Health Care Administration

You are the Vice President of the Dallas/Fort Worth Health and Hospital System. Your system includes 1800 bed-hospital and 30 community-based clinics throughout the metroplex. You and your interdisciplinary team (Quality Improvement, Health Information Management, Pharmacy, and Report/Analytics) are charged with the task of assessing the appropriate clinical, business, and specialty systems applications for the entire hospital system. Provide an assessment for the following applications: administrative, clinical decision support systems, electronic health record and computer-based health record systems, nursing, ancillary service systems, patient numbering systems at master and enterprise levels.

Requirements

For each application assessment (1) select the appropriate clinical setting (inpatient and/or outpatient), (2) explain database architecture and design, and (3) decide which components of an e-health delivery system would be appropriate. Your assessments should be a minimum of 6 pages. 

The study consists of designing databases of various aspects of Dallas Health and Hospital System. The patient and the authorities will access the stored information through the proposed architecture (Go et al., 2013). The architecture of different aspects varies from each other. The team will design the main database in the central part, but the other systems will make use of that tables with additional required tables.

The study also indicates the similarities between the Dallas’s systems and E-health delivery system. The organization has 1800 beds and 30 clinics.

It is a field that relates to management, leadership and administration of hospitals, public health care system, hospital networks, and health care systems (Kongstvedt, 2012).

For in-patient the clinical settings regarding administration of the Dallas Health and Hospital System are managing beds, keep supervision on the scientific research, following legislations, distribution of doctor and nurses in each ward and recruiting doctors, nurses and administrative (Crapo et al., 2015).

For out-patient the administrative manages the thirty community-based clinics, doctors at each clinic and maintain clinical quality.

The administrative of Dallas Health and Hospital Management System have to maintain the quality of the service while managing one hundred beds and thirty clinics. The purpose of the clinics will be handling the out-patient. The entities of the community clinics are placed, id (unique identification number), time, doctor-id and patient-id. The last three components will store that at what time which doctor checked which patient. The individual doctor and patient table will consist of their information.

The ward table will be composed of the bed number, nurse-id, doctor-id and patient-id and time. The patient table will include the admission, discharge and visiting time.

Clinical Settings

Each component is atomic (Coronel & Morris, 2016). So the team can move onto the 2^nd and 3^rd normal form. Through using different tables for patients, doctors and nurses the system will prevent the repetition of groups. The entities of nurse and doctor table will be same. One patient can get admitted or appointed various times. On the other hand, the various patient can get admitted in the same day. The service date depends more on the services than patient so using normalization the team will separate the service dates (O’Leary-Driscoll, 2015). The primary key service_id will serve as the foreign key in the patient table. The Bed table will consist of all the primary keys of other tables. This way the administration department can see all the information regarding a bed.

Doctor Table

Nurse Table

Patient Table

Patient Service Dates Table

Clinic Table

Ward Table

Bed Table

Figure 1: Database Architecture of Dallas’s Administrative

(Source: Created by author)

The viewer, provider information, and public health information are the various elements of electronic health delivery system that is compatible with the administrative database system (Kongstvedt, 2012). The administrative watches over all the operations, observes provider information for future decisions.

The term refers to an essential aspect of the field of the clinical knowledge management technologies using the gathered information to treatment and long-term care for supporting the use of knowledge and clinical processes (Musen, Middleton & Greenes, 2014).

Thus, the settings are.

Administrative: Supporting authorization, clinical coding, referrals and authorize procedures.

Handling clinical details and complexity: Tracking orders, chemotherapy protocols and keeping the patient on research (Go et al., 2013).

Capital control: Preventing unnecessary tests and monitoring medicine orders.

Supporting decisions: Offering support to treatment plan processes and clinical diagnosis (Musen, Middleton & Greenes, 2014).

The database design will consist if mainly on the information of the patients and the services they get. In addition, the various information management tools will assist in analyzing the data that the database stores (Coronel & Morris, 2016). The architecture will be smooth enough to provide all the relevant information to authorized personnel at a single place. The Diagnosis table will assist in finding all the relevant information like which patient did which diagnosis from which clinic or the patient was an inpatient (O’Leary-Driscoll, 2015). The Clinic_id and Bed_id will be nullable as either one or both can have data. Using the foreign key, Patient_id the authorities can access every information regarding the patient.

Database Architecture and Design

Diagnosis

Resource

Resource_provider

Figure 2: Database Architecture of Dallas’s CDS

(Source: Welch & Kawamoto, 2013)

The elements of E-health delivery system that are associated with the Clinical Decision Support Systems are as following.

• E-stakeholder community (Kongstvedt, 2012).
• Potentiality to predict future features.

It refers to a system that holds the whole information of the patients of the Dallas’s for the use by the organization (Weiskopf & Weng, 2013).

• Efficient, organized and well documentation of the administrative process.
• Well, establishment regarding reporting and data collection (Crapo et al., 2015).

The database will consist of the information of Dallas’s patients. The admiration of the organization will utilize this information for constructing a good documentation. The documentation further provides support for better patient treatment (Bright et al., 2012). The patient will give feedback that will store in the system. The administration will examine which doctor and nurses were associated with the treatment of the patient (Rosland et al., 2013). Through the analysis of subscribed drug Dallas’s administration can track the effect of the drug on the various types of patients.

Customer_feedback

Drug_use

Figure 3: Sample Database Architecture of Dallas’s EHR

(Source: “Electronic Health Records-Based Phenotyping | Rethinking Clinical Trials®”, 2016)

The components of the E-health delivery system are as following.

• E-health service model entities (Kongstvedt, 2012).
• Potentiality to predict future features.

Critical Settings:

The nursing system will primarily focus on the in-patients. The nursing system will provide the medicines prescribed to the patients by the doctors. It will assign the nurses to the patients using the electronic health record. The system will try to find which patients feel better under whose supervision and thus improve patient satisfaction (Oshima Lee & Emanuel, 2013).

For out-patent the system will try to assist the organization to help the patient how to follow the doctor’s advice. Catch the patient’s reaction toward the assist Dallas provider in the clinic.

The database will have an additional table called nurse_shifts. This way Dallas will be able to keep track of the nurse’s duty period and provide the best hospitality to patients.

Nurse_shifts

Figure 4: Database Architecture of Dallas’s Nursing System

(Source: “Electronic Health Records-Based Phenotyping | Rethinking Clinical Trials®”, 2016)

• Core value proposition.
• The accommodations for future features (Kongstvedt, 2012).

Critical Settings:

For an in-patient, critical settings are the treatment of critic patients and diagnostic services.

For an out-patient the settings are home health services and occupational therapy, physical therapy (Rosland et al., 2013).

The database will consist of extra two tables Therapist and Home_service_in_charge. The Home_service_in_charge table will give Dallas the ability control the process of providing services to the home.

Home_service_in_charge

Home_service

Therapist

Therapist_service

Figure 5: Database Architecture of Dallas’s Ancillary Services

Components of E-health Delivery System

(Source: Created by author)

• E-health service model entities (Crapo et al., 2015).
• E-stakeholder community involvement.

The Master Patient Index (MPI) or patient numbering system at master and enterprise levels is very vital for Dallas. Dallas will implement independent MPIs within their facility (Crapo et al., 2015).

It has a big responsibility of taking care of the patient while they are admitted to the hospital (Kongstvedt, 2012).

For an out-patient the system will ensure the patient satisfaction by providing all the available facility.

The database will consist of an additional table that will keep track of the payments of the patient.

Payment

Figure 6: Database Architecture of Dallas’s MPI

(Source: Created by author)

• The ability of processing the critical mass of transaction.
• Core value propositions (Rosland et al., 2013).

Conclusion:

The study concludes that the various aspects of the hospital are somehow related to each other. The clinical services are a lot different from the hospital services. The critical settings describe the various important aspects and functions of different systems. The databases may assist different systems for analyzing and storing data, but the core of the databases are all same. The various architecture supports the organization provide services to the patient and put tight control over their services and processes. One simple mistake in designing the database can cause a major issue at the real time experience.

References:

Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., … & Wing, L. (2012). Effect of clinical decision-support systems: a systematic review. Annals of internal medicine, 157(1), 29-43.

Coronel, C., & Morris, S. (2016). Database Systems: Design, Implementation, & Management. Cengage Learning.

Crapo, J., Coyle, D. M., Owen, C. L., Pearson, P., & McRae, K. (2015). U.S. Patent No. 8,949,137. Washington, DC: U.S. Patent and Trademark Office.

Establishment of a Quality Program for the Master Patient Index. (2016). Library.ahima.org. Retrieved 23 March 2016, from https://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_039331.hcsp?dDocName=bok1_039331

Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Borden, W. B., … & Franco, S. (2013). On behalf of the American Heart Association statistics committee and stroke statistics subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation, 127(1), e1-e240.

Kongstvedt, P. R. (2012). Essentials of managed health care. Jones & Bartlett Publishers.

Musen, M. A., Middleton, B., & Greenes, R. A. (2014). Clinical decision-support systems. In Biomedical informatics (pp. 643-674). Springer London.

O’Leary-Driscoll, S. (2015). Database/Resource Acronyms.

Oshima Lee, E., & Emanuel, E. J. (2013). Shared decision making to improve care and reduce costs. New England Journal of Medicine, 368(1), 6-8.

Rosland, A. M., Nelson, K., Sun, H., Dolan, E. D., Maynard, C., Bryson, C., … & Schectman, G. (2013). The patient-centered medical home in the Veterans Health Administration. The American journal of managed care,19(7), e263-72.

Thomson, R. (2016). Clinical Decision Support Systems. Openclinical.org. Retrieved 23 March 2016, from https://www.openclinical.org/dss.html

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.Journal of the American Medical Informatics Association, 20(1), 144-151.

Welch, B., & Kawamoto, K. (2013). The Need for Clinical Decision Support Integrated with the Electronic Health Record for the Clinical Application of Whole Genome Sequencing Information. Journal Of Personalized Medicine, 3(4), 306-325. https://dx.doi.org/10.3390/jpm3040306