scholarly journals Accuracy of ICD-10-CM coding for physical child abuse in a paediatric level I trauma centre

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i71-i74
Author(s):  
Holly Hughes Garza ◽  
Karen E Piper ◽  
Amanda N Barczyk ◽  
Adriana Pérez ◽  
Karla A Lawson
Keyword(s):  
Child Abuse ◽  
Physical Abuse ◽  
Child Protection ◽  
International Classification Of Diseases ◽  
Trauma Centre ◽  
Child Physical Abuse ◽  
Physical Child Abuse ◽  
Predictive Values ◽  
Icd 10 ◽  
Level I

This retrospective study examined the accuracy of the International Classification of Diseases, Clinical Modification (ICD-10-CM) coding for physical child abuse among patients less than 18 years of age who were evaluated due to concern for physical abuse by a multidisciplinary child protection team (MCPT) during 2016–2017 (N=312) in a paediatric level I trauma centre. Sensitivity, specificity, predictive values and diagnostic OR for ICD-10-CM coding were calculated and stratified by admission status, using as a reference standard the abuse determination of the MCPT recorded in a hospital registry. Among inpatients, child physical abuse coding sensitivity was 55.6% (95% CI 41.4% to 69.1%) and specificity was 78.6% (95% CI 59.0% to 91.7%), with diagnostic OR of 4.58 (95% CI 1.64 to 12.70). Among outpatients, sensitivity was 22.2% (95% CI 15.5% to 30.2%) and specificity was 86.3% (95% CI 77.7% to 92.5%), with diagnostic OR of 1.80 (95% CI 0.89 to 3.64). Use of ICD-10-CM coded data sets alone for surveillance may significantly underestimate the occurrence of physical child abuse.

scholarly journals INTERVENCIÓN FAMILIAR EN SITUACIÓN DE RIESGO SOCIAL

2016 ◽  
Vol 1 (1) ◽  
pp. 285 ◽  
Author(s):  
Elena Guillén Guillén ◽  
Mª José Gordillo Montaño ◽  
Mª Isabel Ruíz Fernández ◽  
Mª Dolores Gordillo Gordillo
Keyword(s):  
At Risk ◽  
Child Abuse ◽  
Physical Abuse ◽  
Child Protection ◽  
Family Intervention ◽  
Validity And Reliability ◽  
Physical Child Abuse ◽  
Informative Feedback ◽  
Intervention Process ◽  
Abuse And Neglect

Abstract:Neglect and physical child abuse is a significant present in our environment and growing problem, unfortunately . This review examines the characteristics of programs Intervention with Families and Children and the extent to which some of them are effective in treating this population at risk. The results show that we can find some relatively effective programs but still we have to consider many other aspects to reduce physical abuse and neglect (ie , the use of objective assessments of abuse , children in the register of child protection , claims time , need informative feedback ... etc.) . Moreover, the validity and reliability of notifications in a way they're coming determined by notifying awareness and observation skills of the same , therefore , taking into account the importance of them to go the intervention process , there is a need to create a standardized, stay at all institutions, to make it easy and quick notification.Keywords: Family intervention, Abuse, JuvenileResumen:El abandono y maltrato físico infantil es un problema significativo y cada vez, por desgracia, más presente en nuestros entornos. Esta revisión analiza las características de los programas de Intervención con Familias y Menores y el grado en el que son efectivos algunos de ellos para tratar a esta población en riesgo. Los resultados muestran que podemos encontrar algunos programas relativamente efectivos pero aún así hay que contemplar muchos otras aspectos para reducir el maltrato físico y el abandono (es decir, el uso de evaluaciones objetivas de maltrato; los niños en el registro de protección de niños, demandas a tiempo, necesidad de feedback informativo… etc.). Además, la validez y fiabilidad de las notificaciones en cierto modo van a venir determinadas por la concienciación del notificante y la capacidad de observación del mismo; por lo tanto, y teniendo en cuenta la importancia de las mismas a lo largo del proceso de intervención, se impone la necesidad de crear un instrumento estandarizado, que permanezca en todas las instituciones, para hacer más fácil y rápida la notificación.Palabras clave: Intervención familiar, Maltrato, Menores.

scholarly journals Scoring systems for early prediction of tibial fracture non-union: an update

2021 ◽  
Author(s):  
George D. Chloros ◽  
Nikolaos K. Kanakaris ◽  
James S. H. Vun ◽  
Anthony Howard ◽  
Peter V. Giannoudis
Keyword(s):  
Tibial Fracture ◽  
Scoring Systems ◽  
Trauma Centre ◽  
Predictive Values ◽  
Non Union ◽  
Post Operative Period ◽  
Male Patients ◽  
Level I ◽  
Healing Score ◽  
Tibial Shaft Fractures

Abstract Purpose To evaluate the available tibial fracture non-union prediction scores and to analyse their strengths, weaknesses, and limitations. Methods The first part consisted of a systematic method of locating the currently available clinico-radiological non-union prediction scores. The second part of the investigation consisted of comparing the validity of the non-union prediction scores in 15 patients with tibial shaft fractures randomly selected from a Level I trauma centre prospectively collected database who were treated with intramedullary nailing. Results Four scoring systems identified: The Leeds-Genoa Non-Union Index (LEG-NUI), the Non-Union Determination Score (NURD), the FRACTING score, and the Tibial Fracture Healing Score (TFHS). Patients demographics: Non-union group: five male patients, mean age 36.4 years (18–50); Union group: ten patients (8 males) with mean age 39.8 years (20–66). The following score thresholds were used to calculate positive and negative predictive values for non-union: FRACTING score ≥ 7 at the immediate post-operative period, LEG-NUI score ≥ 5 within 12 weeks, NURD score ≥ 9 at the immediate post-operative period, and TFHS < 3 at 12 weeks. For the FRACTING, LEG-NUI and NURD scores, the positive predictive values for the development of non-union were 80, 100, 40% respectively, whereas the negative predictive values were 60, 90 and 90%. The TFHS could not be retrospectively calculated for robust accuracy. Conclusion The LEG-NUI had the best combination of positive and negative predictive values for early identification of non-union. Based on this study, all currently available scores have inherent strengths and limitations. Several recommendations to improve future score designs are outlined herein to better tackle this devastating, and yet, unsolved problem.

scholarly journals P135: Administrative codes for heat illness: a validation study in Ontario, Canada

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S113-S113
Author(s):  
H. Baassiri ◽  
T. Varghese ◽  
M. Columbus ◽  
K. Clemens ◽  
J. Yan
Keyword(s):  
Gold Standard ◽  
Heat Stroke ◽  
Heat Exposure ◽  
Sample Size Calculation ◽  
Physical Exertion ◽  
International Classification Of Diseases ◽  
Administrative Database ◽  
Heat Illness ◽  
Predictive Values ◽  
Icd 10

Introduction: Extreme heat events due to climate change are becoming increasingly frequent and severe, and may have an impact on human health. Administrative database studies using International Classification of Diseases 10th revision codes (ICD-10) are powerful tools to measure the burden of acute heat illness (AHI) in Canada. We aimed to assess the validity of the coding algorithm for emergency department (ED) encounters for AHI in our region. Methods: Two independent reviewers retrospectively abstracted data from 507 medical records of patients presenting at two EDs in Ontario between May-September 2015-2018. The Gold Standard definition of an AHI is chart-documented heat exposure with a heat related complaint, such as syncope while working outdoors on a hot day. To determine ICD coding algorithm positive predictive value (PPV), records that were previously coded as ICD-10 heat illnesses were compared to the Gold Standard for AHI. To determine sensitivity (Sn), specificity (Sp) and negative predictive values (NPV), the Gold Standard was compared to randomly selected records. A total of 326,702 ED visits were included in study period with 208 having an ICD-10 code related to heat illness. Sample size calculation demonstrated a need to manually review 62 previously coded heat illnesses and 931 random cases, of which 50 and 474 have been reviewed, respectively. In both abstractions, 20% of cases underwent a blinded duplicate review. Results: In our review of 474 random records, 2 cases were identified as AHI but without an appropriate ICD-10 code, 445 were not AHIs, and no cases had been identified as having an AHI ICD-10 inappropriately applied. In our review of 50 previously coded heat illnesses, 34 were found to be appropriately coded and 16 inappropriately coded, as AHI ICD-10. Average patient age and gender of heat illness vs non-heat illness ED presentations were 32 and 48 years of age and 49% and 64% male, respectively. The leading complaint in AHI was heat stroke/exhaustion (39%), followed by headaches (15%), dizziness (9%), shortness of breath (9%) and syncope/presyncope (6%). 76% of all heat illness presentations presented following a period of physical exertion. Conclusion: Final calculation of Sn, Sp, PPV, NPV for the algorithm will occur upon completion of the review. Preliminary results suggest that ICD-10 coding for AHI may be applied correctly in the ED. This study will help to determine if administrative data can accurately be used to measure the burden of heat illness in Canada.

scholarly journals Increased proportion of physical child abuse injuries at a level I pediatric trauma center during the Covid-19 pandemic

2020 ◽  
pp. 104756
Author(s):  
Mark L. Kovler ◽  
Susan Ziegfeld ◽  
Leticia M. Ryan ◽  
Mitchell A. Goldstein ◽  
Rebecca Gardner ◽  
...  
Keyword(s):  
Child Abuse ◽  
Trauma Center ◽  
Pediatric Trauma ◽  
Physical Child Abuse ◽  
Level I ◽  
Pediatric Trauma Center

Child physical abuse: awareness and practices of medical and dental doctors in Pakistan

2021 ◽  
pp. 1-13
Author(s):  
Hafiz Muhammad Ali Khan ◽  
Naveed Mansoori ◽  
Muhammad Hamza Sohail ◽  
Muhammad Ajwad Humayun ◽  
Anam Liaquat ◽  
...  
Keyword(s):  
Child Abuse ◽  
Physical Abuse ◽  
Descriptive Analysis ◽  
Social Indicators ◽  
Sampling Technique ◽  
Child Physical Abuse ◽  
P Value ◽  
Cross Sectional ◽  
Private Hospitals ◽  
Abuse Case

Abstract Objectives: To determine the awareness and practices of medical and dental doctors in detecting and reporting suspected cases of child physical abuse. Methods: A cross-sectional study was done from November 2017 to June 2018 among medical and dental doctors practising in public and private hospitals across Pakistan. Using convienence sampling technique a structured questionnaire was administered. The questionnaire addresses knowledge of the social indicators of child physical abuse, response to child physical abuse, and actions taken by doctors when they believe a child abuse case has been decided. Descriptive analysis was done, and Chi-square test was used for the association of knowledge about child physical abuse and sex.  P-value < 0.05 was considered as statistically significant. Results: Out of total 575 doctors, 347 (60.3%) were males and 446 (77.6%) work in private hospitals. The majority of doctors 384 (66.8%) had <10 years of experience and only 99(17.2%) had received formal training of child abuse.  A fifth of doctors agreed to tell someone immediately after being physically abused for social indicators of child physical abuse and considered statistically significant between the sexes (P<0.05).  Most doctors 450(78.3%) strongly agreed on the value of identifying and documenting child physical abuse while 563(97.9%) doctors did not take any action to suspect a child abuse case. Conclusion: The study revealed sufficient knowledge among doctors about child physical abuse. Although the doctors had a positive attitude regarding child physical abuse, a large proportion remain silent on its suspicion. Keywords: Child, Physical abuse, Physicians, Continuous...

Evaluation of the effect of the child protection act on serious child physical abuse in Taiwan

2019 ◽  
Vol 95 ◽  
pp. 104066
Author(s):  
I-Jun Chou ◽  
Shu-Sing Kong ◽  
Ting-Ting Chung ◽  
Lai-Chu See ◽  
Chang-Fu Kuo ◽  
...  
Keyword(s):  
Physical Abuse ◽  
Child Protection ◽  
Child Physical Abuse

scholarly journals Development of an electronic medical record–based child physical abuse alert system

2017 ◽  
Vol 25 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Rachel P Berger ◽  
Richard A Saladino ◽  
Janet Fromkin ◽  
Emily Heineman ◽  
Srinivasan Suresh ◽  
...  
Keyword(s):  
Medical Record ◽  
Physical Abuse ◽  
Electronic Medical Record ◽  
High Sensitivity ◽  
Child Physical Abuse ◽  
Alert System ◽  
Predictive Values ◽  
System Sensitivity ◽  
Sensitivity Specificity ◽  
Silent Mode

Abstract Objective Physical abuse is a leading cause of pediatric morbidity and mortality. Physicians do not consistently screen for abuse, even in high-risk situations. Alerts in the electronic medical record may help improve screening rates, resulting in early identification and improved outcomes. Methods Triggers to identify children &lt; 2 years old at risk for physical abuse were coded into the electronic medical record at a freestanding pediatric hospital with a level 1 trauma center. The system was run in “silent mode”; physicians were unaware of the system, but study personnel received data on children who triggered the alert system. Sensitivity, specificity, and negative and positive predictive values of the child abuse alert system for identifying physical abuse were calculated. Results Thirty age-specific triggers were embedded into the electronic medical record. From October 21, 2014, through April 6, 2015, the system was in silent mode. All 226 children who triggered the alert system were considered subjects. Mean (SD) age was 9.1 (6.5) months. All triggers were activated at least once. Sensitivity was 96.8% (95% CI, 92.4–100.0%), specificity was 98.5% (95% CI, 98.3.5–98.7), and positive and negative predictive values were 26.5% (95% CI, 21.2–32.8%) and 99.9% (95% CI, 99.9–100.0%), respectively, for identifying children &lt; 2 years old with possible, probable, or definite physical abuse. Discussion/Conclusion Triggers embedded into the electronic medical record can identify young children with who need to be evaluated for physical abuse with high sensitivity and specificity.

scholarly journals A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247404
Author(s):  
Akshaya V. Annapragada ◽  
Marcella M. Donaruma-Kwoh ◽  
Ananth V. Annapragada ◽  
Zbigniew A. Starosolski
Keyword(s):  
Child Abuse ◽  
Natural Language Processing ◽  
Natural Language ◽  
Physical Abuse ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Child Protection ◽  
Medical Records ◽  
Free Text ◽  
Average Accuracy

Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record—including notes from physicians, nurses, and social workers—to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms—Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)—and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse.

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