Vigour: Data Warehouse Design For Healthcare Provider Specializing In Geriatrics

Consultations

Current and Future Decision Making Needs: 

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  1. In order to promote business, Vigour will evaluate clients’ personal details and generate various information. This information will be based on the treatment each of the patient will receive. Therefore, the main decision that Vigour will make for promoting business is clients will be receiving messages of discussants and new features
  2. The second need is payment related information. The organization will be evaluating the payments a client makes. This information will be processed and financial knowledge will be generated. The organization will categorize patients based on payments they make and few other things.

The information requirements are as following.

  1. Client Data:The personal details of the client are essential for business operations. The management will make various crucial decisions based on the client details. The policies, promotions, treatment and many more factors process the client data for generating knowledge.
  2. Medical Condition Progress Data: This data is most vital of all. The system will be storing the medical condition of each patient after consultation and diagnostic. This data will be processed for generating treatment related information. Based on the medical condition progress data, the treatment procedure is modified.
  • Medicine and Effects:Medicines are prescribed to the patients. The employees will store the name of the patient, medicine, side effect of it and after of medicine. These data will be processes later to identify which medicine suits which clients. Most of the decisions related to medication will be made based on the mentioned findings.
  1. Billing:The bills are generated as a prof of service provided to client. The invoice, on the other hand, serves as a proof that client made payment to Vigour. Payment information is crucial for financial decision making.  

Total six dimension models are required to store all the data. The dimension models are as following.

  1. Client Dimension Model:This dimension model will hold personal details of the client.
  2. Invoice Dimension Model: This dimension model will hold all the payment related data.
  • Employee Dimension Model:This dimension model will hold all the details of the employee.
  1. Condition Dimension Model:This dimension model will hold the information of condition against every patient.
  2. Medication Dimension Model:This dimension model holds the information of all the medicines Vigour purchases and prescribes.
  3. Date Dimension Model:Will store every process against a proper timeline.
  • Consultation Dimension Model:The consultation provided to every patient will be needed for better decision making.

SQL Query 1: Select C.Name, CC.CurrentMedication, MC.Symptoms From Client C inner join ClientCondition CC on C.ClientID=CC.ClientID inner join MedicalCondition MC on CC.ConditionID=MC.ConditionID Where C.Gender=Female Order By C.Name;

SQL Query 2: Select Name, Total From Client Inner Join MedicationOrder on Client.ClientID= MedicationOrder.ClientID Group By Name;

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SQL Query 3: Select C.Name, MC.*, From Client as C inner join ClientCondition as CC on C.ClientID = CC.ClientID inner join MedicalCondition as MC on CC.ConditionID  = MC.ConditionID Order By ConsultationDate;

SQL Query 4: Select C.Name, I.Date, I.Discount, I.Total From Client as C inner join Invoice as I on C.ClientID = I.ClientID Where SubTotal>200 AND Discount <5 Order by I.Date ASC;

Figure 1: Fact and Dimension Table of Vigour Date Warehouse

(Source: Created by Author)

Data Dictionary:

Dimension Table: Dim_Client

Attribute

Description

Data Type

Constraint

Purpose

Client_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Name

The given and last name of the client

Varchar (50)

N/A

Identify the name of the client

Date_Of_Birth

The date on which client was born

Date

N/A

The birth date of the client assist in various decision making

Gender

Identify to which sex the client belongs to

Varchar (6)

N/A

The gender assist in identifying what kind of consultation to be offered

MartialStatus

It stores the martial status of the client

Varchar (50)

N/A

Marital status is required for various decision making needs

HomeAddress

The home address will be default address

Varchar (50)

N/A

Delivering the bill to the default address

WorkAddress

The optional address will be work address

Varchar (50)

N/A

In case delivery fails, bill will be sent to the optional address

Occupation

The current job of the client

Varchar (150)

N/A

Consultation is done based on the working nature of the client

Dimension Table: Dim_Medication

Attribute

Description

Data Type

Constraint

Purpose

Medication_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Supplier_name

The name of the medicine supplier

Varchar (50)

N/A

Supplier name is crucial for logistics

Unit_Cost

Cost for each unit of medicine

Decimal (12,2)

N/A

To store the price of medicine

Dimension Table: Dim_Date

Attribute

Description

Data Type

Constraint

Purpose

Date_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Date

The date is stored

Daye

N/A

To assign a process a particular date

Day

The name of the day for a specific date

Varchar (10)

N/A

To find the name of the day on which a process was done or an outcome was generated

Day_of_Week

The number of day on a week

int

N/A

To find the number of the day

Month

The numerical or alphabetical representation of a month

Month

N/A

The date will be assigned for individual months

Month_Name

The name of the mont

Varchar (10)

N/A

The name of the month like January

Quarter_Name

The quarter like first, second and more

Varchar (10)

N/A

To search data or create report for a large period of time

Year

The year is stored

Year

N/A

All the dates, months and quarters will be separated by the year attribute

Dimension Table: Dim_Consultation

Attribute

Description

Data Type

Constraint

Purpose

Consultation_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Consultation_Outcome

The result of consultation

Varchar (200)

N/A

To identify the outcome of the consultation

Diagnostic_Outcome

The result of diagnostic

Varchar (200)

N/A

To see the impact of the diagnostic on the client

Specialist_Outcome

The result a specialist input against each consultation

Varchar (200)

N/A

The specialist outcome servers additional information for treatment

Consultation_Event_Outcome

The final outcome of the consultation

Varchar (200)

N/A

This outcome is very essential

Dimension Table: Dim_Condition

Attribute

Description

Data Type

Constraint

Purpose

Condition_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

CurrentState1

Initial present state of the client

Varchar (200)

N/A

To analyse the initial client state during treatment

CurrentState2

Second storage of client state

Varchar (200)

N/A

To identify and evaluate the second entry on client medical state

CurrentState3

Final information on client state

Varchar (200)

N/A

To identify and evaluate the final entry on client medical state

CurrentMedication

Present medication prescribed to client

Varchar (200)

N/A

To identify and evaluate all the medicines that are prescribed to the client

SignificantEvents

If any special situation occured

Varchar (200)

N/A

To evaluate the events that are special

MedicalConditionName

The name of the condition

Varchar (50)

N/A

To store the name of the condition

MedicalConditionType

In which category the condition reside

Varchar (10)

N/A

To identify the type of the condition and process treatment

MedicalConditionSymptoms

The symptoms of the condition

Varchar (200)

N/A

To identify if the condition is critical or normal

Dimension Table: Dim_Employee

Attribute

Description

Data Type

Constraint

Purpose

Empployee_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Name

The name of the employee

Varchar (50)

N/A

To store the name of the employee

Date_Of_Birth

Date of birth of the employee

Date

N/A

To collect the birth date of employee

Gender

Whether the employee is male or female

Varchar (10)

N/A

To determine the sex of the employee

Date_of_Joining

The date on which the employee joined the organization

Date

N/A

Poetize the employees

Dimension Table: Dim_Invoice

Attribute

Description

Data Type

Constraint

Purpose

Invoice_ID

It separates all the row

Varchar (50)

Primary

Identify each table uniquely

Date

The date on which invoice is generated

Date

N/A

To identify the business profits

Sub_Total

Total expenses of the treatment

Decimal (12,2)

N/A

To identify the business profits

Discount

Discount a client gets

INT

N/A

To make the client loyal

Total

Total amount after discount

Decimal (12,2)

N/A

The amount individual clients pay against treatment

Dim_Client: This dimension table will fulfil the information needs of customer relationship management decision making.

Dim_Invoice: This dimension table will fulfil the information needs of financial decision making.

Dim_Employee: This dimension table will fulfil the information needs of work place related decision making.

Dim_Condition: This dimension table will fulfil the information needs of treatment related decision making.

Dim_Medication: This dimension table will fulfil the information needs of treatment related decision making.

Dim_Condition: This dimension table will fulfil the information needs of treatment related decision making.

Dim_Date: This dimension table will fulfil the information needs of all the decision making.

Andersen, O., Thomsen, C., & Torp, K. (2018). SimpleETL: ETL Processing by Simple Specifications.

Arifin, S. M., Madey, G. R., Vyushkov, A., Raybaud, B., Burkot, T. R., & Collins, F. H. (2017). An online analytical processing multi-dimensional data warehouse for malaria data. Database, 2017.

Arunachalam, S., Page, T., & Thorsteinsson, G. (2016). Healthcare Data Warehousing. i-Manager’s Journal on Computer Science, 4(4), 1.

Bernard, S. K., Tra, G. B., Marcelin, B. K., & Oumtanaga, S. (2016). Determination of Child Vulnerability Level from a Decision-Making System based on a Probabilistic Model. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 7(11), 379-384.

Berrahou, L., Lalande, N., Serrano, E., Molla, G., Berti-Équille, L., Bimonte, S., … & Le Ber, F. (2015). A quality-aware spatial data warehouse for querying hydroecological data. Computers & Geosciences, 85, 126-135.

Bouadi, T., Cordier, M. O., Moreau, P., Quiniou, R., Salmon-Monviola, J., & Gascuel-Odoux, C. (2017). A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management. Environmental Modelling & Software, 97, 229-242.

Chevalier, M., El Malki, M., Kopliku, A., Teste, O., & Tournier, R. (2015, April). How can we implement a Multidimensional Data Warehouse using NoSQL?. In International Conference on Enterprise Information Systems (pp. 108-130). Springer, Cham.

Chevalier, M., El Malki, M., Kopliku, A., Teste, O., & Tournier, R. (2016). Document-oriented models for data warehouses.

Corral, K., Schuff, D., Schymik, G., & St Louis, R. (2015). Enabling Self-Service BI through a Dimensional Model Management Warehouse.

Dehdouh, K., Bentayeb, F., Boussaid, O., & Kabachi, N. (2015, January). Using the column oriented NoSQL model for implementing big data warehouses. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA) (p. 469). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

George, J., Kumar, V., & Kumar, S. (2015). Data Warehouse Design Considerations for a Healthcare Business Intelligence System. In World Congress on Engineering.

Hart, R. (2017). Extending dimensional modeling through the abstraction of data relationships and development of the semantic data warehouse (Doctoral dissertation).

Kannan, V., Fish, J. S., Mutz, J. M., Carrington, A. R., Lai, K., Davis, L. S., … & Bhat, D. G. (2017). Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data: An Agile Framework. Methods of information in medicine, 56(99), e74.

Liu, X., Lei, G., Ren, F., & Ma, H. (2015). Data Warehouse Architecture for Metal Mine Enterprise with UML AND CWM.

Mansmann, S., Rehman, N. U., Weiler, A., & Scholl, M. H. (2014). Discovering OLAP dimensions in semi-structured data. Information Systems, 44, 120-133.

McGlothlin, J. P., Madugula, A., & Stojic, I. (2017). The Virtual Enterprise Data Warehouse for Healthcare. In HEALTHINF (pp. 469-476).