The complexity and challenges of the International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, 10th Revision, Clinical Modification transition in EDs.

Beginning October 2015, the Center for Medicare and Medicaid Services will require medical providers to use the vastly expanded International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) system. Despite wide availability of information and mapping tools for the next generation of the ICD classification system, some of the challenges associated with transition from ICD-9-CM to ICD-10-CM are not well understood. To quantify the challenges faced by emergency physicians, we analyzed a subset of a 2010 Illinois Medicaid database of emergency department ICD-9-CM codes, seeking to determine the accuracy of existing mapping tools in order to better prepare emergency physicians for the change to the expanded ICD-10-CM system. We found that 27% of 1830 codes represented convoluted multidirectional mappings. We then analyzed the convoluted transitions and found that 8% of total visit encounters (23% of the convoluted transitions) were clinically incorrect. The ambiguity and inaccuracy of these mappings may impact the workflow associated with the translation process and affect the potential mapping between ICD codes and Current Procedural Codes, which determine physician reimbursement.

ICD-10 procedure codes produce transition challenges.

The transition of procedure coding from ICD-9-CM-Vol-3 to ICD-10-PCS has generated problems for the medical community at large resulting from the lack of clarity required to integrate two non-congruent coding systems. We hypothesized that quantifying these issues with network topology analyses offers a better understanding of the issues, and therefore we developed solutions (online tools) to empower hospital administrators and researchers to address these challenges. Five topologies were identified: "identity"(I), "class-to-subclass"(C2S), "subclass-toclass"(S2C), "convoluted(C)", and "no mapping"(NM). The procedure codes in the 2010 Illinois Medicaid dataset (3,290 patients, 116 institutions) were categorized as C=55%, C2S=40%, I=3%, NM=2%, and S2C=1%. Majority of the problematic and ambiguous mappings (convoluted) pertained to operations in ophthalmology cardiology, urology, gyneco-obstetrics, and dermatology. Finally, the algorithms were expanded into a user-friendly tool to identify problematic topologies and specify lists of procedural codes utilized by medical professionals and researchers for mitigating error-prone translations, simplifying research, and improving quality.http://www.lussiergroup.org/transition-to-ICD10PCS.

Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?

BACKGROUND: Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the physician perspective via the physician-authored discharge summary, but not nurse documentation. Prior research in this area has focused on collaboration and the usage of similar terminology. OBJECTIVE: The objective of the study is to gain insight into interprofessional care by developing a computational metric to identify similarities, related concepts and differences in physician and nurse work. METHODS: 58 physician discharge summaries and the corresponding nurse plans of care were transformed into Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). MedLEE, a Natural Language Processing (NLP) program, extracted "physician terms" from free-text physician summaries. The nursing plans of care were constructed using the HANDS© nursing documentation software. HANDS© utilizes structured terminologies: nursing diagnosis (NANDA-I), outcomes (NOC), and interventions (NIC) to create "nursing terms". The physician's and nurse's terms were compared using the UMLS network for relatedness, overlaying the physician and nurse terms for comparison. Our overarching goal is to provide insight into the care, by innovatively applying graph algorithms to the UMLS network. We reveal the relationships between the care provided by each professional that is specific to the patient level. RESULTS: We found that only 26% of patients had synonyms (identical UMLS CUIs) between the two professions' documentation. On average, physicians' discharge summaries contain 27 terms and nurses' documentation, 18. Traversing the UMLS network, we found an average of 4 terms related (distance less than 2) between the professions, leaving most concepts as unrelated between nurse and physician care. CONCLUSION: Our hypothesis that physician's and nurse's practice domains are markedly different is supported by the preliminary, quantitative evidence we found. Leveraging the UMLS network and graph traversal algorithms, allows us to compare and contrast nursing and physician care on a single patient, enabling a more complete picture of patient care. We can differentiate professional contributions to patient outcomes and related and divergent concepts by each profession.

Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM.

In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as "convoluted" by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: "identity" (reciprocal), "class-to-subclass," "subclass-to-class," "convoluted," or "no mapping." These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible.Web portal: http://www.lussierlab.org/transition-to-ICD9CM/Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9.

Identifying clinically disruptive International Classification of Diseases 10th Revision Clinical Modification conversions to mitigate financial costs using an online tool.

PURPOSE: To quantify coding ambiguity in International Classification of Diseases Ninth Revision Clinical Modification conversions (ICD-9-CM) to ICD-10-CM mappings for hematology-oncology diagnoses within an Illinois Medicaid database and an academic cancer center database (University of Illinois Cancer Center [UICC]) with the goal of anticipating challenges during ICD-10-CM transition. METHODS: One data set of ICD-9-CM diagnosis codes came from the 2010 Illinois Department of Medicaid, filtered for diagnoses generated by hematology-oncology providers. The other data set of ICD-9-CM diagnosis codes came from UICC. Using a translational methodology via the Motif Web portal ICD-9-CM conversion tool, ICD-9-CM to ICD-10-CM code conversions were graphically mapped and evaluated for clinical loss of information. RESULTS: The transition to ICD-10-CM led to significant information loss, affecting 8% of total Medicaid codes and 1% of UICC codes; 39 ICD-9-CM codes with information loss accounted for 2.9% of total Medicaid reimbursements and 5.3% of UICC billing charges. CONCLUSION: Prior work stated hematology-oncology would be the least affected medical specialty. However, information loss affecting 5% of billing costs could evaporate the operating margin of a practice. By identifying codes at risk for complex transitions, the analytic tools described can be replicated for oncology practices to forecast areas requiring additional training and resource allocation. In summary, complex transitions and diagnosis codes associated with information loss within clinical oncology require additional attention during the transition to ICD-10-CM.

COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p<0.004). Therefore, we suggest that network topology is a scalable and generalizable method predictive of hospitalization.

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

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.