The bird's-eye view: A data-driven approach to understanding patient journeys from claims data

Abstract

Objective: In preference-sensitive conditions such as back pain, there can be high levels of variability in the trajectory of patient care. We sought to develop a methodology that extracts a realistic and comprehensive understanding of the patient journey using medical and pharmaceutical insurance claims data.

Materials and methods: We processed a sample of 10 000 patient episodes (comprised of 113 215 back pain-related claims) into strings of characters, where each letter corresponds to a distinct encounter with the healthcare system. We customized the Levenshtein edit distance algorithm to evaluate the level of similarity between each pair of episodes based on both their content (types of events) and ordering (sequence of events). We then used clustering to extract the main variations of the patient journey.

Results: The algorithm resulted in 12 comprehensive and clinically distinct patterns (clusters) of patient journeys that represent the main ways patients are diagnosed and treated for back pain. We further characterized demographic and utilization metrics for each cluster and observed clear differentiation between the clusters in terms of both clinical content and patient characteristics.

Discussion: Despite being a complex and often noisy data source, administrative claims provide a unique longitudinal overview of patient care across multiple service providers and locations. This methodology leverages claims to capture a data-driven understanding of how patients traverse the healthcare system.

Conclusions: When tailored to various conditions and patient settings, this methodology can provide accurate overviews of patient journeys and facilitate a shift toward high-quality practice patterns.

Keywords: Claims data; clustering; edit distance; patient journey; sequence alignment.

Figure 1.

Variation in the first 4 events of the patient back pain journey. Because back pain is a preference-sensitive condition, high variation exists in the first 6 months of the patient journey. The letters in this Sankey chart correspond to the event types displayed in Table 1. Of the patient back pain episodes, 74% contain 4 or fewer events; 89% are completed within 6 months.

Figure 2.

Example of sequence alignment. Our adaption of Levenshtein’s edit distance maximizes the total score of aligning 2 sequences using matches, substitutions, insertions, and transpositions. Because multiple possible alignments exist for any 2 strings, dynamic optimization is applied to maximize the sequence alignment score based on the given edit values.

Figure 3.

Sample of the n-by-n similarity matrix. The matrix is populated using the normalized similarity scores. The index [i, j] in the similarity matrix s corresponds to the similarity score between patient journey i and patient journey j. Note that diagonal entries all have a normalized similarity score of 1 (as a given patient journey is identical to itself), and the lower diagonal is a reflection of the upper diagonal scores (because $s_{i,j}=s_{j,i}$).

Figure 4.

Aggregating k-means results using ensemble clustering. A single-link method partitions the outputs from multiple iterations of k-means into the final patient journey clusters $C_n$. When the minimum threshold t is set to 90%, 2 clusters form: POWPO-POWPRO and GWGIGW-GXIGW; the other 4 patient journeys drop out as “noise.” When t = 70%, patient journeys are categorized into 1 of 3 clusters: GWGIGW-GXIGW-GXIW, POWPO-POWPRO-PPRW, or EOXW-EPOPW. When t = 50%, 2 clusters merge, resulting in 2 more heterogeneous clusters: POWPO-POWPRO-PPRW-EOXW-EPOPW and GWGIGW-GXIGW-GXIW.