Patients ID First Name Last Name Phone Date Of Birth

Patientspatient Idfirst Namelast Namephonedate Of Birthgenderhealth Pl

Patients Patient ID First Name Last Name Phone Date of Birth Gender Health Plan Allergies 19 Dusty Springfield (/5/1990 M Medicare Codeine derivatives, merperidine 10 Jonathan Cardenas (/22/94 M Dismal Swamp Health Peanuts 23 Jennifer Holiday (/21/1989 F WeCare Health Management 18 Anders Kroll (/11/94 M Claytor Lake Community Health Penicillin 14 Mary Ambrose (/15/90 F Claytor Lake Community Health Shellfish 15 Gina Mercado (/17/89 F Medicare Morphine 4 Steven Nuggent (/12/1986 M Southern Appalachian Mountains Health Plan Penicillin 11 Paula Hargus (/11/90 F WeCare Health Management 22 Jose Hernandez (/30/1987 F Dismal Swamp Health Morphine, morphine derivatives 24 Kimberley Smith (/16/1989 F Claytor Lake Community Health Homework 20 Kevin Bacon (/22/1990 M Southern Appalachian Mountains Health Plan 21 Cesar Romero (/14/1987 M Great American Mid-Atlantic Health Peanuts 1 Ted Williams (/11/94 M Medicare Pine Tar 17 Maria Gabel (/12/90 F WeCare Health Management Penicillin 8 Sonia Cardenas (/12/1988 F Great American Mid-Atlantic Health Penicillin, cephalosporins Doctors Doctor ID First Name Last Name Phone D1 David Warric ( D3 Patricia Loke ( D5 Gene Allen ( D2 Roxanna Freitag ( D4 Robert Gramann ( D9 Sara Jurski ( D11 James Trehass ( D6 Antonio Westenberg ( D8 Joshua Wilson ( D7 Gunther Zamarron ( Drugs UPN Name Generic?

Description Unit Dosage Dosage Form Cost Price/Dose 102 Ampicillin TRUE Antibiotic Pill 250 mg $0.75 $1. Tolbutamide FALSE Bacterial infections Pill 2 mcg $0.24 $0. Avatocin FALSE Allergies Pill 100 mg $0.65 $1. Acebutolol hydrochloride FALSE Arthritis Pill 400 mg $0.55 $1. Dseurton TRUE High blood pressure Pill 175 mg $0.60 $1.

Levothyroxine TRUE Thyroid disorders Pill 25 mg $0.70 $1. Dyotex FALSE Tonsillitis Bottle 2 tsp $0.25 $1. Phalastat FALSE Allergies Bottle 1 tsp $0.75 $1. Clonazepam FALSE Epilepsy Pill 4 mcg $0.65 $1. Epronix FALSE Pain Pill 500 mg $0.85 $1.

Syocil TRUE Diabetes Pill 120 mg $0.45 $1. Diazapam TRUE Anxiety Pill 5 mg $0.45 $1. Glimepiride TRUE Diabetes Pill 2 mg $0.25 $0. Xeroflarol TRUE Acid reflux Bottle 1 tsp $0.50 $1. Cefixime TRUE Antihistamine Pill 400 mg $0.95 $1.

Quentix TRUE High blood pressure Pill 50 mg $0.50 $1. Haloperidol FALSE Diuretic Pill 6 mcg $0.70 $1. Tiron FALSE Beta blocker Pill 150 mcg $0.75 $1. Montelukast sodium TRUE Acne Pill 10 mcg $0.32 $0. Hyometadol FALSE Asthma Bottle 2 tsp $0.65 $1.

Warfarin Sodium TRUE Bronchitis Pill 4 mg $0.65 $1. Nvalax TRUE Depression Pill 200 mg $0.30 $0. Albuterol Sulfate TRUE Asthma Pill 2 mg $0.30 $0. Almotriptan TRUE Conjunctivitis Pill 6.25 mg $0.14 $0. Myobuterol TRUE Antibiotic Bottle 1 tsp $0.55 $1.

Oxaprozin TRUE Anti-inflammatory Pill 1200 mg $0.60 $1. Didanosine TRUE Sinus infection Pill 200 mg $0.65 $1. Tvalaxec TRUE Antihistamine Bottle 2 tsp $0.50 $1. Rivastigmine tartrate TRUE ADHD Pill 4.4 mg $0.40 $1. Rizatriptan Benzoate FALSE Pain Pill 6 mg $0.47 $1.10 Health Plans Plan ID# Plan Name Address City State Zip Phone H1205 WeCare Health Management 269 Monument Ave Richmond VA H10119 Claytor Lake Community Health 11112 W.

University Dr. Radford VA H0971 Medicare 7500 Security Blvd. Alexandria VA H1941 Southern Appalachian Mountains Health Plan 99 Blue Ridge Parkway Front Royal VA H3410 Great American Mid-Atlantic Health 4200 Hwy 123 S. Fairfax VA H1695 Dismal Swamp Health 32111 Swamp Park Place Suffolk VA Refills Prescription ID Refill Date 15 4/22//10//23//1//1//12//29//1//13//16//30//14//23//23//31//3//12//16//1//14//14//15//21//11//11//14//18//14//14//14//16//1//1//3//11//21//21//1//25//18//11//11/11 Rx Prescription ID UPN Quantity Unit Date Expire Date Refills Authorized Auto Refill? Refills Used Instructions Patient ID Doctor mg 6/29/10 6/14/11 4 TRUE 2 1 pill every 4 hours 10 D mg 5/2//30/11 3 TRUE 0 1 pill every 5 hours with food 1 D mg 9/10/10 6/10/11 2 TRUE 0 1 pill every 6 hours 11 D mg 1/16//18/11 0 TRUE 0 2 pills daily 14 D mg 1/10//24/10 2 TRUE 0 2 pills every 12 hours 22 D ml 7/17/10 3/9/11 3 TRUE 0 2 teaspoons full every 4 hours 8 D ml 9/24//25/11 4 TRUE 0 2 teaspoons full every 4 hours 1 D ml 4/15/10 3/3/11 3 TRUE 0 3 teaspoons full every 6 hours 1 D ml 2/2//15/11 3 FALSE 0 2 teaspoons full every 6 hours 19 D ml 2/2//15/11 0 FALSE 0 2 teaspoons full every 6 hours 19 D mg 4/15/10 6/11/11 4 TRUE 1 1 pill every 5 hours with food 18 D mg 5/16/10 7/11/11 4 TRUE 0 1 pill every 5 hours with food 17 D mg 9/10/10 9/12/11 3 TRUE 1 2 pills every 6 hours with food 4 D mg 2/24/10 1/20/11 4 TRUE 0 2 pills every 6 hours with food 21 D ml 1/14/11 8/19/11 3 TRUE 1 2 teaspoons full every 5 hours 4 D mg 7/8/10 1/6/11 4 TRUE 0 2 pills daily 21 D mg 6/21/10 7/30/11 5 FALSE 0 2 pills daily 22 D mg 1/6/11 8/9/11 3 FALSE 1 2 pills daily 24 D mg 7/6/10 6/24/11 4 TRUE 0 2 pills every 5 hours as needed 23 D mg 8/14/10 7/31/11 4 TRUE 0 2 pills every 6 hours 18 D ml 3/14/10 1/22/11 2 TRUE 0 1 teaspoon every 4 hours 11 D ml 8/13/10 7/14/11 5 TRUE 2 1 teaspoon every 4 hours 23 D ml 12/15//11/11 3 FALSE 1 1 teaspoon every 4 hours 15 D mg 5/1/10 4/18/11 4 TRUE 1 2 pills every 4 hours with food 20 D mg 6/13//12/11 2 TRUE 0 2 pills every 4 hours with food 14 D ml 9/24/10 4/16/11 3 TRUE 0 1 teaspoon every 6 hours 17 D ml 1/24/11 9/22/11 2 TRUE 1 1 teaspoon every 6 hours 11 D11 MIS 301 – Homework #5 – “Doc-n-the-Box†Pharmacy In central Fairfax, VA, is a relatively new small medical practice lovingly known as the “Doc-n-the-Box.†There is also a pharmacy affiliated with the medical practice. Although meticulous and professional, recordkeeping at the pharmacy is a bit inefficient since it is done manually. Recordkeeping costs have been rising in recent months as additional people have been hired to meet stricter industry regulations regarding the Health Insurance Portability and Accountability Act (HIPAA) and because of state regulations that affect the sale, storage, and dispensing of prescription drugs. Although the Doc-n-the-Box has succeeded in automating some of the data management for the pharmacy in an Excel spreadsheet, a more substantial change, i.e., a move to Microsoft Access, is needed to properly maintain and store data. That is your job! Individuals who use the Doc-n-the-Box can request prescriptions at the pharmacy, either by presenting a written order from a doctor or asking for a refill of an existing prescription. The pharmacist adds this request to the system by getting the required information to fill it, including information about the drug, the individual’s name, the individual’s health plan, and the prescribing doctor. Use the data that the Doc-n-the-Box has provided for you in the file “HW 2 - Doc-n-the-Box - Data.xls†(which can be found in Blackboard) to complete the following tasks/requirements. (Note that all of these must be successfully accomplished to be able to receive full credit on the assignment.) Requirements: 1. Create a Microsoft Access database for the Doc-n-the-Box pharmacy. 2. Create the table(s), field(s), data type(s), primary key(s), etc. for the database. 3. Create the relationship(s) between/among the tables, as appropriate. 4. Populate the database with the data provided in the Excel file. Once this is done, perform the following data-analysis tasks (i.e., queries) using Access. (Use the generic name for the queries when you save them, e.g., Query 1, Query 2, etc.): Query 1 – Create an alphabetized list of the female patients who have used the Doc-n-the-Box. Your output should include the Last Name, First Name, ID#, Phone, Date of Birth, and allergies, if any, for those individuals. Query 2 – Create a list of patients and the drug(s) that has/have been prescribed for them. Your output should include the Patient’s Last Name, Patient’s First Name, Patient ID #, Drug Name, UPN, Instructions, Number of Refills Authorized, and Rx Expiration Date. This list does not have to be alphabetized. Query 3 – Create a list of the Health Plans with a list of individuals for each plan. Your output should include the Health Plan Name, Health Plan ID#, Patient Last Name, Patient First Name, and Patient ID#. This list should be alphabetized by Health Plan Name, and alphabetized by Patient Last Name within each Health Plan category. Query 4 – Create a list of Prescriptions with 0 (zero) authorized refills. Your output should include UPN, Drug Name, # Refills Authorized, Patient Last Name, Patient First Name, Doctor’s (Last) Name, Doctor ID#, and Doctor’s Phone Number. Query 5 – Create a list of Patients who received prescriptions between May 1, 2010 and December 31, 2010. Your output should be organized by Patient Last Name and should contain the Prescription Date (in ascending order), Expiration Date, Patient’s Last Name, First Name, ID#, Doctor, Doctor ID#, Drug Name, and UPN. Query 6 – Create a query of your own design. This query should require the use of at least four of the tables from the database and the output should include at least one data field (that is not a foreign key) in the output from each of the four tables. (Have some fun with this…)

Paper For Above instruction

The task involves designing and developing a comprehensive Microsoft Access database for a small pharmacy named "Doc-n-the-Box," located in Fairfax, VA. This initiative aims to improve recordkeeping efficiency, ensure compliance with healthcare regulations, and facilitate effective management of prescription data. The process encompasses creating structured tables, establishing relationships among them, populating these tables with existing data from an Excel worksheet, and executing complex queries to analyze the stored information. This paper elaborates on each stage of the database development, including data modeling, implementation, and analytical querying, while illustrating best practices in database management in healthcare settings.

Initially, understanding the data structure is key to creating an effective database design. Based on the provided data, the core entities include Patients, Doctors, Prescriptions (or Refill Requests), Drugs (or Medications), Allergies, and Health Plans. Each entity corresponds to a table in the database, with specific fields and appropriate data types aligned with their real-world attributes. For example, the "Patients" table includes fields such as Patient ID (Primary Key), First Name, Last Name, Phone, Date of Birth, Gender, and Allergies. The "Doctors" table contains Doctor ID (PK), First Name, Last Name, and Phone Number. The "Drugs" table records medication details like UPN, Drug Name, Dosage, Form, Cost, and Price per Dose. The "Health Plans" table catalogs plan names and related contact information.

The relationships among these tables are crucial for maintaining data integrity and enabling comprehensive queries. For instance, a one-to-many relationship exists between Patients and Prescriptions, signifying that each patient can have multiple prescriptions. Similarly, each prescription is associated with one doctor and one drug, establishing foreign key relationships, such as PatientID in the Prescriptions table linking to the Patients table, and so forth. Properly setting these relationships ensures that queries involving multiple tables can be performed efficiently using JOIN operations.

Populating the database involves importing data from the supplied Excel file into respective tables. Data validation during import is essential to maintain consistency, such as ensuring date formats are correct, and foreign keys correspond to existing records. After populating the tables, validation of referential integrity is performed, including ensuring that all prescription records correspond to valid patients, doctors, drugs, and health plans.

Subsequently, the analytical phase involves creating and executing a series of queries tailored to specific informational needs:

  • Query 1: Lists all female patients alphabetically, including their personal details and allergies. This requires filtering by gender and sorting by last name.
  • Query 2: Displays patient-drug relationships, illustrating which drugs have been prescribed to which patients, without specific ordering.
  • Query 3: Presents insurance plan data, listing each plan with its associated members, grouped and sorted for clarity.
  • Query 4: Identifies prescriptions with zero refills remaining, providing insights into prescriptions possibly needing renewal.
  • Query 5: Extracts prescriptions dispensed within a specific date range, ordered by patient name and date for trend analysis.
  • Query 6: An arbitrarily designed query emphasizing complex multi-table joins, including fields from at least four tables, demonstrating advanced querying capabilities.

Throughout this process, adherence to best practices in database normalization, data validation, and query optimization ensures that the final system is reliable, scalable, and compliant with data protection standards. The completion of this project will significantly enhance record management, reduce manual errors, and support efficient data-driven decision-making in the pharmacy setting.

References

  • Chen, P. P. (2008). Database Theory and Design. McGraw-Hill Education.
  • Fundamentals of Database Systems. Pearson.
  • Rob, P., & Coronel, C. (2007). Database Systems: Design, Implementation, & Management. Cengage Learning.
  • Hoffer, J. A., Venkataraman, R., & Topi, H. (2016). Modern Database Management. Pearson.
  • Hipp, J., & Mishra, S. (2012). Healthcare Data Management: A Practical Approach. Springer.
  • Microsoft Access Documentation. (2020). Microsoft Support.
  • Health Level Seven International (HL7). (2019). Standards for Healthcare Data Exchange.
  • CDC. (2022). Guidelines for Healthcare Data Privacy and Security.
  • Schmidt, R., & Kuehn, M. (2018). Data analysis techniques in healthcare informatics. Journal of Medical Systems.
  • Smith, J. (2020). Effective database normalization practices. Information Systems Journal.

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