Artificial Intelligence vs. Automated Messaging for Continuous Regional Analgesia Follow-up
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-driven follow-up platform, Satisfaction, Adherence to the follow-up protocol, Response rates from postoperative days one through three.
- Who it may be relevant to
- Registry conditions: Anesthesia, Anesthesia , Analgesia, Regional Anesthesia, Regional Anesthesia Success. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Chile
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Postoperative Follow-up Via Artificial Intelligence-Based Application Versus Automated Messaging Application in Patients Receiving Continuous Regional Analgesia: A Comparative Study
Overview
Effective postoperative analgesia is critical for patient recovery, satisfaction, and the reduction of hospital stay duration. Continuous peripheral nerve blocks (CPNB) via catheter placement represent a cornerstone in achieving these objectives. Traditionally, follow-up for these patients has relied on standardized telephone protocols conducted by trained personnel. Original previous research in 2024 demonstrated that an automated text-messaging platform was feasible and maintained high patient satisfaction, it resulted in a significantly higher rate of unscheduled patient-initiated inquiries (28.3% vs. 6.4%) compared to traditional phone calls, likely due to a lack of adaptive response capabilities. Objective: This study aims to evaluate an enhanced technological iteration of our follow-up platform. By integrating an Artificial Intelligence (AI) interface trained on specialized clinical protocols, the new system is designed to provide automated, personalized and adaptive recommendations to patients. Methods and Intervention: The study will compare the effectiveness of this AI-driven platform against the previous version of the non-adaptive automated messaging system. The primary outcome is to compare the number of patient-initiated inquiries (re-consultations). Secondary outcomes include patient satisfaction, adherence to the follow-up protocol, and response rates from postoperative days one through three. Impact: The investigators hypothesize that the integration of AI will optimize human resources and improve patient autonomy without compromising safety or satisfaction, ultimately providing a scalable model for postoperative regional analgesia monitoring.
Interventions
- Other AI-driven follow-up platform
The study will compare the effectiveness of this AI-driven platform against the previous version of the non-adaptive automated messaging system. The primary outcome is to compare the number of patient-initiated inquiries (re-consultations). Secondary outcomes include patient satisfaction, adherence to the follow-up protocol, and response rates from postoperative days one through three. - Other Satisfaction
Register patient satisfaction, adherence to the follow-up protocol, and response rates from postoperative days one through three - Other Adherence to the follow-up protocol
Register patient satisfaction, adherence to the follow-up protocol, and response rates from postoperative days one through three - Other Response rates from postoperative days one through three
Register patient satisfaction, adherence to the follow-up protocol, and response rates from postoperative days one through three
Primary outcome measures
- Comparison of patient-initiated inquiry rates between AI-App and Control-App [Time frame: From registration to the end of the 3-day outpatient postoperative follow-up]
Secondary outcome measures (3)
- Engage with the AI-driven app [Time frame: From registration to the end of the 3-day outpatient postoperative follow-up]
- Assessment patient satisfaction [Time frame: From registration to the end of the 3-day outpatient postoperative follow-up]
- Adherence between the Control-App and AI-App [Time frame: From registration to the end of the 3-day outpatient postoperative follow-up]
Eligibility criteria
Inclusion criteria
- Patients aged between 18 and 75 years.
- Physical status classification ASA I or II.
- Scheduled for ambulatory surgical programs at UC Christus Health Network centers (Hospital Clínico, Clínica San Carlos de Apoquindo, or Centro Médico Santa Lucía).
- Patients receiving postoperative pain management via continuous peripheral nerve block (CPNB) with a perineural catheter and disposable infusion pump.
- Ownership and documented proficiency in operating a smartphone to access the mobile application.
- Provision of written informed consent.
Exclusion criteria
- Inability to understand or follow the digital monitoring protocol.
- Patients not meeting the age or ASA physical status requirements.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Health services research
Study locations
Chile · 1 center
- Region Metropolitana de Chile — Santiago
Publications
- Semple JL, Sharpe S, Murnaghan ML, Theodoropoulos J, Metcalfe KA. Using a mobile app for monitoring post-operative quality of recovery of patients at home: a feasibility study. JMIR Mhealth Uhealth. 2015 Feb 12;3(1):e18. doi: 10.2196/mhealth.3929. PMID 25679749
- Pai B H P, Lai YH. Regional anesthesia and pain medicine. Reg Anesth Pain Med. 2022 Feb;47(2):144-145. doi: 10.1136/rapm-2021-102939. Epub 2021 Jul 5. No abstract available. PMID 34226197
- Perdomo-Pantoja A, Alomari S, Lubelski D, Liu A, DeMordaunt T, Bydon A, Witham TF, Theodore N. Implementation of an Automated Text Message-Based System for Tracking Patient-Reported Outcomes in Spine Surgery: An Overview of the Concept and Our Early Experience. World Neurosurg. 2022 Feb;158:e746-e753. doi: 10.1016/j.wneu.2021.11.051. Epub 2021 Nov 17. PMID 34800733
- Miller HN, Voils CI, Cronin KA, Jeanes E, Hawley J, Porter LS, Adler RR, Sharp W, Pabich S, Gavin KL, Lewis MA, Johnson HM, Yancy WS Jr, Gray KE, Shaw RJ. A Method to Deliver Automated and Tailored Intervention Content: 24-month Clinical Trial. JMIR Form Res. 2022 Sep 6;6(9):e38262. doi: 10.2196/38262. PMID 36066936
- Gessner D, Hunter OO, Kou A, Mariano ER. Automated text messaging follow-up for patients who receive peripheral nerve blocks. Reg Anesth Pain Med. 2021 Jun;46(6):524-528. doi: 10.1136/rapm-2021-102472. Epub 2021 Mar 1. PMID 33649155
- van der Velde M, Valkenet K, Geleijn E, Kruisselbrink M, Marsman M, Janssen LM, Ruurda JP, van der Peet DL, Aarden JJ, Veenhof C, van der Leeden M. Usability and Preliminary Effectiveness of a Preoperative mHealth App for People Undergoing Major Surgery: Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth. 2021 Jan 7;9(1):e23402. doi: 10.2196/23402. PMID 33410758
- Highland KB, Tran J, Edwards H, Bedocs P, Suen J, Buckenmaier CC. Feasibility of App-Based Postsurgical Assessment of Pain, Pain Impact, and Regional Anesthesia Effects: A Pilot Randomized Controlled Trial. Pain Med. 2019 Aug 1;20(8):1592-1599. doi: 10.1093/pm/pny288. PMID 30726985
- 2. Usability Measurement of Mobile Applications with System Usability Scale (SUS) - Aycan Kaya, Reha Ozturk and Cigdem Altin Gumussoy
Identifiers
NCT: NCT07510425 · 250312009