Case Summary
The case under investigation is focused on Memorial Hospital in Manchester County, which faced several issues in its practice and is looking for ways to address the problems. The organization has difficulty sharing information, which affects other work processes. In particular, a lack of data interferes with effective coordination of work and monitoring of treatment outcomes and patient status after discharge (Southern New Hampshire University [SNHU], n.d.).
Moreover, if patients first go to Memorial Hospital, service delivery is slowed due to the need for information from other care places. As a result, hospital records were also in disarray, leading to such negative implications as duplication, medication errors, low patient satisfaction, and increased readmissions (SNHU, n.d.). To address the highlighted issues, the organization has joined the new health information exchange (HIE) system – the Manchester Health Access Network (MHAN) – which will bring together information from several health establishments in the county.
Exchanged Information
Healthcare institutions collect a significant amount of information during work, which can be helpful in HIE. Patient health information, including medical history, allergies, chronic diseases, immunization data, test results, and specialist notes on treatment, should be included in the exchange (“Health information,” n.d.). This data helps make evidence-based care decisions to choose the best treatment options.
Patient demographics such as name or contact details may be exchanged cautiously to prevent abuse. However, they can help to keep in contact with the patient and ensure continuous treatment. The information exchange network will also be helpful for the transmission of prescriptions and referrals to facilitate interdisciplinary cooperation in treatment. Thus, MHAN can contain a lot of valuable data and solve the problems faced by Memorial Hospital.
Not Exchanged Information
Although the network is designed to share information, some data should not be included for patients’ protection. Information that providers should not exchange includes sensitive data such as genetic information or information about the psychological condition of the patient (“Health information,” n.d.). Such data can affect trust between the provider and patients and become a barrier to treatment.
Personal data unrelated to a medical condition and not affecting it should also not be part of the exchange as they will cause distrust of specialists. Moreover, the HIE network should not include data protected by privacy, such as financial information or a social security number. When getting to a third party, such details can be used against the patient in fraud and, therefore, must be strictly protected. When deciding what data should be included in the exchange, providers should be guided by the desire to protect patients and their health.
Model
MHAN participants must select the HIE model to establish the network. Among the three standard models, centralized, federated, and hybrid, the third is the most appropriate for the case under study. The hybrid HIE model combines the features of the other two models, striving to use their best features (Lee-Eichenwald, 2018). Most of the participants in the case have implemented electronic health records systems that allow data to be collected and sent to their organization.
However, only a few have the clinical data warehouse (CDW) necessary for exchange (SNHU, n.d.). The hybrid model assumes that the exchange will use both organizations-owned CDWs and one shared CDW for information exchange (Lee-Eichenwald, 2018). Such a framework provides additional protection for patients’ data while maintaining exchange efficiency and supporting rapid delivery of health care services.
Moreover, establishing other models may require additional time and resources or may not have the necessary performance level. In particular, centralized models require a significant investment of resources in their establishment and maintenance, and federated models slow down data transfer due to the need to send requests for receipt (Lee-Eichenwald, 2018). MHAN participants have a limited budget but must effectively address the problems organizations face (SNHU, n.d.). For these reasons, the hybrid model is the optimal choice for the case and will help participants benefit from information sharing.
Data Extraction
After selecting the HIE model, participants should consider how to extract information. Participants must use appropriate technologies like CDWs, application programming interfaces, and electronic health records, allowing data to be collected, transmitted, and stored (Doutreligne et al., 2023). Since a hybrid model is a suitable model in this case, participants will have access to a central CDW, where, with the necessary authorization, they will be able to obtain information about the patient and will also be able to send requests to other facilities to get data that is not available within central CDW. For effective exchange, organizations must strive for interoperability through common standards, terminology, policies, and patient identification systems (Lee-Eichenwald, 2018). As a result, participants can extract and use the data they need in their work.
HIE Network
The use of information available by providers through the exchange will help improve patient outcomes as it provides a rationale for decision-making, helps research, and enhances the quality of services. In particular, knowing the patient’s history, thanks to access to data, specialists can personalize treatment to increase its effectiveness. Moreover, a large amount of data within the network contains demographic information, which will help track disease patterns and make predictions and research. The available data on what treatments and tests patients have received in the past also accelerate treatment and decision-making for care. As a result, the number of errors and readmissions is reduced – problems that need to be solved by Memorial Hospital.
References
Doutreligne, M., Degremont, A., Jachiet, P. A., Lamer, A., & Tannier, X. (2023). Good practices for clinical data warehouse implementation: A case study in France. PLOS Digital Health, 2(7). Web.
Health information exchange (HIE). (n.d.). UC San Diego Health. Web.
Lee-Eichenwald, S. B. (2018). Health information technologies. In P. Oachs & A. Watters (Eds.), Health information management: Concepts, principles, and practice (6th ed, pp. 355-404). AHIMA Press.
Southern New Hampshire University. (n.d.). HIM 350 case study. Web.