238 - Decision Support for Pediatric Neurology Quality Measures

Poster Number: 238
Publication Number: 238.451

Stephen M. Downs, Wake Forest School of Medicine of Wake Forest Baptist Medical Center, Winston Salem, NC, United States; Katie hentges, Child Neurology Foundation, Minneapolis, MN, United States; Ann A. Clark, Indiana University school of medicine, Indianapolis, IN, United States; Anup D. Patel, Nationwide Children's Hospital, Columbus, OH, United States

Background: The American Academy of Neurology (AAN) has published quality measures in child neurology, many applicable to children. Limited available data suggest these child neurology quality metrics are not being met. The Child Neurology Foundation (CNF), in partnership with Digital Health Solutions, LLC (DHS), initiated a national quality improvement effort to develop a clinical decision support system to implement quality measures and assess how it improves care of children in outpatient neurology care.

Objective: Develop a library of clinical care algorithms as part of a larger computer based quality improvement strategy.

Design/Methods: CNF convened an expert panel comprising child neurologists, quality experts, a clinical informatician, and a patient representative. Beginning with published quality measures relevant to child neurology, algorithms were developed, using formal algorithmic flow diagrams. A modified Delphi technique and nominal group method, conducted by teleconference and email, were used to modify each algorithm in several rounds until consensus was achieved. For each algorithm, CNF and the expert panel developed a handout that neurologists can use to educate patients. Medical record abstraction was conducted at one academic health system to establish baseline rates at which four AAN recommended care was delivered.

Results: Baseline rates of four quality metrics confirmed deficiencies (rate, 95% CI): 1) genetic testing for global developmental delay (14%, 0-9%); 2) rescue medication prescription for epilepsy (43%, 15-32%); 3) transition plan of care (38%, 22-53%); and 4) depression screening (75%, 62-88%) and anxiety screening (0%, 0-8%). The panel created 25 algorithms that address quality metrics in child neurology: depression screening, genetic testing, gastrointestinal issues, neurodevelopmental surveillance, school problems, transition of care, migraine management, muscular dystrophy, tic disorder and Tourette syndrome. For each of these a handout was developed. The clinical content has been encoded into a software system (CHICA), embedded into an electronic medical record, that will be pilot tested in neurology clinics in two academic medical centers.

Conclusion(s): Quality metrics are not being met. Expert consensus was achieved on a broad set of clinical algorithms based on published quality metrics. The algorithms were associated with patient handouts and encoded in a clinical system that runs in an electronic medical record. Chart abstraction after implementing the system will demonstrate its effectiveness.