Table 11 Hypothetical scenario illustrating influence of acceptability, fidelity and feasibility on scalability of a health intervention
Scenario title Scalability of a digital health intervention Background A suite of new care models was designed and deployed at a hospital to manage the COVID-19 pandemic. One model supported patients in monitoring symptoms at home and advised patients about when and if they needed medical care and could present to the hospital. This virtual care model leverages technology to connect the patient with best evidence and provide targeted advice from their provider when needed. Outcomes included avoidance of emergency room and hospital overcrowding, lower cost to the patient, increase patient control and peace of mind. Many other conditions could be managed with a virtual care model and, after success of the COVID-19 model, the hospital would like to scale the model to better provide care for older adults with complex comorbidities. They decide to adapt the model to patients with chronic respiratory disease, who are one of the main sources of unnecessary Emergency Department admissions. | |
Barriers to scalability | Scenario |
Acceptability | Initial deployment used a homegrown database with a website link • Older adults often have lower digital literacy • Many older adults do not have daily access to a computer |
Fidelity | The intervention was deployed with urban patients, highly educated, and good internet connection • The deployment will be different with rural patients with poor internet |
Feasibility | Will require primary care integration. • Dispersed, independent GP clinics make it difficult to disseminate • There is no reimbursement model for GPs to look at panels of patients • Cannot currently exchange information between hospital EMR and many different primary care electronic medical records Captures oxygen saturation with a digital device • Emergency funding during pandemic not available for continuing program • Device does not work as well on patients with darker skin shades Enrolling patients relied on their coming to ED with COVID-19 symptoms • Identifying eligible patients will require new technology • All data required for eligibility requirements may not be digital or may not be sensitive/specific enough |
Conclusion Adapting a digital health intervention to a new population or setting can be like starting over again due to differences in IT infrastructure, digital literacy, and funding models Relationship to implementability Scalability of a digital intervention like this scenario encompasses acceptability, fidelity, and feasibility | |