Needles in the haystack: Using open-text fields to identify persons with intellectual and developmental disabilities in administrative home care data.
Open-text notes can surface hidden IDD cases, but only if clinicians swap fuzzy words for precise diagnoses.
01Research in Context
What this study did
McKenzie et al. (2017) read the free-text notes in home-care files.
They hunted for words that hint at intellectual or developmental disability.
The goal: see how many clients official records miss.
What they found
About one in every hundred clients had clear IDD clues in the notes.
Most hints were fuzzy words like "slow" or "forgetful."
These vague terms usually pointed to older, less-impaired people.
How this fits with other research
Amaral et al. (2019) took the idea bigger. They added ICD codes and scanned all-payer claims across an entire state. Their upgrade found even more missed cases.
Anderson et al. (2019) sum up fifty U.S. studies and say we still don’t know how many adults with IDD exist. Katherine’s text-mining trick is one way to fill that gap.
Cornish et al. (2012) urged us to track subtle change over time. Mining notes each year could give the longitudinal data they asked for.
Why it matters
If you work with adult services, you can copy the keyword search today. Ask IT to flag notes that say "Down syndrome," "IQ 55," or similar clear phrases. Push evaluators to drop the vague labels and write the exact diagnosis. Cleaner words now mean easier counts—and better funding arguments—later.
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02At a glance
03Original abstract
BACKGROUND: Use of administrative health data to study populations of interest is becoming more common. Identifying individuals with intellectual and developmental disabilities (IDD) in existing databases can be challenging due to inconsistent definitions and terminologies of IDD over time and across sectors, and the inability to rely on etiologies of IDD as they are frequently unknown. AIMS: To identify diagnoses related to IDD in an administrative database and create a cohort of persons with IDD. METHODS: Open-text diagnostic entries related to IDD were identified in an Ontario home care database (2003-2015) and coded as being either acceptable (e.g. Down syndrome) or ambiguous (e.g. intellectually challenged). The cognitive and functional skills of the resulting groups were compared using logistic regressions and standardized differences, and their age distributions were compared to that of the general home care population. RESULTS: Just under 1% of the home care population had a diagnostic entry related to IDD. Ambiguous terms were most commonly used (61%), and this group tended to be older and less impaired than the group with more acceptable terms used to describe their IDD. CONCLUSIONS: Open-text diagnostic variables in administrative health records can be used to identify and study individuals with IDD. IMPLICATIONS: Future work is needed to educate assessors on the importance of using standard, accepted terminology when recording diagnoses related to IDD.
Research in developmental disabilities, 2017 · doi:10.1016/j.ridd.2017.07.019