Table 3. Example articles from each category and the description of their categorization process.

Category	Title of article in category	Description of categorization
Adverse events	A long-term follow-up evaluation of EHR prescribing safety	Article discusses the analysis of prescription error rates when transitioning between EHRs. Information retrieval for this study required data derivation from a chart review
Clinician cognitive processes	A novel use of the discrete templated notes within an EHR software to monitor resident supervision	This article discusses the specific documentation of resident procedures outside formal procedures allowing monitoring of resident training and potentially cognitive reasoning behind procedures
Data standards creation and data communication	A methodology for a minimum dataset for rare diseases to support national centers of excellence for health care and research	Specifically discusses standard data elements that could be used for rare diseases for epidemiology studies
Genomics	An EHR-driven algorithm to identify incident antidepressant medication users	Discusses a pharmacogenomics platform that focuses on harvesting this type of data for CDS and reporting. The article specifically deals with genomic data for CDS
Medication list data capture	Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration—experience from a pediatric tertiary care facility	Discusses the design an algorithm for derivation of antidepressant users from the EHR data. Indicates missing information on patient medication lists
Patient preferences	An information model for automated assessment of concordance between advance care preferences and care delivered near the end of life	Discusses storage of advance care preference (a patient preference) information in the EHR in an easier-to-retrieve format
Patient-reported data	Assessing older adults' perceptions of sensor data and designing visual displays for ambient environments	Studies the perceptions of elderly patients toward the use of in-home sensors for the collection of medical data (patient-reported due to sensor collection directly from the sensors). Addresses the collection of this information
Phenotyping	A collaborative approach to developing an EHR-phenotyping algorithm for drug-induced liver injury	Discusses the creation of a phenotyping algorithm designed to identify patients in the EHR with drug-induced liver injury

Abbreviations: CDS, clinical decision support; EHR, electronic health record.

Note: Each article's content is described in relation to the reason that it fits in the category (i.e., why it was placed in the category listed).