Hospital_management_system (1)
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IT-650: Principles of Database Design
Southern New Hampshire University
Friday 1
st
, 2023
Dr. Kimberly Queenan
Group Members
Mohcin Bounadar
Meghana Marupudi
Dhanush Yeddula
Table of Contents
Introduction
……………………………………………………………………………...1
Who We Are
……………………………………………………………………………..2
Analyse Business Reason for Hospital Management System DBMS
………………..3
Define Problems Constraints
…………………………………………………………..4
Scope and Boundaries
……………………….………………………………
..........
5
ERD For Hospital Management System
………………………………………………6
Normalization
…………………………………………………………….…………….7
Relationships and Cardinality.
.…………………………………….…………………8
Keys
………………………….……...……………………………….…………………11
Data Examples for Hospital Management System …………….……………………12
Logical Design for the Hospital Management System
….….………………………13
Storage and File Organization
………………………………….……………………14
Backup and Recovery
…………………………………………………………………15
Security Measures
………………..…………….……………….……………………. 16
Maintenance
…………….……………...……….……………….……………………. 17
Scalability and Performance Tuning
………….……………….……………………. 18
Schema definition
………………………………………………………………………19
Hospital Management systems in normal forms
……………………………………..20
Data Dictionary
…………………………………………………………………………21
Data Records
……………………………………………………………………………22
Questionaries
……………………………………………………………………………23
Software Used
…………………………………………………………………………..24
References
…………………….……………….……………………………………….25
1.
Introduction
The landscape of healthcare is undergoing a profound transformation, spurred by the advent of digital solutions that promise to overhaul the traditional paradigms of hospital management. At the forefront of this revolution is our Hospital Management System (HMS) database, a comprehensive platform designed to integrate the multifaceted aspects of hospital administration and patient care into a singular, seamless ecosystem. The HMS database stands as a testament to the synergy between healthcare and information technology, aiming to simplify complex processes, enhance data accessibility, and streamline the day-to-day operations of healthcare facilities.
Our HMS database is not just a piece of technology; it's the heartbeat of a more efficient, agile, and patient-focused healthcare system. It is built with the vision of mitigating the common bottlenecks in hospital workflows such as patient queue management, appointment scheduling, and resource distribution. The system is crafted to handle vast amounts of data, from patient medical records to operational logistics, ensuring that every piece of information is just a few clicks away. 2.
Who We Are
Innovate HealthTech is more than just a name; it is a collective of some of the brightest minds in both the tech and healthcare industries, driven by the mission to create an HMS database that sets new standards in hospital management. Our team is a melting pot of skills and experiences, bringing together veteran software developers, insightful data scientists, and
compassionate healthcare practitioners. Each member of our team is dedicated to the cause of
enhancing healthcare delivery through technological innovation, and it is this dedication that has led to the development of a state-of-the-art HMS database.
The core of our philosophy lies in the belief that technology, when thoughtfully applied, can become a powerful force for good in the healthcare sector. We understand the sensitive nature
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of healthcare data and the critical role it plays in patient care. Therefore, we have designed our HMS database with a relentless focus on security, reliability, and user-friendliness. Our commitment to excellence is reflected in the meticulous attention to detail in every line of code and every user interface element, ensuring that our system is not only powerful but also a pleasure to use.
As change-makers in healthcare IT, we recognize the importance of continuous innovation and responsive support. We do not just deliver a product; we forge partnerships with healthcare providers to ensure that our HMS database evolves in sync with their growing
needs. Our support teams are always ready to assist, train, and guide hospital staff in making the most of the system. We are Innovate HealthTech, and we are your partners in building a healthier future, one patient, one record, one data point at a time.
3.
Analyse Business Reason for Hospital Management System DBMS
The impetus for integrating an HMS database into hospital operations is anchored in the
undeniable business benefits it brings. In the fast-paced world of healthcare, where every second count, an HMS database serves as the central nervous system, coordinating the flow of information and ensuring that critical resources are allocated efficiently. The business case for such a system is clear: it reduces operational costs by automating administrative tasks, mitigates risks associated with manual error, and enhances the overall quality of patient care, all of which contribute to the financial sustainability and growth of healthcare institutions.
An HMS database is not merely about digitizing existing processes; it's about redefining them. It allows hospitals to move away from reactive healthcare models to proactive ones. Through comprehensive data analysis and reporting capabilities, it equips administrators with
the insights needed to make strategic decisions, from optimizing bed occupancy rates to streamlining the supply chain for pharmaceuticals. In essence, the HMS database is a tool that
transforms data into actionable intelligence, driving operational excellence and strategic foresight.
The return on investment in an HMS database is manifested not only in monetary terms
but also in the intangible yet invaluable currency of patient trust and loyalty. In an era where patient experience is as critical as clinical outcomes, an HMS database elevates the standard of service delivery. It ensures that patients are not lost in the shuffle of paperwork, that their time is respected, and that their health journey is as smooth and stress-free as possible. For a healthcare business, this translates into a stronger reputation, higher patient retention, and ultimately, a more robust bottom line.
4.
Define Problems Constraints
Problems
The healthcare industry is rife with operational complexities that can stymie the delivery of timely and effective care. Hospitals often grapple with the arduous task of managing vast amounts of patient data, which can become overwhelming and prone to errors when handled manually. Moreover, the lack of interconnected systems leads to information silos, where critical patient information is compartmentalized, hindering the collaborative effort required for comprehensive care. The HMS database addresses these core issues by providing a unified platform for data management and communication.
A common problem in hospital management is the underutilization of resources, leading to operational inefficiencies that can affect the hospital's ability to deliver care. Inefficient scheduling and resource allocation can lead to longer patient wait times, underused medical equipment, and overworked staff. The HMS database tackles these problems by enabling precise scheduling, real-time resource tracking, and workload distribution, ensuring that the hospital's resources are used to their fullest potential.
In addition to these operational challenges, hospitals face the ongoing issue of maintaining patient privacy and data security. In the digital age, where cyber threats are ever-
present, safeguarding sensitive health information is paramount. The HMS database is engineered with robust security protocols to protect against data breaches, ensuring that patient confidentiality is upheld and that the hospital remains compliant with stringent health information regulations.
Constraints
Implementing an HMS database is a substantial undertaking that comes with its set of constraints. Foremost among these is the technological barrier. Integrating a new HMS database with legacy systems can be a complex and delicate process that requires careful planning and execution. Compatibility issues must be addressed to ensure that the new system works harmoniously with existing software and hardware, which can be a significant constraint in terms of time, cost, and technical expertise required.
Financial constraints also play a pivotal role in defining the scope of an HMS database implementation. Budget limitations can restrict the features and capabilities that can be incorporated into the system. There must be a strategic balance between the desired state-of-
the-art functionalities and the financial resources available. This may necessitate phased rollouts or prioritizing certain modules over others, which in turn affects the timeline and impact of the HMS database deployment.
Objectives of Hospital Management Systems
The objectives of the HMS database are multifaceted, primarily focusing on enhancing operational efficiency, ensuring high-quality patient care, and maintaining data integrity and security. The system is designed to automate administrative functions, such as patient registration, appointment scheduling, and billing, thereby reducing manual workload and allowing healthcare professionals to dedicate more time to patient care. It also aims to
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centralize patient health records, making them readily accessible to authorized personnel, which is crucial for informed clinical decision-making.
Another objective of the HMS database is to optimize resource management within the hospital. By providing real-time visibility into resource utilization, the HMS database enables
administrators to make data-driven decisions that improve service delivery and patient throughput. This includes managing the allocation of beds, scheduling of surgeries, and deployment of medical staff. The system also aims to improve the patient experience by facilitating smoother interactions with hospital services, from admission to discharge.
Lastly, the HMS database aims to provide a platform that not only meets the current needs of healthcare providers but also anticipates future trends and requirements. It is designed with scalability in mind, capable of integrating emerging technologies such as telemedicine, artificial intelligence, and advanced analytics. The system is built to evolve, ensuring that it remains relevant and valuable as the healthcare industry continues to advance and change.
Scope and Boundaries
The scope of the HMS database encompasses the entirety of hospital operations, aiming
to create a digital ecosystem that is comprehensive, integrated, and reflective of the best practices in healthcare management. It spans across all major hospital departments, including outpatient and inpatient services, emergency care, laboratory and diagnostic services, pharmacy management, and administrative functions. The HMS database is designed to be customizable, allowing for tailored solutions that fit the specific needs of each hospital, whether it's a small community hospital or a large multi-specialty facility.
However, the implementation of an HMS database has its boundaries. These boundaries
are often defined by the regulatory framework governing healthcare data management, which
dictates how patient information can be collected, stored, and shared. Compliance with these regulations is non-negotiable, and the HMS database is built to adhere strictly to such standards. Additionally, the boundaries of the HMS database are set by the technological infrastructure of the hospital, as well as the willingness of the staff to embrace new technologies and adapt to new workflows.
The scope of the HMS database also considers the need for interoperability with other healthcare systems, ensuring that it can communicate and exchange data with external entities
such as insurance providers, other hospitals, and government health databases. This interoperability is crucial for continuity of care and for meeting the broader objectives of healthcare networks. Nevertheless, while aiming for inclusivity and expansiveness in features, the system is bound by practical considerations of cost, complexity, and the need for
maintaining a user-friendly interface that does not overwhelm its users.
ERD For Hospital Management System:
Normalization:
1NF (First Normal Form)
The first normal form (1NF) sets the very basic rules for an organized database:
1.
Unique Rows
: Each table must have a primary key that uniquely identifies each row. This primary key ensures that no two rows can be identical, eliminating duplicate records and thereby establishing a unique identifier for each record. In the schema provided, every table has a primary key, such as Patient_ID
, Doctor_ID
, etc., which satisfies this condition.
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2.
Atomicity
: All columns in 1NF must be atomic, which means that each column must hold only a single value in each row, not a set of values or a list. For example, a column should not contain multiple phone numbers; instead, there should be one column for each phone number. The schema adheres to this rule, as each column is designed to hold only one piece of information about the entity it represents.
3.
Consistent Data Types
: Each column in a table must contain values of a single data type. This consistency ensures that all entries in a column are of the same kind of data, which makes it easy to perform operations like sorting and filtering. The tables in the schema follow this rule, with data types specified for each column, such as VARCHAR
, INT
, DATE
, etc.
2NF (Second Normal Form)
To be in the second normal form (2NF), a table must first meet all the criteria of 1NF, and then it must also ensure that:
1.
No Partial Dependencies
: All non-key attributes (i.e., columns that are not part of the
primary key) must be fully functionally dependent on the primary key. This means that the non-key attribute must be dependent on the entire key, not just part of it. For instance, if we have a composite key consisting of multiple columns, no column should be dependent only on part of the composite key. In the provided schema, tables
with a single-column primary key do not have partial dependencies by definition, as there is no composite key to create partiality.
3NF (Third Normal Form)
After satisfying 2NF, a table must meet additional requirements to be in the third normal form
(3NF):
1.
No Transitive Dependencies
: In 3NF, all attributes must depend only on the primary key. This means that there should be no transitive dependency for non-prime attributes
(non-key attributes) in a table. A transitive dependency occurs when a non-prime attribute depends on another non-prime attribute. This rule ensures that each non-key column provides facts about the key, the whole key, and nothing but the key. For the tables in the schema, each non-key attribute depends directly on the primary key, not on any other non-key attribute, thus satisfying the requirement for 3NF.
By ensuring the tables adhere to these normal forms, the database structure avoids redundant data, maintains data integrity, and supports efficient access to the data. The provided schema is designed to meet these normal form requirements, which is crucial for a normalized and well-structured relational database.
Relationships and Cardinality:
Patient to Doctor
Each patient is assigned to a doctor, making this a many-to-one (M:1) relationship. Multiple patients are treated by one doctor, which aligns with the typical patient care process in healthcare settings.
Patient to Department
Patients visit specific departments, and since many patients can be seen by the same department, this is a many-to-one (M:1) relationship. It reflects how departments serve as the functional units that provide medical services to patients.
Doctor to Department
Doctors work within departments; thus, there's a many-to-one (M:1) relationship between doctors and departments. This implies that several doctors can be part of a single department, representing the structure of hospital staffing.
Hospital to Doctor
This is a one-to-many (1:M) relationship where one hospital has many doctors working within it. It shows the capacity of a hospital to employ a range of medical professionals.
Hospital to Location
Each hospital is situated in a location, establishing a one-to-one (1:1) relationship. This assumes that each hospital entity in the database is unique to a location and that each location has only one hospital associated with it as per the ERD.
Medication to Pharmacist
Medications are associated with pharmacists in a many-to-one (M:1) relationship. This means that a pharmacist can dispense various medications, linking their role to patient prescriptions within the system.
Patient to Medication
There is a one-to-many (1:M) relationship from patients to medications. A patient may
be prescribed several medications, which necessitates detailed tracking of all medications for each patient.
Patient to Appointment
A one-to-many (1:M) relationship exists between patients and appointments, as a patient can have multiple appointments over time. This relationship captures the scheduling aspect of patient treatment.
Appointment to Doctor
Appointments are linked to doctors, creating a many-to-one (M:1) relationship. One doctor can have many appointments scheduled with different patients, which is crucial
for managing a doctor's workload and patient care schedule.
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These relationships collectively form the database's structure, crucial for the hospital management system to function effectively. Each relationship captures a specific aspect of how patients, healthcare providers, and resources interact within the healthcare environment.
Keys:
Table Name
Primary Key
Reason for PK Choice
Foreign Keys
Reason for FK Choice
Department
Dept_ID
Unique identifier
for each department ensuring that department data
is unique.
None
N/A
Doctor
Doctor_ID
Unique identifier
for each doctor ensuring no duplicate records
for doctors.
Dept_ID
Links each doctor to
a specific department establishing the department where the doctor works.
Hospital
Hospital_ID
Unique identifier
for each hospital to distinguish between different hospital
entities.
Location_ID; Doctor_ID
Location_ID links the hospital to its physical address; Doctor_ID is used to
associate doctors working at the hospital.
Location
Location_ID
Unique identifier
None
N/A
for each location to prevent duplicates of the same location.
Patient
Patient_ID
Uniquely identifies each patient to ensure
every patient record is unique.
Doctor_ID; Dept_ID
Doctor_ID identifies
the doctor treating the patient; Dept_ID
identifies the department the patient visits.
Pharmacist
Pharmacist_ID
Uniquely identifies each pharmacist to prevent duplication of pharmacist records.
None
N/A
Medication
Presct_Num
Unique identifier
for each prescription to ensure each medication record is unique.
Patient_ID; Pharmacist_I
D; Doctor_ID
Patient_ID links medication to a patient; Pharmacist_ID links it to the dispensing pharmacist; Doctor_ID associates it with
the prescribing doctor.
Appointment
Appointment_Nu
m
Unique identifier
for each appointment to prevent overlap of appointment records.
Patient_ID; Doctor_ID
Patient_ID links the appointment to a specific patient; Doctor_ID links it to the doctor who will conduct the appointment.
Data Examples for Hospital Management System:
Department
Records
: Each record consists of a unique department ID and the name of the department, such as Cardiology or Neurology.
Usage
: These records are used to categorize doctors and services, facilitating the organization of hospital resources by specialty.
Doctor
Records
: Each entry includes a doctor's unique ID, name, area of specialization, and their qualifications, such as MD or Ph.D.
Usage
: These records help manage which doctors are available, their areas of expertise, and their assignment to various departments.
Hospital
Records
: Contains information such as the unique hospital ID, the name of the hospital, and references to its location and the doctors affiliated with it.
Usage
: This data is utilized to manage hospital identities, locations, and the linkage to
the medical staff employed.
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Location
Records
: Each record holds a unique location ID along with the address details like city, street address, and zip code.
Usage
: Location data is essential for mapping where hospitals, departments, and other
facilities are situated.
Patient
Records
: Patient records include a unique patient ID, personal details like name, contact information, date of birth, gender, and references to their assigned doctor and department.
Usage
: Critical for keeping track of patient demographics, their assigned medical personnel, and departmental visits for treatment.
Pharmacist
Records
: Includes pharmacist ID and license number, providing a unique identifier for pharmacists who manage and dispense medications.
Usage
: Used to track the pharmacists' roles in medication management and their authority to dispense drugs.
Medication
Records
: Medication records comprise a unique prescription number, medication name, prescription date, and links to the patient it’s prescribed to, the prescribing doctor, and the dispensing pharmacist.
Usage
: These records are important for managing patient prescriptions, ensuring proper medication tracking, and linking them to the responsible healthcare providers.
Appointment
Records
: Appointments are scheduled with unique appointment numbers, dates, and times, linked to specific patients and doctors.
Usage
: They are essential for organizing doctors’ schedules, planning patient visits, and ensuring that the healthcare process runs smoothly.
Logical Design for the Hospital Management System:
Data Model:
Entities: Patient, Doctor, Department, Hospital, Location, Symptoms, Allergies, Current Prescription, Medication, Pharmacist, Diseases, Appointment, Assigned Test.
Attributes: Defined for each entity, such as Patient_ID
, First_Name
, DOB
, Symptom_Type
, etc.
Relationships: Established between entities; for example, Patients have Appointments,
and Doctors issue Prescriptions.
Schema:
Tables: Created based on entities, with columns mirroring entity attributes.
Constraints: Primary Keys
set for uniqueness, Foreign Keys
for relationships, and CHECK
constraints for valid values (e.g., Gender CHAR(1) CHECK (Gender IN ('M', 'F'))
).
Normalization:
All tables are designed to be in 3NF, with no transitive dependencies, and each non-
key attribute is fully functionally dependent on the primary key.
Physical Design for the Hospital Management System:
Hardware
The hardware infrastructure of the Hospital Management System is designed to offer robust performance, high availability, and redundancy to handle critical healthcare data effectively:
Server
: The choice of Dual Intel Xeon Scalable processors provides ample computational power to manage complex queries and large datasets typical in a hospital environment. The 64GB DDR4 RAM ensures quick data access and efficient handling of concurrent requests, which is essential for a responsive user experience. Utilizing RAID 10 configured SSDs for the database not only offers a balance between high read/write speeds and data redundancy but also provides fault tolerance.
This configuration ensures that even if one SSD fails, the system will continue to operate without data loss and minimal downtime.
Network
: A Gigabit Ethernet LAN backbone is crucial for facilitating high-speed data
transfers within the hospital's intranet. This infrastructure supports the bandwidth requirements of the HMS, ensuring that large medical images and patient records can be accessed promptly by authorized personnel.
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Indexing Strategy: The indexing strategy for the Hospital Management System is crafted to optimize data retrieval and enhance query performance:
Primary Indexes
: By default, primary indexes on all primary keys facilitate quick lookups and ensure the uniqueness of records in each table. This indexing is fundamental to the relational database model and serves as the primary method for query optimization.
Secondary Indexes
: Implementing secondary indexes on Patient_ID
across the Appointment
, Prescription
, and Medication
tables is an informed decision, as these fields are likely to be used frequently in search queries and join operations. These indexes would significantly reduce the query time for common operations, such as retrieving a patient's medication history or upcoming appointments.
Composite Indexes
: The use of composite indexes on (Doctor_ID, Dept_ID)
in the Doctor
table is a strategic choice to accelerate queries that need to correlate doctors to
their respective departments. Such an index would be particularly beneficial for queries that involve filtering or grouping by both doctor and department, which is a common requirement in the system.
Storage and File Organization
The storage and organization of data are structured to ensure system performance and data manageability:
Partitioning
: Partitioning tables by Patient_ID
can drastically improve query performance, especially in a large database. It allows the database to manage data more efficiently by breaking down larger tables into smaller, more manageable pieces.
Archiving
: Regular archiving of historical data, such as past appointments and prescriptions, is a proactive approach to manage data growth. By moving older data to
separate storage bi-annually, the system maintains its performance by reducing the size of the active dataset, ensuring that the most frequently accessed data is retrieved as quickly as possible.
Backup and Recovery
A comprehensive backup and recovery plan is crucial for data integrity and availability:
Nightly Backups
: Complete nightly backups are essential for disaster recovery, ensuring that all data from that day is stored securely. In the event of a system failure, data can be restored up to the last backup, minimizing data loss.
Transaction Log Backups
: Hourly backups of transaction logs capture all the changes made to the database within that hour. This allows for point-in-time recovery,
which is critical for minimizing data loss in case of a failure.
Off-site Storage
: Storing weekly backups off-site is a key disaster recovery strategy. It ensures that, in the event of a catastrophic event such as a natural disaster or major system failure, the data can be recovered from an external location.
Security Measures
Security measures are imperative to protect sensitive health information:
Data Encryption
: Encrypting patient data using AES-256 encryption standards is one
of the strongest encryption methods available and is considered industry-standard for sensitive data.
Access Control
: Implementing role-based access control at the database level ensures
that users have access only to the data necessary for their role, adhering to the principle of least privilege and reducing the risk of insider threats.
Regular Updates
: Keeping the DBMS and operating system up-to-date with regular updates and security patches is critical to protect against known vulnerabilities.
Maintenance
Regular maintenance ensures the database operates at peak efficiency:
Index Rebuilding
: Over time, database indexes can become fragmented, leading to inefficient query performance. Weekly index rebuilding reorganizes the index data and optimizes their storage, which can significantly improve performance.
Health Checks
: Regular health checks are necessary to monitor and ensure the integrity of the database. These can include checking for corrupted data, ensuring that storage space is adequate, and monitoring performance metrics to anticipate and prevent issues.
Scalability and Performance Tuning
Scalability and performance tuning are essential for adapting to growing data and usage needs:
Cloud Integration
: Designing the database with cloud integration in mind prepares the system for future scalability. Cloud platforms can provide flexible resources as needed, such as additional storage or computing power.
Performance Monitoring
: Tools like SQL Profiler and Database Engine Tuning Advisor are valuable for monitoring the system's performance. They can help identify slow-running queries and suggest indexes or other changes to optimize performance.
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Schema definition:
CREATE TABLE Department (
Dept_ID INT PRIMARY KEY,
Dept_Name VARCHAR(255)
);
CREATE TABLE Doctor (
Doctor_ID INT PRIMARY KEY,
Specialization VARCHAR(255),
Qualification VARCHAR(255),
Dept_ID INT,
FOREIGN KEY (Dept_ID) REFERENCES Department(Dept_ID)
);
CREATE TABLE Hospital (
Hospital_ID INT PRIMARY KEY,
Hospital_Name VARCHAR(255),
Doctor_ID INT,
FOREIGN KEY (Doctor_ID) REFERENCES Doctor(Doctor_ID)
);
CREATE TABLE Location (
Location_ID INT PRIMARY KEY,
City VARCHAR(255),
Address VARCHAR(255),
Zip_Code VARCHAR(10)
);
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CREATE TABLE Patient (
Patient_ID INT PRIMARY KEY,
First_Name VARCHAR(255),
Last_Name VARCHAR(255),
DOB DATE,
Gender CHAR(1),
Email VARCHAR(255),
Address VARCHAR(255),
Doctor_ID INT,
Dept_ID INT,
FOREIGN KEY (Doctor_ID) REFERENCES Doctor(Doctor_ID),
FOREIGN KEY (Dept_ID) REFERENCES Department(Dept_ID)
);
CREATE TABLE Pharmacist (
Pharmacist_ID INT PRIMARY KEY,
License_Num VARCHAR(255)
);
CREATE TABLE Medication (
Presct_Num INT PRIMARY KEY,
Presc_Name VARCHAR(255),
Presc_Date DATE,
Patient_ID INT,
Pharmacist_ID INT,
Doctor_ID INT,
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FOREIGN KEY (Patient_ID) REFERENCES Patient(Patient_ID),
FOREIGN KEY (Pharmacist_ID) REFERENCES Pharmacist(Pharmacist_ID),
FOREIGN KEY (Doctor_ID) REFERENCES Doctor(Doctor_ID)
);
CREATE TABLE Appointment (
Appointment_Num INT PRIMARY KEY,
Appointment_Date DATE,
Patient_ID INT,
Doctor_ID INT,
FOREIGN KEY (Patient_ID) REFERENCES Patient(Patient_ID),
FOREIGN KEY (Doctor_ID) REFERENCES Doctor(Doctor_ID)
);
CREATE TABLE Hospital (
Hospital_ID INT PRIMARY KEY,
Hospital_Name VARCHAR(255),
Location_ID INT,
Doctor_ID INT,
FOREIGN KEY (Location_ID) REFERENCES Location(Location_ID),
FOREIGN KEY (Doctor_ID) REFERENCES Doctor(Doctor_ID)
);
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Hospital Management systems in normal forms:
1st Normal Form (1NF)
A table is in 1NF if:
It contains only atomic (indivisible) values.
There are no repeating groups or arrays.
Each column has a unique name.
The order in which data is stored does not matter.
Looking schema, all tables comply with 1NF. Each field holds only atomic values, meaning there are no nested records or repeating groups. For example, First_Name
, Last_Name
, DOB
, Gender
, etc., in the Patient
table, all hold single, indivisible values.
2nd Normal Form (2NF)
A table is in 2NF if:
It is in 1NF.
All non-key attributes are fully functionally dependent on the primary key.
For tables, they all appear to be in 2NF because each column in a table that is not a primary key (e.g., First_Name
, Last_Name
in Patient
) is dependent on the primary key (
Patient_ID
) and nothing else. There are no partial dependencies of any column on a part of a composite primary key, which satisfies the requirement for 2NF.
3rd Normal Form (3NF)
A table is in 3NF if:
It is in 2NF.
There are no transitive dependencies; a non-key attribute does not depend on another non-key attribute.
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From the schema definitions you provided, each non-key attribute depends only on the primary key. For instance, in the Patient
table, Doctor_ID
is a foreign key but is not a determinant for any non-key attribute in the same table. All the non-key attributes like First_Name
, Email
, etc., depend solely on Patient_ID
and not on any other non-key attribute. This indicates that your tables are in 3NF.
To further elaborate:
Patient table:
Patient attributes depend solely on Patient_ID
. There are no attributes that are non-prime and depend on another non-prime attribute.
Doctor table:
Doctor attributes depend on Doctor_ID
, with no transitive dependencies present. The Specialization
and Qualification
depend only on the Doctor_ID
.
Department table:
Here, Dept_Name
depends only on Dept_ID
, adhering to 3NF.
Medication table:
Each attribute like Presc_Name
, Presc_Date
is dependent on the primary key Presct_Num
and not on any other non-key attribute.
All other tables
like Allergies
, Diseases
, Appointment
, etc., follow the same principle where non-key attributes do not depend on other non-key attributes.
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Data Dictionary:
Department:
Doctor
Hospital
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Location
Patient
Pharmacist
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Medication
Appointment
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Data Records(20):
Departments:
Location:
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Pharmacist:
Doctor:
Patient:
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Medication:
Appointment:
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Hospital:
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Questionaries:
1.
How many doctors specialize in 'Cardiology'?
Answer
: To find the number of doctors specializing in Cardiology, you would use the following SQL query:
SELECT COUNT(*) AS CardiologistsCount
FROM Doctor WHERE Specialization = 'Cardiologist';
2.
Which patients have appointments scheduled for '2023/11'?
SELECT p.Patient_ID, p.First_Name, p.Last_Name
FROM Patient p
JOIN Appointment a ON p.Patient_ID = a.Patient_ID
WHERE a.Appointment_Date like '2023-11-%';
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3.
What medications have been prescribed by Doctor with ID '3'?
4.
Which pharmacist dispensed the most medications?
SELECT p.Patient_ID, p.First_Name, p.Last_Name, COUNT(a.Appointment_Num) AS AppointmentCount
FROM Patient p
JOIN Appointment a ON p.Patient_ID = a.Patient_ID
GROUP BY p.Patient_ID;
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Software Used:
For the development and management of the Hospital Management System database, MySQL Workbench was utilized as the primary database management tool. This integrated development environment (IDE) is specifically designed for the MySQL database system, providing a comprehensive suite of tools for efficiently designing, optimizing, and maintaining databases. Operating on a Windows platform, MySQL Workbench offered a user-friendly graphical interface that facilitated the creation and manipulation of database schemas, execution of SQL queries, and visualization of database structures. Its robust functionality and compatibility with Windows OS significantly enhanced the database development process, allowing for streamlined data modelling, query development, and server administration. The use of MySQL Workbench was pivotal in ensuring precise execution of SQL operations, effective database management, and smooth integration of the database with other
components of the Hospital Management System.
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References
Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management
(6th ed.). Pearson.
Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems
(7th ed.). Pearson Education.
Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book
(2nd ed.). Prentice Hall.
Kim, W. (2019). Modern Database Management and Optimization Techniques. In J. L. Smith (Ed.), Proceedings of the 2019 International Conference on Database Technology
(pp.
45-50). Springer.
International Organization for Standardization. (2017). ISO/IEC 9075-1:2016: Information technology — Database languages — SQL — Part 1: Framework (SQL/Framework)
. Retrieved from https://www.iso.org/standard/63555.html
Silberschatz, A., Korth, H. F., & Sudarshan, S. (2011). Database System Concepts and Architecture. Journal of Database Management
, 22(1), 105-123.
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