Classification of Documented Goals of Care Among Hospitalized Patients with High Mortality Risk: a Mixed-Methods Feasibility Study.

Abstract

The ability to classify patients' goals of care (GOC) from clinical documentation would facilitate serious illness communication quality improvement efforts and pragmatic measurement of goal-concordant care. Feasibility of this approach remains unknown. To evaluate the feasibility of classifying patients' GOC from clinical documentation in the electronic health record (EHR), describe the frequency and patterns of changes in patients' goals over time, and identify barriers to reliable goal classification. Retrospective, mixed-methods chart review study. Adults with high (50-74%) and very high (≥ 75%) 6-month mortality risk admitted to three urban hospitals. Two physician coders independently reviewed EHR notes from 6 months before through 6 months after admission to identify documented GOC discussions and classify GOC. GOC were classified into one of four prespecified categories: (1) comfort-focused, (2) maintain or improve function, (3) life extension, or (4) unclear. Coder interrater reliability was assessed using kappa statistics. Barriers to classifying GOC were assessed using qualitative content analysis. Among 85 of 109 (78%) patients, 338 GOC discussions were documented. Inter-rater reliability was substantial (75% interrater agreement; Cohen's kappa = 0.67; 95% CI, 0.60-0.73). Patients' initial documented goal was most frequently "life extension" (N = 37, 44%), followed by "maintain or improve function" (N = 28, 33%), "unclear" (N = 17, 20%), and "comfort-focused" (N = 3, 4%). Among the 66 patients whose goals' classification changed over time, most changed to "comfort-focused" goals (N = 49, 74%). Primary reasons for unclear goals were the observation of concurrently held or conditional goals, patient and family uncertainty, and limited documentation. Clinical notes in the EHR can be used to reliably classify patients' GOC into discrete, clinically germane categories. This work motivates future research to use natural language models to promote scalability of the approach in clinical care and serious illness research.