Table 3. Theoretical feasibility assessment of three artificial intelligence (AI)-based semi-automated workflows to routinely analyse free-text patient-reported experience measures (PREMs) data.

TELOS area	Overall assessment	No code	Low code	Full code
Technical feasibility	Technical workflows rely heavily on academic partners in no/low code solutions and transition to health service ownership in full code. A ‘red flag’ feature enables automated querying of concepts, for example, ‘harm’, ‘unsafe’ and ‘angry’, to quickly identify cases requiring urgent health service or ward attention.	Deductive (forced), inductive (discovery), flagged (‘red flag’ words). Leximancer licence (academia). Prestratified data. Flat reports (portable document format) sent to user profiles.	Deductive (forced), inductive (discovery), flagged (‘red flag’ words). Leximancer licence (academia). PowerBI dashboard. Descriptive analytics. User stratifications (predefined specifications based on PREMs survey).	Deductive (forced), inductive (discovery), flagged (‘red flag’ words). Licencing: enterprise and hospital level. PowerBI dashboard. Descriptive+Leximancer analytics. User stratifications (predefined specifications based on PREMs survey). Read/write data interrogation in Leximancer.
Economic feasibility	Economic investment increased proportionally to technical complexity of the no code, low code and high code solutions. Investment required education and training of healthcare staff. Predicted economic benefits were reduced labour and resource requirements, manual workflows and improved analytical speed. These hypotheses require testing.	Minimal costs for centralised management. Low costs for training and development. No cost for software development.	Moderate costs for centralised management. No costs for Leximancer licensing. Low costs for training and development.	Moderate costs for centralised management. Low costs for Leximancer licensing. Higher costs for training and development.
Legal feasibility	All solutions required shared governance oversight between academia, healthcare and industry, to ensure the security and integrity of patient data workflows. Data must remain inside the health service digital environment and firewall to maintain privacy.	Conjoint governance committee. Data remains inside health system firewall. Designated person to access and analyse PREMs data.	Conjoint governance committee. Data remains inside health system firewall. Healthcare staff+designated persons.	Conjoint governance committee. Data remains inside health system firewall. Healthcare staff+designated persons. Healthcare staff have full read/write control.
Operational feasibility	Seamless workflows to (1) extract (2) analyse and (3) act on data were required. (Near) real-time (eg, 1 month) analysis was required for continuous monitoring and surveillance.	Analysis lead: academia (and health system as desired). Data workflow: extraction, analysis, action. Education and training: Leximancer interpretation.	Analysis lea:– academia and health system together. Data workflow: extraction, analysis, action. Education and training: PowerBI, analytics.	Analysis lead: academia and health system together. Data workflow: extraction, stratification, analysis, action. Education and training: Leximancer, PowerBI, analytics.
Schedule feasibility	Timelines were considered feasible by academic and industry partners.	Time to implementation: ASAP (1–3 months).	Time to implementation: 3–6 months.	Time to implementation: 6–9 months.

TELOSTechnical, Economic, Legal, Operational and Schedule