Validity of claims-based algorithms to identify neurodevelopmental disorders in children.

PURPOSE: To validate healthcare claim-based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. METHODS: Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims-based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. RESULTS: PPVs were 94% (95% CI, 83%-99%) for ASD, 88% (76%-95%) for ADHD, 98% (89%-100%) for learning disability, 98% (89%-100%) for speech/language disorder, 82% (69%-91%) for intellectual disability, and 92% (81%-98%) for behavioral disorder. A total of 19 of the 50 algorithm-based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%-53%). Among the 31 false-positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual-motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. CONCLUSIONS: PPVs were generally high (range: 82%-98%), suggesting that claims-based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.

Enhanced access to healthcare utilization data through medical record review: Lessons learned from a community-based, multi-site project.

Collecting accurate healthcare utilization (HCU) data on community-based interventions is essential to establishing their clinical effectiveness and cost-related impact. Strategies used to enhance receiving medical records for HCU data extraction in a multi-site longitudinal randomized control trial with urban adolescents are presented. Successful strategies included timely assessment of procedures and practice preferences for access to electronic health records and hardcopy medical charts. Repeated outreach to clinical practice sites to identify and accommodate their preferred procedure for medical record release and flexibility in obtaining chart information helped achieve a 75% success rate in this study. Maintaining participant contact, updating provider information, and continuously evaluating site-specific personnel needs are recommended.

Methodological challenges collecting parent phone-call healthcare utilization data.

Recommendations by the National Institute of Nursing Research and other groups have strongly encouraged nurses to pay greater attention to cost-effectiveness analysis when conducting research. Given the increasing prominence of translational science and comparative effective research, cost-effective analysis has become a basic tool in determining intervention value in research. Tracking phone-call communication (number of calls and context) with cross-checks between parents and healthcare providers is an example of this type of healthcare utilization data collection. This article identifies some methodological challenges that have emerged in the process of collecting this type of data in a randomized controlled trial: Parent education Through Simulation-Diabetes (PETS-D). We also describe ways in which those challenges have been addressed with comparison data results, and make recommendations for future research.