Dataset Information

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

ABSTRACT: To identify factors associated with valid Autism Spectrum Disorder (ASD) diagnoses from electronic sources in large healthcare systems. We examined 1,272 charts from ASD diagnosed youth <18 years old. Expert reviewers classified diagnoses as confirmed, probable, possible, ruled out, or not enough information. A total of 845 were classified with 81% as a confirmed, probable, or possible ASD diagnosis. The predictors of valid ASD diagnoses were >2 diagnoses in the medical record (OR 2.94; 95% CI 2.03-4.25; p < 0.001) and being male (OR 1.51; 95% CI 1.05-2.17; p = 0.03). In large integrated healthcare settings, at least two diagnoses can be used to identify ASD patients for population-based research.

SUBMITTER: Coleman KJ

PROVIDER: S-EPMC4474741 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

Coleman Karen J KJ Lutsky Marta A MA Yau Vincent V Qian Yinge Y Pomichowski Magdalena E ME Crawford Phillip M PM Lynch Frances L FL Madden Jeanne M JM Owen-Smith Ashli A Pearson John A JA Pearson Kathryn A KA Rusinak Donna D Quinn Virginia P VP Croen Lisa A LA

Journal of autism and developmental disorders 20150701 7

To identify factors associated with valid Autism Spectrum Disorder (ASD) diagnoses from electronic sources in large healthcare systems. We examined 1,272 charts from ASD diagnosed youth <18 years old. Expert reviewers classified diagnoses as confirmed, probable, possible, ruled out, or not enough information. A total of 845 were classified with 81% as a confirmed, probable, or possible ASD diagnosis. The predictors of valid ASD diagnoses were >2 diagnoses in the medical record (OR 2.94; 95% CI 2 ...[more]

PMID: 25641003

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Project description:BackgroundEvaluation is a challenging but necessary part of the development cycle of clinical information systems like the electronic medical records (EMR) system. It is believed that such evaluations should include multiple perspectives, be comparative and employ both qualitative and quantitative methods. Self-administered questionnaires are frequently used as a quantitative evaluation method in medical informatics, but very few validated questionnaires address clinical use of EMR systems.MethodsWe have developed a task-oriented questionnaire for evaluating EMR systems from the clinician's perspective. The key feature of the questionnaire is a list of 24 general clinical tasks. It is applicable to physicians of most specialties and covers essential parts of their information-oriented work. The task list appears in two separate sections, about EMR use and task performance using the EMR, respectively. By combining these sections, the evaluator may estimate the potential impact of the EMR system on health care delivery. The results may also be compared across time, site or vendor. This paper describes the development, performance and validation of the questionnaire. Its performance is shown in two demonstration studies (n = 219 and 80). Its content is validated in an interview study (n = 10), and its reliability is investigated in a test-retest study (n = 37) and a scaling study (n = 31).ResultsIn the interviews, the physicians found the general clinical tasks in the questionnaire relevant and comprehensible. The tasks were interpreted concordant to their definitions. However, the physicians found questions about tasks not explicitly or only partially supported by the EMR systems difficult to answer. The two demonstration studies provided unambiguous results and low percentages of missing responses. In addition, criterion validity was demonstrated for a majority of task-oriented questions. Their test-retest reliability was generally high, and the non-standard scale was found symmetric and ordinal.ConclusionThis questionnaire is relevant for clinical work and EMR systems, provides reliable and interpretable results, and may be used as part of any evaluation effort involving the clinician's perspective of an EMR system.

Project description:BackgroundMusculoskeletal (MSK) conditions are a common presentation in primary care. This study sought to determine the prevalence of MSK conditions in primary care in Ontario and to validate the extent to which health administrative date billing codes accurately represent MSK diagnoses.MethodsDe-identified electronic medical records (EMR) from the DELPHI database in southwestern Ontario, which contains 2493 patients (55.6% female, mean age 50.3 years (SD = 22.2)) and 21,964 encounters (July 1, 2006-June 30, 2010) were used for the analyses. Outcomes included: validation measures of agreement between International Classification of Diseases (ICD-9) diagnostic codes (health administrative data) and International Classification of Primary Care (ICPC) diagnoses defined as the reference standard, time to first ICD-9 code, prevalence, and healthcare utilization.ResultsThere were 2940 true positive MSK encounters with primary care practitioners for 998 patients. Performance of the ICD-9 diagnostic codes included sensitivity = 76.5%, specificity = 95.2%, PPV = 94.6%, and NPV = 78.7%, compared to the ICPC reference standard. The majority of 998 patients were coded with both an ICPC and ICD-9 MSK code at their first or second encounter (67.4%). However, 23.5% of patients with the ICPC reference standard MSK were never coded with ICD-9. Four-year prevalence of MSK was 52.3% and varied by age (4.5% 0-17 years, 20.1% 18-44, 42.7% 45-64, and 32.7% 65+). Patients at MSK encounters had a higher number of: investigations (17.9% compared to 9.1%, p < .0001); referrals (17.6% compared to 14.3%, p < .0001); and prescriptions for opioids (17.2% compared to 5.3%, p < .0001).ConclusionsThis study determined the prevalence of musculoskeletal conditions in primary care in Ontario using a reference standard definition. The study highlighted the value of using primary care ICPC codes to validate a definition for musculoskeletal conditions. Health administrative data can be used to ascertain the presence of musculoskeletal conditions; however, ICD-9 codes may underrepresent the prevalence of MSK conditions.

Dataset Information

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

Publications

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

Similar Datasets

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