HIM 650 Database Project Proposal-Part 2

HIM 650 Database Project Proposal-Part 2

HIM 650 Database Project Proposal-Part 2

Most drugstores and a growing number of practitioners depend on technology systems to keep patient profiles up to date, including diagnostic data, laboratory data, and prescription data. These systems offer some protection against pharmaceutical mistakes, treatment repetition, and drug reactions (Schiff et al., 2018). They often operate independently, with no knowledge of medications given by other primary care doctors or issued by other pharmacists. For instance, a recent analysis by Schiff et al. (2018) discovered that nearly one in every five patients receiving methadone treatment therapy obtained and filled prescriptions for other drugs, mainly from a separate clinician or drugstore. Therefore, this project proposes a consolidated prescription database which will be a beneficial tool for reducing incorrect prescriptions administration and distribution, especially for prohibited medications that are susceptible to abuse or resale, including opioid analgesics and benzodiazepines.

Goals and Objectives

Prescription opioid misuse affects many people, with approximately 117,000 people in Minnesota abusing them each year (Treatment Solutions, 2021). Prescription drugs addicts visit multiple doctors and pharmacies to obtain multiple prescriptions. Many users have mastered the art of “doctor shopping” to get adequate drugs to fulfill their craving. Therefore, the proposed prescription database’s goal is to provide pharmacists and practitioners with past and current patient prescription data on drug usage. The anticipated database will compile prescription data relating to prescription drugs by various people across the state of Minnesota.

Database Schema

A database schema is the conceptual design of an entire or portion of a database system. It can be represented visually and as a collection of procedures known as integrity constraints that control a database (Khalfallah et al., 2018). These procedures are written in a data definition language like SQL. A database schema, part of a data dictionary, describes how the objects that comprise the database, such as stored procedures, views, tables, and other elements, interact (Khalfallah et al., 2018). Developers often utilize database schemas in software design to lay out how a database must be structured. A database schema is used to design the layout of a database and will assist ensure that data inputs are structured consistently, that each record entry has a unique primary key, and that no vital data is missing.

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A database designer usually builds a database schema to assist developers whose applications communicate with the

HIM 650 Database Project Proposal-Part 2
HIM 650 Database Project Proposal-Part 2

database. Data modeling refers to the process of developing a database schema. This stage would come after establishing a logical model in the three-schema methodology to database architecture. Conceptual schemas are concerned with the knowledge demands instead of the architecture of a database. There are two types of database schema (Imam et al., 2018):

  • A logical database schema expresses the logical restrictions about the system’s data. It can define referential integrity, tables, and views.
  • A physical database schema defines how data is kept on a storage system in records and indexes.

A database schema describes which columns or relationships comprise the database and the attributes featured on every table. As a result, schema design and entity-relationship diagram are sometimes used interchangeably (Imam et al., 2018). The figure below illustrates the schema (ERD) for the proposed prescription database.

Figure 1: Prescription Database Schema

Processes Associated with the Business Rules

A business rule is a guideline, technique, or practice that governs an institution. It is vital to identify and define the business logic when creating a database. These rules allow the database designer to specify the interaction between the participation limitations and guidelines and the appropriate data design. A database designer should comprehend procedures and the kind, role, and scope of the data to create a database that will be useful to the organization. The prescription database is subject to specific business rules listed below.

  • A physician can treat one or more patients.
  • A physician can order one or more prescriptions, including one or more drugs.
  • A pharmacy can only issue drugs prescribed by a physician.
  • A patient can only receive drugs issued by the pharmacy and prescribed by a physician.

Entity Relationship Diagram and Data Dictionary

A Data Dictionary collects labels, descriptions, and properties for data components utilized or recorded in a database, computer system, or research project. It defines the interpretations and goals of data items in the context of a project and gives direction on understanding, accepted meanings, and layout (Buchanan et al., 2021). In addition, a Data Dictionary provides metadata on data items. A Data Dictionary’s metadata can help define the scope and properties of data items and the criteria for their utilization and application. The table below describes the data dictionary for the proposed prescription database.

TABLE NAME ATTRIBUTE NAME TYPE Size REQUIRED PK or FK
PATIENT PatientID INTEGER 10 Yes PK
PatientFirstName VARCHAR 30 Yes  
PatientLastName VARCHAR 30 Yes  
DoB DATE/TIME NA Yes  
Gender BOOLEAN 10 Yes  
Address VARCHAR 100 Yes  
TelephoneNumber VARCHAR 10 Yes  
PHYSICIAN PhysicianID INTERGER 10 Yes PK
PhysicianFirstName VARCHAR 30 Yes  
PhysicianLastName CHAR 30 Yes  
Specialty VARCHAR 50 Yes  
TelephoneNumber CHAR 10 Yes  
PRESCRIPTION PrescriptionID INTEGER 10 Yes PK
DrugDosage VARCHAR 20 Yes  
IssueDate DATE/TIME NA Yes  
DRUG DrugID INTEGER 10 Yes PK
DrugName VARCHAR 50 Yes  
Manufacturer VARCHAR 50 Yes  
PHARMACY PharmacyID INTEGER 10 Yes PK
PharmacyName VARCHAR 20 Yes  
Address VARCHAR 100 Yes  
         
FK – Foreign key
PK – Primary Key

Table 1: Database Data Dictionary

Figure 2 below presents the entity-relationship diagram for the proposed prescription database.

Figure 2: Entity Relationship Diagram

Project Limitations and Possible Extensions

One of the database’s weaknesses is that people with drug addiction will go to other jurisdictions to obtain prescriptions. Individuals have also been known to use various identity documents to get prescription medication. Since the current prescription database does not contain the other state hospital facilities that a person may visit or the numerous patient identifiers that the person has, it does not enable uniform access to the patient’s prescription record. This database might be improved to include all healthcare facilities and drug treatment centers in the United States.

Conclusion

The project has proposed a consolidated prescription database which will be a beneficial tool for reducing incorrect prescriptions administration and distribution, especially for prohibited medications that are susceptible to abuse or resale, including opioid analgesics and benzodiazepines. The proposed database offers some protection against pharmaceutical mistakes, treatment repetition, and drug reactions. Existing systems often operate independently, with no knowledge of medications given by other primary care doctors or issued by other pharmacists. Therefore, the proposed centralized prescription database will bridge this gap.

 

References

Buchanan, E. M., Crain, S. E., Cunningham, A. L., Johnson, H. R., Stash, H., Papadatou-Pastou, M., Isager, P. M., Carlsson, R., & Aczel, B. (2021). Getting started creating data dictionaries: How to create a shareable data set. Advances in Methods and Practices in Psychological Science, 4(1), 251524592092800. https://doi.org/10.1177/2515245920928007

Imam, A. A., Basri, S., Ahmad, R., Watada, J., & González-Aparicio, M. T. (2018). Automatic schema suggestion model for NoSQL document-stores databases. Journal of Big Data, 5(1), 46. https://doi.org/10.1186/s40537-018-0156-1

Khalfallah, N., Ouali, S., & Kraiem, N. (2018). Approach for managing variability in the database schema. Journal of Asian Scientific Research, 8(6), 221–236. https://doi.org/10.18488/journal.2.2018.86.221.236

Schiff, G., Mirica, M. M., Dhavle, A. A., Galanter, W. L., Lambert, B., & Wright, A. (2018). A prescription for enhancing electronic prescribing safety. Health Affairs, 37(11), 1877–1883. https://doi.org/10.1377/hlthaff.2018.0725

Treatment Solutions. (2021). Patient prescription database. Treatment Solutions. https://treatmentsolutions.com/blog/patient-prescription-database/