualitative, and discrete and attribute data. The system can store and manage qualitative data (Hsiao, Chung and Esther, 2014). The qualitative data contains health information and medical processes and procedures. Additionally, the qualitative data contains the medical records, letters, communications and written interaction among different parties. Consequently, quantitative data can be stored in the EHR to mark operations and matters in numbers. The quantitative data handles matters that can be measured, counted or described objectively in numbers. The qualitative data can store medical operations and process in the form of schedules for the medical professionals and the patients. Additionally, medical professionals record different medical conditions and issues in numbers. The quantifying of medical issues in numbers ensures that the treatment progress and medical trend can be tracked to enhance decision making. The different types of data can be presented in multiple interfaces for Assessment, sharing and making medical decisions.
Conducting analytics on medical records across disparate platforms need to be conducted in a defined process and procedure that follows articulate steps (Hsiao, Chung and Esther, 2014). First, the medical facilities and the relevant parties conducting medical analysis need to set measurable goals before the exercise begins. This approach ensures that the right procedures and processes are adopted. Consequently, the analysts need to set priorities for measurement before the exercise continues. Setting priorities ensures that the right data and documents are selected in the course of the exercise. Furthermore, the analysis is conducted through data gathering. The gathering of data provides the analysts with the content to enhance the analytics process. Moreover, the data collected is scrubbed and cleaned to remove superfluous and incorrect data. This approach ensures that the findings will be factual. More so, the data collected goes to the analysis and presentations stage using different methods such as exploratory analysis, data visualization, and data mining. Lastly, the results are interpreted to get the answer to different questions or present facts.
What are the benefits or disadvantages of ER (Entity Relationship) diagrams? Why bother with logical design and not just create tables and columns (physical design) directly? How does understanding the logic of a database Help clinical resources? How does being involved with workflow development Help IT resources?
The entity-relationship (ER) diagrams have different benefits and disadvantages to medical operations. ER presents diagrams with graphical representations to enhance understanding among medical professionals and patients (Brady and Loonam, 2010). Additionally, ER offers an appropriate visual presentation and layout to enable understand different medical aspects. On the other hand, ER diagrams have disadvantages. The ER models result in loss of information content since it does not provide space for such. Additionally, the ER diagrams do not present data manipulation.
The design of the ER diagram is a vital aspect as compared to the columns and tables. The designs enable one to make proper and appropriate presentations that are appealing and informative. Moreover, the understanding of a database has different benefits to clinical resources (Brady and Loonam, 2010). The understanding of the database makes it possible to summarize different medical and clinical aspects that inform the profession. Additionally, the involvement workflow development enables the IT resources to develop customized operations for t=different medical facilities. Therefore, operations and process that suit every medical facility are created.
Who would be the ideal candidate(s) to elicit information from during the requirements gathering and analysis phase? Explain why.
The information technology (IT) will be the appropriate candidates tasked with the requirement of gathering and analysis phase. The IT experts have the relevant information to gather electronic data from the systems they operate (Carman et al., 2015). This makes it possible for them to gather substantial data for the analysis process. On the other hand, the IT professionals have the knowledge and skills to employ various data analysis tools and equipment thus excelling in the field.
Compare bottom-up to top-down modelling. What is the best approach to arrive at an effective data model?
The top-down modelling approach starts with a high-level design and ends with low-level design. The modelling starts with a big picture and breaks it into smaller segments (Mykkanen et al., 2004). On the other hand, the bottom-up approach starts with low-level development or design and commences with high-level design. In this regard, entails bringing together different pieces of the system to create a complex system. Therefore, the best model to develop an effective data model is the bottom-up approach by gathering different aspects of data from the ground to create an effective and efficient data model.
References
Brady, M., & Loonam, J. (2010). Exploring the use of entity-relationship diagramming as a technique to support grounded theory inquiry. Qualitative Research in Organizations and Management: An International Journal, 5(3), 224-237.
Carman, K. L., Mallery, C., Maurer, M., Wang, G., Garfinkel, S., Yang, M., … & Gold, M. (2015). Effectiveness of public deliberation methods for gathering input on issues in healthcare: Results from a randomized trial. Social Science & Medicine, 133, 11-20.
Hsiao, C. J., & Hing, E. (2014). Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2013 (No. 2014). US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
Mykkänen, J., Porrasmaa, J., Korpela, M., Häkkinen, H., Toivanen, M., Tuomainen, M. P., … & Rannanheimo, J. (2004, October). Integration models in health information systems: experiences from the PlugIT project. In Medinfo (pp. 1219-1222).
Zandieh, S. O., Yoon-Flannery, K., Kuperman, G. J., Langsam, D. J., Hyman, D., & Kaushal, R. (2008). Challenges to EHR implementation in electronic-versus paper-based office practices. Journal of general internal medicine, 23(6), 755-761.

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Tags: Health Care Data Management