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The ICD-10 transition will be an evolutionary process. Relying on the EHR or certified coding staff alone will not be sufficient. The EHR can facilitate easy search tools that assist the provider in selecting a diagnosis. Billing staff are an invaluable resource to help validate that coding and documentation are in sync but the burden will clearly rest on the provider. The provider will be juggling a new code structure, drilling down to new levels of complexity and ensuring their documentation supports the specificity of the new codes selected, all while managing a full patient schedule. Education for the provider will be of paramount importance as they navigate this brave new world.

Background: The purpose of this study was to assess whether or not the change in coding classification had an impact on diagnosis and comorbidity coding in hospital discharge data across Canadian provinces., Methods: This study examined eight years (fiscal years 1998 to 2005) of hospital records from the Hospital Person-Oriented Information database (HPOI) derived from the Canadian national Discharge Abstract Database. The average number of coded diagnoses per hospital visit was examined from 1998 to 2005 for provinces that switched from International Classifications of Disease 9(th) version (ICD-9-CM) to ICD-10-CA during this period. The average numbers of type 2 and 3 diagnoses were also described. The prevalence of the Charlson comorbidities and distribution of the Charlson score one year before and one year after ICD-10 implementation for each of the 9 provinces was examined. The prevalence of at least one of the seventeen Charlson comorbidities one year before and one year after ICD-10 implementation were described by hospital characteristics (teaching/non-teaching, urban/rural, volume of patients)., Results: Nine Canadian provinces switched from ICD-9-CM to ICD-I0-CA over a 6 year period starting in 2001. The average number of diagnoses coded per hospital visit for all code types over the study period was 2.58. After implementation of ICD-10-CA a decrease in the number of diagnoses coded was found in four provinces whereas the number of diagnoses coded in the other five provinces remained similar. The prevalence of at least one of the seventeen Charlson conditions remained relatively stable after ICD-10 was implemented, as did the distribution of the Charlson score. When stratified by hospital characteristics, the prevalence of at least one Charlson condition decreased after ICD-10-CA implementation, particularly for low volume hospitals., Conclusion: In conclusion, implementation of ICD-10-CA in Canadian provinces did not substantially change coding practices, but there was some coding variation in the average number of diagnoses per hospital visit across provinces.

Background: The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this context, we undertook this review to discuss: (1) the history of the International Classification of Diseases (a core information "building block" for health systems everywhere) from its introduction to the current era of ICD-11 development; (2) differences across country-specific ICD-10 clinical modifications and the challenges that these differences pose to the international comparability of morbidity data; (3) potential strategic approaches to achieving better international ICD-11 comparability., Literature Review and Discussion: A literature review and stakeholder consultation was carried out. The various ICD-10 clinical modifications (ICD-10-AM [Australia], ICD-10-CA [Canada], ICD-10-GM [Germany], ICD-10-TM [Thailand], ICD-10-CM [United States]) were compared. These ICD-10 modifications differ in their number of codes, chapters, and subcategories. Specific conditions are present in some but not all of the modifications. ICD-11, with a similar structure to ICD-10, will function in an electronic health records environment and also provide disease descriptive characteristics (eg, causal properties, functional impact, and treatment)., Conclusion: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions. One solution to this threat is to develop a meta-database including all country-specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.

Aim: To assess validity of diabetes International Classification of Disease (ICD) 9 and 10 coding algorithms from administrative data using physicians' charts as the 'gold standard' across time periods and geographic regions., Methods: From 48 urban and 16 rural general practitioners' clinics in Alberta and British Columbia, Canada, we randomly selected 50patient charts/clinic for those who visited the clinic in either 2001 or 2004. Reviewed chart data were linked with inpatient discharge abstract and physician claims administrative data. We identified patients with diabetes in the administrative databases using ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x., Results: The prevalence of diabetes was 8.1% among clinic charts. The coding algorithm of "2 physician claims within 2 years or 1 hospitalization with the relevant diabetes ICD codes" had higher validity than other 7 algorithms assessed (sensitivity 92.3%, specificity 96.9%, positive predictive value 77.2%, and negative predictive value 99.3%). After adjustment for age, sex, and comorbid conditions, sensitivity and positive predictive values were not significantly different between time periods and regions., Conclusion: Diabetes could be accurately identified in administrative data using the following case definition "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x"., (Copyright 2010 Elsevier Ireland Ltd. All rights reserved.)

International Classification of Disease coding is widely used by physicians, hospitals, health-care payers, and governments to assess the health of populations and as a means of reimbursement for medical care based on diagnosis and severity of illness. The current classification system, International Classification of Diseases, 9th ed (ICD-9), will soon be replaced by International Classification of Diseases, 10th ed (ICD-10). When the codes that relate to COPD and asthma are examined, the clinical relevance of the categories in International Classification of Disease coding must be questioned. In the future, a more simplified terminology that is consistent with clinical usage could improve accuracy and ease of coding. At present, however, clinicians should become familiar with the present ICD-9 and future ICD-10 codes so that their descriptions of illnesses in the medical records more accurately reflect current coding terminology.

Objective: To develop a tool to allow Australian hospitals to monitor the range of hospital-acquired diagnoses coded in routine data in support of quality improvement efforts., Design and Setting: Secondary analysis of abstracted inpatient records for all episodes in acute care hospitals in Victoria for the financial year 2005-06 (n=2.032 million) to develop a classification system for hospital-acquired diagnoses; each record contains up to 40 diagnosis fields coded with the ICD-10-AM (International Classification of Diseases, 10th revision, Australian modification)., Main Outcome Measure: The Classification of Hospital Acquired Diagnoses (CHADx) was developed by: analysing codes with a "complications" flag to identify high-volume code groups; assessing their salience through an iterative review by health information managers, patient safety researchers and clinicians; and developing principles to reduce double counting arising from coding standards., Results: The dataset included 126,940 inpatient episodes with any hospital-acquired diagnosis (complication rate, 6.25%). Records had a mean of three flagged diagnoses; including unflagged obstetric and neonatal codes, 514,371 diagnoses were available for analysis. Of these, 2.9% (14,898) were removed as comorbidities rather than complications, and another 118,640 were removed as redundant codes, leaving 380,833 diagnoses for grouping into CHADx classes. We used 4345 unique codes to characterise hospital-acquired conditions; in the final CHADx these were grouped into 144 detailed subclasses and 17 "roll-up" groups., Conclusions: Monitoring quality improvement requires timely hospital-onset data, regardless of causation or "preventability" of each complication. The CHADx uses routinely abstracted hospital diagnosis and condition-onset information about in-hospital complications. Use of this classification will allow hospitals to track monthly performance for any of the CHADx indicators, or to evaluate specific quality improvement projects.

Background and Objective: Hospital-acquired catheter-associated urinary tract infection (CAUTI) is one of the first 6 conditions Medicare is targeting to reduce payment associated with hospital-acquired conditions under Congressional mandate. This study was to determine the positive predictive value (PPV) and sensitivity in identifying patients in Medicare claims who had urinary catheterization and who had hospital-acquired CAUTIs., Research Design: CAUTIs identified by ICD-9-CM codes in Medicare claims were compared with those revealed by medical record abstraction in random samples of Medicare discharges in 2005 to 2006. Hospital discharge abstracts (2005) from the states of New York and California were used to estimate the potential impact of a present-on-admission (POA) indicator on PPV., Results: ICD-9-CM procedure codes for urinary catheterization appeared in only 1.4% of Medicare claims for patients who had urinary catheters. As a proxy, claims with major surgery had a PPV of 75% and sensitivity of 48%, and claims with any surgical procedure had a PPV of 53% and sensitivity of 79% in identifying urinary catheterization. The PPV and sensitivity for identifying hospital-acquired CAUTIs varied, with the PPV at 30% and sensitivity at 65% in claims with major surgery. About 80% of the secondary diagnosis codes indicating UTIs were flagged as POA, suggesting that the addition of POA indicators in Medicare claims would increase PPV up to 86% and sensitivity up to 79% in identifying hospital-acquired CAUTIs., Conclusions: The validity in identifying urinary catheter use and CAUTIs from Medicare claims is limited, but will be increased substantially upon addition of a POA indicator.

Objectives: Many of the 2.5 million Americans assaulted annually by intimate partners seek medical care. This project evaluated diagnostic codes indicative of intimate partner violence (IPV) in Oregon hospital and emergency department (ED) records to determine predictive value positive (PVP), sensitivity, and usefulness in routine surveillance. Statewide incidence of care for IPV was calculated and victims and episodes characterized., Methods: The study was a review of medical records assigned > or = 1 diagnostic codes thought predictive of IPV. Sensitivity was estimated by comparing the number of confirmed victims identified with the number predicted by statewide telephone survey. Patients were aged > or = 12 years, treated in any of 58 EDs or hospitals in Oregon during 2000, and discharged with one of three primary or 12 provisional codes suggestive of IPV. Outcome measures were number of victims detected, PPV and sensitivity of codes for detection of IPV, and description of victims., Results: Of 58 hospitals, 52 (90%) provided records. Case finding using primary codes identified 639 victims, 23% of all estimated female victims seen in EDs or hospitalized statewide. PVP was 94% (639/677). Provisional codes increased sensitivity (51%) but reduced PVP (50%). Highest incidence occurred in women aged 20-39 years, and those who were black. Hospitalizations were highest among women aged > or = 50 years, black people, or those with comorbid illness., Conclusions: Three diagnostic codes used for case finding detect approximately one-quarter of ED- and hospital-treated victims, complement surveys, and facilitate description of injured victims.

This proposed rule would modify two of the medical data code set standards adopted in the Transactions and Code Sets final rule published in the Federal Register. It would also implement certain provisions of the Administrative Simplification subtitle of the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Specifically, the proposed rule would modify the standard code sets for coding diagnoses and inpatient hospital procedures by concurrently adopting the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for diagnosis coding, and the International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) for inpatient hospital procedure coding. These new codes would replace the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9- CM) Volumes 1 and 2, and the International Classification of Diseases, Ninth Revision, Clinical Modification (CM) Volume 3 for diagnosis and procedure codes, respectively.

Objective: The goal of this study was to assess the validity of the International Classification of Disease, 10th Version (ICD-10) administrative hospital discharge data and to determine whether there were improvements in the validity of coding for clinical conditions compared with ICD-9 Clinical Modification (ICD-9-CM) data., Methods: We reviewed 4,008 randomly selected charts for patients admitted from January 1 to June 30, 2003 at four teaching hospitals in Alberta, Canada to determine the presence or absence of 32 clinical conditions and to assess the agreement between ICD-10 data and chart data. We then re-coded the same charts using ICD-9-CM and determined the agreement between the ICD-9-CM data and chart data for recording those same conditions. The accuracy of ICD-10 data relative to chart data was compared with the accuracy of ICD-9-CM data relative to chart data., Results: Sensitivity values ranged from 9.3 to 83.1 percent for ICD-9-CM and from 12.7 to 80.8 percent for ICD-10 data. Positive predictive values ranged from 23.1 to 100 percent for ICD-9-CM and from 32.0 to 100 percent for ICD-10 data. Specificity and negative predictive values were consistently high for both ICD-9-CM and ICD-10 databases. Of the 32 conditions assessed, ICD-10 data had significantly higher sensitivity for one condition and lower sensitivity for seven conditions relative to ICD-9-CM data. The two databases had similar sensitivity values for the remaining 24 conditions., Conclusions: The validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions was generally similar though validity differed between coding versions for some conditions. The implementation of ICD-10 coding has not significantly improved the quality of administrative data relative to ICD-9-CM. Future assessments like this one are needed because the validity of ICD-10 data may get better as coders gain experience with the new coding system.

Background: We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance., Methods: A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995-2001 based on the ICD-9-CM coding algorithm, and for 2002-2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995-2005. The corresponding administrative data records were coded in ICD-9-CM for 1995-2001 and in ICD-10 for 2002-2005. The resulting datasets were then evaluated for their ability to predict death at one year., Results: The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit., Conclusion: The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.

Purpose: Coding variation and its impact on identified populations is a major concern in database analyses. We assessed potential differences in demographics and healthcare charges among patients with open-angle glaucoma identified through different International Classification of Diseases, Ninth Revision (ICD-9) coding algorithms., Design: Retrospective database analysis., Methods: Three glaucoma cohorts were identified based on hierarchical inclusion of patients with >/=2 glaucoma ICD-9 codes (PharMetrics, Inc, Watertown, Massachusetts, USA, 1998 to 2003). Descriptive statistics and healthcare charges were assessed for each cohort., Results: The three cohorts included 64,380, 14,705, and 4,225 unique patients each. Although significant differences in age, gender, region, payer type, product type, and medical comorbidities existed among the three cohorts, these differences had little impact on resource consumption when the cohorts were combined, given the smaller number of patients outside of the primary cohort., Conclusions: Glaucoma coding variation in administrative data sets has limited impact on analyses of resource consumption among open-angle glaucoma patients.

Objective: Deployable medical systems patient conditions (PCs) designate groups of patients with similar medical conditions and, therefore, similar treatment requirements. PCs are used by the U.S. military to estimate field medical resources needed in combat operations. Information associated with each of the 389 PCs is based on subject matter expert opinion, instead of direct derivation from standard medical codes. Currently, no mechanisms exist to tie current or historical medical data to PCs. Our study objective was to determine whether reliable conversion between PC codes and International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes is possible., Methods: Data were analyzed for three professional coders assigning all applicable ICD-9-CM diagnosis codes to each PC code. Inter-rater reliability was measured by using Cohen's K statistic and percent agreement. Methods were developed to calculate kappa statistics when multiple responses could be selected from many possible categories., Results: Overall, we found moderate support for the possibility of reliable conversion between PCs and ICD-9-CM diagnoses (mean kappa = 0.61)., Conclusion: Current PCs should be modified into a system that is verifiable with real data.

A retrospective study was conducted in the ambit of Risk Management research, in order to assess adverse events in patients hospitalised in hospitals in one Local Health Authority of the Piemonte region. Specifically, the aims of the study were to: evaluate the relative frequency of ICD-9-CM codes used to define adverse events, with respect to the total number of hospital discharge records submitted in 2003; identify true and false positives, by hospital chart review; estimate the positive predictive value (VPP) of the ICD-9-CM codes used, and determine, in each case, whether the adverse event had led to hospitalisation or if it had occurred during hospitalisation. Results show that the ICD-9-CM codes used effectively identify adverse events. In fact, the probability that an ICD-9-CM code will accurately identify an adverse event is 100% for codes in the "Misadventures of surgical and medical care" category of adverse events, 62.8% for codes indicating "Complications of medications (adverse drug events)" and 56.8% for the "Complications of surgical or medical procedures" category. In most cases the adverse event had occurred prior to hospital admission.

Objective: To examine validity of the International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) codes in discharge data for identifying infants with cardiac defects according to surveillance guidelines., Study Design: Retrospective medical record review of infants born in 2001 at one hospital in Minneapolis, Minnesota. Infants were identified using ICD-9-CM codes from hospital discharge data, and keywords in medical records., Results: Of 2,697 children, ICD-9-CM codes identified 66 infants coded with cardiac defects; physician review confirmed 24 had cardiac defects. Only 35 of 85 (41.2%) ICD-9-CM codes accurately reflected the cardiac defect diagnoses. Additional case finding located four infants with five cardiac defects. Sensitivity of ICD-9-CM codes for identifying these infants was 0.857, predictive value positive was 0.364., Conclusions: ICD-9-CM codes from hospital discharge data identified most infants with cardiac defects, but many were false positives. ICD-9-CM codes were inaccurate for specific cardiac defects., (Journal of Perinatology (2005) 25, 737-742. doi:10.1038/sj.jp.7211382; published online 15 September 2005.)

Objectives: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms., Methods: ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms., Results: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm., Conclusions: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

This short paper highlights some new work being performed at the National Centre for Classification in Health (NCCH), relating newly created term sets developed for specific purposes to existing reference terminologies and classifications such as SNOMED CT and ICD-10-AM. It describes some of the inherent difficulties experienced by the NCCH team in interpreting terms in the term set and therefore in locating equivalent concepts in reference terminologies and classifications, in the absence of a context with which to associate each term. Also examined is the effect that a person's background and past experience has on their understanding and interpretation of clinical terms and how this results in inconsistent "world views".

Background: Administrative hospital discharge data are widely used to assess quality of care in patients undergoing certain procedures. However, little is known about the validity of administrative coding of procedure data. We conducted a detailed chart review to evaluate the accuracy and completeness of information on procedures in administrative data., Methods: We randomly selected 1200 hospital separations in the period April 1, 1996, to March 31, 1997, from administrative discharge data of 3 acute adult hospitals in Calgary, Alberta, Canada. Each separation record in administrative data contains up to 10 procedure coding fields. The corresponding medical charts were reviewed for recording presence or absence of procedures. We then determined sensitivity to quantify the accuracy of coding presence of procedures in administrative data when these are present in the chart data (criterion standard)., Results: The agreement between the 2 databases varied greatly across 35 procedures studied. The sensitivity ranged from 0% to 94%. Of 6 major procedures studied, validity of coding was generally good, with 5 procedures having coding sensitivity of 69% and over and only 1 (lysis of peritoneal adhesion) with a low sensitivity of 41%. In contrast, many minor procedures had low sensitivities. Of 29 minor procedures studied, sensitivity was lower than 50% for 15 procedures, between 50% and 79% for 10, and 80% and over for 4., Conclusion: Validity of information on procedures in administrative discharge data appears to be related to type of procedures. Major procedures that are usually performed in operating rooms are reasonably well-coded. Meanwhile, minor procedures that are routinely performed on wards or in radiology departments are generally undercoded.

Practices depend on proper coding of procedures and diagnoses to obtain maximal reimbursement. Proficient coding by both educated physicians and staff is dependent on transmission of correct information. A well-designed encounter form is one key to enhance the process. This article provides some practical guidelines for making the encounter form more effective.

Unlabelled: Physicians are required to code information concerning a patient's stay in order to measure the medical activity in hospitals. They use the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). Coding is usually performed manually and computerized tools may be useful in speeding up and facilitating the tedious task of coding patient information. The aim of this work is to build a surface semantic model of ICD-10 in order to ameliorate a coding help system., Methods: This work was focused on chapter XI of the ICD-10, Diseases of the Digestive System. Each term from both analytical and alphabetical indexes about this chapter were submitted to a morphological analysis in order to extract the medical concepts within. After a statistical analysis of these concepts and the way they connect themselves, a semantic model based on a "semantic frame" approach was built., Results: Although this model could represent a reasonable amount of medical knowledge within chapter XI of the ICD-10 in a quite satisfactory way, it shows lack of efficiency for some other chapters., Conclusion: Difficulties have to be overcome when modelling a classification meant for manual utilisation, and a lot of work still has to be done to obtain an effective coding help system using the ICD-10.

Hospital separation data are used to study suicidal behaviour; however, there is little information about the appropriateness of these data for research and planning activities. The study purpose is to examine how consistently hospital separation E-code data reflect suicidal behaviours. Expert clinicians reviewed medical records of individuals who had a separation for self-poisoning to determine whether the self-poisoning was deliberate. Agreement among clinicians was evaluated and latent class analysis performed to derive a summary estimate of the prevalence of deliberate self-poisoning. This estimate was then compared to the prevalence of deliberate self-poisoning based on the external cause of injury (E-codes). Clinicians estimated the prevalence to be 63% higher than the E-code based prevalence. Much larger discrepancies were apparent among older age groups, those whose care was primarily medical in nature and those with a longer length of hospital stay. In acute care settings, self-poisonings among the elderly may not receive adequate attention and/or documentation. Estimating the prevalence of admissions for suicidal behaviour using hospital separation data is of questionable validity, particularly among older age groups.