Develop a library of clinical care algorithms based on evidence and expert consensus.

The ºÃÉ«ÏÈÉú (AAN) has published a compendium of quality measures, many applicable to children. Limited data exist showing how well child neurologists perform relative to these child neurology quality metrics. The data that are available show a need for ways to track and improve the quality of care. The Child Neurology Foundation (CNF) in partnership with Digital Health Solutions, LLC (DHS) initiated a national quality improvement initiative aimed at developing a clinical decision support system to implement quality measures and improve care quality and patient education for child neurology.

CNF convened an expert panel comprising child neurologists, quality experts, a clinical informatician, and a patient representative. Beginning with quality measures published by the AAN and 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.

The panel created 25 algorithms that address quality metrics related to epilepsy, 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 now been encoded into a software system, embedded into an electronic medical record, that will be pilot tested in neurology clinics in two academic medical centers.