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Accuracy of Clinical Coding in Healthcare Data

Works Count: 35649

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Description

This cluster of papers focuses on the accuracy and impact of clinical coding, particularly related to the International Classification of Diseases (ICD-9-CM and ICD-10) in healthcare administrative data. It addresses challenges, implications, and improvements in clinical coding, data quality, and the use of hospital discharge data for research and financial purposes.

Keywords

ICD-10; Clinical Coding; Health Information Management; Data Quality; Hospital Discharge Data; Administrative Data; Medical Documentation; Coding Accuracy; Healthcare System Productivity; ICD-11

Designing clinical research: An epidemiologic approach

1989-03-01

Stephen B. Hulley , Steven R. Cummings , Warren S. Browner +3 more

Comparability of cause of death between ICD-9 and ICD-10: preliminary estimates.

2001-05-18

Robert N. Anderson , Arialdi M. Miniño , Donna L. Hoyert +1 more

This report presents preliminary results describing the effects of implementing the Tenth Revision of the International Classification of Diseases (ICD-10) on mortality statistics for selected causes of death effective with … This report presents preliminary results describing the effects of implementing the Tenth Revision of the International Classification of Diseases (ICD-10) on mortality statistics for selected causes of death effective with deaths occurring in the United States in 1999. The report also describes major features of the Tenth Revision (ICD-10), including changes from the Ninth Revision (ICD-9) in classification and rules for selecting underlying causes of death. Application of comparability ratios is also discussed.The report is based on cause-of-death information from a large sample of 1996 death certificates filed in the 50 States and the District of Columbia. Cause-of-death information in the sample includes underlying cause of death classified by both ICD-9 and ICD-10. Because the data file on which comparability information is derived is incomplete, results are preliminary.Preliminary comparability ratios by cause of death presented in this report indicate the extent of discontinuities in cause-of-death trends from 1998 through 1999 resulting from implementing ICD-10. For some leading causes (e.g., Septicemia, Influenza and pneumonia, Alzheimer's disease, and Nephritis, nephrotic syndrome and nephrosis), the discontinuity in trend is substantial. The ranking of leading causes of death is also substantially affected for some causes of death.Results of this study, although preliminary, are essential to analyzing trends in mortality between ICD-9 and ICD-10. In particular, the results provide a means for interpreting changes between 1998, which is the last year in which ICD-9 was used, and 1999, the year in which ICD-10 was implemented for mortality in the United States.

Assessing Validity of ICD‐9‐CM and ICD‐10 Administrative Data in Recording Clinical Conditions in a Unique Dually Coded Database

2008-03-11

Hude Quan , Bing Li , L. Duncan Saunders +4 more

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 … 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.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.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.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.

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The Patient Record in Epidemiology

1981-10-01

Leonard T. Kurland , Craig A. Molgaard

Population-based linkage of health records in Western Australia: development of a health services research linked database

1999-10-01

C. D’Arcy J. Holman , A. Bass , Ian Rouse +1 more

A demographically based index of premorbid intelligence for the WAIS—R.

1984-10-01

Andrés Barona , Cecil R. Reynolds , Robert L. Chastain

Measuring Diagnoses: ICD Code Accuracy

2005-08-11

Kimberly J. O’Malley , Karon F. Cook , Matt D. Price +3 more

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 … 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.

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Validity of diagnostic coding within the General Practice Research Database: a systematic review

2010-02-26

Nada Khan , Siân Harrison , Peter W. Rose

The UK-based General Practice Research Database (GPRD) is a valuable source of longitudinal primary care records and is increasingly used for epidemiological research.To conduct a systematic review of the literature … The UK-based General Practice Research Database (GPRD) is a valuable source of longitudinal primary care records and is increasingly used for epidemiological research.To conduct a systematic review of the literature on accuracy and completeness of diagnostic coding in the GPRD.Systematic review.Six electronic databases were searched using search terms relating to the GPRD, in association with terms synonymous with validity, accuracy, concordance, and recording. A positive predictive value was calculated for each diagnosis that considered a comparison with a gold standard. Studies were also considered that compared the GPRD with other databases and national statistics.A total of 49 papers are included in this review. Forty papers conducted validation of a clinical diagnosis in the GPRD. When assessed against a gold standard (validation using GP questionnaire, primary care medical records, or hospital correspondence), most of the diagnoses were accurately recorded in the patient electronic record. Acute conditions were not as well recorded, with positive predictive values lower than 50%. Twelve papers compared prevalence or consultation rates in the GPRD against other primary care databases or national statistics. Generally, there was good agreement between disease prevalence and consultation rates between the GPRD and other datasets; however, rates of diabetes and musculoskeletal conditions were underestimated in the GPRD.Most of the diagnoses coded in the GPRD are well recorded. Researchers using the GPRD may want to consider how well the disease of interest is recorded before planning research, and consider how to optimise the identification of clinical events.

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Essentials of medical statistics.

1988-01-01

Betty Kirkwood

Basics Frequencies, frequency distributions and histograms Means, standard deviations and standard errors The normal distribution Confidence interval for a mean Significance tests for a single mean Comparison of two means … Basics Frequencies, frequency distributions and histograms Means, standard deviations and standard errors The normal distribution Confidence interval for a mean Significance tests for a single mean Comparison of two means Comparison of several means - analysis of variance Correlation and linear regression Multiple regression Probability Proportions The chi-squared test for contingency tables Further methods for contingency tables Measures of mortality and morbidity Survival analysis The Poisson distribution Goodness of fit of frequency distributions Transformations Non-parametric methods Planning and conducting an investigation Sources of error Sampling methods Cohort and case-control studies Clinical trials and intervention studies Calculation of required sample size Use of computers Appendix: Statistical tables

Presentation adapting a clinical comorbidity index for use with ICD-9-CM administrative data: Differing perspectives

1993-10-01

Patrick S. Romano , Leslíe L. Roos , James G. Jollis

Accuracy of ICD-9-CM Codes for Identifying Cardiovascular and Stroke Risk Factors

2005-04-18

Elena Birman-Deych , Amy D. Waterman , Ying Yan +3 more

We sought to determine which ICD-9-CM codes in Medicare Part A data identify cardiovascular and stroke risk factors.This was a cross-sectional study comparing ICD-9-CM data to structured medical record review … We sought to determine which ICD-9-CM codes in Medicare Part A data identify cardiovascular and stroke risk factors.This was a cross-sectional study comparing ICD-9-CM data to structured medical record review from 23,657 Medicare beneficiaries aged 20 to 105 years who had atrial fibrillation.Quality improvement organizations used standardized abstraction instruments to determine the presence of 9 cardiovascular and stroke risk factors. Using the chart abstractions as the gold standard, we assessed the accuracy of ICD-9-CM codes to identify these risk factors.ICD-9-CM codes for all risk factors had high specificity (>0.95) and low sensitivity (< or =0.76). The positive predictive values were greater than 0.95 for 5 common, chronic risk factors-coronary artery disease, stroke/transient ischemic attack, heart failure, diabetes, and hypertension. The sixth common risk factor, valvular heart disease, had a positive predictive value of 0.93. For all 6 common risk factors, negative predictive values ranged from 0.52 to 0.91. The rare risk factors-arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis-had high negative predictive value (> or =0.98) but moderate positive predictive values (range, 0.54-0.77) in this population.Using ICD-9-CM codes alone, heart failure, coronary artery disease, diabetes, hypertension, and stroke can be ruled in but not necessarily ruled out. Where feasible, review of additional data (eg, physician notes or imaging studies) should be used to confirm the diagnosis of valvular disease, arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis.

Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients

2014-07-01

David W. Bates , Suchi Saria , Lucila Ohno‐Machado +2 more

The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made … The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics—techniques for analyzing large quantities of data and gleaning new insights from that analysis—which is part of what is known as big data . As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases—that is, key examples—where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient’s condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure—analytics, algorithms, registries, assessment scores, monitoring devices, and so forth—that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.

A Quality Study of a Medical Birth Registry

1990-06-01

Sven Cnattingius , Anders Ericson , Jan Gunnarskog +1 more

A quality control study was made of the Swedish Medical Birth Registry. This registry used one mode of data collection during 1973-1981 and another from 1982 onwards. The number of … A quality control study was made of the Swedish Medical Birth Registry. This registry used one mode of data collection during 1973-1981 and another from 1982 onwards. The number of errors in the register was checked by comparing register information with a sample of the original medical records, and the variability in the use of diagnoses between hospitals was studied. Different types of errors were identified and quantified and the efficiency of the two methods of data collection evaluated.

A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data

2009-05-26

Carl van Walraven , Peter C. Austin , Alison Jennings +2 more

Background: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the … Background: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. Objective: Modify the Elixhauser classification system into a single numeric score for administrative data. Methods: For all hospitalizations at the Ottawa Hospital, Canada, between 1996 and 2008, we determined if International Classification of Disease codes for chronic diagnoses were in any of the 30 Elixhauser comorbidity groups. We then used backward stepwise multivariate logistic regression to determine the independent association of each comorbidity group with death in hospital. Regression coefficients were modified into a scoring system that reflected the strength of each comorbidity group's independent association with hospital death. Results: Hospitalizations that were included were 345,795 (derivation: 228,565; validation 117,230). Twenty-one of the 30 groups were independently associated with hospital mortality. The resulting comorbidity score had an equivalent discrimination in the derivation and validation groups (overall c-statistic 0.763, 95% CI: 0.759–0.766). This was similar to models having all Elixhauser groups (0.760, 95% CI: 0.756–0.764) or significant groups only (0.759, 95% CI: 0.754–0.762), but significantly exceeded discrimination when comorbidity was expressed using the Charlson score (0.745, 95% CI: 0.742–0.749). Conclusion: When analyzing administrative data, the Elixhauser comorbidity system can be condensed to a single numeric score that summarizes disease burden and is adequately discriminative for death in hospital.

Basic and clinical biostatistics

1991-04-01

Wayne A. Price

Validity of Information on Comorbidity Derived From ICD-9-CCM Administrative Data

2002-08-01

Hude Quan , Gerry A. Parsons , William A. Ghali

Background. The comorbidity variables that constitute the Charlson index are widely used in health care research using administrative data. However, little is known about the validity of administrative data in … Background. The comorbidity variables that constitute the Charlson index are widely used in health care research using administrative data. However, little is known about the validity of administrative data in these comorbidities. The agreement between administrative hospital discharge data and chart data for the recording of information on comorbidity was evaluated. The predictive ability of comorbidity information in the two data sets for predicting in-hospital mortality was also compared. Methods. One thousand two hundred administrative hospital discharge records were randomly selected in the region of Calgary, Alberta, Canada in 1996 and used a published coding algorithm to define the 17 comorbidities that constitute the Charlson index. Corresponding patient charts for the selected records were reviewed as the "criterion standard" against which validity of the administrative data were judged. Results. Compared with the chart data, administrative data had a lower prevalence in 10 comorbidities, a higher prevalence in 3 and a similar prevalence in 4. The κ values ranged from a high of 0.87 to a low of 0.34; agreement was therefore near perfect for one variable, substantial for six, moderate for nine, and only fair for one variable. For the Charlson index score ranging from 0 to 5 to 6 or higher, agreement was moderate to substantial (κ = 0.56, weighted κ = 0.71). When 16 Charlson comorbidities from administrative data were used to predict in-hospital mortality, 10 comorbidities and the index scores defined using administrative data yielded odds ratios that were similar to those derived from chart data. The remaining six comorbidities yielded odds ratios that were quite different from those derived from chart data. Conclusions. Administrative data generally agree with patient chart data for recording of comorbidities although comorbidities tend to be under-reported in administrative data. The ability to predict in-hospital mortality is less reliable for some of the individual comorbidities than it is for the summarized Charlson index scores in administrative data.

Applied Mixed Models in Medicine

2000-11-01

Helen Brown , Robin Presco

Applied mixed models in medicine , Applied mixed models in medicine , کتابخانه دیجیتال جندی شاپور اهواز Applied mixed models in medicine , Applied mixed models in medicine , کتابخانه دیجیتال جندی شاپور اهواز

Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

2005-10-13

Hude Quan , Vijaya Sundararajan , Patricia Halfon +7 more

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 … 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.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.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.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.

Practical considerations on the use of the charlson comorbidity index with administrative data bases

1996-12-01

William D’Hoore , A. Bouckaert , Charles Tilquin

International Classification of Primary Care

1986-01-01

Bent Guttorm Bentsen

"Health for all by year 2000" was the subject of the WHO Conference at Alma-Ata in 1978. It was evident that good primary care was a requirement to reach this … "Health for all by year 2000" was the subject of the WHO Conference at Alma-Ata in 1978. It was evident that good primary care was a requirement to reach this goal. However, knowledge about this was scanty, and the instrument, an acceptable classification for analyses of primary care, was lacking. Since 1978 a WHO Working Party on Classifications of Primary Care has been working on a Reason for Encounter Classification. A RFEC test form was produced. In 1983 a feasibility study was conducted in nine countries: Australia, Barbados, Brazil, Hungary, Malaysia, The Netherlands, Norway, the Philippines, and the USA. The results of this were changing the original proposal very much. In addition, the WONCA/WHO Classification of Health Problems in Primary Care was included in the final version. In 1984 this final version was accepted by WONCA Classification Committee. This is called ICPC = The International Classification of Primary Care. ICPC is biaxial with the chapters of organ/organ systems along the one axis, in addition of three chapters: General, Mental, and Social problems. The other axis comprises seven components: Complaints, Process and Diagnosis. An alphanumeric code is used. The feasibility study of RFEC comprised ten test sites, and 138 primary care professionals recorded a total of 100 452 reasons for encounter. The English version of the RFEC was translated into five other languages, and these versions were used during the study. ICPC is a comprehensive, simple and practicable classification which can be used in medical records and in different areas of primary care research.

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The accuracy of Medicare's hospital claims data: progress has been made, but problems remain.

1992-02-01

Elliot S. Fisher , Fredrick S. Whaley , W. Mark Krushat +4 more

Health care databases provide a widely used source of data for health care research, but their accuracy remains uncertain. We analyzed data from the 1985 National DRG Validation Study, which … Health care databases provide a widely used source of data for health care research, but their accuracy remains uncertain. We analyzed data from the 1985 National DRG Validation Study, which carefully reabstracted and reassigned ICD-9-CM diagnosis and procedure codes from a national sample of 7050 medical records, to determine whether coding accuracy had improved since the Institute of Medicine studies of the 1970s and to assess the current coding accuracy of specific diagnoses and procedures.We defined agreement as the proportion of all reabstracted records that had the same principal diagnosis or procedure coded on both the original (hospital) record and on the reabstracted record. We also evaluated coding accuracy in 1985 using the concepts of diagnostic test evaluation.Overall, the percentage of agreement between the principal diagnosis on the reabstracted record and the original hospital record, when analyzed at the third digit, improved from 73.2% in 1977 to 78.2% in 1985. However, analysis of the 1985 data demonstrated that the accuracy of diagnosis and procedure coding varies substantially across conditions.Although some diagnoses and all major surgical procedures that we examined were accurately coded, the variability in the accuracy of diagnosis coding poses a problem that must be overcome if claims-based research is to achieve its full potential.

Clinical Epidemiology: The Architecture of Clinical Research.

1985-10-01

Alvan R. Feinstein

Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries

2011-02-17

Hude Quan , Bing Li , Chantal Marie Couris +5 more

With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed … With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.

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International statistical classification of diseases and related health problems. Tenth revision.

1988-01-01

G. R. Brämer

The International Classification of Diseases has, under various names, been for many decades the essential tool for national and international comparability in public health. This statistical tool has been customarily … The International Classification of Diseases has, under various names, been for many decades the essential tool for national and international comparability in public health. This statistical tool has been customarily revised every 10 years in order to keep up with the advances of medicine. At first intended primarily for the classification of causes of death, its scope has been progressively widening to include coding and tabulation of causes of morbidity as well as medical record indexing and retrieval. The ability to exchange comparable data from region to region and from country to country, to allow comparison from one population to another and to permit study of diseases over long periods, is one of the strengths of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD). WHO has been responsible for the organization, coordination and execution of activities related to ICD since 1948 (Sixth Revision of the ICD) and is now proceeding with the Tenth Revision. For the first time in its history the ICD will be based on an alphanumeric coding scheme and will have to function as a core classification from which a series of modules can be derived, each reaching a different degree of specificity and adapted to a particular specialty or type of user. It is proposed that the chapters on external causes of injury and poisoning, and factors influencing health status and contact with health services, which were supplementary classifications in ICD-9, should form an integral part of ICD-10. The title of ICD has been amended to "International Statistical Classification of Diseases and Related Health Problems"', but the abbreviation "ICD" will be retained.(ABSTRACT TRUNCATED AT 250 WORDS)

Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

1992-06-01

Richard A. Deyo

Counting the dead and what they died from: an assessment of the global status of cause of death data.

2005-03-01

Colin Mathers , Doris Ma Fat , Mie Inoue +2 more

We sought to assess the current status of global data on death registration and to examine several indicators of data completeness and quality.We summarized the availability of death registration data … We sought to assess the current status of global data on death registration and to examine several indicators of data completeness and quality.We summarized the availability of death registration data by year and country. Indicators of data quality were assessed for each country and included the timeliness, completeness and coverage of registration and the proportion of deaths assigned to ill-defined causes.At the end of 2003 data on death registration were available from 115 countries, although they were essentially complete for only 64 countries. Coverage of death registration varies from close to 100% in the WHO European Region to less than 10% in the African Region. Only 23 countries have data that are more than 90% complete, where ill-defined causes account for less than 10% of total of causes of death, and where ICD-9 or ICD-10 codes are used. There are 28 countries where less than 70% of the data are complete or where ill-defined codes are assigned to more than 20% of deaths. Twelve high-income countries in western Europe are included among the 55 countries with intermediate-quality data.Few countries have good-quality data on mortality that can be used to adequately support policy development and implementation. There is an urgent need for countries to implement death registration systems, even if only through sample registration, or enhance their existing systems in order to rapidly improve knowledge about the most basic of health statistics: who dies from what?

Statistical Evaluation of Prognostic versus Diagnostic Models: Beyond the ROC Curve

2007-11-17

Nancy R. Cook

Abstract Background: Diagnostic and prognostic or predictive models serve different purposes. Whereas diagnostic models are usually used for classification, prognostic models incorporate the dimension of time, adding a stochastic element. … Abstract Background: Diagnostic and prognostic or predictive models serve different purposes. Whereas diagnostic models are usually used for classification, prognostic models incorporate the dimension of time, adding a stochastic element. Content: The ROC curve is typically used to evaluate clinical utility for both diagnostic and prognostic models. This curve assesses how well a test or model discriminates, or separates individuals into two classes, such as diseased and nondiseased. A strong risk predictor, such as lipids for cardiovascular disease, may have limited impact on the area under the curve, called the AUC or c-statistic, even if it alters predicted values. Calibration, measuring whether predicted probabilities agree with observed proportions, is another component of model accuracy important to assess. Reclassification can directly compare the clinical impact of two models by determining how many individuals would be reclassified into clinically relevant risk strata. For example, adding high-sensitivity C-reactive protein and family history to prediction models for cardiovascular disease using traditional risk factors moves approximately 30% of those at intermediate risk levels, such as 5%–10% or 10%–20% 10-year risk, into higher or lower risk categories, despite little change in the c-statistic. A calibration statistic can asses how well the new predicted values agree with those observed in the cross-classified data. Summary: Although it is useful for classification, evaluation of prognostic models should not rely solely on the ROC curve, but should assess both discrimination and calibration. Risk reclassification can aid in comparing the clinical impact of two models on risk for the individual, as well as the population.

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International Classification of Diseases

1978-12-01

F. Avery Jones

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PRACTICAL STATISTICS FOR MEDICAL RESEARCH

1991-12-01

John Ludbrook

Systematic review of discharge coding accuracy

2011-07-27

Elaine M. Burns , Elizabeth Rigby , Ravikrishna Mamidanna +4 more

IntroductionRoutinely collected data sets are increasingly used for research, financial reimbursement and health service planning. High quality data are necessary for reliable analysis. This study aims to assess the published … IntroductionRoutinely collected data sets are increasingly used for research, financial reimbursement and health service planning. High quality data are necessary for reliable analysis. This study aims to assess the published accuracy of routinely collected data sets in Great Britain.

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Development of a comorbidity index using physician claims data

2000-12-01

Carrie N. Klabunde , Arnold L. Potosky , Julie Legler +1 more

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

1982-04-01

J A Hanley , Barbara J. McNeil

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It … A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.

Sorting Things Out: Classification and Its Consequences

2000-09-01

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New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality

2004-12-01

Vijaya Sundararajan , Toni Henderson , Catherine Perry +3 more

Coding of Stroke and Stroke Risk Factors Using <i>International Classification of Diseases</i> , Revisions 9 and 10

2005-07-15

R Kokotailo , Michael D. Hill

Background and Purpose— Surveillance is necessary to understand and meet the future demands stroke will place on health care. Administrative data are the most accessible data source for stroke surveillance … Background and Purpose— Surveillance is necessary to understand and meet the future demands stroke will place on health care. Administrative data are the most accessible data source for stroke surveillance in Canada. The International Classification of Diseases , 10th revision (ICD-10) coding system has potential improvements over ICD-9 for stroke classification. Our purpose was to compare hospital discharge abstract coding using ICD-9 and ICD-10 for stroke and its risk factors. Methods— We took advantage of a switch in coding systems from ICD-9 to ICD-10 to independently review stroke patient charts. From time periods April 2000 to March 2001, 717 charts, and from April 2002 to March 2003, 249 charts were randomly selected for review. Using a before-and-after time period design, the accuracy of hospital coding of stroke (part I) and stroke risk factors (part II) using ICD-9 and ICD-10 was compared. We used careful definitions of stroke and its types based on ICD-9 using the fourth and fifth digit modifier codes. Results— Stroke coding was equally good with ICD-9 (90% [CI 95 86 to 93] correct) and ICD-10 [92% (CI 95 88 to 95 correct) with ICD-10. There were some differences in coding by stroke type, notably with transient ischemic attack, but these differences were not statistically significant. Atrial fibrillation, coronary artery disease/ischemic heart disease, diabetes mellitus, and hypertension were coded with high sensitivity (81% to 91%) and specificity (83% to 100%). ICD-10 was as good as ICD-9 for stroke risk factor coding. Conclusions— Passive surveillance using administrative data are a useful tool for identifying stroke and its risk factors using both ICD-9 and ICD-10.

Clinical Prediction Models

2019-01-01

Ewout W. Steyerberg

Clinical epidemiology: The essentials

1988-12-01

Pinuccia Valagussa

International Classification of Diseases, 10th Revision

2018-01-01

P Loy

International Classification of Diseases, Ninth Revision

2020-02-07

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Validity of diagnoses, procedures, and laboratory data in Japanese administrative data

2017-01-28

Hayato Yamana , Mutsuko Moriwaki , Hiromasa Horiguchi +3 more

Validation of recorded data is a prerequisite for studies that utilize administrative databases. The present study evaluated the validity of diagnoses and procedure records in the Japanese Diagnosis Procedure Combination … Validation of recorded data is a prerequisite for studies that utilize administrative databases. The present study evaluated the validity of diagnoses and procedure records in the Japanese Diagnosis Procedure Combination (DPC) data, along with laboratory test results in the newly-introduced Standardized Structured Medical Record Information Exchange (SS-MIX) data.Between November 2015 and February 2016, we conducted chart reviews of 315 patients hospitalized between April 2014 and March 2015 in four middle-sized acute-care hospitals in Shizuoka, Kochi, Fukuoka, and Saga Prefectures and used them as reference standards. The sensitivity and specificity of DPC data in identifying 16 diseases and 10 common procedures were identified. The accuracy of SS-MIX data for 13 laboratory test results was also examined.The specificity of diagnoses in the DPC data exceeded 96%, while the sensitivity was below 50% for seven diseases and variable across diseases. When limited to primary diagnoses, the sensitivity and specificity were 78.9% and 93.2%, respectively. The sensitivity of procedure records exceeded 90% for six procedures, and the specificity exceeded 90% for nine procedures. Agreement between the SS-MIX data and the chart reviews was above 95% for all 13 items.The validity of diagnoses and procedure records in the DPC data and laboratory results in the SS-MIX data was high in general, supporting their use in future studies.

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Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum

2019-02-25

Achim Wolf , Daniel Dedman , Jennifer Campbell +4 more

Data resource basicsClinical Practice Research Datalink (CPRD) is a UK government, not-for-profit research service that has been supplying anonymized primary care data for public health research for more than 30 … Data resource basicsClinical Practice Research Datalink (CPRD) is a UK government, not-for-profit research service that has been supplying anonymized primary care data for public health research for more than 30 years.In October 2017 CPRD launched a new data resource called CPRD Aurum.CPRD Aurum is a database containing routinely collected data from primary care practices in England, capturing diagnoses, symptoms, prescriptions, referrals and tests for over 19 million patients as of September 2018 (Figure 1).Primary care data in CPRD Aurum have been linked to national secondary care databases as well as deprivation and death registration data (Table 1). UK primary careThe United Kingdom's (UK) National Health Service (NHS) is a publicly funded health service, free at the point of use.General practitioners (GPs) are considered the 'gatekeepers' of the NHS, referring patients to secondary care and diagnostic tests. 1 Over 98% of the population is registered at one of the 7300 GP practices in England. 2 A unique patient identifier, the NHS number, is used in primary, secondary and tertiary care settings, enabling linkages to other data sources. 3There are four principal GP IT systems (primary care patient management software system) suppliers in England 4 and the largest coverage is provided by EMIS Health V R (EMIS Web V R software is used in 56% of English practices). 5CPRD Aurum, discussed in this Data Resource Profile, encompasses EMIS Web V R GP practices that have agreed to contribute data to this database on a daily basis.CPRD also collects data from practices using Vision V R software that contribute to the CPRD GOLD database, which has been used in epidemiological research for 30 years. 6 CPRD AurumCPRD Aurum includes patient electronic healthcare records (EHR) collected routinely in primary care.When a practice agrees to contribute patient data to CPRD Aurum, CPRD receives a full historic collection of the coded part of the practice's electronic health records, which includes data on deceased patients and those who have left the practice.Since 25 May 2018, individuals in England can optout of sharing their confidential patient information for research purposes 7 and, as of 1 September 2018, 2.7% of the English primary care registered population had opted-out. 8 As of September 2018, CPRD Aurum included 7 million patients who were alive and registered at EMIS Web V R currently contributing practices (Table 2), representing around 13% of the population of England.This number

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Vital and health statistics

1978-09-01

Multiple imputation: a primer

1999-01-01

Joseph L. Schafer

International Statistical Classification of Diseases and Related Health Problems

2010-12-14

Vital Health Statistics

2017-01-01

R Sudha

International Statistical Classification of Diseases and Related Health Problems 10th Revision

2013-08-01

Subject Psychiatry Collection: Oxford Medicine Online Subject Psychiatry Collection: Oxford Medicine Online

Achievements and Obstacles in the Late Phase of the Global Polio Eradication Initiative.

2025-07-25

Olaf Müller , Guangyu Lu , Peter Meißner +3 more

The Global Polio Eradication Initiative (GPEI), founded in 1988, has contributed to a drastic reduction in the number of cases of wild poliovirus (WPV) infection. Progress has stalled for years, … The Global Polio Eradication Initiative (GPEI), founded in 1988, has contributed to a drastic reduction in the number of cases of wild poliovirus (WPV) infection. Progress has stalled for years, however, even though the GPEI has become a very costly global health program. Poliomyelitis is caused by WPV types 1, 2, and 3, as well as by mutated vaccine viruses. This review is based on publications retrieved by a selective literature search relating to challenges that currently face the GPEI, with an emphasis on the situation in Germany, e.g., the problem of maintaining the high rate of vaccination coverage. WPV1 remains endemic in Pakistan and Afghanistan. In addition, outbreaks caused by viral mutants of oral live polio vaccines (OPV) have become a problem in countries with low vaccination coverage, with several thousand cases since 2000. Industrialized countries have also had rare cases of poliomyelitis in recent years, caused by mutated vaccine viruses, which often circulate undetected. Aside from the dysfunctionality of the health care systems of many countries, geopolitical tensions, international and civil wars, mass human migration, hesitancy and skepticism of the population about vaccination, and funding fatigue on the part of donor countries, there are a variety of technical problems confronting the GPEI in its quest for success. Maintaining high polio vaccination rates may be a more realistic solution to the problem of polio than continuing to pursue the GPEI's objective of putting all polioviruses out of existence. Doctors in Germany can actively contribute to the achievement of both these goals by checking the polio vaccination status of their patients, as recommended by the German Standing Committee on Vaccination (STIKO). This is especially important when doctors care for refugees and asylum-seekers who have arrived in Germany from abroad.

Analisis Kelengkapan Resume Medis dan Akurasi Koding Terhadap Ketepatan Klaim BPJS Di RSUD Provinsi NTB Tahun 2024

2025-06-24

Mega Sara Yulianti , Husnuzzia Urrahmah | JUKEJ Jurnal Kesehatan Jompa

Ketidaklengkapan resume medis menjadi salah satu penyebab tertundanya klaim BPJS yang berdampak pada keterlambatan pembayaran dan potensi kerugian finansial bagi rumah sakit. Kelengkapan dokumen serta keakuratan kode diagnosis merupakan komponen … Ketidaklengkapan resume medis menjadi salah satu penyebab tertundanya klaim BPJS yang berdampak pada keterlambatan pembayaran dan potensi kerugian finansial bagi rumah sakit. Kelengkapan dokumen serta keakuratan kode diagnosis merupakan komponen penting dalam mempercepat pengajuan dan pencairan klaim sesuai ketentuan INA-CBGs. Penelitian ini bertujuan untuk mengetahui persentase kelengkapan resume medis rawat inap dan keakuratan kode diagnosis terhadap ketepatan waktu klaim BPJS di RSUD Provinsi NTB. Penelitian menggunakan metode deskriptif kuantitatif dengan pendekatan cross-sectional. Populasi sebanyak 7.885 resume medis pasien rawat inap Oktober–Desember 2023, dengan sampel 99 dokumen. Data dikumpulkan melalui observasi dan dianalisis menggunakan SPSS. Kelengkapan tertinggi tercatat pada aspek tidak adanya tipe-x sebesar 100%, sedangkan ketidaklengkapan tertinggi pada autentikasi nama dan gelar dokter sebesar 53,5%. Keakuratan kode diagnosis menunjukkan 84 dokumen (84,7%) dikode benar, 3 dokumen (3,1%) tidak sesuai, dan 12 dokumen (12,1%) tidak dikode. Ketepatan waktu klaim BPJS menunjukkan 84 dokumen (84,7%) tepat waktu dan 15 dokumen (15,3%) terlambat. Kelengkapan autentikasi dan akurasi kode diagnosis masih belum optimal dan menjadi faktor yang memengaruhi ketepatan waktu klaim. Disarankan untuk menyusun dan menerapkan SOP pengisian resume medis yang menekankan autentikasi dan akurasi kode diagnosis. Perlu dilakukan pelatihan rutin bagi dokter dan petugas rekam medis sesuai standar verifikasi BPJS. Audit internal berkala juga perlu dilakukan untuk memastikan kesesuaian resume medis dengan standar INA-CBGs dan PMK No. 76 Tahun 2016.

Verification of the high-sensitivity troponin I assay method on Abbott Alinity I: Experience of the central Biochemistry Laboratory of Ibn Sina University Hospital in Rabat

2025-06-23

Salima Lakrami , Loubna Zerrouki , Mounya Bouabdellah +3 more | World Journal of Biology Pharmacy and Health Sciences

Verification of an analytical method in clinical laboratories is an essential process under the responsibility of biologists. It consists in assessing the analytical performance of a method and/or technique for … Verification of an analytical method in clinical laboratories is an essential process under the responsibility of biologists. It consists in assessing the analytical performance of a method and/or technique for assaying a biological parameter, in order to guarantee accurate and reliable results that benefit both the patient and the prescriber. The aim of our work, carried out in the Central Biochemistry Laboratory of the Ibn Sina University Hospital in Rabat-salé, is to verify the Troponin I (cTnI) assay « Range A » on Abbott's Alinity i automated system, using the chemiluminescent microparticle immunoassay (CMIA) method. The methodology adopted was based on the recommendations of the COFRAC (Comité Français d'Accréditation) accreditation technical guide, involving evaluation of the main analytical performances: repeatability, reproducibility and method comparison, plus an external quality assessment (EQA) to measure accuracy. The results obtained from this evaluation were compliant, and the CVs (coefficients of variation) produced were compliant and satisfactory with the supplier's data and with the Ricos learned society.

Pengodean Kasus Cedera, Keracunan Dan External Cause Pada Sistem Informasi Puskesmas

2025-06-19

Hendra Rohman , Ais Prawirya , Fadia Fadia | JURNAL ILMU KESEHATAN BHAKTI SETYA MEDIKA

In health center, there was still errors in filling in disease codes. In this case was on cases of injury, poisoning and external cause codes that were not coded up … In health center, there was still errors in filling in disease codes. In this case was on cases of injury, poisoning and external cause codes that were not coded up to the 5th character. This happened because doctors and nurses did not understand the coding procedures. This study aims to identify coding process, calculate the percentage of code accuracy, identify factors causing inaccuracy in coding injuries, poisoning and external causes at the Bambanglipuro Health Center. The provision of disease diagnosis codes at health center was carried out after the nurse had finished filling in the assessment, doctor input diagnosis in SIMPUS, and ICD code automatically appeared. The percentage of correct diagnosis codes for cases of poisoning injuries and external causes of outpatients at Bambanglipuro Health Center, Bantul in period 2023 from a total sample of 71 medical records, number of correct diagnosis codes was 20 medical records (28%), and number of incorrect diagnosis codes was 51 medical records (72%). The cause was human factor (human), namely human resources who did not meet the competence of medical creators, special training had not been provided for coding officers and external causes were not coded. Method factors are that there is no SOP on disease coding system. Measuring implementation of disease diagnosis coding is carried out by re-examination by medical record officers who have competence in disease coding. Fulfilment of human resources according to qualifications affects work outcomes in UKRM. The ICD database on SIMPUS needs to be reviewed and data updated by vendors so that code selection can be available more specifically. Classification of code determination with ICD rules can describe the journey of a patient's medical record history more specifically.

New GP registrars: A toolkit for learning

2025-06-14

Elizabeth McGeorge | InnovAiT Education and inspiration for general practice

Exploring discrepancies in clinical coding between rural and urban hospitals in Aotearoa New Zealand in patients who underwent interhospital transfer

2025-06-12

Anna Donaldson , Rory Miller , Garry Nixon +1 more | Rural and Remote Health

The agreement of clinical coding between rural and urban hospitals in Aotearoa New Zealand (NZ) is unknown, and data from comparable international health systems is scarce, dated or inconclusive. There … The agreement of clinical coding between rural and urban hospitals in Aotearoa New Zealand (NZ) is unknown, and data from comparable international health systems is scarce, dated or inconclusive. There is a reliance upon administrative datasets that store clinically coded information to complete numerous rural-urban health analyses, which inform health policy and resource allocation decisions. Anecdotally, clinical coding in NZ rural hospitals is often performed by clinicians or reception staff without formal coding training; in urban NZ hospitals this would usually be completed by formally trained clinical coders. This study aimed to determine whether discrepancies existed between the primary diagnosis codes assigned in the National Minimum Dataset (hospital events) (NMDS) of hospital discharges by NZ's publicly funded hospitals, for patients who underwent an interhospital transfer from a rural to an urban hospital. This was a retrospective observational study using the NMDS. NZ's publicly funded hospitals were classified into three categories: rural hospitals, hospitals in small urban centres and hospitals in large urban centres. Interhospital transfers were identified by bundling events in the NMDS into healthcare encounters. The primary diagnosis codes assigned at discharge from the rural hospital were compared against the codes assigned at discharge from the urban hospital, and corresponding diagnosis groups based on the WHO chapter definitions were assigned to each code. The number and percentage, with 95% confidence intervals (CIs), of encounters where there was discordance between primary diagnosis codes from the rural and urban hospitals were calculated. The study included 31,691 patients, from 54 publicly funded hospitals, who underwent an interhospital transfer from an NZ rural to an urban hospital between 1 January 2015 and 31 December 2019. There were discrepancies in 64.1% (95%CI 63.5-64.6%) of the primary diagnosis codes assigned between the rural and urban hospitals, and in 32.1% (95%CI 31.6-32.6%) of broader diagnosis groups. In both cases, higher discrepancies existed for transfers to hospitals in small urban centres compared to hospitals in large urban centres. The most frequently assigned diagnosis group at discharge from rural hospitals was the non-specific group 'other', constituting 24.4% of all diagnosis groups assigned by a rural hospital. For 4.8% of all healthcare encounters, a specific diagnosis group assigned on discharge from the rural hospital was subsequently changed to 'other' at the urban transfer hospital. This reassignment to 'other' following interhospital transfer occurred within every diagnosis group assigned at a rural hospital. Two-thirds of primary diagnosis codes and one-third of diagnosis groups were discordant after transfer from rural to urban hospitals in NZ. Further investigation is needed into why these discrepancies are occurring.

OP0123-PARE HOW CAN WE HELP: INTRODUCTION MAPS FOR PEOPLE WITH A RECENT RMD DIAGNOSIS

2025-06-01

N. Caeyers , V. De Pourcq , D. Segers | Annals of the Rheumatic Diseases

NOVEL CTCL RISK STRATIFIER INTEGRATING LYMPH NODE VOLUMETRICS AND mSWAT TO PREDICT SURVIVAL AND THERAPY RESPONSE

2025-06-01

Larisa J. Geskin , A Kaminsky , Lauren M. Fahmy +7 more | Hematological Oncology

Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia

2025-06-01

Chris A Rees , Rodrick Kisenge , Evance Godfrey +7 more | BMJ Paediatrics Open

Background The time after hospital discharge carries high rates of mortality in neonates and young children in sub-Saharan Africa. Previous work using logistic regression to develop risk assessment tools to … Background The time after hospital discharge carries high rates of mortality in neonates and young children in sub-Saharan Africa. Previous work using logistic regression to develop risk assessment tools to identify those at risk for postdischarge mortality has yielded fair discriminatory value. Our objective was to determine if machine learning models would have greater discriminatory value to identify neonates and young children at risk for postdischarge mortality. Methods We conducted a planned secondary analysis of a prospective observational cohort at Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia. We enrolled neonates and young children near the time of discharge. The outcome was 60-day postdischarge mortality. We collected socioeconomic, demographic, clinical, and anthropometric data during hospital admission and used machine learning (ie, eXtreme Gradient Boosting (XGBoost), Hist-Gradient Boost, Support Vector Machine, Neural Network, and Random Forest) to develop risk assessment tools to identify: (1) neonates and (2) young children at risk for postdischarge mortality. Results A total of 2310 neonates and 1933 young children enrolled. Of these, 71 (3.1%) neonates and 67 (3.5%) young children died after hospital discharge. XGBoost, Hist Gradient Boost, and Neural Network models yielded the greatest discriminatory value (area under the receiver operating characteristic curves range: 0.94–0.99) and fewest features, which included six features for neonates and five for young children. Discharge against medical advice, low birth weight, and supplemental oxygen requirement during hospitalisation were predictive of postdischarge mortality in neonates. For young children, discharge against medical advice, pallor, and chronic medical problems were predictive of postdischarge mortality. Conclusions Our parsimonious machine learning-based models had excellent discriminatory value to predict postdischarge mortality among neonates and young children. External validation of these tools is warranted to assist in the design of interventions to reduce postdischarge mortality in these vulnerable populations.

Using machine learning to identify ophthalmology subspecialty care and advance workforce research with the IRIS® Registry

2025-06-01

Ju Hyun Jeon , Ju‐Yeun Lee , Tobias Elze +7 more | Ophthalmology Science

Towards Precision One-year Postpartum Surveillance: Methodological Considerations for Temporal and Sample Size Stratification in Complication Reporting

2025-06-01

Hong Chen , Xuefeng Li , Xueqing Wu | American Journal of Obstetrics and Gynecology

Analisis Tren Temporal Kasus Penyakit Pernapasan Berdasarkan ICD-10 Di Kota Semarang

2025-06-01

Ghina Fatin Qothrunnida , Okti Truhastuti Dyah R. , Ahmad Zaini | Malahayati Nursing Journal

ABSTRACT The human respiratory system is susceptible to various diseases that are influenced by exposure to bacteria, viruses and air pollutants. Respiratory tract diseases are a major cause of morbidity … ABSTRACT The human respiratory system is susceptible to various diseases that are influenced by exposure to bacteria, viruses and air pollutants. Respiratory tract diseases are a major cause of morbidity and death, with symptoms varying from mild flu to severe conditions. Delay in diagnosis increases the risk of death, making prevention and management of this disease a public health priority. This study analyzes the temporal trends of respiratory tract diseases in Semarang City from January 2014 to July 2023 using ICD-10 data. Weather factors, air pollution and seasonal allergens influence case fluctuations, with a spike in cases from January to March. Exposure to air pollution, especially PM2.5 and ozone, contributes to an increase in respiratory diseases. Climate change is also affecting the prevalence of the disease, with extreme temperatures and high humidity increasing the risk. The results show the importance of season-based interventions to reduce the burden of respiratory diseases, including pollution control and public education. Keywords: Epidemiological Trends, Air Pollution, Climate Change, Air Temperature, Respiratory Tract Diseases ABSTRAK Sistem pernapasan manusia rentan terhadap berbagai penyakit yang dipengaruhi oleh paparan bakteri, virus, dan polutan udara. Penyakit saluran pernapasan merupakan penyebab utama kesakitan dan kematian, dengan gejala yang bervariasi, dari flu ringan hingga kondisi berat. Keterlambatan diagnosis meningkatkan risiko kematian, sehingga pencegahan dan pengelolaan penyakit ini menjadi prioritas kesehatan masyarakat. Penelitian ini menganalisis tren temporal penyakit saluran pernapasan di Kota Semarang dari Januari 2014 hingga Juli 2023 menggunakan data ICD-10. Faktor cuaca, polusi udara, dan alergen musiman mempengaruhi fluktuasi kasus, dengan lonjakan kasus pada bulan Januari hingga Maret. Paparan polusi udara, terutama PM2.5 dan ozon, berkontribusi terhadap peningkatan penyakit pernapasan. Perubahan iklim juga mempengaruhi prevalensi penyakit ini, dengan suhu ekstrim dan kelembaban tinggi dapat meningkatkan risiko. Hasil penelitian menunjukkan pentingnya intervensi berbasis musim untuk mengurangi beban penyakit saluran pernapasan, termasuk pengendalian polusi dan edukasi masyarakat. Kata Kunci: Tren Epidemiologi, Pencemaran Udara, Perubahan Iklim, Suhu Udara, Penyakit Saluran Pernapasan.

NSQIP and NHSN: A Tale of Two Definitions

2025-05-31

Margaret M. Gilman , Kathleen Kiesel | American Journal of Infection Control

Examining Benefits of a Multi-Hospital System Centralized Surveillance Team

2025-05-31

Megan E. Dizon , Amy Goodsell , Glenn Schellinger | American Journal of Infection Control

INSIGHTS FROM A 2024 CLINICAL AUDIT OF THYROID STORM CASES AT HOSPITAL SULTAN ISMAIL, JOHOR BAHRU

2025-05-30

Krishnaveni Kandasamy , Min Yi Lau , Ken Seng Chiew | Journal of the ASEAN Federation of Endocrine Societies

INTRODUCTION/BACKGROUNDThyroid storms require immediate medical intervention due to the risk of rapid multi-organ failure and high mortality. Therefore, a retrospective audit of thyroid storm management at Hospital Sultan Ismail, Johor … INTRODUCTION/BACKGROUNDThyroid storms require immediate medical intervention due to the risk of rapid multi-organ failure and high mortality. Therefore, a retrospective audit of thyroid storm management at Hospital Sultan Ismail, Johor Bahru (HSIJB) was done, to ascertain strengths and identify areas for improvement. METHODOLOGYThis audit analyzed all 17 thyroid storm cases admitted to HSIJB in 2024 (1st January 2024 to 31st December 2024). Data was extracted from electronic medical records. RESULTMost were female (70%, n = 12) with mean age of 48 years (range 26 to 75 years). All had Bursch-Wartofsky Point Scale of at least 45 (range 45 to 140). The commonest presentation was cardiovascular manifestations (100% tachycardia, 76% atrial fibrillation, 58% heart failure), followed by gastrointestinal-hepatic dysfunction (53%) and CNS effects (47%). All 5 ventilated patients were co-managed in the ICU. Predominant etiology was Graves' disease (88%, n = 15), with a case of gestational trophoblastic disease. Main precipitants were medication non-adherence (50%, n = 8), infection (23%, n = 4), and new thyroid diagnosis (29%, n = 5). Treatment was initiated within 6 hours of presentation in 82% of cases (n = 14). In the remaining 3 cases, treatment was delayed by up to 9 hours while awaiting TFT results, as these patients had no prior history of thyrotoxicosis. Aside from one death within 3 days due to thyroid storm and tubo-ovarian abscess, there was no other mortality at up to 180 days after discharge. CONCLUSIONTimely intervention in thyroid storms is critical to optimize patient outcomes. However, diagnosis can be challenging, particularly in patients without known thyroid disorders, which may result in delayed treatment. As such, it is essential to initiate therapy promptly based on strong clinical suspicion, even prior to laboratory results. Additionally, addressing issues related to treatment non-adherence through targeted patient education is vital to reduce the incidence of thyroid storm.

Decreasing inappropriate MRCP with contrast exams: impact of an EMR-Embedded clinical care pathway

2025-05-29

Daniella Asch , Gowthaman Gunabushanam , Kelsey O. Cole +2 more | Abdominal Radiology

Risk of acute care events: Designing prognostic models for real world use.

2025-05-28

Jennifer Elston Lafata , Jacob Stein , Soroush Fariman +2 more | Journal of Clinical Oncology

e13562 Background: Electronic health records (EHRs) combined with advances in cloud processing and computing power have made prognostic modeling a growth area in biomedical research. Despite calls to deploy these … e13562 Background: Electronic health records (EHRs) combined with advances in cloud processing and computing power have made prognostic modeling a growth area in biomedical research. Despite calls to deploy these models in practice, they are rarely used in part because of challenges integrating models within EHRs, clinical workflows or both. We address this translational bottleneck in the context of risk for emergency department visits and/or unplanned hospitalizations (i.e., acute care events, ACEs) among people receiving systemic cancer treatment by partnering with a Clinical Advisory Panel (CAP) and EHR specialists to consider clinical integration during model development. Here we present the resulting prognostic model, illustrating the influence of advisors on model development. Methods: We identified adults aged 21+ with cancer initiating systemic therapy in 2022 at an academic medical center or six affiliated community sites. We compiled clinical and socio-demographic characteristics from structured EHR data, including age, sex, cancer diagnosis, anti-cancer drug orders and infusions, supportive medication orders, and prior ACEs. We used geocoding to characterize patients' residential community. Data were divided into 50% training, 25% validation and 25% test sets. Prognostic models were fit iteratively alongside discussions with the CAP and other advisors to optimize model design, variable construction, and guidance for use with risk-stratified supportive interventions. After initial discussion, elastic net regression was used to guide initial variable selection. Final models were estimated with ridge regression. Results: 4697 study eligible patients were identified. Based on feedback, we identified a need for two prognostic models. First, a baseline model that used information from oncology treatment plans to predict ACE risk prior to therapy initiation, enabling proactive intervention. Second, a time-updated model that used infusion room dispensing information to continually re-calculate ACE risk in 30-day increments based on updated treatment and clinical events over patients’ course of therapy. In both models, ACE in the past month emerged as the strongest predictor, displaying the largest standardized coefficient magnitude following penalization (OR = 1.91 and 1.60, respectively). The initial model had a C-statistic of 0.70 and the time-updated model had a C-statistic of 0.72. Conclusions: We present prognostic models designed for clinical use with risk-stratified supportive interventions. By limiting predictors to structured EHR data fields, considering clinical workflows, and planning for integration during model development we have illustrated a way to address a translational bottleneck with prognostic modeling. Engaging diverse subject matter experts and end users prior to and during model development has the potential to accelerate the adoption and use of prognostic models in practice.

The application of quality control circle in improving the accuracy of ICD coding on medical records

2025-05-28

Wenjing Ou , P.Y. Chang , Jinghua Zhou +3 more | BMC Health Services Research

This study aims to explore effective methods for reducing the International Classification of Diseases (ICD) coding error rate on the first page of inpatient cases using Quality Control Circle (QCC) … This study aims to explore effective methods for reducing the International Classification of Diseases (ICD) coding error rate on the first page of inpatient cases using Quality Control Circle (QCC) management tools. The goal is to enhance the quality of data on the initial page of cases and elevate the competency of coders. A total of 4,613 medical case front pages from patients discharged between March 1, 2024, and March 31, 2024, were selected as the pre-QCC intervention group. Additionally, 4,489 inpatient case front pages from discharges occurring between July 1, 2024, and July 31, 2024, were designated as the post-QCC intervention group. The QCC team was established within the Department of Case Management, focusing on the theme: "Improving the Accuracy of ICD Coding on Medical Case Front Pages." Guided by the Plan-Do-Check-Act (PDCA) cycle framework, the team implemented QCC activities following its ten-step methodology. Both tangible outcomes (e.g., coding error rates) and intangible outcomes (e.g., staff skill enhancement) were evaluated before and after the intervention. A follow-up examination of the coding on the first page of 4,489 inpatient cases discharged in July 2024 revealed a significant reduction in the overall error rate, from 7.02% before the intervention to 2.90% afterward (χ²=81.791, P < 0.001). Specifically, the error rate for primary diagnoses decreased from 1.86 to 0.65% (χ²=27.067, P < 0.001), other diagnoses from 2.88 to 1.54% (χ²=18.996, P < 0.001), surgical operations from 1.99 to 0.71% (χ²=27.804, P < 0.001), and pathologic diagnosis error rates dropped from 0.17 to 0% (P = 0.008). Additionally, the coding error rate for external causes of injury and poisoning fell from 0.11 to 0% (P = 0.033). The goal achievement rate reached 111.6%, with a progress rate of 58.68%. Among the intangible achievements, participants demonstrated notable improvements in problem-solving, communication, quality control methodologies, and innovation skills. The application of QCC tools not only significantly enhances the accuracy rate of ICD coding in inpatient medical records but also improves coders' problem-solving capabilities and communication skills. Consequently, it facilitates continuous quality improvement in the coding of inpatient medical record front pages, thereby promoting sustained advancement in healthcare data management standards.

International Classification of Primary Care (ICPC-2) and search engines: an exploration of three algorithms for information retrieval to aid medical coding

2025-05-28

Vinícius Augusto Reis Almeida , Egbert J. van der Haring , Kees van Boven +1 more | Journal of Medical Artificial Intelligence

Exploring Contemporary Classification Practices

2025-05-27

Jack Andersen , Joacim Hansson

ICD-10 Tanı Kodları İle İlgili Yapılan Lisanüstü Tezlerin Bibliyometrik Analizi

2025-05-27

Osman Özen , Mustafa Nal

Amaç: Bu araştırmanın amacı Türkiye’de Hastalıkların Uluslararası Sınıflandırılması (ICD) ile ilgili yapılan lisansüstü tez çalışmalarının bibliyometrik analizini çıkarmaktır. Gereç ve Yöntem: Bu araştırma, nitel araştırma türünde olup literatür taraması, doküman … Amaç: Bu araştırmanın amacı Türkiye’de Hastalıkların Uluslararası Sınıflandırılması (ICD) ile ilgili yapılan lisansüstü tez çalışmalarının bibliyometrik analizini çıkarmaktır. Gereç ve Yöntem: Bu araştırma, nitel araştırma türünde olup literatür taraması, doküman incelemesi tekniği kullanılmıştır. Araştırmanın evrenini YÖKTEZ merkezindeki tezler oluşturmaktadır. Araştırma kapsamında “ICD” ve “ICD-10” kelimeleri taranmıştır. Örneklem olarak elde edilen sonuçların tamamı çalışılmaya dâhil edilmiştir. Verilerin analizinde Excel programı kullanılmıştır. Bulgular: Türkiye’de ICD ile ilgili toplam 102 lisansüstü tez çalışmasının yapıldığı belirlenmiştir. Araştırmanın örneklemini 79’u tıpta uzmanlık, 17’si lisansüstü ve 6’sı doktora tez çalışmasından oluşmaktadır. Tezlerde çoğunlukla retrospektif veriler kullanılmıştır. Araştırmalarda etik izinlerinin büyük çoğunluğunda alındığı tespit edilmiştir. Tezlerde danışmanlık yapan akademisyenlerin büyük çoğunluğu profesördür. Yapılan çalışmaların çoğunun nicel araştırma türünde olduğu belirlenmiştir. Tezlerin konularına bakıldığında “kuruma başvuranların hastalıklarının dağılımının değerlendirilmesi” üzerine yapıldığı tespit edilmiştir. Sonuç ve Öneriler: Bu araştırma sonuçları ICD-10 tanı kodları ile yapılan tezlerin bibliyometrik özellikleri hakkında bilgi sunmaktadır. Literatür taramasında ICD-10 tanı kodları ile ilgili yapılan araştırmaların bibliyometrik analizine yönelik çalışmaya rastlanmamıştır. Yapılan bu çalışmanın literatüre ve ICD-10 tanı kodları konusunda araştırma yapacak araştırmacılara katkı sağlayacağı düşünülmektedir.

Increasing Awareness of Your BFRB

2025-05-26

The Data: Who Cares?

2025-05-20

Resampling Methods for Class Imbalance in Clinical Prediction Models: A Systematic Review and Meta-Regression Protocol

2025-05-20

Osama Abdelhay , Aymen Shatnawi , Hassan Najadat +1 more

Abstract Introduction Class imbalance—situations where clinically important “positive” cases form <30 % of the dataset—systematically degrades the sensitivity and fairness of medical prediction models. Although data-level techniques such as random … Abstract Introduction Class imbalance—situations where clinically important “positive” cases form <30 % of the dataset—systematically degrades the sensitivity and fairness of medical prediction models. Although data-level techniques such as random oversampling, random undersampling and SMOTE, and algorithm-level approaches like cost-sensitive learning, are widely used, the empirical evidence describing when these corrections improve model performance remains fragmented across diseases and modelling frameworks. This protocol outlines a scoping systematic review with meta-regression that will map and quantitatively summarise 15 years of research on resampling strategies in imbalanced clinical datasets, addressing a critical methodological gap in trustworthy medical AI. Methods and analysis We will search MEDLINE, EMBASE, Scopus, Web of Science Core Collection and IEEE Xplore, plus grey-literature sources (medRxiv, arXiv, bioRxiv) for primary studies (2009 – 31 Dec 2024) that apply at least one resampling or cost-sensitive method to binary clinical prediction tasks with a minority-class prevalence <30 %. No language restrictions will be applied. Two reviewers will screen records, extract data with a piloted form and document the process in a PRISMA flow diagram. A descriptive synthesis will catalogue clinical domain, sample size, imbalance ratio, resampling technique, model type and performance metrics where≥10 studies report compatible AUCs, a random-effects mixed-effects meta-regression (logit-transformed AUC) will examine moderators including imbalance ratio, resampling class, model family and sample size. Small-study effects will be probed with funnel plots, Egger’s test, trim-and-fill and weight-function models; influence diagnostics and leave-one-out analyses will assess robustness. Because this is a methodological review, formal clinical risk-of-bias tools are optional; instead, design-level screening, influence diagnostics and sensitivity analyses will ensure transparency. Discussion By combining a broad conceptual map with quantitative estimates, this review will establish when data-level versus algorithm-level balancing yields genuine improvements in discrimination, calibration and cost-sensitive metrics across diverse medical domains. The findings will guide researchers in choosing parsimonious, evidence-based imbalance corrections, inform journal and regulatory reporting standards, and highlight research gaps, such as the under-reporting of calibration and misclassification costs, that must be addressed before balanced models can be trusted in clinical practice. Systematic review registration INPLASY202550026

It’s hard to ignore the data when the data is in the room

2025-05-19

Margaret Ann Bolick , Leilani Pai , Rachel Funk +1 more

A growing body of research demonstrates the benefits of engaging students as partners to improve tertiary education. Yet, more research is needed to understand how students can support critical transformations … A growing body of research demonstrates the benefits of engaging students as partners to improve tertiary education. Yet, more research is needed to understand how students can support critical transformations outside of the classroom context. In this qualitative study, we explored how a networked improvement community (NIC) engaged students as partners toward critically transforming introductory tertiary mathematics courses in spring 2023. Using an open coding process, we analyzed field notes, interviews, and journals from NIC members to develop themes describing the NIC’s positioning of students. We compared these themes to Holen et al.’s (2021) framework on student-institutional partnerships. Findings reveal four positions students may adopt in critical transformation efforts: democratic participant, apprentice, consultant, and beneficiary. This study contributes to the field’s understanding of ways students can influence larger structural and cultural systems that impact student success, as well as challenges inherent in this work.

Generating a FHIR ConceptMap from WHO’s ICD-10 to ICD-11 Mapping Tables

2025-05-15

Tessa Ohlsen , Josef Ingenerf , Joshua Wiedekopf

The mapping from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to the 11th Revision (ICD-11), initiated by the World Health Organization (WHO), presents a … The mapping from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to the 11th Revision (ICD-11), initiated by the World Health Organization (WHO), presents a challenge for healthcare systems, most of which currently rely on extensive ICD-10 coded data for billing purposes. This paper introduces a methodology to generate a FHIR (Fast Healthcare Interoperability Resources) ConceptMap from the WHO-provided ICD-10 to ICD-11 mapping tables. The resulting ConceptMap allows healthcare organizations to automate the mapping process, facilitating the integration of ICD-11. The final ConceptMap includes ICD-11 mappings for 12,952 ICD-10 codes. This approach prepares healthcare systems for the transition to ICD-11.

Indexing clinical documents for long-term preservation through an international coding system

2025-05-15

Eugenia Pasceri , Maria Teresa Chiaravalloti

This paper describes the experience of mapping the document types defined by Medas company, a Digital Preservation Organization for healthcare institutions, to the Logical Observation Identifiers Names and Codes (LOINC) … This paper describes the experience of mapping the document types defined by Medas company, a Digital Preservation Organization for healthcare institutions, to the Logical Observation Identifiers Names and Codes (LOINC) international standard, and consequently assess its adequacy in representing specificities of the Italian context. Mapping operations were manually performed by LOINC Italia experts. The LOINC database was searched using the LOINC Search web browser and REgenstrief LOINC Mapping Assistant (RELMA) software, employing local names and synonyms of Medas document types. Out of 483 Medas document types, 144 fully match with a LOINC code; 211 were associated to a LOINC code which only partially covers the meaning; 128 are not covered by LOINC. Although the axes of LOINC Document Ontology are quite adequate for document types representation, an extension of Kind of document and Subject Matter Domain axes is desirable. Local Medas codes reflects the variety of clinical document types produced in the Italian healthcare domain and the results of this case study can be used as new terms submissions to enrich the LOINC standard, especially in relation to the specificities that characterize the Italian national context.

Initial Evaluation of ICD-11’s Adherence to Acceptable Terminology Practices

2025-05-15

Kathy Giannangelo , Susan H. Fenton , Mary H Stanfill

This research evaluates ICD-11 using the cancer Biomedical Informatics Grid (caBIG®) Terminology Review Criteria Matrix version 3.3 and the National Committee on Vital and Health Statistics (NCVHS) criteria for adoption … This research evaluates ICD-11 using the cancer Biomedical Informatics Grid (caBIG®) Terminology Review Criteria Matrix version 3.3 and the National Committee on Vital and Health Statistics (NCVHS) criteria for adoption and implementation and guidelines for curation and dissemination of health terminology and vocabulary standards. The aim is to determine if ICD-11 meets acceptable terminology practices and to identify its strengths and weaknesses.

Coding Clinic: A Multidisciplinary Approach Supporting Early-Stage Medical Data Science Research

2025-05-15

Elias Grünewald , Jon Barrenetxea , Niklas Giesa +4 more

The Coding Clinic at Charité - Universitätsmedizin Berlin offers early-career researchers support in data science and software development, addressing challenges in medical research. Through consultations, pair programming, and open-source resources, … The Coding Clinic at Charité - Universitätsmedizin Berlin offers early-career researchers support in data science and software development, addressing challenges in medical research. Through consultations, pair programming, and open-source resources, the Coding Clinic enhances data management, EHR analysis, and fosters interdisciplinarity. Our team of several computer and data scientists developed an increasingly popular consultation format in a novel setting.

Preparing for ICD-11: Exploring the Current Use of ICD-10 in Dutch Hospital Mortality and Morbidity Coding

2025-05-15

Myrthe I. van Heerde , Ronald Cornet

The healthcare sector uses the International Classification of Diseases (ICD) to record and analyze mortality and morbidity, as well as to monitor public health, reimbursement, quality measurement, and clinical decision … The healthcare sector uses the International Classification of Diseases (ICD) to record and analyze mortality and morbidity, as well as to monitor public health, reimbursement, quality measurement, and clinical decision support. This paper explores the use of ICD-10 in Dutch hospital care and discusses how this could inform the transition to ICD-11. The study conducts a rapid literature review along semi-structured interviews with key stakeholders. A model is created to visualize the current use of ICD-10 mortality and morbidity data streams in hospitals. The results indicate that ICD-10 serves an important function for clinical, financial, and epidemiological purposes in the Netherlands. To ensure a smooth transition to ICD-11, stakeholder collaboration, investment in AI-tools, training of users, technical integration with Electronic Health Record (EHR) systems, improved hospital registration, and alignment with SNOMED CT are important. This paper emphasizes the need for further research into the practical implementation of ICD-11 in the Netherlands and its impact on patient-related outcome measures.

A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation

2025-05-14

Dan Wu , Jiye An , Shan Nan +3 more

Health examination identifies risk factors and diseases at an early stage through a series of health examination items. In China, however, the incidence of consulting services for health examination items … Health examination identifies risk factors and diseases at an early stage through a series of health examination items. In China, however, the incidence of consulting services for health examination items is low and the current health examination item package is insufficiently personalized. Therefore, we created and evaluated a clinical decision support system (CDSS) for personalized health examination items. An ontology with the data properties as the core design was created to guide the knowledge expression. A knowledge graph composed of ontology-guided property graphs was developed to provide rich and clear decision-making knowledge. The system, including the web for primary care clinicians and the app for participants, was constructed to directly assist primary care clinicians through personalized and interpretable health examination item recommendations. The enter rate and mapping rate were created to evaluate the system's capability to process input health feature data. The two-step expert evaluation was designed to assess whether recommendations with several health examination items were appropriate for participants. The system recommendations and existing packages were compared to the expert's gold standard. There were 15 classes, 2-level class hierarchies, 3 types of object properties, and 16 types of data properties in the health examination item recommendation ontology. Several different data properties could express a piece of complex decision-making knowledge and reduce the number of classes. There were 584 classes, 781 object properties, and 1094 data properties in the knowledge graph. Retrospective data from 70 participants, with a total of 472 health features, were selected for system evaluation. The ontology can cover 96.2% of the health features. 56.4% health features entered into the system had corresponding health examination items. The precision and recall of the system were 96.3% and 84.8%, and the packages were 72.5% and 69.1%. The performance of this system was close to experts and outperformed the current impersonalized health examination item packages. This system could improve the personalization of health examination items and the health examination consultation services, and promote participants' engagement in the health examination.

Panorama da implementação da CID-11 no Brasil

2025-05-12

Ana Carolina Aires Cerqueira Prata , Silvia von Tiesenhausen de Sousa-Carmo , Maria Cristina Horta Vilar +5 more

<title>RESUMO</title> O Brasil iniciou, em 2021, a implementação da 11ª Revisão da Classificação Internacional de Doenças (CID-11), com término previsto para 2027. A CID-11 representa um salto tecnológico e conceitual … <title>RESUMO</title> O Brasil iniciou, em 2021, a implementação da 11ª Revisão da Classificação Internacional de Doenças (CID-11), com término previsto para 2027. A CID-11 representa um salto tecnológico e conceitual que oferece novas oportunidades de análise epidemiológica e de gestão da saúde, porém impõe desafios de interoperabilidade e de preservação das séries históricas que tornam mais complexa a sua implementação. Considerando a grande extensão territorial do Brasil e a existência de sistemas de informação em saúde consolidados, a implementação no país será escalonada, tendo cinco eixos prioritários: publicação, tradução e uso da CID-11 no Brasil; infraestrutura de tecnologia da informação; comparabilidade e qualidade dos dados; desenvolvimento de capacidades; e promoção e disseminação. O presente artigo tem como objetivo apresentar o panorama atual do processo de implantação da CID-11, as etapas já realizadas e as perspectivas futuras. A implementação segue as diretrizes do Guia de Implementação da Organização Mundial da Saúde e a Nota Técnica nº 91/2024 do Ministério da Saúde do Brasil, que aborda procedimentos como a atualização dos sistemas de informação em saúde e áreas de atenção, considerando a diversidade das realidades regionais do país e a magnitude da mudança nas classificações internacionais, com objetivo de garantir segurança e efetividade, com preservação das séries temporais e agilidade para adaptações. O processo representa mais um marco na saúde pública nacional, ao mesmo tempo em que moderniza e aprimora os sistemas de informação de estatísticas vitais, consolidando o papel do Brasil no avanço da saúde pública global.

Perancangan Aplikasi Daftar Istilah Medis Prosedur Klinis Sistem Pencernaan berdasarkan ICD-9-CM Berbasis Website

2025-05-12

Ghina Sabila Salma , Yanto Haryanto , Fitria Dewi Rahmawati +1 more

Penguasaan keterampilan dalam klasifikasi klinis serta kodefikasi penyakit dan prosedur klinis merupakan kompetensi esensial yang harus dimiliki oleh seorang Perekam Medis dan Informasi Kesehatan (PMIK). Kemampuan ini sangat bergantung pada … Penguasaan keterampilan dalam klasifikasi klinis serta kodefikasi penyakit dan prosedur klinis merupakan kompetensi esensial yang harus dimiliki oleh seorang Perekam Medis dan Informasi Kesehatan (PMIK). Kemampuan ini sangat bergantung pada pemahaman yang baik terhadap istilah medis dan informasi medis yang menjadi dasar dalam proses kodefikasi. Program Studi D-III Rekam Medis dan Informasi Kesehatan (RMIK) Cirebon, Poltekkes Kemenkes Tasikmalaya sebagai salah satu institusi pendidikan yang mencetak tenaga PMIK, berdasarkan hasil observasi, masih menerapkan metode pembelajaran istilah medis secara manual. Kondisi ini dinilai kurang optimal dalam menghadapi tuntutan perkembangan teknologi pada era digital saat ini. Pemanfaatan media pembelajaran berbasis teknologi, seperti aplikasi berbasis website, menjadi salah satu solusi untuk meningkatkan efektivitas dan efisiensi pembelajaran. Penelitian ini bertujuan untuk merancang aplikasi daftar istilah medis prosedur klinis sistem pencernaan berbasis website sebagai sarana pembelajaran mahasiswa. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model pengembangan aplikasi waterfall. Adapun metode pengujian aplikasi dilakukan dengan metode blackbox testing dan pengujian konten dimana pada tahap ini pengujian dilakukan terhadap 32 (tiga puluh dua) orang mahasiswa dan 1 (satu) orang koordinator laboratorium koding. Hasil dari pengujian tersebut menunjukkan bahwa aplikasi, baik dari aspek system maupun konten menunjukkan hasil yang positif dan layak untuk digunakan sebagai media pembelajaran mahasiswa.

Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy

2025-05-12

E. Alonso , Andoni Beristain , Jorge Burgos +1 more

This paper presents a novel approach for screening women in their first trimester of pregnancy to identify those at high risk of having a child with Down syndrome (DS), using … This paper presents a novel approach for screening women in their first trimester of pregnancy to identify those at high risk of having a child with Down syndrome (DS), using machine learning algorithms. Various machine learning models, including statistical, linear, and ensemble models, were trained using a pseudo-anonymized dataset of 90,532 screening patients. This dataset, containing less than 1% positive cases, was obtained from Cruces University Hospital, a public health hospital (Osakidetza) in Baracaldo, Basque Country, Spain. The models incorporate a set of input variables, including demographic variables such as maternal age, weight, ethnicity, smoking status, and diabetes status, as well as laboratory variables like nuchal translucency (NT), pregnancy-associated plasma protein-A (PAPP-A), and beta-human chorionic gonadotropin hormone (B-HCG) levels. The trained classification algorithms achieved ROC-AUC values between 0.970 and 0.982, with sensitivity and specificity of 0.94. The results indicate that machine learning techniques can effectively predict Down syndrome risk in first-trimester screening programs.