Epic Skills Assessment Algorithmic Problem Solving Questions and Answers Flashcards
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An Epic integration engine must match incoming HL7 messages to patient records using fuzzy string matching. Which algorithm is commonly used to measure the edit distance between two patient names?
Answer: Levenshtein distance algorithm
The Levenshtein distance algorithm calculates the minimum number of single-character edits needed to transform one string into another, making it ideal for fuzzy name matching.
When optimizing an Epic report that joins multiple large datasets, which algorithmic concept explains why breaking the problem into smaller sorted subsets before merging improves performance?
Answer: Divide and conquer
Divide and conquer splits a problem into smaller subproblems, solves them independently, and merges results, which is the principle behind efficient merge-based joins.
In Epic's Best Practice Alert system, an algorithm must evaluate multiple clinical rules against patient data in real time. What is the time complexity concern when rules have overlapping conditions?
Answer: O(n * m) where n is rules and m is conditions per rule
Evaluating n rules each with m conditions results in O(n * m) complexity, which becomes a performance concern as both rules and conditions scale.
An algorithm must detect circular referral patterns in Epic where Provider A refers to B, B refers to C, and C refers back to A. Which technique is most appropriate?
Answer: Cycle detection using DFS
Cycle detection using depth-first search can identify back edges in a directed graph, which indicate circular referral patterns.
When building a predictive algorithm in Epic that must consider the optimal subset of clinical features for patient risk scoring, which approach avoids evaluating all possible feature combinations?
Answer: Greedy feature selection
Greedy feature selection iteratively adds or removes features based on performance improvement, avoiding the exponential cost of evaluating all subsets.
An Epic algorithm processes a stream of lab results and must maintain a running median for trend analysis. Which combination of data structures most efficiently supports this?
Answer: Two heaps: a max-heap and a min-heap
Maintaining a max-heap for the lower half and a min-heap for the upper half allows O(log n) insertion and O(1) median retrieval from a data stream.