Predicting anticipated benefit from an extended consultation to personalise care in multimorbidity: a development and internal validation study of a prioritisation algorithm in general practice

Abstract

Background: Persons with multimorbidity may gain from person-centred care compared with the current protocolised chronic-disease management in Dutch general practice. Given time constraints and limited resources, it is essential to prioritise those most in need of an assessment of person-centred chronic-care needs.

Aim: To develop and validate a prioritisation algorithm based on routine electronic medical record (EMR) data that distinguishes between patients with multimorbidity who would, and those who would not, benefit from an extended person-centred consultation to assess person-centred chronic-care needs, as judged by GPs.

Design and setting: A mixed-methods study was conducted in five general practices in the north-west region of the Netherlands. Four out of the five practices were situated in rural areas.

Method: Multivariable logistic regression using EMR data to predict the GPs' judgement on patients' anticipated benefit from an extended consultation, as well as a thematic analysis of a focus group exploring GPs' clinical reasoning for this judgement were conducted. Internal validation was performed using 10-fold cross-validation. Multimorbidity was defined as the presence of ≥3 chronic conditions.

Results: In total, EMRs from 1032 patients were included in the analysis; of these, 352 (34.1%) were judged to have anticipated benefit. The model's cross-validated C-statistic was 0.72 (95% confidence interval = 0.70 to 0.75). Calibration was good. Presence of home visit(s) and history of myocardial infarction were associated with anticipated benefit. Thematic analysis revealed three dimensions feeding anticipated benefit: GPs' cause for concern, patients' mindset regarding their conditions, and balance between received care/expected care needed.

Conclusion: This algorithm may facilitate automated prioritisation, potentially avoiding the need for GPs to personally triage the whole practice population that has multimorbidity. However, external validation of the algorithm and evaluation of actual benefit of consultation is recommended before implementation.

Keywords: general practice; multimorbidity; person-centred care; primary care.

Figure 1.

Flowchart sample selection. ^aPotentially eligible participants were selected based on an EMR search query that included data from the years 2017 and 2018 (baseline measurement [T0] of the COPILOT project). ^bAt T0 of the COPILOT project, GPs checked whether these potentially eligible patients met the inclusion or exclusion criteria based on their personal knowledge. This was done as EMR data relating to the criteria might not be accurate. If necessary, GPs were allowed to exclude patients on this basis. ^cThis study utilised data from the COPILOT project, an action-based research study aimed at co-creating and piloting a proactive, person-centred, chronic-care approach for patients with multimorbidity. Data were collected during three measurement waves: 2019 (T0), 2020 (T1), and 2021 (T2). For the current study, T2 data were used. However, some patients were lost to follow-up because of reasons specified in this figure or excluded because of the unavailability of EMR data. ^dPatients included in the shaded boxes (moderate and high anticipated benefit) were merged in the analysis. EMR = electronic medical record.

Figure 2.

Measures to determine the optimal cut-off value of the diagnostic model for anticipated benefit from an EPCC. EPCC = extended person-centred consultation. NPV = negative predictive value.