Population Segmentation Based on Healthcare Needs: Validation of a Brief Clinician-Administered Tool

Abstract

Background: As populations age with increasingly complex chronic conditions, segmenting populations into clinically meaningful categories of healthcare and related service needs can provide healthcare planners with crucial information to optimally meet needs. However, while conventional approaches typically involve electronic medical records (EMRs), such records do not always capture information reliably or accurately.

Objective: We describe the inter-rater reliability and predictive validity of a clinician-administered tool, the Simple Segmentation Tool (SST) for categorizing older individuals into one of six Global Impression (GI) segments and eight complicating factors (CFs) indicative of healthcare and related social needs.

Design: Observational study ( ClinicalTrials.gov , number NCT02663037).

Participants: Patients aged 55 years and above.

Main measures: Emergency department (ED) subjects (between May and June 2016) had baseline SST assessment by two physicians and a nurse concurrently seeing the same individual. General medical (GM) ward subjects (February 2017) had a SST assessment by their principal physician. Adverse events (ED visits, hospitalizations, and mortality over 90 days from baseline) were determined by a blinded reviewer. Inter-rater reliability was measured using Cohen's kappa. Predictive validity was evaluated using Cox hazard ratios based on time to first adverse event.

Key results: Cohen's kappa between physician-physician, service physician-nurse, and physician-nurse pairs for GI were 0.60, 0.71, and 0.68, respectively. Cox analyses demonstrated significant predictive validity of GI and CFs for adverse outcomes.

Conclusions: With modest training, clinicians can complete a brief instrument to segment their patient into clinically meaningful categories of healthcare and related service needs. This approach can complement and overcome current limitations of EMR-based instruments, particularly with respect to whole-patient care.

Trial registration: ClinicalTrials.gov Identifier: NCT02663037.

Keywords: aging; health services research; psychometrics.

Conflict of interest statement

The authors declare that they do not have a conflict of interest.

Figures

Figure 1

Flowchart of subjects recruited and available for analysis from the emergency department and General Medical Ward.

Figure 2

a Kaplan-Meier survival estimates for ED visit outcome by Global Impression categories (n = 248). b Kaplan-Meier survival estimates for non-elective hospital admission outcome by Global Impression categories (n = 248). c Kaplan-Meier survival estimates for mortality outcome by Global Impression categories (n = 248). A, healthy; B, chronic condition, asymptomatic; C, chronic condition, symptomatic; D, long course of decline; E, limited reserve; F, short decline before dying.