Memorial Hospital Milestone Case Study


Milestone One: Case Study

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            Memorial Hospital is facing a series of challenges, according to the information provided in the case study. Among the first concerns is an obstacle preventing them from sharing sensitive patient information with the patient’s physician since it is such a vast hospital. Except for sending and mailing documents, there is no way to get updates on the patient’s progress until he or she is discharged from Memorial Hospital. After a patient is released, there is no way to monitor their progress until they return to the hospital or are readmitted. Due to the lack of an HIE setup, the hospital has experienced profound medical and prescription faults, a rise in duplicate tests, rampant complications in healthcare, a surge in re-admissions, and a tremendous reduction in patient satisfaction. To solve these issues, the hospital has formed a board spearheading the HIE initiative, which aims at putting together healthcare providers in a single network to improve patient care better and make the transition from a paper-based to an electronic medical record system.

Data to be Exchanged

            Recent care, episode logs, labs, radiology reports, vaccines, prescriptions, vitals, medicines, and hospital admissions, and discharges are all examples of documents and information that will have to be exchanged via the HIE. This information is critical to the recovery plan for the patient. For instance, all records should be forwarded to the primary care provider after a patient is discharged from the hospital to indicate what examinations have been carried out to ascertain the reason behind the patient’s appointment in the first place. By doing this, doctors would be at par, knowing the reasons behind the patient was admitted to the hospital. At the same time, all the tests regarding the patient would have been issued, thus eliminating the need for them to be redone. So long as the documentation from the primary doctor is available and updated, doctors at the hospital can proceed efficiently with the patient care plan.

Data not to be Exchanged

             The hospital should not share information regarding patients who have not agreed to participate in the HIE. Additionally, unless the patient expressly requests it, mental health, substance addiction, and other sensitive diagnoses should not be shared. It also relies on the policies in place to make data transfer easier. Therefore, patients ought to sign agreement papers to enable their private data made accessible. Assuring that the patient fully comprehends the facts to be communicated and that the patient’s decision is adequately recorded and upheld. According to the meaningful and consent outline, the patient must decide whether and when the details can be exchanged in six ways.

First and foremost, before making any decision, the patient out to be taught in complete honesty and transparency. Secondly, the agreed-upon finding should be rendered after the patient has had enough time to carefully study the provider’s instructional materials. Three, explain that the material has to be shared with the patient. Four, Enable the patient to recognize that his or her records will not be used for derogatory reasons, nor will it be used to impose any restrictions on seeking medical attention. Five, explain to the patient that the more the knowledge exchange deviates from a realistic patient standard, the more time and education the patient may need to make an informed decision. Enable the patient to delete the details at any moment, which is number six.


Memorial Hospital will be best served by a hybrid model, as previously proposed. The hybrid model incorporates the best features of other platforms, employs a single database for querying, and provides improved data access. A centralized CDW in the hybrid model will connect with each remote CDW, allowing for a more efficient data flow. The hybrid model employs the decentralized approach to provide the most up-to-date information. As a consequence, the unified model will only handle a certain amount of data, and adding additional hardware and software would not increase results, resulting in a negative cost-benefit ratio. Decentralized systems, on the other hand, allow for the addition of more hardware to improve data storage and capacity. It also keeps track of individual members, resulting in a redundant data function that increases data availability and consistency (Oachs, 2016).

Data Extraction

            The best data extraction technique, in my view, is directed exchange. This technique utilizes an encoded electronic mail. Information is efficiently transferred from one provider to the other through sending a message in a secure format that can only be read by the intended receiver, rather than asking a database a question. This technique was designed to provide an easy, guarded and innovative way for members to transmit valid encoded healthcare data unswervingly over the internet to pre-approved clients (Direct Project 2015). All the data extraction techniques (Health Information Exchanges) utilizing the hybrid model are regarded to be the most versatile with the capability of enhanced HER and data analysis at more accessible rates compared to other forms of technology. At the same time, bracing the objectives of the nationwide health IT interoperability, patient commitment and management of public health. According to Bresnick (2015), each form of HIE has its own set of benefits and drawbacks with the hybrid solution striking a balance between the necessity for supplier control and data warehouse’s strong effect for significant performance.

Network Data Analysis

I would evaluate data in the network by running reports on a regular basis to ensure that information is being entered accurately and in a timely manner. Patients’ treatment management will be aided as a result of this action. Evaluation of the information would allow simple identification of flaws in data entry thus enabling a faster resolution to the problem. In return, patients would get high-quality care that they need.




Bresnick, J. (2015). How health information exchange models impact data analytics. Retrieved from data-analytics.

Oachs, P. K., & Watters, A. (2016). Health information management: Concepts, principles, and practice. Chicago, IL: AHIMA, American Health Information Management Association.

Patient Consent for Electronic Health Information Exchange. (2019). Retrieved from Health

What is HIE? Query-Based Exchange. (2019). Retrieved from hie#query-based_exchange.


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