Jianrong John Li

Bio: Jianrong John Li is an academic researcher from University of Arizona. The author has contributed to research in topics: Diagnosis code & Poison control. The author has an hindex of 5, co-authored 5 publications receiving 87 citations. Previous affiliations of Jianrong John Li include University of Illinois at Chicago.

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

Abstract: 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.

Identifying Clinically Disruptive International Classification of Diseases 10th Revision Clinical Modification Conversions to Mitigate Financial Costs Using an Online Tool

Abstract: Complex transitions and diagnosis codes associated with information loss within clinical oncology require additional attention during the transition to ICD-10-CM.

ICD-10 procedure codes produce transition challenges.

Abstract: 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.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Abstract: Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Development and Evaluation of a New Frailty Index for Older Surgical Patients With Cancer.

Abstract: Importance Frailty based on the modified Frailty Index is associated with poor postoperative outcomes. However, the index requires high levels of personnel time and effort and often has missing data. Objective To evaluate the association of the Memorial Sloan Kettering–Frailty Index (MSK-FI) with established geriatric assessment (GA) and surgical outcomes. Design, Setting, and Participants This cohort study included prospectively evaluated patients with cancer 75 years and older who were referred to MSK Geriatrics Service clinics for preoperative evaluation before undergoing surgery requiring hospitalization between February 2015 and September 2017. Patients were comanaged by the Geriatrics Service and Surgery Service in the postoperative period. Exposures Impairments identified by GA and comorbid conditions retrieved from submittedInternational Classification of Diseases, Ninth Revision (ICD-9) andICD-10codes within the first 48 hours of hospitalization. Main Outcomes and Measures The association of MSK-FI score (which includedICD-9andICD-10codes) with GA impairments (based on clinical interview and examination as well as patient reports) was examined. The associations of MSK-FI score with short-term surgical outcomes (ie, frequency of complications, length of stay, 30-day surgical complications, 30-day intensive care unit admissions, and 30-day readmissions) and 1-year survival, estimated by Kaplan-Meier methods, were determined. Results In total, 1137 patients (median [interquartile range] age, 80 [77-84] years; 583 [51.2%] women) were included in the study. A higher MSK-FI score was associated with the number of GA impairments (ρ = 0.52; bootstrapped 95% CI, 0.47-0.56). Each 1-point increase in MSK-FI score was associated with longer length of stay (0.58 d; 95% CI, 0.22-0.95;P = .002) and higher odds of intensive care unit admission (odds ratio, 1.28; 95% CI, 1.04-1.58;P = .02). Median (interquartile range) follow-up among survivors was 12.1 (5.6-19.1) months. The MSK-FI score was associated with overall mortality; 12-month risk of death was 5% for a score of 0 and approximately 20% for scores of 4 and higher (nonlinear association,P = .005). Conclusions and Relevance In this study, the MSK-FI was associated with the previously validated GA and postoperative outcomes in older patients with cancer and may be a feasible tool for perioperative assessment of older surgical patients with cancer. Future studies should assess the association of MSK-FI score with postoperative care and outcomes of older, frail patients with cancer.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario.

Abstract: Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.

Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions

Abstract: Background: The national mandate for health systems to transition from ICD-9-CM to ICD-10-CM in October 2015 has an impact on research activities. Clinical phenotypes defined by ICD-9-CM codes need to be converted to ICD-10-CM, which has nearly four times more codes and a very different structure than ICD-9-CM. Methods: We used the Centers for Medicare & Medicaid Services (CMS) General Equivalent Maps (GEMs) to translate, using four different methods, condition-specific ICD-9-CM code sets used for pragmatic trials (n=32) into ICD-10-CM. We calculated the recall, precision, and F‑score of each method. We also used the ICD-9-CM and ICD-10-CM value sets defined for electronic quality measure as an additional evaluation of the mapping methods. Results: The forward-backward mapping (FBM) method had higher precision, recall and F‑score metrics than simple forward mapping (SFM). The more aggressive secondary (SM) and tertiary mapping (TM) methods resulted in higher recall but lower precision. For clinical phenotype definition, FBM was the best (F=0.67), but was close to SM (F=0.62) and TM (F=0.60), judging on the F‑scores alone. The overall difference between the four methods was statistically significant (one-way ANOVA, F=5.749, p=0.001). However, pairwise comparisons between FBM, SM, and TM did not reach statistical significance. A similar trend was found for the quality measure value sets. Discussion: The optimal method for using the GEMs depends on the relative importance of recall versus precision for a given use case. It appears that for clinically distinct and homogenous conditions, the recall of FBM is sufficient. The performance of all mapping methods was lower for heterogeneous conditions. Since code sets used for phenotype definition and quality measurement can be very similar, there is a possibility of cross-fertilization between the two activities. Conclusion: Different mapping approaches yield different collections of ICD-10-CM codes. All methods require some level of human validation.

Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements.

Abstract: Objectives To provide an overview of statewide hospital discharge databases (HDD), including their uses in health services research and limitations, and to describe Agency for Healthcare Research and Quality (AHRQ) Enhanced State Data grants to address clinical and race–ethnicity data limitations. Principal Findings Almost all states have statewide HDD collected by public or private data organizations. Statewide HDD, based on the hospital claim with state variations, contain useful core variables and require minimal collection burden. AHRQ's Healthcare Cost and Utilization Project builds uniform state and national research files using statewide HDD. States, hospitals, and researchers use statewide HDD for many purposes. Illustrating researchers' use, during 2012–2014, HSR published 26 HDD-based articles on health policy, access, quality, clinical aspects of care, race–ethnicity and insurance impacts, economics, financing, and research methods. HDD have limitations affecting their use. Five AHRQ grants focused on enhancing clinical data and three grants aimed at improving race–ethnicity data. Conclusion ICD-10 implementation will significantly affect the HDD. The AHRQ grants, information technology advances, payment policy changes, and the need for outpatient information may stimulate other statewide HDD changes. To remain a mainstay of health services research, statewide HDD need to keep pace with changing user needs while minimizing collection burdens.