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Strehlow M, Alvarez A, Blomkalns AL, Caretta-Wyer H, Gharahbaghian L, Imler D, Khan A, Lee M, Lobo V, Newberry JA, Riberia R, Sebok-Syer S, Shen S, Gisondi MA. Precision emergency medicine. Acad Emerg Med 2024. [PMID: 38940478 DOI: 10.1111/acem.14962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/13/2024] [Accepted: 05/23/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health. METHODS In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward. RESULTS Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty. CONCLUSIONS Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.
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Affiliation(s)
- Matthew Strehlow
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Al'ai Alvarez
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Andra L Blomkalns
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Holly Caretta-Wyer
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Laleh Gharahbaghian
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Daniel Imler
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Ayesha Khan
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Moon Lee
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Viveta Lobo
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jennifer A Newberry
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Ryan Riberia
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Stefanie Sebok-Syer
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Sam Shen
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Michael A Gisondi
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
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Álvaro de la Parra JA, Del Olmo Rodríguez M, Caramés Sánchez C, Blanco Á, Pfang B, Mayoralas-Alises S, Fernandez-Ferro J, Calvo E, Gómez Martín Ó, Fernández Tabera J, Plaza Nohales C, Nieto C, Short Apellaniz J. 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. BMJ Open 2024; 14:e081158. [PMID: 38267242 PMCID: PMC10824031 DOI: 10.1136/bmjopen-2023-081158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
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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Affiliation(s)
| | - Marta Del Olmo Rodríguez
- Quirónsalud, Madrid, Spain
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
| | - Cristina Caramés Sánchez
- Quirónsalud, Madrid, Spain
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
| | | | - Bernadette Pfang
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
| | | | - Jose Fernandez-Ferro
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Neurology Department, Hospital Universitario Rey Juan Carlos, Mostoles, Spain
| | - Emilio Calvo
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Orthopaedic Surgery and Traumatology, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
| | - Óscar Gómez Martín
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
| | - Jesús Fernández Tabera
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Villalba General University Hospital, Collado Villalba, Spain
| | - Carmen Plaza Nohales
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Hospital Universitario Rey Juan Carlos, Mostoles, Spain
| | - Carlota Nieto
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
| | - Jorge Short Apellaniz
- Instituto de Investigacion Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
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