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 (original) (raw)
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The Complexity and Challenges of the ICD-9-CM to ICD-10-CM Transition in Emergency Departments
The American Journal of Emergency Medicine, 2015
Beginning October 2015, the Center for Medicare and Medicaid Services (CMS) will require medical providers to utilize the vastly expanded 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 1,830 codes represented convoluted multidirectional mappings. We then analyzed the convoluted transitions and found 8% of total visit encounters (23% of the convoluted transitions) were clinically incorrect. The ambiguity and inaccuracy of these mappings may impact the work flow associated with the translation process and affect the potential mapping between ICD codes and CPT (Current Procedural Codes) codes, which determine physician reimbursement.
Simulation of ICD-9 to ICD-10-CM Transition for Family Medicine: Simple or Convoluted?
Journal of the American Board of Family Medicine, 2016
The objective of this study was to examine the impact of the transition from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), to Interactional Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), on family medicine and to identify areas where additional training might be required. Methods: Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million in claims). Using the science of networks, we evaluated each ICD-9-CM code used by family medicine physicians to determine whether the transition was simple or convoluted. A simple transition is defined as 1 ICD-9-CM code mapping to 1 ICD-10-CM code, or 1 ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is nonreciprocal and complex, with multiple codes for which definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy. Results: Of the 1635 diagnosis codes used by family medicine physicians, 70% of the codes were categorized as simple, 27% of codes were convoluted, and 3% had no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims was similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only <0.1% of the overall diagnosis codes. Conclusions: The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, and for which additional resources need to be invested to ensure a successful transition to ICD-10-CM.
Perspectives in health information management / AHIMA, American Health Information Management Association, 2012
This article will examine the benefits and challenges of the US healthcare system's upcoming conversion to use of the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) and will review the cost implications of the transition. Benefits including improved quality of care, potential cost savings from increased accuracy of payments and reduction of unpaid claims, and improved tracking of healthcare data related to public health and bioterrorism events are discussed. Challenges are noted in the areas of planning and implementation, the financial cost of the transition, a shortage of qualified coders, the need for further training and education of the healthcare workforce, and the loss of productivity during the transition. Although the transition will require substantial implementation and conversion costs, potential benefits can be achieved in the areas of data integrity, fraud detection, enhanced cost analysis cap...
ICD-10 procedure codes produce transition challenges
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018
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 algorith...
Journal of the American Medical Informatics Association, 2013
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.
Annals of Emergency Medicine, 2019
decrease in left without being seen rate, and overall, improvement in throughput. Methods: We conducted a retrospective observational study at a single urban academic tertiary care center, with an annual ED census of 100,000 visits. The adapted direct bedding model was implemented in July 2018. We analyzed the average and median time for door-to-evaluation before and after the implementation of our modified direct bedding model. This was defined as the time from patient registration to the time a provider in a local area assigned themselves to care for the patient. We also analyzed our left without being seen rate before and after system implementation. This was defined as the percentage of patients who leave the emergency department before being seen by a provider. We did not make exclusions on ESI level or patient demographics when evaluating the data. Results: We analyzed data from January 1, 2018-December 2018. Direct bedding was implemented in mid July 2018. Median time to full evaluation before and after direct bedding was 60.6 minutes and 48 minutes, respectively. When looking at the average time to full evaluation before and after direct bedding, we found that to be 76.7 and 61.6, respectively. There was 15.1 minutes reduction in average, and 12.6 minutes reduction in median. In addition, we looked at our left without being seen rate before and after direct bedding to be 5% and 3.6%, respectively, a 1.4% reduction. Conclusion: Improving the accessibility of patients to the primary care team led to patients being seen faster which also resulted in less patients walking out before being seen. In the event that available patient beds became full or there was a backup of to-be-seen patients, the area team captain started triage and initiated telemedicine in a designated room. This allowed for our remote providers to start the initial evaluation and workup on these patients. Asynchronous work between the nurses and the providers made time more efficient, especially in a staffing-constraint ED. Our limitation of this pilot is the inability to derive the effect of overall throughput based on this model due to the various constraints involved in patient's ED stay. More studies need to be done to truly see the success of this novel front-end approach.
ICD-9 to ICD-10: evolution, revolution, and current debates in the United States
Perspectives in health information management / AHIMA, American Health Information Management Association, 2013
The International Statistical Classification of Diseases and Related Health Problems (ICD) has undergone a long evolution from its initial inception in the late 18th century. Today, ICD is the internationally recognized classification that helps clinicians, policy makers, and patients to navigate, understand, and compare healthcare systems and services. Currently in the United States, hot debates surround the transition from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). This article presents an analysis of the views of the proponents and opponents of the upcoming change. We also briefly present and analyze the quality of the most frequently cited scientific evidence that underpins the recent debates focusing on two major issues: ICD-10-CM implementation costs and revenue gains and the projected clinical data quality improvement. We conc...
Measuring Diagnoses: ICD Code Accuracy
Health Services Research, 2005
Objective. To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process.Data Sources/Study Setting. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications.Study Design/Methods. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy.Principle Findings. Main error sources along the “patient trajectory” include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the “paper trail” include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding.Conclusions. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.