Dataset Information

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.

ABSTRACT:

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.

SUBMITTER: Alvaro de la Parra JA

PROVIDER: S-EPMC10824031 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

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.

Álvaro de la Parra Juan Antonio JA Del Olmo Rodríguez Marta M Caramés Sánchez Cristina C Blanco Ángel Á Pfang Bernadette B Mayoralas-Alises Sagrario S Fernandez-Ferro Jose J Calvo Emilio E Gómez Martín Óscar Ó Fernández Tabera Jesús J Plaza Nohales Carmen C Nieto Carlota C Short Apellaniz Jorge J

BMJ open 20240124 1

<h4>Objective</h4>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.<h4>Design</h4>Interrupted time series analysis.<h4>Setting</h4>4 academic hospitals from Madrid, Spain.<h4>Participants</h4>Patients referred from primary care attending 10 033 470 out ...[more]

PMID: 38267242

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Project description:BackgroundNon-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.MethodsThe study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.ResultsDecision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.ConclusionsThe risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Project description:BackgroundFollow-up with a primary care provider within 1-2 weeks of discharge from hospital has been associated with reduced readmissions. We sought to determine appointment attendance with primary care providers postdischarge and identify factors associated with attendance.MethodsWe conducted a retrospective cohort study involving general medicine patients who had been discharged from hospital between Sept. 1, 2014, and Dec. 30, 2015, from 2 Ontario academic hospitals, and who had been supported by a transitional care specialist and advised to see a primary care provider within 1 week. Attendance was determined by self-report during follow-up by telephone. We used multivariable logistic regression to assess whether patient factors (e.g., comorbidity) or system factors (e.g., booking the appointment before discharge) predicted attendance. We used Cox proportional hazards modelling to assess whether attendance predicted readmission within 30 days.ResultsOf the 214 patients included in our study, 35% (n = 75) attended a primary care appointment within 1 week of discharge; 52% (n = 124) of patients attended an appointment within 2 weeks. After adjusting for age, sex and comorbidity, significant predictors of attendance were booking the appointment before discharge (odds ratio [OR] 2.14, 95% confidence interval [CI] 1.07-4.40), familiarity with the primary care provider (OR 5.43, 95% CI 2.25-14.1) and inclusion of a reminder, callback number and appointment time in the discharge summary (OR 15.3, 95% CI 2.09-326). Predictors of nonattendance were the presence of a home support worker (OR 0.38, 95% CI 0.17-0.80) and a booked specialist appointment before discharge (OR 0.37, 95% CI 0.18-0.73). Attendance was not associated with reduced readmissions (hazard ratio 0.66, 95% CI 0.40-1.09).InterpretationTimely follow-up with PCPs postdischarge remains challenging. Efforts to improve attendance should focus on reinforcing need for follow-up and coordinating follow-up before discharge, particularly for those poorly connected with the health care system.

Dataset Information

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

Design

Setting

Participants

Intervention

Primary and secondary outcome measures

Results

Conclusion

Publications

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.

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