Health Information Improvement Proposal

Health Information Improvement Proposal

Most healthcare organizations and healthcare providers, such as the Independence Medical Center, use health information management (HIM) systems and have attained meaningful use certification. However, market changes and regulatory evolutions create the need to continuously improve data collection and analysis based on the current HER systems to make better use of patient data and improve clinical outcomes, quality, and efficiency. This article presents a full proposal with recommendations based on the most current and relevant information related to HIM systems. The provided information focuses on recommendations for technological and logistical changes in HIM, data services and potential outcomes, and a review of contemporary data analysis trends to improve current HIM practices in the organization. The proposal also provides recommendations for best practices for data collection, safe and secure data storage, and conversion of data analytics into useful and understandable deliverables.

Recommendations on Technological and Logistical Changes in the HIM System

The current focus for the Independence Medical Center is to improve data collection and analysis within the current EHR in order to make better use of patient data and improve clinical outcomes, quality, and efficiency. The improvement of the organization’s HIM system requires the implementation of both technological and logistical changes in the current HIM system. In this regard, the recommended changes include integrating data capture systems at all levels of interactions with patients and improving HIM interoperability.

The recommended data capture system includes the use of various technologies that monitor the patient, capture and feed data to the HIM system, and, at the same time, allow patient access to the collected information. The data capture technologies need to be able to collect patient health data, including practices and behaviors, and provide health information to patients regardless of whether the patients are within the facility or at home. An instance of such technologies is the use of mHealth technologies integrated with the current HIM system. mHealth technologies such as mobile apps, smart bracelets, and smartwatches are becoming essential in remote patient monitoring and data collection. Such technologies have been successfully deployed in remote patient surveillance post-surgery, allowing care providers to monitor and collect data on recovery remotely and identify and prevent post-surgery complications (Dawes et al., 2021). Others, such as mobile apps, have been applied in successful contact tracing and delivering timely information to patients and populations to prevent the pandemic from spreading further (Cho et al., 2020). In both cases, mHealth technologies can significantly improve the HIM’s data collection capacity.

The second change focuses on HIM system logistics. The current HIM’s interoperability needs to be enhanced in order to improve the efficiency of data exchange between Independence Medical Center’s EHR systems and other providers, as well as the recommended data capture technologies. As a result, Independence Medical Center will need to adopt the currently acceptable standardized data exchange protocols in the healthcare industry. In this case, it will facilitate data sharing and collaboration with others in the healthcare environment and, at the same time, support improved patient access to their data and information (Blumenthal & Squires, 2015).

Data Services, Potential Outcomes, and Alignment with Strategic Goals

Various data services with potential outcomes align with the Independence Medical Center’s strategic goals. The two data services proposed for adoption include the utilization of advanced data analytics services and the use of cloud computing services for health. To begin with, advanced data analytics services focus on the use of various data collection, storage, analysis, extraction, and presentation of data. Advanced data analysis services such as big data analytics are necessary as one of the proposed new technological changes is the addition of data capture technologies, including mHealth technologies. These data capture technologies will create enormous amounts of data that cannot be managed or analyzed using current system tools. Advanced data analytics, such as big data analytics, will help the organization analyze, understand, and highlight meaningful trends from the massive generated data (Hariri et al., 2019). In addition, the use of big data analytics has the potential to optimize resource allocation and the efficiency of both administrative and patient care services.

Secondly, the use of cloud computing services in healthcare, such as Microsoft Azure Health Data Services, provides various tools, such as appointment planning, data collection, and analysis tools, and supports secure storage and accessibility of healthcare data from anywhere. Cloud services can also provide a user-friendly dashboard where patients and populations can access their health data and healthcare information. The cloud services, including data analytics tools, can provide real-time performance metrics and other quality indicators that the management can utilize to improve care planning and enhance clinical outcomes, quality, and efficiency.

Contemporary Data Analysis Trends

Health information management systems are integrating advanced data analysis technologies to improve data management, identify trends in healthcare, and guide healthcare decision-makers. Two contemporary data analysis trends in health information systems include the integration of artificial intelligence in health information management systems and the use of big data analytics.

Artificial Intelligence (AI) allows machines and computers to simulate human intelligence and support quick data collection, analysis, and presentation. AI is gaining application in the analysis of health data with an increased potential through AI algorithms to identify would-be missed trends and insights into healthcare, predict care outcomes, and help personalize care planning for individual patients. As the Independence Medical Center focuses on using mobile technologies to improve data collection, AI is currently being implemented in data analysis and processing for mobile health technologies with the capacity for medical diagnosis and quality of treatments and care (Sannino et al., 2019).

Healthcare organizations are embracing and using Big Data Analytics (BDA) to manage healthcare data. Big Data Analytics involves the use of technological techniques and tools that help collect, analyze, and extract information from the enormous amounts of structured and unstructured data available from multiple sources (Batko & Ślęzak, 2022). The use of big Data Analytics in healthcare aligns with the Independence Medical Center’s strategic goals of better data collection, analysis, and utilization, as Big Data Analytics helps collect big data, analyze the data, and identify current and future trends that can guide patient care and management decisions (Batko & Ślęzak, 2022).

Recommended Best Practices for Data Collection, Secure Storage, and Data Analytics and Conversion

Based on the review and analysis of the current HIM system data collection, storage, and analysis practices, the following recommendations have been made to enhance best practices. First, data collection needs to be standardized across all levels of patient interaction within Independence Medical Center and outside of the facility, with a focus on mostly patient-generated data from mobile data capture technologies. Second, all collected data must be stored within AI-security-assisted systems with controlled access. Third, Big Data Analytics combined with AI systems should be utilized to analyze, present, and convert data into more understandable formats for use by the management and all healthcare professionals.

Conclusion

As the healthcare environment has grown more complicated with new diseases and conditions emerging, populations are becoming more diverse, and health wants, needs, and expectations vary across individuals and patients, the use of big data has become essential in healthcare delivery. For these reasons, healthcare providers must improve their HIM systems and integrate them with emerging technologies and techniques that enhance data collection, storage, and analysis techniques. By considering the recommendations presented in this proposal, such as the use of mHealth technologies for data capturing, Big Data Analytics, and AI for data analytics and management, the Independence Medical Center will be able to sufficiently enhance data collection and analysis in a way that aligns with strategic goals.

References

Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/S40537-021-00553-4

Blumenthal, D., & Squires, D. (2015). Giving patients control of their EHR data. Journal of General Internal Medicine, 30(Suppl 1), 42. https://doi.org/10.1007/S11606-014-3071-Y

Cho, H., Ippolito, D., & Yu, Y. W. (2020). Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. ArXiv Preprint ArXiv:2003.11511. https://arxiv.org/abs/2003.11511v2

Dawes, J., Lin, A. Y., Varghese, C., Russell, M. M., & Lin, A. Y. (2021). Mobile health technology for remote home monitoring after surgery: A meta-analysis. British Journal of Surgery, 108(11), 1304–1314. https://doi.org/10.1093/BJS/ZNAB323

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1), 1–16. https://doi.org/10.1186/S40537-019-0206-3/TABLES/2

Sannino, G., Bouguila, N., De Pietro, G., & Celesti, A. (2019). Artificial intelligence for mobile health data analysis and processing. Mobile Information Systems, 2019. https://doi.org/10.1155/2019/2673463

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You have been asked to create a proposal for improving data collection and analysis based on the current EHR at Independence Medical Center and the recommendations you already made during your work. Despite being meaningful use certified, the leadership team feels that the organization needs to make better use of patient data to improve clinical outcomes, quality, and efficiency.

Your board of directors has asked you to prepare a full proposal and is relying on you to provide them with the most current and relevant information to help them decide on the next steps related to improvements and analysis.

Instructions
For this assignment, write a 4–6-page, APA-formatted paper that summarizes proposed data services and their potential outcomes. Next, align the appropriate data with the organization’s strategic goals, identify current trends in data analytics, and provide recommendations for the next steps based on your assessment of the organization’s health information needs. Feel free to draw on relevant aspects of the previous assignments in this course to help you complete this assignment.

In your proposal:

Explain your recommendations related to technological and logistical changes to an organization’s HIM system (1–2 pages).
Describe 2–3 data services and how their potential outcomes align with an organization’s administrative and clinical goals (1–2 pages).
Analyze how contemporary data analysis trends could be leveraged to improve current practices in an organization (1 page).
Recommend best practices for collecting data, securely storing data, and converting data analytics into useful and understandable deliverables (1 page).
Criteria:
1. Explains recommendations related to technological and logistical changes to an organization’s HIM system and identifies potential challenges to implementing recommendations.
2. Describes very appropriate data services and how their potential outcomes align with an organization’s administrative and clinical goals with support from specific examples.
3. Analyze how contemporary data analysis trends could be leveraged to improve current practices in an organization and identify processes that could be implemented to ensure continuous improvement.
4. Recommends best practices for collecting data, securely storing data, and converting data analytics into useful and understandable deliverables. Identifies specific processes to ensure that recommendations can be sustainably performed.
5. Applies relevant evidence and best practices to target proposal messaging to stakeholders. Identifies challenges in persuading all relevant stakeholders.
6. Uses communication style and vocabulary appropriate for the specific context of the target audience.