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Journal articles on the topic 'ICD-10-CM'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Waguespack, Richard W., and Rhonda Buckholtz. "ICD-9-CM to ICD-10-CM Transition." Otolaryngology–Head and Neck Surgery 145, no. 2_suppl (August 2011): P10—P11. http://dx.doi.org/10.1177/0194599811415818a8.

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2

Parman, Cindy. "ICD-10-CM Updates!" Oncology Issues 31, no. 6 (November 2016): 12–16. http://dx.doi.org/10.1080/10463356.2016.11884133.

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3

Cartwright, Donna J. "ICD-9-CM to ICD-10-CM Codes: What? Why? How?" Advances in Wound Care 2, no. 10 (December 2013): 588–92. http://dx.doi.org/10.1089/wound.2013.0478.

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Parman, Cindy. "Still More ICD-10-CM Updates!" Oncology Issues 33, no. 6 (November 2, 2018): 8–12. http://dx.doi.org/10.1080/10463356.2018.1536109.

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5

Hodorowicz, Mary Ann. "New ICD-10-CM Coding System." AADE in Practice 4, no. 2 (February 19, 2016): 12–15. http://dx.doi.org/10.1177/2325160316629899.

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6

Wu, Patrick, Aliya Gifford, Xiangrui Meng, Xue Li, Harry Campbell, Tim Varley, Juan Zhao, et al. "Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation." JMIR Medical Informatics 7, no. 4 (November 29, 2019): e14325. http://dx.doi.org/10.2196/14325.

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Background The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). Objective The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. Methods We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. Results We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). Conclusions This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

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Cartagena, F. Phil, Molly Schaeffer, Dorothy Rifai, Victoria Doroshenko, and Howard S. Goldberg. "Leveraging the NLM map from SNOMED CT to ICD-10-CM to facilitate adoption of ICD-10-CM." Journal of the American Medical Informatics Association 22, no. 3 (March 13, 2015): 659–70. http://dx.doi.org/10.1093/jamia/ocu042.

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Abstract Objective Develop and test web services to retrieve and identify the most precise ICD-10-CM code(s) for a given clinical encounter. Facilitate creation of user interfaces that 1) provide an initial shortlist of candidate codes, ideally visible on a single screen; and 2) enable code refinement. Materials and Methods To satisfy our high-level use cases, the analysis and design process involved reviewing available maps and crosswalks, designing the rule adjudication framework, determining necessary metadata, retrieving related codes, and iteratively improving the code refinement algorithm. Results The Partners ICD-10-CM Search and Mapping Services (PI-10 Services) are SOAP web services written using Microsoft's.NET 4.0 Framework, Windows Communications Framework, and SQL Server 2012. The services cover 96% of the Partners problem list subset of SNOMED CT codes that map to ICD-10-CM codes and can return up to 76% of the 69 823 billable ICD-10-CM codes prior to creation of custom mapping rules. Discussion We consider ways to increase 1) the coverage ratio of the Partners problem list subset of SNOMED CT codes and 2) the upper bound of returnable ICD-10-CM codes by creating custom mapping rules. Future work will investigate the utility of the transitive closure of SNOMED CT codes and other methods to assist in custom rule creation and, ultimately, to provide more complete coverage of ICD-10-CM codes. Conclusions ICD-10-CM will be easier for clinicians to manage if applications display short lists of candidate codes from which clinicians can subsequently select a code for further refinement. The PI-10 Services support ICD-10 migration by implementing this paradigm and enabling users to consistently and accurately find the best ICD-10-CM code(s) without translation from ICD-9-CM.

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Seare, J., J. Yang, S. Yu, and V. Zarotsky. "Building a bridge: ICD-9-CM to ICD-10-CM mapping challenges and solutions." Value in Health 17, no. 3 (May 2014): A187. http://dx.doi.org/10.1016/j.jval.2014.03.1094.

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9

DiSantostefano, Jan. "Getting to Know the ICD-10-CM." Journal for Nurse Practitioners 6, no. 2 (February 2010): 149–50. http://dx.doi.org/10.1016/j.nurpra.2009.11.001.

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Bhakoo, A., and B. P. Olivieri. "ICD-10-CM for the interventional radiologist." Journal of Vascular and Interventional Radiology 26, no. 2 (February 2015): S210. http://dx.doi.org/10.1016/j.jvir.2014.12.560.

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11

McCarty, Janet, and Neela Swanson. "Are You Ready for ICD-10-CM?" ASHA Leader 17, no. 2 (February 2012): 3–8. http://dx.doi.org/10.1044/leader.bml2.17022012.3.

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12

Venepalli, Neeta K., Ardaman Shergill, Parvaneh Dorestani, and Andrew D. Boyd. "Conducting Retrospective Ontological Clinical Trials in ICD-9-CM in the Age of ICD-10-CM." Cancer Informatics 13s3 (January 2014): CIN.S14032. http://dx.doi.org/10.4137/cin.s14032.

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Objective To quantify the impact of International Classification of Disease 10th Revision Clinical Modification (ICD-10-CM) transition in cancer clinical trials by comparing coding accuracy and data discontinuity in backward ICD-10-CM to ICD-9-CM mapping via two tools, and to develop a standard ICD-9-CM and ICD-10-CM bridging methodology for retrospective analyses. Background While the transition to ICD-10-CM has been delayed until October 2015, its impact on cancer-related studies utilizing ICD-9-CM diagnoses has been inadequately explored. Materials and Methods Three high impact journals with broad national and international readerships were reviewed for cancer-related studies utilizing ICD-9-CM diagnoses codes in study design, methods, or results. Forward ICD-9-CM to ICD-10-CM mapping was performing using a translational methodology with the Motif web portal ICD-9-CM conversion tool. Backward mapping from ICD-10-CM to ICD-9-CM was performed using both Centers for Medicare and Medicaid Services (CMS) general equivalence mappings (GEMs) files and the Motif web portal tool. Generated ICD-9-CM codes were compared with the original ICD-9-CM codes to assess data accuracy and discontinuity. Results While both methods yielded additional ICD-9-CM codes, the CMS GEMs method provided incomplete coverage with 16 of the original ICD-9-CM codes missing, whereas the Motif web portal method provided complete coverage. Of these 16 codes, 12 ICD-9-CM codes were present in 2010 Illinois Medicaid data, and accounted for 0.52% of patient encounters and 0.35% of total Medicaid reimbursements. Extraneous ICD-9-CM codes from both methods (Centers for Medicare and Medicaid Services general equivalent mapping [CMS GEMs, n = 161; Motif web portal, n = 246]) in excess of original ICD-9-CM codes accounted for 2.1% and 2.3% of total patient encounters and 3.4% and 4.1% of total Medicaid reimbursements from the 2010 Illinois Medicare database. Discussion Longitudinal data analyses post-ICD-10-CM transition will require backward ICD-10-CM to ICD-9-CM coding, and data comparison for accuracy. Researchers must be aware that all methods for backward coding are not comparable in yielding original ICD-9-CM codes. Conclusions The mandated delay is an opportunity for organizations to better understand areas of financial risk with regards to data management via backward coding. Our methodology is relevant for all healthcare-related coding data, and can be replicated by organizations as a strategy to mitigate financial risk.

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Glerum, Kimberly M., and Mark R. Zonfrillo. "Validation of an ICD-9-CM and ICD-10-CM map to AIS 2005 Update 2008." Injury Prevention 25, no. 2 (November 10, 2017): 90–92. http://dx.doi.org/10.1136/injuryprev-2017-042519.

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Although the Abbreviated Injury Scale (AIS) is the most widely used severity scoring system for traumatic injuries, hospitals are required to document and bill based on the International Classification of Diseases (ICD). An expert panel recently developed a map between ICD-9-CM and ICD-10-CM to AIS 2005 Update 2008. This study aimed to validate the recently developed map using a large trauma registry. The map demonstrated moderate to substantial agreement for maximum AIS (MAIS) scores per body region based on expert chart review versus map-derived values (range: 44%–86%). Injury Severity Scores (ISSs) calculated from expert coders versus map-derived values were also compared and demonstrated moderate agreement (ICD-9-CM: 48%, ICD-10-CM: 54%). Although not a perfect conversion tool, the new ICD-AIS map provides a systematic method to assign injury severity for datasets with only ICD-9-CM and ICD-10-CM codes available and can be used for future injury-related research and data analysis.

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Uysal, Suzan. "ICD-10-CM Diagnosis Coding for Neuropsychological Assessment." Archives of Clinical Neuropsychology 34, no. 5 (October 24, 2018): 721–30. http://dx.doi.org/10.1093/arclin/acy084.

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Abstract Objective This paper summarizes the basic principles of diagnosis coding for neuropsychological evaluation of patients with known or suspected brain injury or disease. Method The resources forming the basis of this article are the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) and the ICD-10-CM Official Guidelines for Coding and Reporting. Conclusion Diagnosis codes are used to communicate the specific reason for health care encounters and the conditions treated. All health care providers in all health care settings are mandated to implement ICD-10-CM for coding all health care encounters and transactions. It is the providers of health care services who ultimately are responsible for medical record documentation and diagnosis coding. The neuropsychologist’s knowledge base, therefore, should include a basic understanding of the structure of the ICD-10-CM, the conventions and rules for diagnosis coding, and the rules for what constitutes accurate coding.

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Kusnoor, Sheila V., Mallory N. Blasingame, Annette M. Williams, Spencer J. DesAutels, Jing Su, and Nunzia Bettinsoli Giuse. "A narrative review of the impact of the transition to ICD-10 and ICD-10-CM/PCS." JAMIA Open 3, no. 1 (December 26, 2019): 126–31. http://dx.doi.org/10.1093/jamiaopen/ooz066.

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Abstract Objectives The United States transitioned to the tenth version of the International Classification of Diseases (ICD) system (ICD-10) for mortality coding in 1999 and to the International Classification of Diseases, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS) on October 1, 2015. The purpose of this study was to conduct a narrative literature review to better understand the impact of the implementation of ICD-10/ICD-10-CM/PCS. Materials and Methods We searched English-language articles in PubMed, Web of Science, and Business Source Complete and reviewed websites of relevant professional associations, government agencies, research groups, and ICD-10 news aggregators to identify literature on the impact of the ICD-10/ICD-10-CM/PCS transition. We used Google to search for additional gray literature and used handsearching of the references of the most on-target articles to help ensure comprehensiveness. Results Impact areas reported in the literature include: productivity and staffing, costs, reimbursement, coding accuracy, mapping between ICD versions, morbidity and mortality surveillance, and patient care. With the exception of morbidity and mortality surveillance, quantitative studies describing the actual impact of the ICD-10/ICD-10-CM/PCS implementation were limited and much of the literature was based on the ICD-10-CM/PCS transition rather than the earlier conversion to ICD-10 for mortality coding. Discussion This study revealed several gaps in the literature that limit the ability to draw reliable conclusions about the overall impact, positive or negative, of moving to ICD-10/ICD-10-CM/PCS in the United States. Conclusion These knowledge gaps present an opportunity for future research and knowledge sharing and will be important to consider when planning for ICD-11.

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Fung, Kin Wah, ,. Julia Xu, and Olivier Bodenreider. "The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM." Journal of the American Medical Informatics Association 27, no. 5 (May 1, 2020): 738–46. http://dx.doi.org/10.1093/jamia/ocaa030.

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Abstract Objective To study the newly adopted International Classification of Diseases 11th revision (ICD-11) and compare it to the International Classification of Diseases 10th revision (ICD-10) and International Classification of Diseases 10th revision-Clinical Modification (ICD-10-CM). Materials and Methods : Data files and maps were downloaded from the World Health Organization (WHO) website and through the application programming interfaces. A round trip method based on the WHO maps was used to identify equivalent codes between ICD-10 and ICD-11, which were validated by limited manual review. ICD-11 terms were mapped to ICD-10-CM through normalized lexical mapping. ICD-10-CM codes in 6 disease areas were also manually recoded in ICD-11. Results Excluding the chapters for traditional medicine, functioning assessment, and extension codes for postcoordination, ICD-11 has 14 622 leaf codes (codes that can be used in coding) compared to ICD-10 and ICD-10-CM, which has 10 607 and 71 932 leaf codes, respectively. We identified 4037 pairs of ICD-10 and ICD-11 codes that were equivalent (estimated accuracy of 96%) by our round trip method. Lexical matching between ICD-11 and ICD-10-CM identified 4059 pairs of possibly equivalent codes. Manual recoding showed that 60% of a sample of 388 ICD-10-CM codes could be fully represented in ICD-11 by precoordinated codes or postcoordination. Conclusion In ICD-11, there is a moderate increase in the number of codes over ICD-10. With postcoordination, it is possible to fully represent the meaning of a high proportion of ICD-10-CM codes, especially with the addition of a limited number of extension codes.

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Ogden, Kate, Neela Swanson, and Janet McCarty. "Answers to Your ICD-10-CM Coding Questions." ASHA Leader 21, no. 1 (January 2016): 30–32. http://dx.doi.org/10.1044/leader.bml.21012016.30.

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Barsoumian, Alice E., Jason F. Okulicz, and Joseph M. Yabes. "Lyme Disease Prevalence by ICD-10-CM Codes." Military Medicine 185, no. 7-8 (June 9, 2020): 404. http://dx.doi.org/10.1093/milmed/usaa116.

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Smithee, Ryan B., Tiffanie M. Markus, Elizabeth Soda, Carlos G. Grijalva, Wei Xing, Nong Shang, Marie R. Griffin, and Fernanda C. Lessa. "Pneumonia Hospitalization Coding Changes Associated With Transition From the 9th to 10th Revision of International Classification of Diseases." Health Services Research and Managerial Epidemiology 7 (January 1, 2020): 233339282093980. http://dx.doi.org/10.1177/2333392820939801.

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Objectives: To evaluate the impact of International Classification of Disease, 10th revision, Clinical Modification ( ICD-10-CM) implementation on pneumonia hospitalizations rates, which had declined following pneumococcal conjugate vaccine introduction for infants in 2000. Methods: We randomly selected records from a single hospital 1 year before (n = 500) and after (n = 500) October 2015 implementation of ICD-10-CM coding. We used a validated ICD-9-CM algorithm and translation of that algorithm to ICD-10-CM to identify pneumonia hospitalizations pre- and post-implementation, respectively. We recoded ICD-10-CM records to ICD-9-CM and vice versa. We calculated sensitivity and positive predictive value (PPV) of the ICD-10-CM algorithm using ICD-9-CM coding as the reference. We used sensitivity and PPV values to calculate an adjustment factor to apply to ICD-10 era rates to enable comparison with ICD-9-CM rates. We reviewed primary diagnoses of charts not meeting the pneumonia definition when recoded. Results: Sensitivity and PPV of the ICD-10-CM algorithm were 94% and 92%, respectively, for young children and 74% and 79% for older adults. The estimated adjustment factor for ICD-10-CM period rates was −2.09% (95% credible region [CR], −7.71% to +3.0%) for children and +6.76% (95% CR, −3.06% to +16.7%) for older adults. We identified a change in coding adult charts that met the ICD-9-CM pneumonia definition that led to recoding in ICD-10-CM as chronic obstructive pulmonary disease (COPD) exacerbation. Conclusions: The ICD-10-CM algorithm derived from a validated ICD-9-CM algorithm should not introduce substantial bias for evaluating pneumonia trends in children. However, changes in coding of pneumonia associated with COPD in adults warrant further study.

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Utter, Garth H., Preston R. Miller, Nathan T. Mowery, Gail T. Tominaga, Oliver Gunter, Turner M. Osler, David J. Ciesla, et al. "ICD-9-CM and ICD-10-CM mapping of the AAST Emergency General Surgery disease severity grading systems." Journal of Trauma and Acute Care Surgery 78, no. 5 (May 2015): 1059–65. http://dx.doi.org/10.1097/ta.0000000000000608.

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Loftis, Kathryn L., Janet P. Price, Patrick J. Gillich, Kathy J. Cookman, Amy L. Brammer, Trish St. Germain, Jo Barnes, et al. "Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008." Traffic Injury Prevention 17, sup1 (September 2, 2016): 1–5. http://dx.doi.org/10.1080/15389588.2016.1191069.

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Quan, Hude, Vijaya Sundararajan, Patricia Halfon, Andrew Fong, Bernard Burnand, Jean-Christophe Luthi, L. Duncan Saunders, Cynthia A. Beck, Thomas E. Feasby, and William A. Ghali. "Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data." Medical Care 43, no. 11 (November 2005): 1130–39. http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83.

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Manchikanti, Laxmaiah. "The Tragedy of the Implementation of ICD-10- CM as ICD-10: Is the Cart Before the Horse or Is There a Tragic Paradox of Misinformation and Ignorance?" July 2015 18;4, no. 4;18 (July 14, 2015): E485—E495. http://dx.doi.org/10.36076/ppj.2015/18/e485.

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The forced implementation of ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes that are specific to the United States, scheduled for implementation October 1, 2015, which is vastly different from ICD-10 (International Classification of Diseases, Tenth Revision), implemented worldwide, which has 14,400 codes, compared to ICD-10-CM with 144,000 codes to be implemented in the United States is a major concern to practicing U.S. physicians and a bonanza for health IT and hospital industry. This implementation is based on a liberal interpretation of the Health Insurance Portability and Accountability Act (HIPAA), which requires an update to ICD-9- CM (International Classification of Diseases, Ninth Revision, Clinical Modification) and says nothing about ICD-10 or beyond. On June 29, 2015, the Supreme Court ruled that the Environmental Protection Agency unreasonably interpreted the Clean Air Act when it decided to set limits on the emissions of toxic pollutants from power plants, without first considering the costs on the industry. Thus, to do so is applicable to the medical industry with the Centers for Medicare and Medicaid Services (CMS) unreasonably interpreting HIPAA and imposing existent extensive regulations without considering the cost. In the United States, ICD-10-CM with a 10-fold increase in the number of codes has resulted in a system which has become so complicated that it no longer compares with any other country. Moreover, most WHO members use the ICD-10 system (not ICD-10-CM) only to record mortality in 138 countries or morbidity in 99 countries. Currently, only 10 countries employ ICD-10 (not ICD-10-CM) in the reimbursement process, 6 of which have a single payer health care system. Development of ICD-10-CM is managed by 4 non-physician groups, known as cooperating parties. They include the Centers for Disease Control and Prevention (CDC), CMS, the American Hospital Association (AHA), and the American Health Information Management Association (AHIMA). The AHIMA has taken the lead with the AHA just behind, both with escalating profits and influence, essentially creating a statutory monopoly for their own benefit. Further, the ICD-10-CM coalition includes 3M which will boost its revenues and profits substantially with its implementation and Blue Cross Blue Shield which has its own agenda. Physician groups are not a party to these cooperating parties or coalitions, having only a peripheral involvement. ICD-10-CM creates numerous deficiencies with 500 codes that are more specific in ICD-9-CM than ICD-10-CM. The costs of an implementation are enormous, along with maintenance costs, productivity, and cash disruptions. Key words: ICD-10-CM, ICD-10, ICD-9-CM (International Classification of Diseases, 10th Revision, Ninth revision, Clinical Modification), Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology (HIT), costs of implementation

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Clery, Michael J., Philip Joseph Hudson, Jasmine C. Moore, Laura M. Mercer Kollar, and Daniel T. Wu. "Exploring injury intentionality and mechanism via ICD-10-CM injury codes and self-reported injury in a large, urban emergency department." Injury Prevention 27, Suppl 1 (March 2021): i62—i65. http://dx.doi.org/10.1136/injuryprev-2019-043508.

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Health systems capture injuries using International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) diagnostic codes and share data with public health to inform injury surveillance. This study analyses provider-assigned ICD-10-CM injury codes among self-reported injuries to determine the effectiveness of ICD-10-CM coding in capturing injury and assault.MethodsSelf-reported injury screen records from an urban, level 1 trauma centre collected between 20 November 2015 and 30 September 2019 were compared with corresponding provider-assigned ICD-10-CM codes discerning the frequency in which intentions are indicated among patients reporting (1) any injury and (2) assault.ResultsOf 380 922 patients screened, 32 788 (8.61%) reported any injury and 6763 (1.78%) reported assault. ICD-10-CM codes had a sensitivity of 67.40% (95% CI 66.89% to 67.91%) for any injury and specificity of 89.79% (95% CI 89.69% to 89.89%]). For assault, ICD-10-CM codes had sensitivity of 2.25% (95% CI 1.91% to 2.63%) and specificity of 99.97% (95% CI 99.97% 99.98%).DiscussionThis study found provider-assigned ICD-10-CM had limited sensitivity to identify injury and low sensitivity for assault. This study more fully characterises ICD-10-CM coding system effectiveness in identifying assaults.

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Louis, Elan D. "Essential tremor: a unique diagnostic code in ICD-10-CM." Lancet Neurology 12, no. 3 (March 2013): 223–24. http://dx.doi.org/10.1016/s1474-4422(12)70325-3.

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McNicholas, Faith C. M. "Medical Record Documentation Yields Accuracy in ICD-10-CM Coding." Journal of the Dermatology Nurses’ Association 8, no. 1 (2016): 64–67. http://dx.doi.org/10.1097/jdn.0000000000000189.

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Schubert, Sara L., and Vanessa R. Melanson. "Response to Lyme Disease Prevalence by ICD-10-CM Codes." Military Medicine 185, no. 7-8 (June 9, 2020): 405. http://dx.doi.org/10.1093/milmed/usaa117.

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Gillespie, S., M. Allison, C. Cleveland, D. Bachman, X. Yang, M. Schmidt, and A. Bauck. "A4-2: ICD-10 CM Transition across Three Research Centers." Clinical Medicine & Research 12, no. 1-2 (September 1, 2014): 109–10. http://dx.doi.org/10.3121/cmr.2014.1250.a4-2.

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Caskey, R., J. Zaman, H. Nam, S. R. Chae, L. Williams, G. Mathew, M. Burton, J. J. Li, Y. A. Lussier, and A. D. Boyd. "The Transition to ICD-10-CM: Challenges for Pediatric Practice." PEDIATRICS 134, no. 1 (June 2, 2014): 31–36. http://dx.doi.org/10.1542/peds.2013-4147.

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Feng, Jui-Ying, Wan-Lin Chiang, and Tsung-Hsueh Lu. "What's new in ICD-10-CM in classifying child maltreatment?" Child Abuse & Neglect 35, no. 8 (August 2011): 655–57. http://dx.doi.org/10.1016/j.chiabu.2011.04.002.

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Yang, E., AM Patel, J. Whiteley, and W. Yeh. "Transition from ICD-9-CM to ICD-10-CM Diagnosis Coding System in the United States – Findings from Hemophilia A." Value in Health 21 (May 2018): S217. http://dx.doi.org/10.1016/j.jval.2018.04.1473.

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Armstrong, Joanne, Patricia McDermott, George R. Saade, and Sindhu K. Srinivas. "Coding update of the SMFM definition of low risk for cesarean delivery from ICD-9-CM to ICD-10-CM." American Journal of Obstetrics and Gynecology 217, no. 1 (July 2017): B2—B12.e56. http://dx.doi.org/10.1016/j.ajog.2017.04.013.

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Grief, S. N., J. Patel, K. M. Kochendorfer, L. A. Green, Y. A. Lussier, J. Li, M. Burton, and A. D. Boyd. "Simulation of ICD-9 to ICD-10-CM Transition for Family Medicine: Simple or Convoluted?" Journal of the American Board of Family Medicine 29, no. 1 (January 1, 2016): 29–36. http://dx.doi.org/10.3122/jabfm.2016.01.150146.

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De Coster, Carolyn, Bing Li, and Hude Quan. "Comparison and Validity of Procedures Coded With ICD-9-CM and ICD-10-CA/CCI." Medical Care 46, no. 6 (June 2008): 627–34. http://dx.doi.org/10.1097/mlr.0b013e3181649439.

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Boyd, Andrew D., Young Min Yang, Jianrong Li, Colleen Kenost, Mike D. Burton, Bryan Becker, and Yves A. Lussier. "Challenges and remediation for Patient Safety Indicators in the transition to ICD-10-CM." Journal of the American Medical Informatics Association 22, no. 1 (September 3, 2014): 19–28. http://dx.doi.org/10.1136/amiajnl-2013-002491.

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Abstract Reporting of hospital adverse events relies on Patient Safety Indicators (PSIs) using International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes. The US transition to ICD-10-CM in 2015 could result in erroneous comparisons of PSIs. Using the General Equivalent Mappings (GEMs), we compared the accuracy of ICD-9-CM coded PSIs against recommended ICD-10-CM codes from the Centers for Medicaid/Medicare Services (CMS). We further predict their impact in a cohort of 38 644 patients (1 446 581 visits and 399 hospitals). We compared the predicted results to the published PSI related ICD-10-CM diagnosis codes. We provide the first report of substantial hospital safety reporting errors with five direct comparisons from the 23 types of PSIs (transfusion and anesthesia related PSIs). One PSI was excluded from the comparison between code sets due to reorganization, while 15 additional PSIs were inaccurate to a lesser degree due to the complexity of the coding translation. The ICD-10-CM translations proposed by CMS pose impending risks for (1) comparing safety incidents, (2) inflating the number of PSIs, and (3) increasing the variability of calculations attributable to the abundance of coding system translations. Ethical organizations addressing ‘data-, process-, and system-focused’ improvements could be penalized using the new ICD-10-CM Agency for Healthcare Research and Quality PSIs because of apparent increases in PSIs bearing the same PSI identifier and label, yet calculated differently. Here we investigate which PSIs would reliably transition between ICD-9-CM and ICD-10-CM, and those at risk of under-reporting and over-reporting adverse events while the frequency of these adverse events remain unchanged.

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Manchikanti, Laxmaiah. "Survival Strategies for Tsunami of ICD-10-CM for Interventionalists: Pursue or Perish!" Pain Physician 5;18, no. 5;9 (September 14, 2015): E685—E712. http://dx.doi.org/10.36076/ppj.2015/18/e685.

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The unfunded mandate for the implementation of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) is scheduled October 1, 2015. The development of ICD-10-CM has been a complicated process. We have endeavored to keep Interventional Pain Management doctors apprised via a variety of related topical manuscripts. The major issues relate to the lack of formal physician participation in its preparation. While the American Health Information Management Association (AHIMA) and American Hospital Association (AHA) as active partners in its preparation. Centers for Medicare and Medicaid Services (CMS) and Centers for Disease Control and Prevention (CDC) are major players; 3M and Blue Cross Blue Shield Association are also involved. The cost of ICD-10-CM implementation is high, similar to the implementation of electronic health records (EHRs), likely consuming substantial resources. While ICD-10, utilized worldwide, includes 14,400 different codes, ICD-10-CM, specific for the United States, has expanded to 144,000 codes, which also includes procedural coding system. It is imperative for physicians to prepare for the mandatory implementation. Conversion from ICD-9-CM to ICD-10-CM coding in interventional pain management is not a conversion of one to one that can be easily obtained from software packages. It is a both a difficult and time-consuming task with each physician, early on, expected to spend on estimation at least 10 minutes per visit on extra coding for established and new patients. For interventional pain physicians, there have been a multitude of changes, including creation of new codes and confusing conversion of existing codes. This manuscript describes a variety of codes that are relevant to interventional pain physicians and often utilized in daily practices. It is our objective that this manuscript will provide coding assistance to interventional pain physicians. Key words: ICD-9-CM (International Classification of Diseases, Ninth revision, Clinical Modification), ICD-10, ICD-10-CM (International Classification of Diseases, 10th Revision), Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology (HIT)

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Rose, Eric, Steven Rube, Andrew Kanter, Matthew Cardwell, and Frank Naeymi-Rad. "Integration of Postcoordination Content into a Clinical Interface Terminology to Support Administrative Coding." Applied Clinical Informatics 10, no. 01 (January 2019): 051–59. http://dx.doi.org/10.1055/s-0038-1676972.

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Background Clinical interface terminologies (CITs) consist of terms designed for clinical documentation and, through mappings to standardized vocabularies, to support secondary uses of patient data, including clinical decision support, quality measurement, and billing for health care services. The latter purpose requires maps to administrative coding systems, such as the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), for diagnoses in the United States. Objectives The transition from ICD-9-CM to ICD-10-CM posed a challenge to CIT users due to the substantially increased details in ICD-10-CM. To address this, we developed a content layer within a CIT that provides postcoordination prompts for the details required for accurate ICD-10-CM coding. Methods We developed content to support prompting for and capture of additional information specified by the user in a single, clinically relevant term that is added to the patient's record, and whose mapping to other coding systems (like Systematized Nomenclature of Medicine—Clinical Terms [SNOMED CT]) reflects the details added during postcoordination. We worked with clinical information system developers to incorporate this into user interfaces, and with end-users to refine the design. Results While the prompts were designed around the precoordinated elements implicit in ICD-10-CM, irregularities in ICD-10-CM required some additional design measures, such as providing postcoordination options that interpolate gaps in ICD-10-CM to avoid user confusion. The system we describe has been implemented by ∼30,000 health care provider organizations, with content that covers the vast majority of encounter diagnoses. User feedback has been largely positive, though concerns have been raised about expanding postcoordination content beyond that required for ICD-10-CM coding. Conclusion We have demonstrated the design and development of what, to our knowledge, is the first system that uses postcoordination to capture ICD-10-CM-relevant details in a CIT while also reflecting the details added by the user in maps to other vocabularies.

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Hall, Katelyn E., Hannah Yang, DeLayna Goulding, Elyse Contreras, and Katherine A. James. "Interrupted time series analysis of cannabis coding in Colorado during the ICD-10-CM transition." Injury Prevention 27, Suppl 1 (March 2021): i66—i70. http://dx.doi.org/10.1136/injuryprev-2019-043511.

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The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), implemented in 2015, has more codes than ICD-9-CM for events involving cannabis. We examined cannabis indicator trends across the transition from ICD-9-CM to ICD-10-CM in Colorado, where state law regulates adult cannabis use. Using 2011 to 2018 data from hospital and emergency department (ED) discharges, we calculated monthly rates per 1000 discharges for two indicators: (1) cannabis use disorders and (2) poisoning and adverse effects of psychodysleptics. Immediate, point-of-transition (level) and gradual, post-transition (slope) changes across the ICD-9-CM to ICD-10-CM transition were tested using interrupted time series models adjusted for legalisation, seasonality and autocorrelation. We observed a level increase and slope increase in the rate of ED discharges with cannabis use disorders. Hospital discharges with cannabis use disorders had a negative slope change after the transition and no level change. ED discharges with poisoning and adverse effects of psychodysleptics showed an increase in slope after the transition. No effects of the transition were observed on hospital discharges with poisoning and adverse effects of psychodysleptics. Shifts in the level and slope of cannabis indicator rates after implementation of the new coding scheme suggest the use of caution when interpreting trends spanning the ICD-9-CM to ICD-10-CM transition.

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Allori, Alexander C., Janet D. Cragan, Gina C. Delia Porta, John B. Mulliken, John G. Meara, Richard Bruun, Stephen Shusterman, et al. "Clinician's Primer to ICD-10-CM Coding for Cleft Lip/Palate Care." Cleft Palate-Craniofacial Journal 54, no. 1 (January 2017): 7–13. http://dx.doi.org/10.1597/15-219.

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On October 1,2015, the United States required use of the Clinical Modification of the International Classification of Diseases, 10th Revision (ICD-10-CM) for diagnostic coding. This primer was written to assist the cleft care community with understanding and use of ICD-10-CM for diagnostic coding related to cleft lip and/or palate (CL/P).

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Mayhew, Meghan, Lynn L. DeBar, Richard A. Deyo, Robert D. Kerns, Joseph L. Goulet, Cynthia A. Brandt, and Michael Von Korff. "Development and Assessment of a Crosswalk Between ICD-9-CM and ICD-10-CM to Identify Patients with Common Pain Conditions." Journal of Pain 20, no. 12 (December 2019): 1429–45. http://dx.doi.org/10.1016/j.jpain.2019.05.006.

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Hamedani, Ali G., Leah Blank, Dylan P. Thibault, and Allison W. Willis. "Impact of ICD-9 to ICD-10 Coding Transition on Prevalence Trends in Neurology." Neurology: Clinical Practice 11, no. 5 (January 29, 2021): e612-e619. http://dx.doi.org/10.1212/cpj.0000000000001046.

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ObjectiveTo determine the effect of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) coding transition on the point prevalence and longitudinal trends of 16 neurologic diagnoses.MethodsWe used 2014–2017 data from the National Inpatient Sample to identify hospitalizations with one of 16 common neurologic diagnoses. We used published ICD-9-CM codes to identify hospitalizations from January 1, 2014, to September 30, 2015, and used the Agency for Healthcare Research and Quality's MapIt tool to convert them to equivalent ICD-10-CM codes for October 1, 2015–December 31, 2017. We compared the prevalence of each diagnosis before vs after the ICD coding transition using logistic regression and used interrupted time series regression to model the longitudinal change in disease prevalence across time.ResultsThe average monthly prevalence of subarachnoid hemorrhage was stable before the coding transition (average monthly increase of 4.32 admissions, 99.7% confidence interval [CI]: −8.38 to 17.01) but increased after the coding transition (average monthly increase of 24.32 admissions, 99.7% CI: 15.71–32.93). Otherwise, there were no significant differences in the longitudinal rate of change in disease prevalence over time between ICD-9-CM and ICD-10-CM. Six of 16 neurologic diagnoses (37.5%) experienced significant changes in cross-sectional prevalence during the coding transition, most notably for status epilepticus (odds ratio 0.30, 99.7% CI: 0.26–0.34).ConclusionsThe transition from ICD-9-CM to ICD-10-CM coding affects prevalence estimates for status epilepticus and other neurologic disorders, a potential source of bias for future longitudinal neurologic studies. Studies should limit to 1 coding system or use interrupted time series models to adjust for changes in coding patterns until new neurology-specific ICD-9 to ICD-10 conversion maps can be developed.

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Stewart, Christine C., Christine Y. Lu, Tae K. Yoon, Karen J. Coleman, Phillip M. Crawford, Matthew D. Lakoma, and Gregory E. Simon. "Impact of ICD-10-CM Transition on Mental Health Diagnoses Recording." eGEMs (Generating Evidence & Methods to improve patient outcomes) 7, no. 1 (April 12, 2019): 14. http://dx.doi.org/10.5334/egems.281.

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"ICD-10-CM Challenge." AAP Pediatric Coding Newsletter 14, no. 7 (April 1, 2019). http://dx.doi.org/10.1542/pcco_book180_document008.

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"ICD-10-CM Update." AAP Pediatric Coding Newsletter 12, no. 2 (November 1, 2016): 1–8. http://dx.doi.org/10.1542/pcco_book151_document001.

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In the July 2016 AAP Pediatric Coding Newsletter™, the changes to International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), were previewed. With the new codes now implemented on October 1, 2016, this article includes a few more updates.

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Neimeyer, Greg J. "The World of Health: An ICD–10–CM PrimerThe World of Health: An ICD–10–CM Primer." PsycCRITIQUES 5959, no. 2323 (2014). http://dx.doi.org/10.1037/a0036561.

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"Preparing for ICD-10-CM." AAP Pediatric Coding Newsletter 5, no. 7 (April 1, 2010). http://dx.doi.org/10.1542/pcco_book072_document003.

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"ICD-10-CM Guidelines Updated." AAP Pediatric Coding Newsletter 8, no. 3 (December 1, 2012). http://dx.doi.org/10.1542/pcco_book104_document006.

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"ICD-10-CM: Biting Questions." AAP Pediatric Coding Newsletter 12, no. 8 (May 1, 2017): 3. http://dx.doi.org/10.1542/pcco_book157_document002.

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Deerin, Jessica, Jean-Paul Chretien, and Paul Lewis. "Evaluation of DoD Syndrome Mapping and Baseline for ICD-9-CM to ICD-10-CM Transition." Online Journal of Public Health Informatics 9, no. 1 (May 2, 2017). http://dx.doi.org/10.5210/ojphi.v9i1.7594.

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ObjectiveThe transition from ICD-9-CM to ICD-10-CM requires evaluationof syndrome mappings to obtain a baseline for syndromic surveillancepurposes. Two syndrome mappings are evaluated in this report.IntroductionThe Department of Defense conducts syndromic surveillanceof health encounter visits of Military Health System (MHS)beneficiaries. Providers within the MHS assign up to 10 diagnosiscodes to each health encounter visit. The diagnosis codes are groupedinto syndrome and sub-syndrome categories. On October 1, 2015,the Health and Human Services-mandated transition from ICD-9-CM to ICD-10-CM required evaluation of the syndrome mappingsto establish a baseline of syndrome rates within the DoD. The DoDdata within the BioSense system currently utilizes DoD ESSENCEsyndrome mappings. The Master Mapping Reference Table (MMRT)was developed by the CDC to translate diagnostic codes across theICD-9-CM and ICD-10-CM encoding systems to prepare for thetransition. The DoD ESSENCE and MMRT syndrome definitions arepresented in this analysis for comparison.MethodsDoD data was pulled from the BioSense Platform through aRStudio server on October 11, 2016, querying data from October1, 2014 to September 30, 2016. This time period provides twelvemonths of ICD-9-CM data and twelve months of ICD-10-CM data.The ICD codes were binned to both DoD ESSENCE syndromes andMMRT macro syndromes for comparison. Although a patient visitmay contain up to 10 ICD codes, only the first four were includedfor this analysis. Providers are trained to prioritize diagnosis codesby position. Only 2.2% of visits had greater than 4 diagnostic codes.Each ICD code in a visit is binned to an applicable syndrome. Thetotal number of visits includes visits that binned and did not bin toa syndrome. Multiple syndromes may be assigned to one patient’shealth encounter visit if multiple ICD codes are binned. Additionally,more than one code per visit may bin to the same syndrome; however,only unique syndromes are counted in the total syndrome rate. Thetotal syndrome rate was calculated by total unique syndrome visitsas the numerator and total number of visits during the ICD-9-CM orICD-10-CM time period as the denominator. The rates per 1000 totalvisits were calculated.ResultsAmong the DoD ESSENCE syndromes, the ICD-9-CM ratefor ILI was 36.3 per 1,000 compared to the ICD-10-CM rate of38.6 per 1,000. The ICD-9-CM rate for neurological was 18.1 per1,000 compared to the ICD-10-CM rate of 0.2 per 1,000.Among the MMRT syndromes, the ICD-9-CM rate for ILI was16.7 per 1,000 compared to the ICD-10-CM rate of 38.4 per 1,000.The ICD-9-CM rate for mental disorders was 73.8 per 1,000 comparedto the ICD-10-CM rate of 73.2 per 1,000.ConclusionsThis analysis provides baseline rates of MMRT syndromes andsub-syndromes for syndromic surveillance during the ICD-9-CM toICD-10-CM transition. These data will serve for future comparisonand tracking of syndrome-specific trends for military-relevant healththreats.

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"Partial Code Freeze for ICD-9-CM and ICD-10-CM Finalized." AAP Pediatric Coding Newsletter 6, no. 6 (March 1, 2011). http://dx.doi.org/10.1542/pcco_book083_document004.

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