The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools

Abstract

Objective: Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties.

Materials and methods: Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty.

Results: We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with 'abdominal pain' and 'gastroenteritis' accounting for approximately 3.5%.

Discussion: Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables.

Conclusions: Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition.

Keywords: ICD-10-CM; ICD-9-CM; billing complexity; motifs; networks; transition to ICD-10-CM.

Figure 1

ICD-9-CM to ICD-10-CM conversion: from bipartite mapping maps to insightful network motifs (see Methods section). The mapping of ICD-9-CM to ICD-10-CM and back yields complex networks that we simplified into elementary motifs represented in this figure. Seventy-five per cent of the ICD-9-CM codes are represented by the top seven mapping motifs. Importantly, 63% of ICD-9-CM codes occur in simple mapping motifs (motifs with no dashed arrows). Interestingly, 1% of ICD-9-CM codes have no corresponding ICD-10-CM codes. However, the remaining 36% of convoluted motifs (pink background, dashed arrows) are likely to be harder to understand for coders, clinicians, and managers. An ICD-9-CM mapping that proceeds via a convoluted motif leads to a complex interpretation of its corresponding ICD-10-CM code(s). Indeed, there is no straightforward way to query patient data across the ICD-9-CM and ICD-10-CM divide of convoluted motifs. Due to the non-reciprocal mappings, the majority of convoluted motifs are unbounded (dashed arrows). Blurred matrix cells contain no ICD-9-CM codes (legend; empty set). Each of the matrix cells comprises one or more mapping motifs that are further synthesized into five mapping categories utilized in figures 2–4 (background color, legend). Of note, the illustrated motifs represent 98.9% of the discovered ones.

Figure 2

From ICD-9-CM to ICD-10-CM mapping network to actionable categories of motifs. Using Center for Medicare and Medicaid Services ICD-9-CM to ICD-10-CM mapping tables, the full network of (A) illustrates the complexity of mappings attributable to mappings (lines) between ICD-9-CM (blue circles) and ICD-10-CM (purple circles). In (A), large purple networks correspond to thousands of ICD-10-CM codes associated with a single ICD-9-CM code, while large blue networks are the converse. In addition, the mappings are not reciprocal leading to entanglements between the meanings of different codes (figure 1). Twenty-seven distinct patterns of mapping motifs (figure 1, background color) were observed and classified into five mapping categories organized by increasing complexity (B, first column) each category has a specific color scheme (B, fifth column) utilized in the background of figure 1 and the bar graph of figures 3 and 4. The abbreviation, Mapp., refers to mapping. Each mapping category is illustrated with an example (A, B, columns 3 and 4). The examples of the two last categories demonstrate the difficulties that may arise from interpreting data collected in ICD-9-CM or in ICD-10-CM, which may affect a clinical practice beyond billing practices. For example, the concept of ‘Accidental poisoning by unspecified drug’ does not exist anymore in ICD-10-CM, where emergency department physicians will be required to specify the drug category, which requires a certainty not reflecting clinical practice.

Figure 3

Discrimination by clinical specialty. Furthermore, clinical specialty is unequally impacted as shown with the percentage of ICD-9-CM codes per mapping category (color coding of the bars from figure 2B, column 5). Clinical classes with a larger proportion of convoluted network motifs and higher ICD-10-CM to ICD-9-CM codes ratios are most likely to be affected by the transition. Mapping categories range from simple (identity) to convoluted, and are used as a proxy to estimate the impact of ICD-10-CM transition to clinical practice. Convoluted and no mapping will incur disproportionally more costs than simple motifs of mappings due to the inability to compare clinical practice before and after transition using ICD codes. In addition, a ratio was calculated comparing the number of total codes per clinical class (figure 3, rightmost column [#ICD-10-CM]/[#ICD-9-CM]). ‘Injury and poisoning's’ outstandingly high ratio is highlighted in yellow).

Figure 4

Case study: identifying ICD-10-CM conversion challenges in 24 000 clinical encounters in 217 emergency departments. (A) The convoluted mapping categories correspond to approximately 27% of the emergency department (ED) costs, encounters and codes, increasing the risk of inaccuracies and errors and has significant implications on the data reliability pre and post-ICD-10-CM transition; 31% of the billed ED codes were convoluted and corresponded to 28% of visits and 27% of costs, while 56% of codes were the less complex mapping motifs (blue and purple) which correspond to 57% of encounters and 60% of costs. Interestingly, there was a 3.6% decrease of ED payments for encounters coding to convoluted mapping category and an increase of 5.2% for those associated witho less complex mapping categories. There is no inherent inconsistency of the payment variations because complexity of mapping from ICD-9-CM to ICD-10-CM is not associated with the amounts of diagnoses payments. (B) Example of convoluted mapping in the ED: ‘Abdominal pain’ with associated cost data. Of note, Center for Medicare and Medicaid Services mapping confounds mappings of male and female genital symptoms (ICD-9-CM) with abdominal pain location (ICD-10-CM). Post-transition, gender-specific information will be required in addition to the ICD codes for inventory management of speculum. (C) Example of convoluted mapping in the ED: ‘diarrhea’ and ‘non-infection gastroenteritis’ are confounded in ICD-10-CM with implication for infectious disease protocols and inventories (eg, culture sampling, disposable isolation supplies).