DialysisBot: AI Chatbot for Hemodialysis Self-Management

NCT07830810 | Not Yet Recruiting

• 1 other identifier: 2992/CEL
• Study Type: interventional
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

End-stage kidney disease requiring hemodialysis is a chronic condition with substantial clinical, functional, and psychosocial burden. Maintaining an adequate health status between dialysis sessions depends largely on patients' ability to self-manage key aspects of treatment, including fluid restriction, dietary control, and vascular access care. Adherence in this population remains frequently suboptimal, and elevated interdialytic weight gain (IDWG), hyperphosphatemia, and hyperkalemia are among the most common complications associated with non-adherence. DialysisBot is a multiplatform, artificial intelligence-based conversational agent designed to support hemodialysis patients in the day-to-day self-management of their disease. The system combines a generative large language model for natural dialogue management, a fine-tuned BERT classifier for recognizing the domain of each patient request (diet, fluid intake, vascular access care, or organizational aspects of dialysis), and a vector-similarity retrieval engine that limits every response to a clinically validated knowledge base preloaded by the research team and approved by the hospital institution. The system performs no autonomous web search. When the cosine similarity between a user query and the indexed reference documents falls below a predefined threshold, the system withholds a clinical answer and instead informs the patient that it cannot respond, directing them to contact healthcare staff; this mechanism is the main technical safeguard against hallucinated or unvalidated responses. This is a prospective experimental pilot study evaluating the feasibility, acceptability, impact, and user satisfaction associated with DialysisBot as a self-management support tool for patients undergoing hemodialysis. Secondary objectives include assessing support for dietary management (phosphorus, potassium, and sodium restriction), support for interdialytic fluid intake control, whether system-provided guidance on vascular access management (arteriovenous fistula and central venous catheter) is put into practice and consistent with current standards of care, the impact on treatment adherence, and the barriers, facilitators, and overall user experience associated with the intervention. The study is organized into two methodological phases: a quantitative longitudinal phase (T0 baseline, T1 at 1 month, T2 at 3 months) using standardized patient-reported outcome measures and routine clinical parameters, followed by a qualitative phase conducted after T2, based on semi-structured interviews analyzed through reflexive thematic analysis. A convenience sample of 20-40 adult patients undergoing chronic hemodialysis, recruited through a participating dialysis center and/or an online patient community, will be enrolled and trained on the application before use. As a pilot feasibility study, its results are intended to inform the design and sample size of future, larger-scale confirmatory trials.

Conditions

Chatbot-Based Training; Hemodialysis; Self-management

Keywords

hemodialysis; chatbot; conversational agent; Chronic kidney disease; End-stage kidney disease; Usability; empowerment; Artificial intelligence; Large language model; mHealth; Digital health; Arteriovenous fistula; Patient education; Patient empowerment; Vascular access; Treatment adherence; Fluid restriction; Interdialytic weight gain

Outcome Measures

Primary Outcomes (3)

• Feasibility of the DialysisBot Intervention

  Feasibility will be assessed through four indicators: recruitment rate (proportion of eligible patients who consent to participate), retention rate (proportion of participants completing the study at the final assessment), questionnaire completion rate (proportion of instruments adequately completed from the intermediate to the final assessment), and application engagement (mean number of weekly patient-system interactions, derived from anonymized system logs).

  Baseline (T0) through 3 months (T2)

• Usability of DialysisBot

  Usability will be assessed using the Italian version of the Chatbot Usability Scale (BUS-11), an 11-item, 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) validated for AI-based conversational systems. Total score ranges from 11 to 55; higher scores indicate greater perceived usability. \[Time Frame: 1 month (T1)\]

  1 month (T1)

• Change in Treatment Adherence Indicators

  Adherence will be measured through routinely collected clinical parameters: interdialytic weight gain (IDWG, from pre- and post-dialysis body weight), serum phosphate (mmol/L), serum potassium (mmol/L), and attendance rate at scheduled dialysis sessions (%).

  Baseline (T0), 1 month (T1), and 3 months (T2)

Secondary Outcomes (1)

• Change in Health-Related Quality of Life

  Baseline (T0) compared to 3 months (T2)

Study Arms (1)

DialysisBot: AI Conversational Agent for Self-Management Support

EXPERIMENTAL

All enrolled participants receive access to DialysisBot, a multiplatform AI-based conversational agent designed to support self-management in patients undergoing chronic hemodialysis. The intervention combines a generative large language model for natural dialogue, a fine-tuned BERT classifier for intent recognition (diet, fluid intake, vascular access care), and a vector-similarity retrieval engine restricting responses to a clinically validated knowledge base approved by the research team and the hospital institution. Before starting use of the application, participants receive individual training, including a guided walkthrough of the interface, an integrated digital user manual, and ongoing technical support for the duration of the study (approximately 3 months, from baseline/T0 to the final assessment/T2). Participants use DialysisBot as needed in their daily self-management routine between dialysis sessions, with no comparator or control group.

Behavioral: DialysisBot

Interventions

DialysisBot BEHAVIORAL

DialysisBot is a multiplatform, AI-based conversational agent (chatbot) providing personalized, self-management support to patients undergoing chronic hemodialysis. It combines a generative large language model for dialogue management, a fine-tuned BERT classifier for request-domain recognition (diet, fluid intake, vascular access care, dialysis organization), and a vector-similarity retrieval engine that restricts all responses to a clinically validated educational knowledge base preloaded by the research team. The system performs no autonomous web search. When query-to-source similarity falls below a predefined threshold, the system withholds a response and directs the patient to healthcare staff, serving as a safeguard against unvalidated or hallucinated content. Participants use the application as needed in their daily routine over a 3-month period, after receiving individual training on its use.

DialysisBot: AI Conversational Agent for Self-Management Support

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age ≥ 18 years
• Patients undergoing chronic hemodialysis treatment
• Availability of a compatible digital device (smartphone, tablet, or computer)
• Adequate understanding of the Italian language
• Sufficient basic digital literacy
• Signed informed consent A purposive subsample (n = 10-15) of enrolled participants will additionally take part in semi-structured qualitative interviews at the end of the study.

You may not qualify if:

• Age under 18 years
• Significant cognitive impairment preventing use of the application
• Significant inability to use digital tools
• Absence of signed informed consent

Sponsors & Collaborators

• University of Rome Tor Vergata: lead

Study Sites (1)

Centro Dialisi Molfetta

Molfetta, Bari, 70056, Italy

Location

Related Publications (6)

• Borsci S, Prati E, Malizia A, et al. (2023) - "Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale", Pers Ubiquitous Comput (DOI: 10.1007/s00779-023-01731-2)

  BACKGROUND

• Flythe JE, Mangione TW, Brunelli SM, Lynch KE (2015) - "Psychometric properties and validation of a patient-centered interdialytic weight gain questionnaire", J Ren Nutr

  BACKGROUND

• Torabikhah M, Farsi Z, Sajadi SA. Comparing the effects of mHealth app use and face-to-face training on the clinical and laboratory parameters of dietary and fluid intake adherence in hemodialysis patients: a randomized clinical trial. BMC Nephrol. 2023 Jun 29;24(1):194. doi: 10.1186/s12882-023-03246-7.

  PMID: 37386428 BACKGROUND

• Wen Y, Ruan Y, Yu Y. Mobile health management among end stage renal disease patients: a scoping review. Front Med (Lausanne). 2024 Jul 11;11:1366362. doi: 10.3389/fmed.2024.1366362. eCollection 2024.

  PMID: 39055692 BACKGROUND

• Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Normando E, Meinert E. The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review. J Med Internet Res. 2020 Oct 22;22(10):e20346. doi: 10.2196/20346.

  PMID: 33090118 BACKGROUND

• Kugler C, Maeding I, Russell CL. Non-adherence in patients on chronic hemodialysis: an international comparison study. J Nephrol. 2011 May-Jun;24(3):366-75. doi: 10.5301/JN.2010.5823.

  PMID: 20954134 BACKGROUND

MeSH Terms

Conditions

Renal Insufficiency, Chronic; Kidney Failure, Chronic; Empowerment; Arteriovenous Fistula; Patient Participation; Treatment Adherence and Compliance

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Social Behavior; Behavior; Arteriovenous Malformations; Vascular Malformations; Cardiovascular Abnormalities; Cardiovascular Diseases; Vascular Fistula; Vascular Diseases; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Fistula; Pathological Conditions, Anatomical; Patient Acceptance of Health Care; Health Behavior

Study Officials

• Michela Piredda, Prof

  Unicampus Roma

  STUDY DIRECTOR

• ELENA BARILE

  Università di Roma Tor Vergata

  PRINCIPAL INVESTIGATOR

Central Study Contacts

ELENA BARILE, PhD Student

CONTACT

3313905617; e.barile95@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Elena Barile

Study Record Dates

• First Submitted: July 29, 2026
• First Posted: September 21, 2026
• Study Start: October 1, 2026
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): November 15, 2026
• Last Updated: September 21, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)