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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Feb 11, 2020
Date Accepted: Mar 28, 2020

The final, peer-reviewed published version of this preprint can be found here:

The Impact of Digital-First Consultations on Workload in General Practice: Modeling Study

Salisbury C, Murphy M, Duncan P

The Impact of Digital-First Consultations on Workload in General Practice: Modeling Study

J Med Internet Res 2020;22(6):e18203

DOI: 10.2196/18203

PMID: 32543441

PMCID: 7327596

The impact of digital-first consultations on workload in general practice: modelling study

  • Chris Salisbury; 
  • Mairead Murphy; 
  • Polly Duncan

ABSTRACT

Background:

Health services in many countries are promoting digital-first models of access to general practice based on offering online, video or telephone consultations before a face-to-face consultation. It is claimed that this will both improve access for patients and moderate the workload of doctors. However improved access could also potentially increase doctors’ workload.

Objective:

To explore whether and under what circumstances digital-first access to general practice is likely to reduce or increase general practice workload.

Methods:

A process map to delineate primary care access pathways was developed and a model to estimate general practice workload constructed in Microsoft Excel. The model was populated using estimates of key variables obtained from a systematic review of published studies. A MEDLINE search was conducted for studies published in English between 1 January 2000 and 30 September 2019. Included papers provided quantitative data about online, telephone or video consultations for unselected patients requesting a general practice in-hours consultation for any problem. We excluded studies of GPs consulting specialists, consultations not conducted by doctors, and consultations conducted out-of-hours, in secondary care, in specialist services or for a specific health care problem. Data about the following variables were extracted from the included papers to form the model inputs: the proportion of consultations managed digitally, the proportion of digital consultations completed without a subsequent consultation, the proportion of subsequent consultations conducted by telephone rather than face-to-face, consultation duration, and the proportion of digital consultations which represent new demand. The outcome was general practice workload. The model was used to test the likely impact of different digital-first scenarios, based on the best available evidence and the plausible range of estimates from the published studies. The model allows others to test the impact on workload of varying assumptions about model inputs.

Results:

Digital-first approaches are likely to increase general practice workload unless they are shorter, and a higher proportion are managed without a subsequent consultation, than observed in most published studies. In our base-case scenarios (based on the best available evidence), digital-first access models using online, telephone or video consultations are likely to increase GP workload by 25%, 3% and 31% respectively. An important determinant of workload is whether the availability of digital-first approaches changes the demand for general practice consultations, but there is little robust evidence about this question.

Conclusions:

Digital-first approaches to primary care could increase general practice workload unless stringent conditions are met. Justification for these approaches should be based on evidence about the benefits in relation to the costs, rather than assumptions about reductions in workload. Given the potential increase in workload, which in due course could worsen problems of access, these initiatives should be rolled out in a staged way alongside careful evaluation.


 Citation

Please cite as:

Salisbury C, Murphy M, Duncan P

The Impact of Digital-First Consultations on Workload in General Practice: Modeling Study

J Med Internet Res 2020;22(6):e18203

DOI: 10.2196/18203

PMID: 32543441

PMCID: 7327596

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