Hospital_management_system
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Date
Dec 6, 2023
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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.
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 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.
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.
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.
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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
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.
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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.
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.
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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.
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.
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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
for each location
to prevent
duplicates of the
None
N/A
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
Patient_ID;
Doctor_ID
Patient_ID links the
appointment to a
appointment to
prevent overlap
of appointment
records.
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.
Location
Records
: Each record holds a unique location ID along with the address details like
city, street address, and zip code.
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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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