Accuracy of diagnoses of schizophrenia in Medicaid claims.
ABSTRACT Medical insurance claims are increasingly important as a source of data in monitoring health care utilization and patient outcomes and in identifying patient cohorts for research. In a study that attempted to verify that those with Medicaid claims for treatment of schizophrenia did indeed have the disorder, two psychiatrists evaluated clinical information obtained from primary mental health care providers in relation to DSM-III-R criteria. The psychiatrists classified 86.8 percent of 319 patients with claims for treatment of schizophrenia and 27.5 percent of 156 patients with claims for treatment of other psychiatric diagnoses as definitely or probably having schizophrenia. The authors conclude that most diagnoses of schizophrenia listed on Medicaid claims are accurate, but that a substantial number of individuals with schizophrenia may not be identified by claims data.
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ABSTRACT: Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as "high validity"; those with positive predictive value ≥70% and sensitivity <70% were graded as "moderate validity". To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions.BMC Medical Informatics and Decision Making 04/2015; 15(1):31. DOI:10.1186/s12911-015-0155-5 · 1.50 Impact Factor
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ABSTRACT: My HealtheVet (MHV) is the personal health record and patient portal developed by the United States Veterans Health Administration (VA). While millions of American veterans have registered for MHV, little is known about how a patient's health status may affect adoption and use of the personal health record. Our aim was to characterize the reach of the VA personal health record by clinical condition. This was a cross-sectional analysis of all veterans nationwide with at least one inpatient admission or two outpatient visits between April 2010 and March 2012. We compared adoption (registration, authentication, opt-in to use secure messaging) and use (prescription refill and secure messaging) of MHV in April 2012 across 18 specific clinical conditions prevalent in and of high priority to the VA. We calculated predicted probabilities of adoption by condition using multivariable logistic regression models adjusting for sociodemographics, comorbidities, and clustering of patients within facilities. Among 6,012,875 veterans, 6.20% were women, 61.45% were Caucasian, and 26.31% resided in rural areas. The mean age was 63.3 years. Nationwide, 18.64% had registered for MHV, 11.06% refilled prescriptions via MHV, and 1.91% used secure messaging with their clinical providers. Results from the multivariable regression suggest that patients with HIV, hyperlipidemia, and spinal cord injury had the highest predicted probabilities of adoption, whereas those with schizophrenia/schizoaffective disorder, alcohol or drug abuse, and stroke had the lowest. Variation was observed across diagnoses in actual (unadjusted) adoption and use, with registration rates ranging from 29.19% of patients with traumatic brain injury to 14.18% of those with schizophrenia/schizoaffective disorder. Some of the variation in actual reach can be explained by facility-level differences in MHV adoption and by differences in patients' sociodemographic characteristics (eg, age, race, income) by diagnosis. In this phase of early adoption, opportunities are being missed for those with specific medical conditions that require intensive treatment and self-management, which could be greatly supported by functions of a tethered personal health record.Journal of Medical Internet Research 12/2014; 16(12):e272. DOI:10.2196/jmir.3751 · 4.67 Impact Factor
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ABSTRACT: Patients with sexually transmitted infection (STI) diagnosis should be tested for human immunodeficiency virus (HIV), regardless of previous HIV test results.Medical Care 10/2014; DOI:10.1097/MLR.0000000000000253 · 2.94 Impact Factor