Increasing demand for haematological specialist care makes the optimisation of referrals and outpatient workflow a priority. Automated placing of standardised test orders prior to the first appointment may provide haematologists with necessary information to reach diagnoses and initiate treatment at the first patient encounter, reducing low-value follow-up appointments. We aimed to evaluate rates of patient participation in an initiative using artificial intelligence to place standardised test orders as well as reasons for non-participation, differences in the number of participants and non-participants discharged back to primary care with a diagnosis or appropriate treatment plan, and potentially avoidable referrals.

A retrospective, multicentric cohort study.

Four academic hospitals in Madrid, Spain.

18 190 patients referred for a first haematologist appointment for 11 included presenting complaints.

Referral notes from primary care were classified using natural language processing and automated placement of standardised test order sets was carried out prior to first appointment for participating patients.

We compared demographic differences between participants and non-participants, the main motives for not participating, and the number of patients discharged back to primary care at first appointment with a diagnosis and treatment plan. Most frequent International Classification of Diseases, tenth revision codes for each of the included presenting complaints were described.

During the study period, 18 190 (41%) patients were referred for a first haematologist appointment for presenting complaints included in the intervention (‘eligible patients’), of which 612 (3.3%) patients agreed to participate in the intervention. Participants were significantly younger than non-participants. Most common motives for not participating were administrative reasons (6268, 76.9%). Only 122 (1.5%) patients expressed explicit unwillingness to participate. A significant increase in the number of patients discharged upon first appointment was observed for participants (146 (23.9%) vs 3375 (19.36%); p=0.041), signifying a 22% relative reduction in avoidable follow-up. The diagnosis ‘haematological disorders ruled out’ was constantly observed as one of the ten most common diagnoses made by the haematology specialist for all but one of the included presenting complaints.

Natural language processing of referrals from primary to specialist haematology care with automated placing of standardised test orders can decrease low-value follow-up appointments. Explicit refusal to participate was low. Participants tended to be younger than non-participants, underlining the importance of designing strategies to target the older population in order to improve participation.

Post-COVID-19 conditions (PCC) may include pulmonary sequelae, fatigue and other symptoms, but its mechanisms are not fully elucidated.

This study investigated the correlation between fatigue and the presence of pulmonary abnormalities in PCC patients with respiratory involvement 6–12 months after hospitalisation.

Cross-sectional study.

A tertiary hospital in Brazil.

315 patients, aged ≥18 years, were considered eligible based on SARS-CoV-2 infection confirmed by reverse transcription-PCR.

Pulmonary function tests (PFT), cardiopulmonary exercise tests (CPET), chest CT and hand grip were performed. The following scales were applied: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, Euroqol 5 Dimensions quality of life (EQ-5D) and Hospital Anxiety and Depression Scale (HADS). Participants were divided between the fatigue group (FACIT-F≤30) and the non-fatigue group (FACIT-F>30). For the statistical analysis, the primary outcome was the difference in the diffusing capacity of the lungs for carbon monoxide (DLCO) between groups. Considered secondary outcomes were differences in PFT, CPET, chest CT, hand grip, EQ-5D and HADS.

The fatigue group had 81 patients (25.7%) against 234 (74.3%). PFT and CPET showed no significant difference in DLCO and oxygen consumption peak values between groups. The fatigue group had a lower workload (mean 55.3±21.3 watts vs 66.5±23.2 watts, p=0.003), higher breathing reserve (median 41.9% (33.8–52.5) vs 37.7% (28.9–47.1), p=0.028) and lower prevalence of ground glass opacity (60.8% vs 77.7%, p=0.003) and reticulation (36.7% vs 54.9%, p=0.005) in chest CT. The fatigue group had higher anxiety (57% vs 24%, p

Fatigue in patients with PCC 6–12 months after hospitalisation is relatively common and had weak correlation with pulmonary disorders. Our results suggested fatigue could be strongly related with peripheral disorders such as reduced musculoskeletal strength or psychosocial limitations.