Effect of an algorithm for automatic placing of standardised test order sets on low-value appointments and attendance rates at four Spanish teaching hospitals: an interrupted time series analysis.

OBJECTIVE: Reducing backlogs for elective care is a priority for healthcare systems. We conducted an interrupted time series analysis demonstrating the effect of an algorithm for placing automatic test order sets prior to first specialist appointment on avoidable follow-up appointments and attendance rates. DESIGN: Interrupted time series analysis. SETTING: 4 academic hospitals from Madrid, Spain. PARTICIPANTS: Patients referred from primary care attending 10 033 470 outpatient appointments from 16 clinical specialties during a 6-year period (1 January 2018 to 30 June 2023). INTERVENTION: An algorithm using natural language processing was launched in May 2021. Test order sets developed for 257 presenting complaints from 16 clinical specialties were placed automatically before first specialist appointments to increase rates of diagnosis and initiation of treatment with discharge back to primary care. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes included rate of diagnosis and discharge to primary care and follow-up to first appointment index. The secondary outcome was trend in 'did not attend' rates. RESULTS: Since May 2021, a total of 1 175 814 automatic test orders have been placed. Significant changes in trend of diagnosis and discharge to primary care at first appointment (p=0.005, 95% CI 0.5 to 2.9) and 'did not attend' rates (p=0.006, 95% CI -0.1 to -0.8) and an estimated attributable reduction of 11 306 avoidable follow-up appointments per month were observed. CONCLUSION: An algorithm for placing automatic standardised test order sets can reduce low-value follow-up appointments by allowing specialists to confirm diagnoses and initiate treatment at first appointment, also leading to early discharge to primary care and a reduction in 'did not attend' rates. This initiative points to an improved process for outpatient diagnosis and treatment, delivering healthcare more effectively and efficiently.

Implementation of an Algorithm of Cohort Classification to Prevent the Spread of COVID-19 in Nursing Homes.

Older adults living in nursing homes are the most vulnerable group of the COVID-19 pandemic. There are many difficulties in isolating residents and limiting the spread in this setting. We have developed a simple algorithm with a traffic light format for resident classification and sectorization within nursing homes, based on basic diagnostic tests, surveillance of symptoms onset, and close contact monitoring. We have implemented the algorithm in several centers with good data on adherence. Suggestions for implementation and evaluation are discussed.