International Classification of Diseases, 10th Revision Training

Harris, Susie T.; Zeng, Xiaoming; Roß, Thomas; Ford, Lee T.

doi:10.1097/hcm.0b013e3182a9d640

Cited by 9 publications

(8 citation statements)

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“…Comorbidities were descriptively reported using the International Classification of Disease, 10th version. 25 Nutritional status was investigated with the Mini Nutritional Assessment Short Form (MNA-SF), 26 which includes information on (i) anthropometric measures (body mass index and weight loss), (ii) neuropsychological problems and recent psychological stress, (iii) mobility, and (iv) decline in food intake.Risk of developing pressure sores was evaluated through the Exton-Smith Scale, a five-item questionnaire determining physical and mental condition, activity, mobility, and incontinence. 27 Medication use was defined according to the Anatomical Therapeutics Chemical Classification code system (ATC classification) and the number of drugs used by patients was recorded.…”

Section: Methodsmentioning

confidence: 99%

“…Comorbidities were descriptively reported using the International Classification of Disease, 10th version. 25…”

Section: Methodsmentioning

confidence: 99%

“…Comorbidity: CIRS comorbidity is the number of health problems/diseases that are so severe to require chronic drug therapies in 13 aspects of health. 25 CIRS can be consequently self-assessed by reporting health problems/diseases that require medications for their treatment.…”

Section: Methodsmentioning

confidence: 99%

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Development and Validation of a Self-Administered Multidimensional Prognostic Index to Predict Negative Health Outcomes in Community-Dwelling Persons

Pilotto

Veronese

Guerrero

et al. 2019

Rejuvenation Research

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The multidimensional prognostic index (MPI) is a comprehensive geriatric assessment (CGA)-based tool that accurately predicts negative health outcomes in older subjects with different diseases and settings. To calculate the MPI several validated tools are assessed by health care professionals according to the CGA, whereas self-reported information by the patients is not available, but it could be of importance for the early identification of frailty. We aimed to develop and validate a self-administered MPI (SELFY-MPI) in community-dwelling subjects. For this reason, we enrolled 167 subjects (mean age = 67.3, range = 20–88 years, 51% = men). All subjects underwent a CGA-based assessment to calculate the MPI and the SELFY-MPI. The SELFY-MPI included the assessment of (1) basic and instrumental activities of daily living, (2) mobility, (3) memory, (4) nutrition, (5) comorbidity, (6) number of medications, and (7) socioeconomic situation. The Bland–Altman methodology was used to measure the agreement between MPI and SELFY-MPI. The mean MPI and SELFY-MPI values were 0.147 and 0.145, respectively. The mean difference was +0.002 ± standard deviation of 0.07. Lower and upper 95% limits of agreement were −0.135 and +0.139, respectively, with only 5 of 167 (3%) of observations outside the limits. Stratified analysis by age provided similar results for younger (≤65 years old, n = 45) and older subjects (>65 years, n = 122). The analysis of variances in subjects subdivided according to different year decades showed no differences of agreement according to age. In conclusion, the SELFY-MPI can be used as a prognostic tool in subjects of different ages.

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Section: Methodsmentioning

confidence: 99%

“…Comorbidities were descriptively reported using the International Classification of Disease, 10th version. 25…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development and Validation of a Self-Administered Multidimensional Prognostic Index to Predict Negative Health Outcomes in Community-Dwelling Persons

Pilotto

Veronese

Guerrero

et al. 2019

Rejuvenation Research

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“…The impact that quality data has on the socioeconomic and environmental situations makes it a priceless entity in the disease classification process as well. The uses of input from health data span broad applications such as analysis and prediction of outcomes, policy creation and amendment, and trend establishment of mortality and diseases outbreaks [3]. Barriers relating to social and economic status, as well as geographical, hinder the access to quality health data.…”

Section: Introductionmentioning

confidence: 99%

Health Professionals Factors Influencing Utilization of Disease Classification Systems in Public Hospitals in Kiambu County, Kenya

Maina¹,

Rucha²,

Otieno³

et al. 2021

SCIRJ

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Background: Despite the globally acclaimed efficacy of the ICD-10 as a tool for disease classification, existing evidence focus on quality of disease coding outcomes with limited evidence on determinants of utilization. The starting point to sustaining the quality of disease coding is establishing its utilization and its determinants. There is paucity of evidence on role of health professional factors on use of ICD systems in Kenya.Objective: To determine health professionals' factors associated with utilization of ICD-10 in Public Hospitals.Methods: This study was conducted in Kiambu County, Kenya among public health facilities. Cross-sectional descriptive study design was used. A total of 46 health records and information officers [HRIOs] were sampled using total population sampling. Data was collected using self-administered questionnaires. Descriptive statistics, chi-square and regression analysis was used to analyse and synthesis the data using SPSS version 22.Results: Out of the 42 respondents, 28 (67%) indicated that the systems were utilized either frequently or always. Chi-square analysis revealed that ICD perception was the only health professional factor significantly associated with ICD-10 utilization. Positive perception of ICD-10 utility increased the likelihood of its frequent use by approximately 4.63 times (OR = 4.63; p=0.049).Conclusion: Staff perception of ICD systems is an important determinant of its utilization. Providing tailored capacity building and sensitization of ICD system benefits and value-add to staff can significantly counter negative perceptions and improve utilization in hospitals.

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“…1 In 1979, the United States modified the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) system with increased diagnostic codes and incorporated inpatient procedure codes. [2][3][4] However, with increasing information needs of our health care organizations, including electronic medical records, bioinformatics, and other technological improvements, the ICD-9-CM could not meet the needs, and the Centers for Medicare & Medicaid Services decided to move the US health care system to the International Classification of Diseases, 10th Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS). 2,5,6 The transition from ICD-9 7 to ICD-10 8 system, which was initially scheduled for October 2013, has been delayed in the United States until October 2015.…”

mentioning

confidence: 99%

International Classification of Diseases, 10th Revision, Coding for Prematurity

Chabra¹

2015

The Health Care Manager

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The International Classification of Diseases, Ninth Revision, Clinical Modification was introduced in the United States in 1979 as a coding system to document inpatient diagnostic and procedural codes. However, transition to International Classification of Diseases, 10th Revision (ICD-10) has been federally mandated by October 2015 due to advances in medical technology and procedures in medicine. This includes ICD-10, Clinical Modification and ICD-10, Procedure Coding System. The ICD-10 coding set is more detailed and specific and will add 54 000 diagnosis codes and 83 000 procedure codes to the International Classification of Diseases, Ninth Revision, Clinical Modification code set, thereby accounting for increased granularity of codes needed for evidence-based medicine. This article describes the importance of need for specificity of the codes and emphasizes the role of training in preparing for implementation of the ICD-10 coding system. An example is made for the need for accuracy in ICD-10 codes for prematurity as regards defining the premature population using standardized nomenclature.

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International Classification of Diseases, 10th Revision Training

Cited by 9 publications

References 0 publications

Development and Validation of a Self-Administered Multidimensional Prognostic Index to Predict Negative Health Outcomes in Community-Dwelling Persons

Development and Validation of a Self-Administered Multidimensional Prognostic Index to Predict Negative Health Outcomes in Community-Dwelling Persons

Health Professionals Factors Influencing Utilization of Disease Classification Systems in Public Hospitals in Kiambu County, Kenya

International Classification of Diseases, 10th Revision, Coding for Prematurity

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