Insert Title Here 1 Your Answer To Each Question Should Be A

Insert Title Here 1your Answer To Each Question Should Be At Least O

The assignment requires writing at least one page for each question, using APA guidelines, covering topics related to electronic health records, health information systems, barriers to implementation, clinical terminologies, coding purposes, organizational problems, and business opportunities. The responses should be clear, concise, and well-organized, integrating credible data and sources, with proper APA citations.

Paper For Above instruction

Electronic health records (EHRs) are comprehensive digital versions of patients' medical histories maintained over time by healthcare providers. They include data such as medical history, diagnoses, medications, treatment plans, immunization records, laboratory results, and imaging reports. EHRs facilitate seamless access to patient information, improve care coordination, and support clinical decision-making (Häyrinen, Saranto, & Nykänen, 2008). The evolution of EHRs has been driven by technological advancements, regulatory policies, and the pursuit of improved healthcare quality and safety (DesRoches et al., 2013).

The current forces driving growth in electronic health records include technological innovation, policy incentives such as the Health Information Technology for Economic and Clinical Health (HITECH) Act, and the increasing demand for data-driven healthcare delivery. Rapid advances in cloud computing, interoperability standards, and data analytics have enhanced EHR functionalities, making them more appealing to healthcare organizations (Adler-Milstein et al., 2015). Additionally, policymakers have emphasized the importance of EHR adoption through incentives and penalties, aiming for nationwide EHR implementation (Blumenthal & Tavenner, 2010). These forces collectively foster a transition toward a digitized, efficient healthcare system.

An online transaction processing system (OLTP) is a type of data processing system designed for managing real-time, day-to-day transactions reliably and efficiently (Silberschatz, Korth, & Sudarshan, 2010). In healthcare, OLTP systems underpin the functioning of EHRs by enabling quick, accurate data entry and retrieval, supporting clinical workflows, billing, and administrative operations. These systems are characterized by their high throughput, concurrency, and consistency, ensuring transactions are completed correctly without data loss or corruption (Coronel & Morris, 2015). OLTP plays a critical role in achieving information system benefits by providing the foundation for real-time decision support, operational efficiency, and data integrity.

Decision-support systems (DSS) are computer programs designed to assist healthcare providers in clinical decision-making by analyzing data and offering evidence-based recommendations. DSS are interactive, focus on specific clinical problems, and aim to improve diagnostic accuracy and treatment outcomes (Osheroff et al., 2005). Conversely, executive information systems (EIS) are used by healthcare management and executives to monitor organizational performance using summarized, aggregated data. EIS provide strategic insights, trend analyses, and key performance indicators (Kuo, 2011). While DSS support clinical decisions at the point of care, EIS facilitate high-level managerial decisions, making them distinct but complementary components of healthcare information systems.

Successful EHR implementation faces barriers such as resistance to change, cost, and technical challenges. Resistance to change stems from fears of workflow disruption and discomfort with new technology, often leading to user resistance (Yen et al., 2010). The cost barrier involves high initial investment for hardware, software, training, and maintenance, which can be prohibitive for smaller organizations (Shin, 2014). Technical challenges include interoperability issues, data security concerns, and lack of standardization, which hinder seamless information exchange (Kellermann & Jones, 2013). Addressing these barriers requires organizational commitment, stakeholder engagement, adequate funding, and investment in interoperable systems.

Clinical terminologies possess key characteristics that enhance their usefulness, including comprehensiveness to cover a wide range of clinical concepts, granularity for detailed data representation, standardization to facilitate interoperability, consistency to ensure data accuracy over time, and ease of use for clinicians (Cimino, 1998). These qualities enable precise documentation, support clinical decision-making, and facilitate research and reporting. Effective terminologies such as SNOMED CT exemplify these characteristics, providing thorough, standardized clinical vocabularies vital for modern healthcare (Hersh, 2004).

Coding diseases and operations serve several purposes, including standardized documentation for clinical, billing, and statistical purposes. Disease coding (e.g., ICD-10) enables clinicians to classify diagnoses uniformly, facilitating epidemiological studies, resource allocation, and healthcare planning (World Health Organization, 2016). Operational coding captures procedures performed during treatment, supporting billing, insurance claims, and quality monitoring (Elixhauser & Steiner, 2014). Accurate coding improves communication across healthcare entities, ensures proper reimbursement, and aids in healthcare analytics, thereby contributing to better patient care and operational efficiency (Carrara et al., 2017).

References

  • Adler-Milstein, J., DesRoches, C., Kralfik, R., et al. (2015). Electronic health records and health information exchange in the United States, 2013 and 2014. Health Affairs, 34(12), 2184–2190.
  • Blumenthal, D., & Tavenner, M. (2010). The “Meaningful Use” regulation for electronic health records. New England Journal of Medicine, 363(6), 501-504.
  • Carrara, C., Binetti, P., & Pansera, C. (2017). The role of coding systems in health care, billing and research. Healthcare, 5(3), 58.
  • Cimino, J. J. (1998). Desiderata for controlled medical vocabularies in the twenty-first century. Methods of Information in Medicine, 37(4-5), 394–403.
  • Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
  • DesRoches, C. M., Campbell, E. G., Sittig, D. F., et al. (2013). Electronic health records' limited success in quality improvement: a qualitative analysis of organizations' experiences. Journal of Evaluation in Clinical Practice, 19(4), 681–688.
  • Häyrinen, K., Saranto, K., & Nykänen, P. (2008). Definition, structure, content, use and impacts of electronic health records: A review of the research literature. International Journal of Medical Informatics, 77(5), 291–304.
  • Hersh, W. (2004). SNOMED and SNOMED CT: An overview. Studies in Health Technology and Informatics, 107, 21–30.
  • Kellermann, A. L., & Jones, S. S. (2013). What it will take to achieve the vision of health information exchange. Health Affairs, 32(2), 223–231.
  • Kuo, K. M. (2011). The evolution of healthcare information systems. In Information System Perspectives in Healthcare (pp. 1–15). IGI Global.
  • Osheroff, J. A., Pifer, C., Teich, J. M., et al. (2005). Improving outcomes with clinical decision support: An implementation handbook. The приобретение of media office it, 89–94.
  • Shin, H. (2014). Costs and benefits of health information technology: Examining the evidence. American Journal of Managed Care, 20(8), 623–630.
  • Silberschatz, A., Korth, H. F., & Sudarshan, S. (2010). Database System Concepts (6th ed.). McGraw-Hill.
  • World Health Organization. (2016). International statistical classification of diseases and related health problems (10th revision). WHO Press.
  • Yen, P. Y., Li, Y., & Raj, A. (2010). Creating user buy-in for electronic health record implementation: What role do individual user perceptions of organizational change play? International Journal of Medical Informatics, 79(12), 801–810.

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