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Can quality indicators be automatically extracted from resident records in long term care facilities? An assessment
Peter D Hibbert1, Charlotte J Molloy1, Louise K Wiles2
1Australian Institute of Health Innovation, Macquarie University, 75 Talavera Rd, Macquarie Park, NSW 2113, Australia.
Abstract:
Problem description Measuring quality of care, represented by quality indicators (QIs), in long term care (LTC) residents is a key imperative for care systems globally. To measure at scale, algorithms can be developed to automatically extract QIs from residents' records. However, much information documented in residents' care records is free-text, making the automated extraction of QIs challenging.
Objectives:
To analyse the proportion of QIs that are extracted from structured data fields in LTC residents' care records and therefore are amenable to automated extraction.
Methods:
A total of 236 QIs were developed for 16 conditions, e.g., Cognitive impairment, End of life care. Trained LTC nurses then undertook a manual care record review to assess the percentage of CareTrack Aged QIs that were collected and stored in structured data fields in the care record.
Results:
The underlying QI data field type was extracted from six electronic care record systems across 8,333 QI assessments (encounters of care) for 118 residents in 42 facilities. To determine QI eligibility, 17 % (n = 1425/8333) of QIs used structured data fields, with 39 % hybrid (structured and free-text) and 44 % free-text. QI eligibility assessment using structured data fields varied across the care record systems (Range 11-26 %). To determine QI adherence, 5 % (n = 251/4930) of QIs used structured data fields, with 60 % hybrid and 35 % free-text. QI adherence assessment using structured data fields varied across the care record systems (Range 1-12 %). Combining QI eligibility and adherence, 13 % (n = 1676/13263) of indicator question information used structured data fields.
Conclusion:
This study challenges the assumption that QIs that are representative of care delivered in LTCs can be largely algorithmically electronically extracted from structured data fields. QI assessments for evidence-based care were extracted predominantly from free-text and hybrid data fields in LTC residents' records.
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