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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; Del Olmo Rodríguez, Marta; Caramés Sánchez, Cristina; Blanco, Ángel; Pfang, Bernadette; Mayoralas-Alises, Sagrario; Fernandez-Ferro, Jose; Calvo, Emilio; Gómez Martín, Óscar; Fernández Tabera, Jesús; Plaza Nohales, Carmen; Nieto, Carlota; Short Apellaniz, Jorge.
Afiliação
  • Álvaro de la Parra JA; Quirónsalud, Madrid, Spain.
  • Del Olmo Rodríguez M; Quirónsalud, Madrid, Spain.
  • Caramés Sánchez C; Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.
  • Blanco Á; Quirónsalud, Madrid, Spain.
  • Pfang B; Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.
  • Mayoralas-Alises S; Quirónsalud, Madrid, Spain.
  • Fernandez-Ferro J; Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.
  • Calvo E; Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain.
  • Gómez Martín Ó; Hospital Quirónsalud San Jose, Madrid, Spain.
  • Fernández Tabera J; Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.
  • Plaza Nohales C; Neurology Department, Hospital Universitario Rey Juan Carlos, Mostoles, Spain.
  • Nieto C; Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.
  • Short Apellaniz J; Orthopaedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain.
BMJ Open ; 14(1): e081158, 2024 01 24.
Article em En | MEDLINE | ID: mdl-38267242
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Líquidos Corporais / Hospitais de Ensino Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Líquidos Corporais / Hospitais de Ensino Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha