Abstract
Introduction
Remote monitoring programs for kidney failure patients undergoing automated peritoneal dialysis (APD-RMP) are increasing worldwide and improving clinical outcomes. This study evaluated both patient-reported perceptions and experiences of healthcare professionals when using the new Sharesource Analytics 1.0 APD-RMP platform and generated proposals for easily implemented decision rules.
Methods
This prospective observational study of 284 adult patients receiving APD-RMP and switching to the Sharesource Analytics 1.0 platform was conducted from March 1 to April 12, 2024, in six dialysis centers. Patient-reported experience was measured using the Kidney PREM survey, and healthcare usability was measured using the Telehealth Usability Questionnaire. Expert consensus and focus group techniques were used to create decision rules in response to data presented in the Sharesource Analytics 1.0 platform.
Results
Patients rated their overall experience with the service provided by their dialysis center at the end of the study period as 6.9 (SD 0.3), with seven being the best possible score. Nephrologists and nurses rated their satisfaction with the new tool at 6.8 (SD 0.3) and its usability at 6.8 (SD 0.3); the maximum possible score was 7. Twenty-seven healthcare professionals (20 women), including nephrologists and nurses, participated in the expert consensus and focus group, which – based on type of alarm, findings and causes, and review of possible actions – proposed decision rules for interventions.
Conclusion
Care delivered using a remote monitoring platform with analytics was associated with high patient-reported experience ratings and very good usability and satisfaction ratings from healthcare professionals. Integrating structured decision rules alongside remote analytics could facilitate a more proactive, personalized, and efficient APD-RMP care.
Keywords: Peritoneal dialysis, Remote monitoring program, Engagement, Usability, Sharesource Analytics 1.0, Clinical decision
Plain Language Summary
This study evaluated a new digital platform called Sharesource Analytics 1.0, which helps healthcare teams monitor treatment information and identify problems early. We included 284 adult patients treated at six dialysis centers in Colombia. Patients responded to a survey about their experience with care, and healthcare professionals completed a questionnaire on the ease of use of the new remote monitoring platform for automatized peritoneal dialysis (PD). Patients rated their overall experience very positively, nearly giving it the highest possible score. Nephrologists and nurses also reported excellent usability and satisfaction. The study also showed that remote monitoring using Sharesource Analytics 1.0 represented only 3% of nurses’ time and 1% of nephrologists’ time, indicating that telemonitoring requires minimal effort while maintaining effective clinical supervision. These results suggest that a remote monitoring platform Sharesource Analytics 1.0 can improve the quality of care and make PD programs more efficient, while maintaining strong communication between patients and their care teams.
Introduction
Peritoneal dialysis (PD) offers several advantages in terms of lifestyle and patient autonomy compared to hemodialysis. However, PD programs worldwide face significant challenges, including a shortage of specialized personnel, a high workload, and the complexity of home care that contribute to underutilization of this dialysis treatment modality. These limitations underscore the need for digital solutions that support clinical decision-making, optimize processes, and improve therapeutic outcomes [1–3].
In this context, eHealth has demonstrated to be both useful and easy to operate for patients undergoing automated peritoneal dialysis (APD). Since 2015, intelligent cyclers have enabled remote monitoring via a cloud-based platform, facilitating two-way communication between patients and their clinical care teams, and allowing continuous treatment oversight [4]. The advancement of remote monitoring through Sharesource Analytics 1.0 in 2021 may further improve the quality of patient-centered care through early problem detection and adjustments to prescriptions without the need for frequent in-person visits. It employs algorithms to evaluate critical clinical indicators, such as treatment adherence (based on number and completeness of sessions), peritoneal catheter functionality (using PD solution flow patterns), and alarm occurrences (recorded and time stamped). These data are organized over a 180-day span and is automatically presented to the clinicians in the Sharesource Clinical Portal. This integration helps clinical staff identify early deviations and trends in treatment parameters and prioritize proactive interventions.
In recent years, there has been a shift toward the adoption of patient-centered care delivery models. Patient-reported experience measures (PREMs) are now recognized as an important aspect of the quality of care for people with chronic kidney disease undergoing dialysis [5, 6], and positive experiences have been linked to better treatment adherence and clinical outcomes [7–10].
However, successfully implementing digital tools like Sharesource Analytics 1.0 also requires understanding how healthcare professionals perceive and use them. This can be accomplished by conducting an evaluation of their usability, which measures attributes such as usefulness, ease of use, learning, interaction, and reliability, as well as a satisfaction assessment [11].
Furthermore, it has recently been recognized that there is a need for practical documents and guidance to assist nephrologists and nurses in how to routinely implement remote monitoring in their own clinics [12, 13]. In this regard, this study aimed to conduct a comprehensive evaluation of a digital remote monitoring solution for PD, analyzing its clinical utility including with a time and motion study, and produce guidance for implementation while also assessing the user experience of healthcare professionals, and patient-reported perceptions. This multidimensional assessment provides a comprehensive understanding of the platform’s real-world impact and its potential to enhance PD care.
Materials and Methods
Study Design and Patients
This prospective observational study was conducted at six dialysis centers from March 1 to April 12, 2024. Adult patients receiving automated peritoneal dialysis (APD) with an active remote monitoring program (RMP) for at least 30 days prior to the start of the study were included. Those with a Charlson Comorbidity Index greater than eight, those not expected to survive more than 6 months, those with cirrhosis or liver cancer, and those with sensory and/or cognitive impairment preventing them from answering the surveys were excluded. Following the screening period, eligible patients transitioned to their clinicians using the Sharesource Analytics 1.0 system, PD prescriptions were not changed at the time of that transition. Clinical follow-up was conducted over a 12-week period. The study protocol was approved by the Clinical Research Ethics Committee of Renal Care Services Colombia (February 16, 2023, Minute, item number 004).
Data Collection
The sociodemographic and clinical baseline characteristics included age, sex, ethnicity, history of diabetes mellitus, dialysis vintage, Charlson comorbidity index, body mass index, urine output day, systolic blood pressure, diastolic blood pressure, serum urea nitrogen, Kt/V, hemoglobin, phosphorus, potassium, and serum albumin. Additionally, prescription variables included total treatment time, number of cycles, dwell time, wet day percentage, infusion volume, and use of glucose-based peritoneal dialysis (PD) solutions with different concentrations of glucose and calcium, and icodextrin-based solutions.
Study Outcomes
The primary outcome was the PREMs, measured through the Kidney PREM survey [10], a comprehensive tool consisting of 38 items grouped into 13 themes, which was used to assess patient experience. All items were rated on a 7-point Likert scale ranging from 1 (never) to 7 (always).
The secondary outcome involved the assessment of usability, which was measured using the Telehealth Usability Questionnaire (TUQ), a tool that had been developed to evaluate the usability of telehealth implementation and services [11]. In the present study, TUQ was adapted specifically for application within the APD program featuring remote monitoring. This assessment was conducted among nephrologists and nurses. The TUQ evaluates five principal usability dimensions: usefulness, ease of use, effectiveness, reliability, and satisfaction. Usability scores were determined as the average of all items; each rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Additionally, clinical outcomes such as the number of hospitalizations, emergency room visits, and preemptive consultations were documented.
Furthermore, time and motion measurements were conducted to evaluate the efficiency of remote monitoring utilizing Sharesource Analytics 1.0. This methodology was developed by an external consultant [14] using continuous time measurement techniques. The study was carried out across four dialysis clinics operated by renal care services in Colombia. The duration of time spent by nurses and nephrologists was documented, with particular focus on the time allocated to Sharesource Analytics 1.0 for daily remote monitoring, as well as the review of the remote monitoring platform performed during the monthly comprehensive peritoneal program consultations. In addition, the takt time consultations were also evaluated. This is the period during which a patient usually arrives to be seen by the nephrologist or the nurse.
Sharesource Analytics 1.0
Figure 1 illustrates the essential components of this remote monitoring system, emphasizing two critical features: notification panel and evidence based clinical decision support. APD cycler data analysis provides insight into treatment adherence, catheter function, and alarms. It allows the clinician to monitor progressive problems, anticipate therapeutic patterns over time, by converting daily cycler data into useful analytics, so allowing proactive clinical decisions by identifying changes or trends over 180 days. Furthermore, the notification panel indicates the status of completed treatments and priority alerts, informing users of important changes or deviations. This facilitates remote monitoring by alerting the clinical team when attention is required and enabling longitudinal analysis to personalize care.
Fig. 1.
Description of Sharesource Analytics 1.0. The figure describes some of the essential components of remote monitoring for APD and examples of information provided on treatment adherence, catheter function, and alarms.
Statistical Analysis
Descriptive statistics were used to analyze the data. For continuous variables, the mean and standard deviation or the median and interquartile range were used depending on the distribution. For categorical variables, proportions were calculated.
Qualitative methodology included expert consensus and focus group techniques; these were used to develop the remote monitoring implementation framework when introducing and applying the Sharesource Analytics 1.0 platform and create decision rules in response to the findings observed in the connectivity platform. The focus group participants were nurses and nephrologists from the Renal Care Services Colombia Renal Clinic Network with at least 3 years of experience in PD who were recognized for their expertise in patient care. They were selected using purposive non-probability sampling to achieve maximum variability. The criterion of redundant information was used to determine the number of groups; therefore, three focus groups were held. An expert in qualitative focus group methodology led the sessions.
No data imputation procedures were performed to address missing data, and a complete case analysis approach was used. Stata 16® (StataCorp [2019]. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC) was used for all statistical analyses.
Results
Study Population
A total of 324 patients were screened, of whom 284 met the eligibility criteria and began follow-up under Sharesource Analytics 1.0. The retention rate was 92.6%, with 263 patients completing the study. Twenty-one patients (7.4%) discontinued early due to causes including death (2.1%), transfer to hemodialysis (1.8%), kidney transplantation (1.8%), suspension of treatment (1.1%), change of provider (0.3%), and absence of data transmission for more than 30 days (0.3%) (see Figure 2). The mean age was 56.9 years; the majority were men (55.2%), 33.2% were diabetic, and 43.2% had residual kidney function (see Table 1).
Fig. 2.
Flowchart of the recruitment of patients into the study. In the first box, 324 adult patients receiving automated peritoneal dialysis (APD) with an active remote monitoring platform (RMP) for at least 30 days prior to the start of the study were included.
Table 1.
Baseline demographic and clinical characteristics of 284 patients undergoing APD with remote monitoring using the Sharesource Analytics 1.0 platform
| Characteristics | Full sample |
|---|---|
| N = 284 | |
| Age, mean (SD), years | 57.6 (16.2) |
| Sex, n (%) | |
| Male | 160 (56.3) |
| Female | 124 (43.7) |
| Ethnicity, n (%) | |
| Afro-American | 11 (3.9) |
| Mestizo | 273 (96.1) |
| Diabetes history, n (%) | |
| Yes | 97 (34.2) |
| Previous kidney replacement therapy, median (IQR), years | 2.8 (1.6; 4.9) |
| Charlson comorbidity index; median (IQR) | 2 (0; 3) |
| 0–2 | 171 (60.2) |
| 3–5 | 109 (38.4) |
| ≥6 | 4 (1.4) |
| Karnofsky scale, mean (SD) | 72.8 (14.1) |
| Body mass index, mean (SD) | 25.4 (4.5) |
| Urine output; n (%) | |
| < 250 mL/day | 159 (55.9) |
| ≥ 250 mL/day | 125 (44.1) |
| Blood pressure systolic, mean (SD), mm Hg | 125.8 (22.2) |
| Blood pressure diastolic, mean (SD), mm Hg | 76.1 (14.6) |
| Hemoglobin, mean (SD), g/dL | 12.1 (1.8) |
| Albumin, mean (SD), g/dL | 3.8 (0.5) |
| Phosphorus, mean (SD), mg/dL | 5.1 (1.3) |
| BUN, mean (SD), mg/dL | 51.5 (14.0) |
| Potassium, mean (SD), mEq/L | 4.5 (0.8) |
| Kt/V, mean (SD) | 2.1 (0.5) |
| Prescription of peritoneal dialysis treatment, n (%) | |
| Total treatment time, mean (SD), hours | 10.5 (1.3) |
| Cycles, mean (SD), n | 4.4 (0.9) |
| Dwell time, mean (SD), minutes | 121.5 (29.3) |
| Wet day, n (%) | 141 (49.1) |
| Infusion volume, mean (SD), mL | 1,963.8 (223.2) |
| Use of Dianeal low calcium | 20 (7.0) |
| Icodextrin use, n (%) | 40 (13.9) |
| Additional exchange | 28 (9.8) |
| Glucose concentration | |
| Dianeal 1.5% (15 g/1,000 mL), n | 120 |
| Dianeal 2.5% (25 g/1,000 mL), n | 284 |
| Dianeal 4.25% (42.5 g/1,000 mL), n | 33 |
Patient-Reported Experience Measures
Patients rated their overall experience with the service provided by their dialysis center monitoring their therapy with Sharesource Analytics 1.0 as 6.9 (SD 0.3), a score of seven being the best they could achieve. Some aspects that were highly rated by patients included the treatment provided by the renal team, access to the renal team, sharing decisions about their care, privacy, and dignity, among others. In addition, the study identified several areas for improvement, including accessibility, comfort, cleanliness, parking, transportation, and aspects of laboratory testing. For instance, patients expressed a need for improved transportation services to and from the dialysis center, as well as a desire for more comfortable and cleaner facilities (see details in Table 2).
Table 2.
PREM evaluated by the Kidney PREM survey in 284 APD patients with remote monitoring using the Sharesource Analytics 1.0 platform
| Domains | Mean score (SD) |
|---|---|
| N = 252 | |
| Access to the renal team | 6.9 (0.4) |
| Support | 6.2 (0.9) |
| Communication | 6.2 (0.7) |
| Patient information | 6.2 (0.7) |
| Fluid intake and diet | 7.0 (0.1) |
| Needling | 7.0 (0.1) |
| Test | 5.5 (0.7) |
| Sharing decisions about your care | 6.9 (0.2) |
| Privacy and dignity | 6.9 (0.3) |
| Scheduling and planning | 6.5 (1.4) |
| How the renal team treats you | 6.9 (0.4) |
| Transport | 4.1 (1.1) |
| The environment | 5.9 (0.6) |
| Your overall experience | 6.9 (0.3) |
PREM, patient-reported experience measure.
Usability and Satisfaction
The usability and satisfaction of the APD-RMP were evaluated using the Telehealth Usability Questionnaire (TUQ) among 27 healthcare professionals (including nephrologists and nurses) who were responsible for monitoring 284 APD patients remotely. Twenty of the participants were over 40 years old and had an average of 11.2 years (SD 8.2) of experience with PD programs. The APD-RMP demonstrated high levels of usability and acceptance among healthcare professionals. The usefulness, ease of use, and learnability domains achieved high mean scores (both 6.9), indicating that the platform was perceived as intuitive, efficient, and adequate to meet clinical care needs. Interface quality and interaction quality domains were also rated highly (mean scores of 6.7 and 6.8, respectively), reflecting clear information presentation and effective support for clinical decision-making. The reliability domain showed the lowest mean score (6.3), mainly due to a lower perception of equivalence with in-person visits, whereas satisfaction and future use remained high (6.9), indicating overall favorable acceptance of the system. (see details in Table 3).
Table 3.
Usability and satisfaction assessed by the Telehealth Usability Questionnaire among 27 healthcare professionals (nephrologists and nurses) using Sharesource Analytics 1.0 for the remote monitoring of 284 patients undergoing APD
| Items | Telehealth Usability Questionnaire | Mean Score (SD) |
|---|---|---|
| | Domain: usefulness | 6.9 (0.3) |
| 1 | APD-RMP platform improves the access to healthcare services | 6.9 (0.4) |
| 2 | APD-RMP platform saves patient’s time traveling to a hospital or specialist clinic | 6.9 (0.3) |
| 3 | RMP platform provides for patient’s healthcare needs | 6.9 (0.3) |
| | Domain: ease of use and learnability | 6.9 (0.2) |
| 4 | It was simple to use this system (APD-RMP platform) | 6.9 (0.3) |
| 5 | It was easy to learn to use the system (APD-RMP platform) | 6.9 (0.2) |
| 6 | I believe I could become productive quickly using this system (RMP platform) | 6.9 (0.2) |
| | Domain: interface quality | 6.7 (0.3) |
| 7 | The way I interact with this APD-RMP platform is pleasant | 6.9 (0.3) |
| 8 | I like using the system (APD-RMP platform) | 6.9 (0.3) |
| 9 | The APD-RMP platform is simple and easy to understand | 6.8 (0.4) |
| 10 | This APD-RMP platform is able to do everything I would want it to be able to do | 6.4 (0.8) |
| | Domain: interaction quality | 6.8 (0.4) |
| 11 | I could easily realize that I need to talk to the patient using the APD-RMP platform | 6.8 (0.5) |
| 12 | I could understand clearly the patient’s condition using the APD-RMP platform | 6.7 (0.5) |
| 13 | The information contained in the APD-RMP platform is sufficient and is presented clearly, so that it generates effective actions in patient’s care | 6.5 (0.9) |
| 14 | Do I feel that the APD-RMP platform brings me closer to the patient in their environment | 6.9 (0.3) |
| | Domain: reliability | 6.3 (0.7) |
| 15 | I think the interaction provided over the APD-RMP system are the same as in-person visits | 5.5 (1.7) |
| 16 | Whenever I made a mistake using the APD-RMP platform, I could easily and quickly correct it | 6.8 (0.4) |
| 17 | APD-RMP platform gave error messages that clearly told me how to fix problems | 6.5 (0.9) |
| | Domain: satisfaction and future use | 6.9 (0.3) |
| 18 | I feel comfortable communicating with the patient using the APD-RMP platform | 6.9 (0.4) |
| 19 | APD-RMP platform is a good way to improve healthcare services | 6.9 (0.5) |
| 20 | I would use APD-RMP platform services again | 6.9 (0.3) |
| 21 | Overall, I am satisfied with this APD-RMP platform | 6.9 (0.3) |
| Overall score | 6.8 (0.3) | |
APD-RMP, automated peritoneal dialysis with an active remote monitoring program.
Clinical Outcomes
During the follow-up period, 49 hospitalizations were reported. The most frequent causes were cardio-cerebrovascular (17, or 34.7%), infectious (7, or 14.3%), and endocrine/metabolic (7, or 14.3%) complications. Seven emergency room visits were recorded during follow-up; the main cause was syncope (28.5%). A total of 35 preventive visits were made during follow-up. The main reasons were abnormal vital signals patterns (22.9%), ultrafiltration/dry weight problems (20%), and catheter problems (14.3%).
Decision Rules
A total of 27 healthcare professionals (20 women and seven men), including nephrologists and nurses, with mean experience of 11.2 years (SD 8.2) in PD programs, participated in the expert consensus and focus group that defined appropriate decision rules. The decision rules regarding adherence, catheter function, ultrafiltration, weight, and blood pressure are presented in Table 4. This is a practical document that provides instructions for using and further developing remote monitoring in PD. Additionally, a graphic illustration (Fig. 3) summarizes the implementation of the suggested remote monitoring scheme when using Sharesource Analytics 1.0.
Table 4.
Decision rules regarding adherence, catheter function, ultrafiltration, weight, and blood pressure
| Type of alarm | Findings/causes | Actions | Decision |
|---|---|---|---|
| ✓Lost treatment time | If this type of alarm occurs for 3 or more consecutive treatments, contact the patient and inquire about the following causes: |
|
Based on the information provided by the patient, define the most appropriate intervention:
|
| ✓Lost dwell time | 1. What is the reason for early disconnection or early termination of drainage, e.g., power failure, medical appointments, and need to go to the bathroom. | ||
| ✓Lost therapy volume | 2. Do you experience discomfort/pain during infusion, dwelling or drainage that will force you to bypass? | ||
| ✓Drain ended early | 3. What are the characteristics of the peritoneal fluid? | ||
| ✓Fill/dwell bypass count | 4. Ask about the presence of constipation | ||
| ✓Initial drain variance | If this type of alarm occurs for 2 or more consecutive treatments, contact the patient and inquire about the following causes: |
|
Based on the information provided by the patient, define the most appropriate intervention:
|
| ✓Initial drain bypass | 1. Did you have any complications on the day the alarm was triggered? | ||
| ✓Device program variance | 2. Ask the patient about signs of dehydration or overload | ||
| 3. Do you experience discomfort/pain during drainage that will force you to bypass? | |||
| 4. Did you make any changes to the parameters programmed in the cycler? Example: temperature, initial drain value | |||
| ✓Events during treatment | Contact immediately the patient if the events (yellow and/or red flags) affect the treatment time. If the time is not affected, contact the patient if these events continue at 2 or more consecutive treatments. Inquire about the following causes: |
|
Based on the information provided by the patient, define the most appropriate intervention:
|
| 1. Do you experience any problems with the catheter? | |||
| 2. Do you suffer from constipation? | |||
| 3. What are the characteristics of the peritoneal fluid? | |||
| 4. Do you have any problem with the cycler? | |||
| 5. Do you encounter problems when assembling the cassette, lines, therapy bags? | |||
| 6. What are your sleeping postures? | |||
| ✓Ultrafiltration (UF) | Examine the UF profile graph: if it deviates from trend or UF is < 750 mL/day in an anuric patient on 2 or more consecutive treatments, contact the patient | Inquire about the following causes:
|
Based on the information provided by the patient, define the most appropriate intervention:
|
| ✓Weight | Examine the weight profile graph. If the weight profile is 2 kg above or 3 kg below the patient’s target weight on 2 or more consecutive treatments, contact the patient | Inquire about the following causes:
|
Based on the information provided by the patient, define the most appropriate intervention:
|
| ✓Blood pressure | Examine the blood pressure (BP) values graph. If you find values below 90/50 mm Hg or above 160/100 mm Hg, contact immediately the patient | Inquire about the following causes:
|
Based on the information provided by the patient, define the most appropriate intervention:
|
Expert consensus and focus group techniques were used to create the following decision rules in response to findings observed in the Sharesource Analytics 1.0 platform.
Fig. 3.
Remote monitoring scheme with Sharesource Analytics 1.0.
Time and Motion
A total of 13 nurses and 7 nephrologists participated in this assessment while using Sharesource Analytics 1.0; 336 h were recorded for nurses and 196 h for nephrologists. The ratio of PD patients to nurses was 45:1, and to nephrologists was 140 dialysis patients:1. The nurses’ time dedicated to daily remote monitoring follow-up and the monthly comprehensive outpatient consultation averaged 2 min per patient. Similar results were observed in the nephrologists’ review of Sharesource Analytics 1.0 during the monthly comprehensive outpatient consultation (see Figure 4).
Fig. 4.
Ratio between PD patients per nephrologist and nurse and takt time.
The takt time for nephrology was 35 min; of these, 16 min are spent directly evaluating the patient. Nurses see a new patient every 34 min and spend 24 of those min directly assessing the patient. These findings indicate that looking at the remote monitoring accounts for only 1% of the time allocated by nephrologists and 3% by nurses (Fig. 5).
Fig. 5.
Diagram of daily activities of the nephrologist and the nurse.
Discussion
This prospective observational study evaluated patient experience, clinical usability, and care processes after implementing Sharesource Analytics 1.0 for a group of patients undergoing APD with RMP at six dialysis centers in Colombia. The findings demonstrate high satisfaction levels among both patients and healthcare professionals, showing it is possible to integrate digital health technologies into PD programs in real-world clinical practice.
Patients rated their overall healthcare experience as very high, with an average Kidney Patient-Reported Experience Measure (Kidney PREM) score of 6.9 out of 7. These results align with previous studies that have documented improved patient experiences in home dialysis programs using remote patient monitoring technologies [15, 16]. On the other hand, more clinical specific areas such as transportation, facility infrastructure, and laboratory logistics were identified as areas with opportunities for improvement. This underscores the fact that while digital solutions can improve medical oversight and perceived care quality, there are other structural challenges that must also be addressed to ensure comprehensive patient satisfaction.
The high scores for usability and satisfaction reported by healthcare professionals using the Sharesource Analytics 1.0 platform were equally notable. The Telehealth Usability Questionnaire yielded high scores in all categories, especially usefulness and satisfaction. These results are consistent with previous reports that describe increased efficiency and clinical confidence with remote monitoring platforms in PD programs [17–21]. The positive reception among nephrologists and nurses suggests that integrating advanced analytics into routine practice is desirable, including settings with limited nephrologist and nurse numbers.
This study shows that the implementation of a remote monitoring platform with analytics enabled early detection of ultrafiltration and catheter-related issues, leading to 35 preventive visits. Such visits can result in proactive clinical interventions that potentially may reduce hospitalization, extend technique and patient survival, and improve other issues related to PD treatment [19, 21].
An additional outcome of this study is the development of structured decision rules based on focus groups and expert consensus. These rules, which are adapted to patterns detected using Sharesource Analytics 1.0 (e.g., adherence, weight changes, and catheter alerts), provide an outline for standardizing remote responses, promoting timely interventions, and ensuring the correct interpretation of results by new or training personnel [12, 13].
The time and motion analysis offers supplementary insights into the allocation of clinical workload among healthcare providers utilizing a remote monitoring platform with analytics (Sharesource Analytics 1.0). There has been some concern that digital tools could increase clinician workload, but the observation that remote monitoring necessitates merely 1% of nephrologists’ time and 3% of nurses’ time indicates the effectiveness of digital follow-up when integrated into routine clinical practice. These findings align with previous evidence indicating that telemonitoring can improve time management without increasing workload or compromising clinical oversight [22].
Overall, the integration of telemonitoring tools such as a remote monitoring platform with analytics appears to improve operational efficiency while maintaining high standards of care and user satisfaction. Future research should explore whether these time efficiencies translate into measurable long-term outcomes, including improved treatment adherence, reduced hospitalization, cost savings, and enhanced patient and technique survival.
This study has certain limitations, particularly its observational nature and relatively short observation period with limited geographic scope. Further multicenter studies with longer follow-up are warranted to assess the sustained clinical impact of telemonitoring systems and their role in promoting wider adoption of PD therapy.
In conclusion, in kidney failure patients undergoing APD, implementation of remote monitoring using a remote monitoring platform with analytics was associated with high patient-reported experience ratings, as well as very high usability and satisfaction ratings from healthcare professionals. Integrating structured decision rules and guidance on how to use SSA 1.0 remote analytics into routine clinical practice could facilitate a more proactive, personalized, and efficient APD-RMP care.
Acknowledgments
The authors wish to express their gratitude to all the patients and nursing teams who participated in the study.
Statement of Ethics
This study was performed in accordance with the Declaration of Helsinki. This human study was approved by the Clinical Research Ethics Committee of the Renal Care Services Colombia: Minute, item number 004, dated February 16, 2023. The study was not registered as a clinical trial because of observational study. All adult participants provided written informed consent to participate in this study.
Conflict of Interest Statement
Dra Dazzarola is a full-time employee of Renal Care Services STR del Valle. Dr. Cely is a full-time employee of Renal Care Services Agency National University. Dr. Doria is a full-time employee of Renal Care Services Sucursal Bucaramanga. Dr. Castillo is a full-time employee of Renal Care Services Agency Soacha. Dr. Pulido is a full-time employee of Renal Care Services Agency San Rafael. Dr. Rojas is a full-time employee of Renal Care Services Sucursal Duitama. Dr. Sanabria is a full-time employee of Renal Care Services Latin-America. RN Vesga is a full-time employee of Renal Care Services Colombia. RN Alba is a full-time employee of Renal Care Services Colombia. Dr. Lindholm is an employee of Karolinska Institutet, Stockholm, Sweden, and was previously an employee of Baxter Healthcare Corporation. Dr. Rivera is a full-time employee of Vantive, Deerfield, IL, USA. Dr. Rutherford is a full-time employee of Vantive, Zurich, Switzerland.
Funding Sources
This study was supported by Baxter International, Inc. The funder had no role in the study design; data collection, analysis, or reporting; or the decision to submit for publication.
Author Contributions
Dra Dazzarola, Dr Cely, Dr Doria, Dr Castillo, Dr Pulido, Dr Rojas, and RN Alba: Original research project conception and design, data acquisition, and data interpretation. Dr Sanabria, Dr Lindholm, Dr Rivera, and Dr Rutherford: original research project conception and design, and data interpretation. RN Vesga: original research project conception and design, statistical analysis, and data interpretation. All authors have been involved in the drafting of the manuscript or revising it critically for important intellectual content and provided final approval of the version to be published, verify that they have met all the journal’s requirements for authorship, agree to be accountable for all aspects of the work, ensuring the accuracy and integrity of the publication, and approved the final manuscript draft submitted for publication.
Funding Statement
This study was supported by Baxter International, Inc. The funder had no role in the study design; data collection, analysis, or reporting; or the decision to submit for publication.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author (mauricio.sanabria@vantive.com). A secure database that meets the requirements of confidentiality that safeguards patient privacy is maintained as part of the study protocol.
References
- 1. Bonenkamp AA, van Eck van der Sluijs A, Hoekstra T, Verhaar MC, van Ittersum FJ, Abrahams AC, et al. Health-related quality of life in home dialysis patients compared to in-center hemodialysis patients: a systematic review and meta-analysis. Kidney Med. 2020;2(2):139–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Canaud B, Kooman J, Davenport A, Campo D, Carreel E, Morena-Carrere M, et al. Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the withings toolkit health sensing tools. Front Nephrol. 2023;3:1148565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Perl J, Brown EA, Chan CT, Couchoud C, Davies SJ, Kazancioğlu R, et al. Home dialysis: conclusions from a kidney disease: improving global outcomes (KDIGO) controversies conference. Kidney Int. 2023;103(5):842–58. [DOI] [PubMed] [Google Scholar]
- 4. Milan Manani S, Baretta M, Giuliani A, Virzì GM, Martino F, Crepaldi C, et al. Remote monitoring in peritoneal dialysis: benefits on clinical outcomes and on quality of life. J Nephrol. 2020;33(6):1301–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Rhee CM, Brunelli SM, Subramanian L, Tentori F. Measuring patient experience in dialysis: a new paradigm of quality assessment. J Nephrol. 2018;31(2):231–40. [DOI] [PubMed] [Google Scholar]
- 6. Aiyegbusi OL, Kyte D, Cockwell P, Anderson N, Calvert M. CalvertM. A patient-centred approach to measuring quality in kidney care: patient-reported outcome measures and patient-reported experience measures. Curr Opin Nephrol Hypertens. 2017;26(6):442–9. [DOI] [PubMed] [Google Scholar]
- 7. Kovac JA, Patel SS, Peterson RA, Kimmel PL. Patient satisfaction with care and behavioral compliance in end-stage renal disease patients treated with hemodialysis. Am J Kidney Dis. 2002;39(6):1236–44. [DOI] [PubMed] [Google Scholar]
- 8. Bennett PN, St Clair Russell J, Atwal J, Brown L, Schiller B. Patient-to-patient peermentor support in dialysis: improving the patient experience. Semin Dial. 2018;31(5):455–61. [DOI] [PubMed] [Google Scholar]
- 9. Srinivas R, Chavin KD, Baliga PK, Srinivas T, Taber DJ. Association between patient satisfaction and outcomes in kidney transplant. Am J Med Qual. 2015;30(2):180–5. [DOI] [PubMed] [Google Scholar]
- 10. Hawkins J, Wellsted D, Corps C, Fluck R, Gair R, Hall N, et al. Measuring patients' experience with renal services in the UK: development and validation of the kidney PREM. Nephrol Dial Transpl. 2022;37(8):1507–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Parmanto B, Lewis AN Jr, Graham KM, Bertolet MH. Development of the telehealth usability questionnaire (TUQ). Int J Telerehabil. 2016;8(1):3–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Neri L, Di Liberato L, Alfano G, Allegrucci V, Appio N, Bussi C, et al. Precision medicine in peritoneal dialysis: an expert opinion on the application of the sharesource platform for the remote management of patients. J Pers Med. 2024;14(8):807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Brown EA, Jha V, Bavanandan S, Chan CT, Davies S, Figueirido A, et al. International home dialysis consortium: declaration advocating for the promotion of home dialysis globally. Kidney Int Rep. 2025;10(6):1633–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Diveti Boutique de consultores. [internet]. Colombia; 2025. Accessed 29 October 2025. Available from: https://diveti.com.co/
- 15. Walker RC, Tong A, Howard K, Darby N, Palmer SC. Patients’ and caregivers’ expectations and experiences of remote monitoring for peritoneal dialysis: A qualitative interview study. Perit Dial Int. 2020;40(6):540–7. [DOI] [PubMed] [Google Scholar]
- 16. Cuevas-Budhart MÁ, Celaya Pineda IX, Perez Moran D, Trejo Villeda MA, Gomez Del Pulgar M, Rodríguez Zamora MC, et al. Patient experience in automated peritoneal dialysis with telemedicine monitoring during the COVID-19 pandemic in Mexico: Qualitative study. Nurs Open. 2023;10(2):1092–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Paniagua R, Ramos A, Ávila M, Ventura MJ, Nevarez-Sida A, Qureshi AR, et al. Remote monitoring of automated peritoneal dialysis reduces mortality, adverse events and hospitalizations: a cluster-randomized controlled trial. Nephrol Dial Transpl. 2025;40(3):588–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Sanabria M, Buitrago G, Lindholm B, Vesga J, Nilsson LG, Yang D, et al. Remote patient monitoring program in automated peritoneal dialysis: impact on hospitalizations. Perit Dial Int. 2019;39(5):472–8. [DOI] [PubMed] [Google Scholar]
- 19. Sanabria M, Vesga J, Lindholm B, Rivera A, Rutherford P. Time on therapy of automated peritoneal dialysis with and without remote patient monitoring: a cohort study. Int J Nephrol. 2022;2022:8646775. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Corzo L, Wilkie M, Vesga J, Lindholm B, Buitrago G, Rivera AS, et al. Technique failure in remote patient monitoring program in patients undergoing automated peritoneal dialysis: a retrospective cohort study. Perit Dial Int. 2022;42(3):288–96. [DOI] [PubMed] [Google Scholar]
- 21. Centellas-Pérez FJ, Ortega-Cerrato A, Vera M, Devesa-Buch RJ, Muñoz-de-Bustillo E. Prats M,et al. Impact of Remote Monitoring on Standardized Outcomes in Nephrology-Peritoneal Dialysis. Kidney Int Rep. 2023;9(2):266–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Mata-Lima A, Paquete AR, Serrano-Olmedo JJ. Remote patient monitoring and management in nephrology: a systematic review. Nefrologia. 2024;44(5):639–67. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author (mauricio.sanabria@vantive.com). A secure database that meets the requirements of confidentiality that safeguards patient privacy is maintained as part of the study protocol.
