Health Information Exchange (HIE) Models - Special Academic Support

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Health Information Exchange (HIE) Models

The management of the Memorial Hospital has faced the challenge of coordinating patient data between its different facilities, which has led to unnecessary tests, diagnostics issues, and poorer patient outcomes. Hence, this hospital has to implement a hybrid HIE model with a consumer-mediated exchange. This report will analyze the three key HIE models: centralized, decentralized, and hybrid, and explain why the last approach should be applied and how to extract data using this model.

The various types of HIE models include centralized, decentralized, and hybrid. According to Blais et al. (n.d.), with the centralized model, all data is stored centrally, in one single or consolidated repository, and each participants data is regularly submitted directly to this entity where it is then stored and accessed (para. 44). Also, the HIE manages the data under this approach, which makes it suitable for the implementation within the existing organizational structure. However, this model is not appropriate for the Memorial Hospital because Blais et al. (n.d.) argue that it is best suited for managing and analyzing population data. The main disadvantage of the centralized system is that since the data is stored in one place, there is an issue with matching the patient with their data as there are no shared identifiers.

A decentralized model is also referred to as the federated approach, and its design requires all the participants of the HIE group to store and manage data locally; however, they agree to share and send data to the other members of their group. A challenge with this system is a need to implement a locator service that will respond to queries, identify the needed patient information, and send it to the inquirer (Blais et al., n.d.). The challenge with this model is that each participant of the federated system maintains a system that the HIE does not manage. There can be issues with sharing data, and a proper data identification system is needed. However, the benefit of the federated system is that if one participant has issues with their data storage system, the others are not affected and can continue working. Blais et al. (n.d.) argue that this model is best suited for regional or state-wide healthcare facilities.

The hybrid model is the best-suited one for Memorial Hospital because it combines the benefits of the centralized and the federated systems. Blais et al. (n.d.) state that such systems have centralized data storage and a record locator service to retrieve the patients information. Ways to extract data using the hybrid model that will be incorporated in the hybrid system include the following: Automatic Recognition Technologies, for instance, Intelligent document recognition technologies, Enterprise Master Patient Indices and Identity Management(EMPIs), Cloud-Based Technologies and applications such as Web portals. Intelligent document recognition systems are algorithms powered by artificial intelligence (AI) (A beginners guide to intelligent document recognition, n.d.). AI allows recognizing structured and unstructured data and storing it in the data management system. Since AI is capable of recognizing the different types of data, it is also applied to retrieve it as the system categorizes the files based on their type and contents. Next, the EMPIs is a database that is used to maintain consistent and accurate information about each patient registered by a healthcare organization (DelVecchio, 2017). EMPIs are suitable for use when there is a need to link data between different facilities since each patient is represented in the system only once. Hence, this technology will help Memorial Hospital to link its different facilities without having the issue of one patient being represented through several records in the system.

Finally, a cloud-based web portal helps create an easy-to-access and customizable user interface that will provide the facilitys managers with access to patient data (A cloud technology-based web portal, n.d.). The cloud technology allows to provide access to information on demand, and it will be the resource that allows the physicians to access information from the HIEs members. The web portal will be linked with the data storage system and the AI-powered EMPIs.

To analyze the data from the identified technologies, improve patient outcomes, and reach the organizations objectives, one will have to implement the described systems in combination. The described hybrid model and technologies will help Memorial Hospital collect data in each facility. The web portal and AI-powered EMPIs will recognize the documents and assign them with indices related to each patient. This way, the system will not have duplicates, and the physicians and other specialists will be able to access all patient information through a cloud-based portal. Overall, this paper discusses the different types of data management systems for an HIE and addresses the ways of storing, managing and accessing data that are suitable for the memorial Hospital. The recommended system is the hybrid one, which will allow the different facilities at Memorial to manage their data separately while having access to the files from other departments.

References

A beginners guide to intelligent document recognition. (n.d.). Web.

Cloud technology-based web portal. (n.d.).

Blais, A. F., Borut, C., Alwan, C., Casey, N., Bramble, R., & Code, S. (n.d.). Health information exchange (hie): A primer and a provider selection guide. Leading Change.

DelVecchio, A. (2018). Enterprise master patient index (EMPI). TechTarget.

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