Extracting Data from the Electronic Health Record of Patients with ADHD Reveals Pediatricians' Discussions of Educational Support and Document Collection

Primary care physicians (PCPs) have an important role in the identification and management of Attention Deficit Hyperactivity Disorder (ADHD). There is a paucity of research on PCPs' practices related to the discussion of educational interventions. We conducted a retrospective chart review using Natural Language Processing to extract data on how often PCPs in an outpatient clinic: 1) discuss educational support with patients and caregivers; and 2) obtain educational records. About three-quarters of patients had at least one term related to educational support included in at least one note, but only 13 percent of patients had at least one educational record uploaded into the electronic health record (EHR). There was no association between having an educational document uploaded into the EHR and inclusion of a term related to educational support in a note. Almost half (48 percent) of these records were unclearly labeled. Further education of PCPs is warranted to increase discussions of educational support and obtaining educational records, as is collaboration with health information management professionals around labeling.

Linking education and hospital data in England: linkage process and quality

INTRODUCTION

Linkage of administrative data for universal state education and National Health Service (NHS) hospital care would enable research into the inter-relationships between education and health for all children in England.

OBJECTIVES

We aim to describe the linkage process and evaluate the quality of linkage of four one-year birth cohorts within the National Pupil Database (NPD) and Hospital Episode Statistics (HES).

METHODS

We used multi-step deterministic linkage algorithms to link longitudinal records from state schools to the chronology of records in the NHS Personal Demographics Service (PDS; linkage stage 1), and HES (linkage stage 2). We calculated linkage rates and compared pupil characteristics in linked and unlinked samples for each stage of linkage and each cohort (1990/91, 1996/97, 1999/00, and 2004/05).

RESULTS

Of the 2,287,671 pupil records, 2,174,601 (95%) linked to HES. Linkage rates improved over time (92% in 1990/91 to 99% in 2004/05). Ethnic minority pupils and those living in more deprived areas were less likely to be matched to hospital records, but differences in pupil characteristics between linked and unlinked samples were moderate to small.

CONCLUSION

We linked nearly all pupils to at least one hospital record. The high coverage of the linkage represents a unique opportunity for wide-scale analyses across the domains of health and education. However, missed links disproportionately affected ethnic minorities or those living in the poorest neighbourhoods: selection bias could be mitigated by increasing the quality and completeness of identifiers recorded in administrative data or the application of statistical methods that account for missed links.

HIGHLIGHTS

Longitudinal administrative records for all children attending state school and acute hospital services in England have been used for research for more than two decades, but lack of a shared unique identifier has limited scope for linkage between these databases.We applied multi-step deterministic linkage algorithms to 4 one-year cohorts of children born 1 September-31 August in 1990/91, 1996/97, 1999/00 and 2004/05. In stage 1, full names, date of birth, and postcode histories from education data in the National Pupil Database were linked to the NHS Personal Demographic Service. In stage 2, NHS number, postcode, date of birth and sex were linked to hospital records in Hospital Episode Statistics.Between 92% and 99% of school pupils linked to at least one hospital record. Ethnic minority pupils and pupils who were living in the most deprived areas were least likely to link. Ethnic minority pupils were less likely than white children to link at the first step in both algorithms.Bias due to linkage errors could lead to an underestimate of the health needs in disadvantaged groups. Improved data quality, more sensitive linkage algorithms, and/or statistical methods that account for missed links in analyses, should be considered to reduce linkage bias.