MCQ Quiz: Data Science in Pharmacy

Data science is a multidisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from health data to drive decision-making. For pharmacists, this involves leveraging skills from health informatics, biostatistics, and pharmacoepidemiology to improve patient outcomes, optimize pharmacy operations, and advance public health.and advance public health. This quiz will test your knowledge on the core concepts and applications of data science in pharmacy practice.

1. Data science in pharmacy is best described as:

• a. The practice of compounding medications.
• b. The use of data, algorithms, and systems to solve complex problems in the medication-use process.
• c. The study of pharmacy law and ethics.
• d. The process of dispensing prescriptions.

Answer: b. The use of data, algorithms, and systems to solve complex problems in the medication-use process.

2. The foundational element of any data science project in healthcare is:

• a. A complex algorithm.
• b. A powerful computer.
• c. High-quality, well-structured data.
• d. A team of software developers.

Answer: c. High-quality, well-structured data.

3. A pharmacist using a patient’s electronic health record (EHR) is interacting with what type of data?

• a. Pre-clinical data
• b. Anecdotal data
• c. Real-World Data (RWD)
• d. Financial data only

Answer: c. Real-World Data (RWD)

4. A machine learning model that predicts a patient’s risk of hospital readmission based on their past clinical history is an example of:

• a. Descriptive analytics
• b. Diagnostic analytics
• c. Predictive analytics
• d. Prescriptive analytics

Answer: c. Predictive analytics

5. A major challenge in using EHR data for data science is that much of the most valuable clinical information is in the form of:

• a. Structured lab values.
• b. Unstructured free-text notes.
• c. Billing codes.
• d. Demographic information.

Answer: b. Unstructured free-text notes.

6. The field of study focused on analyzing drug effects in large populations, which is a core application of data science, is called:

• a. Medicinal Chemistry
• b. Pharmaceutics
• c. Pharmacogenomics
• d. Pharmacoepidemiology

Answer: d. Pharmacoepidemiology

7. “Natural Language Processing” (NLP) is a data science technique used to:

• a. Analyze numerical lab data.
• b. Extract meaningful information from human language (e.g., progress notes).
• c. Optimize pharmacy workflow.
• d. Predict drug shortages.

Answer: b. Extract meaningful information from human language (e.g., progress notes).

8. The principle of “Garbage In, Garbage Out” means that a data science model:

• a. Can fix any errors in the input data.
• b. Will produce unreliable results if trained on poor-quality data.
• c. Is only as good as its algorithm.
• d. Requires a large amount of garbage data to function.

Answer: b. Will produce unreliable results if trained on poor-quality data.

9. A pharmacist contributes to the quality of healthcare data by:

• a. Performing accurate medication reconciliation.
• b. Documenting interventions clearly.
• c. Verifying patient allergies.
• d. All of the above.

Answer: d. All of the above.

10. A hospital uses a data-driven algorithm to identify patients at high risk for opioid-induced respiratory depression. This is an application of data science in:

• a. Patient safety and risk stratification.
• b. Inventory management.
• c. Drug discovery.
• d. Billing.

Answer: a. Patient safety and risk stratification.

11. The principles of evidence-based practice are fundamental to interpreting the output of data science models.

• a. True
• b. False

Answer: a. True

12. In “supervised machine learning,” the model learns from a dataset that is:

• a. Unlabeled
• b. Labeled with the correct outcomes/answers
• c. Small and incomplete
• d. Random

Answer: b. Labeled with the correct outcomes/answers

13. A key ethical consideration in healthcare data science is:

• a. Ensuring patient privacy and data security.
• b. Preventing algorithmic bias against certain populations.
• c. Transparency in how models make decisions.
• d. All of the above.

Answer: d. All of the above.

14. A pharmacist using a dashboard that shows real-time adherence rates for patients on a specific medication is using:

• a. Predictive analytics
• b. Descriptive analytics
• c. Prescriptive analytics
• d. A crystal ball

Answer: b. Descriptive analytics

15. “Health information and informatics” is a key foundational topic for data science in pharmacy.

• a. True
• b. False

Answer: a. True

16. Which of the following is NOT a typical source of Real-World Data used in pharmacy data science?

• a. Insurance claims data
• b. Patient social media posts
• c. Data from a randomized controlled trial
• d. Electronic Health Records

Answer: c. Data from a randomized controlled trial

17. “Unsupervised learning” is a machine learning technique used to:

• a. Predict a specific, known outcome.
• b. Discover hidden patterns or structures in unlabeled data (e.g., clustering).
• c. Train a model through rewards and penalties.
• d. Verify prescription accuracy.

Answer: b. Discover hidden patterns or structures in unlabeled data (e.g., clustering).

18. The pharmacist’s role in the age of data science will increasingly involve:

• a. Less clinical judgment.
• b. Interpreting complex data to make individualized patient care decisions.
• c. Only manual tasks.
• d. Writing computer code.

Answer: b. Interpreting complex data to make individualized patient care decisions.

19. A data science model that suggests the optimal next-line therapy for a patient based on their unique characteristics is an example of:

• a. Prescriptive analytics
• b. Descriptive analytics
• c. Diagnostic analytics
• d. A simple alert

Answer: a. Prescriptive analytics

20. The “Medication Safety” module in the curriculum covers informatics tools that are applications of data science.

• a. True
• b. False

Answer: a. True

21. A pharmacy can use data science to optimize its inventory by:

• a. Ordering one of every medication.
• b. Predicting seasonal demand and identifying slow-moving stock.
• c. Letting the stock run out before reordering.
• d. Asking physicians what to order.

Answer: b. Predicting seasonal demand and identifying slow-moving stock.

22. A major source of bias in healthcare data science models is:

• a. Using too much data.
• b. Using training data that does not accurately represent the diversity of the patient population.
• c. Using algorithms that are too complex.
• d. Using data that is too clean.

Answer: b. Using training data that does not accurately represent the diversity of the patient population.

23. “Data interoperability” refers to the ability of:

• a. A single computer system to store data.
• b. Different computer systems and applications to communicate and exchange data effectively.
• c. Patients and providers to communicate.
• d. A pharmacist to use a computer.

Answer: b. Different computer systems and applications to communicate and exchange data effectively.

24. The field of pharmacogenomics relies on data science to:

• a. Analyze genetic and clinical data to predict drug response.
• b. Determine the cost of genetic testing.
• c. Dispense medications.
• d. Manage side effects.

Answer: a. Analyze genetic and clinical data to predict drug response.

25. A pharmacist’s ability to critically appraise a study is a skill that is _________ when evaluating a study based on data science.

• a. irrelevant
• b. essential
• c. outdated
• d. optional

Answer: b. essential

26. Which of the following is an example of “structured data” in an EHR?

• a. A physician’s free-text progress note.
• b. A lab value for serum creatinine entered into a specific field.
• c. A scanned PDF of an outside hospital record.
• d. A dictated operative report.

Answer: b. A lab value for serum creatinine entered into a specific field.

27. A key benefit of using data science in population health is the ability to:

• a. Treat individual patients only.
• b. Identify at-risk populations and proactively deliver interventions.
• c. Focus only on healthy individuals.
• d. Reduce the need for pharmacists.

Answer: b. Identify at-risk populations and proactively deliver interventions.

28. The use of “automated systems” to decrease medication errors is an application of data-driven processes.

• a. True
• b. False

Answer: a. True

29. A pharmacist is involved in a project that uses machine learning to analyze clinical notes to identify patients experiencing unreported adverse drug events. This is an application of:

• a. NLP and pharmacovigilance
• b. Inventory management
• c. Drug compounding
• d. Financial analysis

Answer: a. NLP and pharmacovigilance

30. The “Principles of Personalized Medicine” are heavily reliant on data science.

• a. True
• b. False

Answer: a. True

31. The term “algorithm” in data science refers to:

• a. A type of medication.
• b. A set of rules or instructions to be followed in calculations or other problem-solving operations.
• c. A physical computer chip.
• d. A patient’s care plan.

Answer: b. A set of rules or instructions to be followed in calculations or other problem-solving operations.

32. A data-driven approach to antimicrobial stewardship might involve:

• a. Using algorithms to analyze local antibiograms and patient data to recommend the optimal empiric antibiotic.
• b. Prescribing broad-spectrum antibiotics for every infection.
• c. Never using antibiotics.
• d. Using data from other countries to guide local therapy.

Answer: a. Using algorithms to analyze local antibiograms and patient data to recommend the optimal empiric antibiotic.

33. What is the most important skill for a pharmacist in a data-rich healthcare environment?

• a. The ability to type quickly.
• b. The ability to critically think and apply clinical context to data-driven insights.
• c. The ability to write complex code.
• d. The ability to sell retail products.

Answer: b. The ability to critically think and apply clinical context to data-driven insights.

34. The “black box” nature of some AI models presents a challenge for:

• a. Clinical explainability and trust.
• b. Ease of use.
• c. Data storage.
• d. Computer speed.

Answer: a. Clinical explainability and trust.

35. A key role for pharmacists is to ensure that the implementation of data science tools:

• a. Increases the number of medication errors.
• b. Improves patient safety and the quality of care.
• c. Makes the pharmacist’s job more difficult.
• d. Replaces all human interaction.

Answer: b. Improves patient safety and the quality of care.

36. A dashboard that visualizes medication adherence trends across a patient population is a product of:

• a. Data science
• b. Guesswork
• c. A single pharmacist’s opinion
• d. A manual chart review

Answer: a. Data science

37. Which of the following is NOT a primary component of data science?

• a. Computer science/IT
• b. Statistics and mathematics
• c. Domain expertise (e.g., pharmacy/clinical knowledge)
• d. Marketing and sales

Answer: d. Marketing and sales

38. The curriculum’s focus on evidence-based practice provides the skills needed to evaluate data science applications.

• a. True
• b. False

Answer: a. True

39. A “predictive model” in data science is designed to:

• a. Describe what has happened in the past.
• b. Forecast what is likely to happen in the future.
• c. Explain why something happened.
• d. Prescribe a specific action.

Answer: b. Forecast what is likely to happen in the future.

40. A pharmacist’s active participation is crucial for the success of data science projects related to the medication-use system.

• a. True
• b. False

Answer: a. True

41. What is the most likely application of data science in a community pharmacy setting?

• a. Identifying patients who are non-adherent or due for a vaccine.
• b. Performing genomic sequencing.
• c. Developing new drug molecules.
• d. Managing inpatient insulin drips.

Answer: a. Identifying patients who are non-adherent or due for a vaccine.

42. The quality of a pharmacist’s documentation in the EHR directly impacts the quality of the data available for analysis.

• a. True
• b. False

Answer: a. True

43. Which of the following is an ethical imperative when using patient data for data science projects?

• a. To share the data publicly.
• b. To de-identify the data to protect patient privacy.
• c. To use the data for marketing purposes only.
• d. To ignore all regulations.

Answer: b. To de-identify the data to protect patient privacy.

44. The use of data science in pharmacy will likely lead to a practice model that is more:

• a. Reactive
• b. Proactive and preventative
• c. Product-focused
• d. Inefficient

Answer: b. Proactive and preventative

45. What is the biggest risk of a biased algorithm in healthcare?

• a. It might make the computer run slower.
• b. It might perpetuate or worsen health disparities for certain patient populations.
• c. It will be too expensive.
• d. It will be difficult to program.

Answer: b. It might perpetuate or worsen health disparities for certain patient populations.

46. A data-driven approach allows a pharmacy department to:

• a. Justify the value of its clinical services.
• b. Identify areas for improvement.
• c. Allocate resources more effectively.
• d. All of the above.

Answer: d. All of the above.

47. The future pharmacist will need to be comfortable working alongside:

• a. Only other pharmacists.
• b. Only physicians.
• c. Data-driven tools and AI-powered systems.
• d. Robots exclusively.

Answer: c. Data-driven tools and AI-powered systems.

48. Health informatics is a foundational field for health data science.

• a. True
• b. False

Answer: a. True

49. The overall goal of applying data science to pharmacy is to:

• a. Create more data.
• b. Use data to generate actionable insights that improve patient care and medication safety.
• c. Replace all pharmacists with computers.
• d. Make healthcare more technologically complex for its own sake.

Answer: b. Use data to generate actionable insights that improve patient care and medication safety.

50. The ultimate reason for a student pharmacist to learn about data science is to:

• a. Become a computer programmer.
• b. Be prepared to practice effectively in a future healthcare system that is increasingly data-driven.
• c. Pass an elective course.
• d. Be able to analyze their own social media data.

Answer: b. Be prepared to practice effectively in a future healthcare system that is increasingly data-driven.