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I started with the obvious ones: name, date of birth, SSN, MRN.
Start by framing the problem: duplicate detection is about identifying records that refer to the same real-world patient. Then discuss a tiered approach using a combination of identifiers, demographic attributes, and fuzzy matching, explaining why each attribute matters and its trade-offs.
Pro tip: Mention that no single attribute is perfect, so a weighted scoring system with thresholds is often used, and that you should consider the cost of false positives vs. false negatives in a healthcare context.
Discuss attributes like SSN, MRN, or insurance ID that are designed to be unique. Explain that they are strong signals but may be missing or have errors.
Cover name, date of birth, address, phone number, and gender. Explain how these can be combined and that some are more stable than others (e.g., DOB vs. address).
Explain that names and addresses can have variations (typos, nicknames, formatting), so fuzzy matching techniques like Levenshtein distance or phonetic algorithms are needed.
Discuss how the importance of each attribute depends on data quality and the cost of false positives (merging different patients) vs. false negatives (missing duplicates).
Suggest a weighted scoring approach where each attribute contributes to a similarity score, and a threshold determines if records are duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part where I felt most out of my depth.
Start by framing the problem as a classic record linkage pipeline: first apply deterministic rules for high-precision matches, then use probabilistic scoring for the rest. Walk through each stage—blocking, fuzzy similarity, weighted scoring—and explain how you'd calibrate thresholds and rank candidates by confidence. Emphasize trade-offs between precision and recall, and how you'd validate with labeled data.
Pro tip: Mention that you'd treat the confidence score as a calibrated probability (e.g., via logistic regression or Platt scaling) so thresholds can be set based on business cost of false positives vs false negatives. This shows you understand that ranking is not just about raw scores but about decision-making under uncertainty.
Apply exact-match rules on high-confidence identifiers (e.g., SSN, email, exact name+DOB) to catch obvious duplicates with 100% precision. These matches bypass scoring and are auto-merged or flagged as high confidence.
Partition records into blocks using one or more keys (e.g., first 3 letters of last name, zip code) to reduce the comparison space. Explain that blocking keys should be chosen to balance recall (not missing true matches) and efficiency.
For each candidate pair within a block, compute similarity scores on individual attributes using metrics like Jaro-Winkler, Levenshtein, or token-based (e.g., Jaccard) for names and addresses. Normalize scores to a 0-1 range.
Combine attribute-level similarities into an overall match score using weights that reflect each attribute's discriminative power (e.g., name weight > address weight). Weights can be learned via logistic regression on labeled pairs or set by domain experts.
Rank candidate pairs by their composite score and apply thresholds to classify as match, non-match, or review. Use precision-recall curves to choose thresholds that align with business goals (e.g., minimize false merges in healthcare).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said you set a high precision threshold for auto-merging and route borderline cases to a review queue.
Start by defining what a false positive means in the context of duplicate detection and its impact on user experience and data quality. Then, outline a multi-layered strategy combining algorithmic improvements (e.g., threshold tuning, feature engineering) and human-in-the-loop review, emphasizing trade-offs between precision and recall. Conclude by explaining how human review can be integrated efficiently to continuously improve the system.
Pro tip: Frame the discussion around the business context: in healthcare, false positives can lead to patient safety issues or data integrity problems, so it's crucial to balance automation with human oversight. Mention that human review should focus on borderline cases and provide feedback to retrain models, creating a virtuous cycle.
Clarify what constitutes a false positive in duplicate detection (e.g., incorrectly flagging two distinct records as duplicates) and discuss the consequences such as user frustration, data loss, or compliance risks.
Describe techniques like adjusting similarity thresholds, using ensemble methods, incorporating domain-specific features, and leveraging active learning to improve model precision.
Explain how to route ambiguous cases to human reviewers, set up a queue with prioritization, and provide tools for efficient decision-making (e.g., side-by-side comparison).
Detail how human decisions are fed back into the model as labeled data to retrain and refine the algorithm, reducing future false positives.
Discuss metrics to track (precision, recall, human review rate) and how to monitor system performance over time to ensure false positives remain low.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about edit distance for typos, address normalization via a third-party service, and treating missing fields as a soft miss rather than a hard disqualifier.
Start by clarifying the specific system and data flow, then walk through a layered strategy: input normalization, fuzzy matching, validation, and fallback mechanisms. Emphasize how you balance accuracy, performance, and user experience, and mention any trade-offs or metrics you'd track.
Pro tip: Show that you think about ambiguity not just as a technical problem but also as a product and compliance issue—especially in healthcare where incorrect data can have serious consequences. Mention how you'd surface low-confidence matches to users for confirmation rather than silently guessing.
Ask about the specific data types (e.g., addresses, drug names), volume, latency requirements, and tolerance for errors. This shows you tailor solutions to constraints.
Explain how you standardize inputs: lowercasing, trimming, removing punctuation, expanding abbreviations, and using libraries like libpostal for addresses.
Discuss algorithms like Levenshtein distance, Jaro-Winkler, or phonetic matching (Soundex, Metaphone) for typos, and rule-based or ML-based validation for missing fields.
Describe how you handle low-confidence matches: suggest alternatives, ask for user confirmation, or log for manual review. Mention continuous improvement via feedback.
Talk about tracking metrics like match rate, false positive rate, and latency, and how you'd iterate on thresholds and models based on real-world data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Queue it for review, mark it as suspected duplicate, or prompt for additional input at the point of care.
Start by clarifying what 'low confidence' means in the context of the system (e.g., a match score below a threshold) and the potential impact of a wrong match. Then, propose a tiered response strategy that balances automation with human oversight, and discuss how to measure and improve confidence over time.
Pro tip: Emphasize that low-confidence matches should be treated as opportunities for learning—log them, analyze patterns, and feed them back into the model or rules to reduce future ambiguity. This shows you think beyond immediate fixes and consider long-term system health.
Establish a clear threshold or range for low confidence and assess the risk of false positives vs. false negatives in the specific domain (e.g., patient data matching at Blink Health).
Decide on a safe default behavior, such as flagging for manual review, requesting additional data, or returning a 'no match' result, based on the cost of errors.
If manual review is chosen, outline how to route low-confidence cases to human experts, provide them with context, and capture their decisions to improve the system.
Set up logging and metrics to track low-confidence occurrences, their outcomes, and use that data to refine confidence thresholds or model retraining.
If the system interacts with users, design UI/UX to transparently convey low confidence (e.g., 'We're not sure—please verify') to set appropriate expectations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the strangest part of the whole thing.
Start by clarifying the plan's goals and constraints, then break it into small, independently verifiable tasks with clear prompts and quality gates. Emphasize iterative development, automated verification, and human review at critical points to ensure correctness and maintainability.
Pro tip: Treat the AI assistant as a junior engineer: provide precise context, define 'done' criteria upfront, and always include a rollback or fallback plan for each task. This shows you balance speed with safety, a key trait for high-stakes healthcare software.
Understand the overall goal, constraints (e.g., compliance, performance), and what 'done' looks like for the entire plan. Identify key milestones and risks.
Break the plan into small tasks that can be completed and verified independently. Each task should have a clear input, output, and acceptance criteria.
For each task, write a prompt that includes context, specific requirements, examples, and constraints. Use iterative prompting to refine outputs.
For each task, specify automated checks (e.g., unit tests, linters, type checks) and manual reviews (e.g., code review, security scan) that must pass before proceeding.
Run tasks in sequence, verify outputs at each gate, and integrate successful results. Adjust prompts and tasks based on feedback and failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.